idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
|---|---|---|---|---|---|---|
2,101 | What do you call an average that does not include outliers? | It's called the trimmed mean. Basically what you do is compute the mean of the middle 80% of your data, ignoring the top and bottom 10%. Of course, these numbers can vary, but that's the general idea. | What do you call an average that does not include outliers? | It's called the trimmed mean. Basically what you do is compute the mean of the middle 80% of your data, ignoring the top and bottom 10%. Of course, these numbers can vary, but that's the general idea | What do you call an average that does not include outliers?
It's called the trimmed mean. Basically what you do is compute the mean of the middle 80% of your data, ignoring the top and bottom 10%. Of course, these numbers can vary, but that's the general idea. | What do you call an average that does not include outliers?
It's called the trimmed mean. Basically what you do is compute the mean of the middle 80% of your data, ignoring the top and bottom 10%. Of course, these numbers can vary, but that's the general idea |
2,102 | What do you call an average that does not include outliers? | A statistically sensible approach is to use a standard deviation cut-off.
For example, remove any results +/-3 standard deviations.
Using a rule like "biggest 10%" doesn't make sense. What if there are no outliers? The 10% rule would eliminate some data anyway. Unacceptable. | What do you call an average that does not include outliers? | A statistically sensible approach is to use a standard deviation cut-off.
For example, remove any results +/-3 standard deviations.
Using a rule like "biggest 10%" doesn't make sense. What if there a | What do you call an average that does not include outliers?
A statistically sensible approach is to use a standard deviation cut-off.
For example, remove any results +/-3 standard deviations.
Using a rule like "biggest 10%" doesn't make sense. What if there are no outliers? The 10% rule would eliminate some data anyw... | What do you call an average that does not include outliers?
A statistically sensible approach is to use a standard deviation cut-off.
For example, remove any results +/-3 standard deviations.
Using a rule like "biggest 10%" doesn't make sense. What if there a |
2,103 | What do you call an average that does not include outliers? | Another standard test for identifying outliers is to use LQ $-$ (1.5$\times$IQR) and UQ $+$ (1.5$\times$ IQR). This is somewhat easier than computing the standard deviation and more general since it doesn't make any assumptions about the underlying data being from a normal distribution. | What do you call an average that does not include outliers? | Another standard test for identifying outliers is to use LQ $-$ (1.5$\times$IQR) and UQ $+$ (1.5$\times$ IQR). This is somewhat easier than computing the standard deviation and more general since it | What do you call an average that does not include outliers?
Another standard test for identifying outliers is to use LQ $-$ (1.5$\times$IQR) and UQ $+$ (1.5$\times$ IQR). This is somewhat easier than computing the standard deviation and more general since it doesn't make any assumptions about the underlying data being... | What do you call an average that does not include outliers?
Another standard test for identifying outliers is to use LQ $-$ (1.5$\times$IQR) and UQ $+$ (1.5$\times$ IQR). This is somewhat easier than computing the standard deviation and more general since it |
2,104 | What do you call an average that does not include outliers? | For a very specific name, you'll need to specify the mechanism for outlier rejection. One general term is "robust".
dsimcha mentions one approach: trimming. Another is clipping: all values outside a known-good range are discarded. | What do you call an average that does not include outliers? | For a very specific name, you'll need to specify the mechanism for outlier rejection. One general term is "robust".
dsimcha mentions one approach: trimming. Another is clipping: all values outside a | What do you call an average that does not include outliers?
For a very specific name, you'll need to specify the mechanism for outlier rejection. One general term is "robust".
dsimcha mentions one approach: trimming. Another is clipping: all values outside a known-good range are discarded. | What do you call an average that does not include outliers?
For a very specific name, you'll need to specify the mechanism for outlier rejection. One general term is "robust".
dsimcha mentions one approach: trimming. Another is clipping: all values outside a |
2,105 | What do you call an average that does not include outliers? | The "average" you're talking about is actually called the "mean".
It's not exactly answering your question, but a different statistic which is not affected by outliers is the median, that is, the middle number.
{90,89,92,91,5} mean: 73.4
{90,89,92,91,5} median: 90
This might be useful to you, I dunno. | What do you call an average that does not include outliers? | The "average" you're talking about is actually called the "mean".
It's not exactly answering your question, but a different statistic which is not affected by outliers is the median, that is, the midd | What do you call an average that does not include outliers?
The "average" you're talking about is actually called the "mean".
It's not exactly answering your question, but a different statistic which is not affected by outliers is the median, that is, the middle number.
{90,89,92,91,5} mean: 73.4
{90,89,92,91,5} median... | What do you call an average that does not include outliers?
The "average" you're talking about is actually called the "mean".
It's not exactly answering your question, but a different statistic which is not affected by outliers is the median, that is, the midd |
2,106 | What do you call an average that does not include outliers? | There is no official name because of the various mechanisms, such as Q test, used to get rid of outliers.
Removing outliers is called trimming.
No program I have ever used has average() with an integrated trim() | What do you call an average that does not include outliers? | There is no official name because of the various mechanisms, such as Q test, used to get rid of outliers.
Removing outliers is called trimming.
No program I have ever used has average() with an inte | What do you call an average that does not include outliers?
There is no official name because of the various mechanisms, such as Q test, used to get rid of outliers.
Removing outliers is called trimming.
No program I have ever used has average() with an integrated trim() | What do you call an average that does not include outliers?
There is no official name because of the various mechanisms, such as Q test, used to get rid of outliers.
Removing outliers is called trimming.
No program I have ever used has average() with an inte |
2,107 | What do you call an average that does not include outliers? | I don't know if it has a name, but you could easily come up with a number of algorithms to reject outliers:
Find all numbers between the 10th and 90th percentiles (do this by sorting then rejecting the first $N/10$ and last $N/10$ numbers) and take the mean value of the remaining values.
Sort values, reject high and l... | What do you call an average that does not include outliers? | I don't know if it has a name, but you could easily come up with a number of algorithms to reject outliers:
Find all numbers between the 10th and 90th percentiles (do this by sorting then rejecting t | What do you call an average that does not include outliers?
I don't know if it has a name, but you could easily come up with a number of algorithms to reject outliers:
Find all numbers between the 10th and 90th percentiles (do this by sorting then rejecting the first $N/10$ and last $N/10$ numbers) and take the mean v... | What do you call an average that does not include outliers?
I don't know if it has a name, but you could easily come up with a number of algorithms to reject outliers:
Find all numbers between the 10th and 90th percentiles (do this by sorting then rejecting t |
2,108 | What do you call an average that does not include outliers? | The most common way of having a Robust (the usual word meaning resistant to bad data) average is to use the median. This is just the middle value in the sorted list (of half way between the middle two values), so for your example it would be 90.5 = half way between 90 and 91.
If you want to get really into robust stati... | What do you call an average that does not include outliers? | The most common way of having a Robust (the usual word meaning resistant to bad data) average is to use the median. This is just the middle value in the sorted list (of half way between the middle two | What do you call an average that does not include outliers?
The most common way of having a Robust (the usual word meaning resistant to bad data) average is to use the median. This is just the middle value in the sorted list (of half way between the middle two values), so for your example it would be 90.5 = half way be... | What do you call an average that does not include outliers?
The most common way of having a Robust (the usual word meaning resistant to bad data) average is to use the median. This is just the middle value in the sorted list (of half way between the middle two |
2,109 | What do you call an average that does not include outliers? | ...
{90,89,92,91(,5)} avg = 90.5
How do you describe this average in statistics?
...
There's no special designation for that method. Call it any name you want, provided that you always tell the audience how you arrived at your result, and you have the outliers in hand to show them if they request (and believe... | What do you call an average that does not include outliers? | ...
{90,89,92,91(,5)} avg = 90.5
How do you describe this average in statistics?
...
There's no special designation for that method. Call it any name you want, provided that you always tell | What do you call an average that does not include outliers?
...
{90,89,92,91(,5)} avg = 90.5
How do you describe this average in statistics?
...
There's no special designation for that method. Call it any name you want, provided that you always tell the audience how you arrived at your result, and you have th... | What do you call an average that does not include outliers?
...
{90,89,92,91(,5)} avg = 90.5
How do you describe this average in statistics?
...
There's no special designation for that method. Call it any name you want, provided that you always tell |
2,110 | What do you call an average that does not include outliers? | If all you have is one variable (as you imply) I think some of the respondents above are being over critical of your approach. Certainly other methods that look at things like leverage are more statistically sound; however that implies you are doing modeling of some sort. If you just have for example scores on a test o... | What do you call an average that does not include outliers? | If all you have is one variable (as you imply) I think some of the respondents above are being over critical of your approach. Certainly other methods that look at things like leverage are more statis | What do you call an average that does not include outliers?
If all you have is one variable (as you imply) I think some of the respondents above are being over critical of your approach. Certainly other methods that look at things like leverage are more statistically sound; however that implies you are doing modeling o... | What do you call an average that does not include outliers?
If all you have is one variable (as you imply) I think some of the respondents above are being over critical of your approach. Certainly other methods that look at things like leverage are more statis |
2,111 | What do you call an average that does not include outliers? | I love the discussion here - the trimmed mean is a powerful tool to get a central tendency estimate concentrated around the middle of the data.
The one thing I would add is that there is a choice to be made about which "metric" to use in the cases of small and large sample sizes. In some cases we talk about
means in ... | What do you call an average that does not include outliers? | I love the discussion here - the trimmed mean is a powerful tool to get a central tendency estimate concentrated around the middle of the data.
The one thing I would add is that there is a choice to b | What do you call an average that does not include outliers?
I love the discussion here - the trimmed mean is a powerful tool to get a central tendency estimate concentrated around the middle of the data.
The one thing I would add is that there is a choice to be made about which "metric" to use in the cases of small and... | What do you call an average that does not include outliers?
I love the discussion here - the trimmed mean is a powerful tool to get a central tendency estimate concentrated around the middle of the data.
The one thing I would add is that there is a choice to b |
2,112 | What do you call an average that does not include outliers? | There are superior methods to the IQR or SD based methods. Due to outliers being present, the distribution likely has issues with normality already (unless ouliers are evenly distributed at both ends of the distribution). This inflates the SD a lot, making the SDs use less than desirable, however the SD method has some... | What do you call an average that does not include outliers? | There are superior methods to the IQR or SD based methods. Due to outliers being present, the distribution likely has issues with normality already (unless ouliers are evenly distributed at both ends | What do you call an average that does not include outliers?
There are superior methods to the IQR or SD based methods. Due to outliers being present, the distribution likely has issues with normality already (unless ouliers are evenly distributed at both ends of the distribution). This inflates the SD a lot, making the... | What do you call an average that does not include outliers?
There are superior methods to the IQR or SD based methods. Due to outliers being present, the distribution likely has issues with normality already (unless ouliers are evenly distributed at both ends |
2,113 | What do you call an average that does not include outliers? | disclaimer - this method is ad hoc and without rigorous study. Use at your own risk :)
What I found to be quite good was to reduce the relevancy of a points contribution to the mean by the square of its number of standard deviations from the mean but only if the point is more than one standard deviation from the mean.
... | What do you call an average that does not include outliers? | disclaimer - this method is ad hoc and without rigorous study. Use at your own risk :)
What I found to be quite good was to reduce the relevancy of a points contribution to the mean by the square of i | What do you call an average that does not include outliers?
disclaimer - this method is ad hoc and without rigorous study. Use at your own risk :)
What I found to be quite good was to reduce the relevancy of a points contribution to the mean by the square of its number of standard deviations from the mean but only if t... | What do you call an average that does not include outliers?
disclaimer - this method is ad hoc and without rigorous study. Use at your own risk :)
What I found to be quite good was to reduce the relevancy of a points contribution to the mean by the square of i |
2,114 | What do you call an average that does not include outliers? | It can be the median. Not always, but sometimes. I have no idea what it is called in other occasions. Hope this helped. (At least a little.) | What do you call an average that does not include outliers? | It can be the median. Not always, but sometimes. I have no idea what it is called in other occasions. Hope this helped. (At least a little.) | What do you call an average that does not include outliers?
It can be the median. Not always, but sometimes. I have no idea what it is called in other occasions. Hope this helped. (At least a little.) | What do you call an average that does not include outliers?
It can be the median. Not always, but sometimes. I have no idea what it is called in other occasions. Hope this helped. (At least a little.) |
2,115 | What do you call an average that does not include outliers? | My statistics textbook refers to this as a Sample Mean as opposed to a Population Mean. Sample implies there was a restriction applied to the full dataset, though no modification (removal) to the dataset was made. | What do you call an average that does not include outliers? | My statistics textbook refers to this as a Sample Mean as opposed to a Population Mean. Sample implies there was a restriction applied to the full dataset, though no modification (removal) to the data | What do you call an average that does not include outliers?
My statistics textbook refers to this as a Sample Mean as opposed to a Population Mean. Sample implies there was a restriction applied to the full dataset, though no modification (removal) to the dataset was made. | What do you call an average that does not include outliers?
My statistics textbook refers to this as a Sample Mean as opposed to a Population Mean. Sample implies there was a restriction applied to the full dataset, though no modification (removal) to the data |
2,116 | Is there a minimum sample size required for the t-test to be valid? | There is no minimum sample size for the t test to be valid other than it be large enough to calculate the test statistic. Validity requires that the assumptions for the test statistic hold approximately. Those assumptions are in the one sample case that the data are iid normal (or approximately normal) with mean 0 un... | Is there a minimum sample size required for the t-test to be valid? | There is no minimum sample size for the t test to be valid other than it be large enough to calculate the test statistic. Validity requires that the assumptions for the test statistic hold approximat | Is there a minimum sample size required for the t-test to be valid?
There is no minimum sample size for the t test to be valid other than it be large enough to calculate the test statistic. Validity requires that the assumptions for the test statistic hold approximately. Those assumptions are in the one sample case t... | Is there a minimum sample size required for the t-test to be valid?
There is no minimum sample size for the t test to be valid other than it be large enough to calculate the test statistic. Validity requires that the assumptions for the test statistic hold approximat |
2,117 | Is there a minimum sample size required for the t-test to be valid? | With all deference to him, he doesn't know what he's talking about. The t-test was designed for working with small samples. There isn't really a minimum (maybe you could say a minimum of 3 for a one-sample t-test, IDK), but you do have a concern regarding adequate power with small samples. You may be interested in rea... | Is there a minimum sample size required for the t-test to be valid? | With all deference to him, he doesn't know what he's talking about. The t-test was designed for working with small samples. There isn't really a minimum (maybe you could say a minimum of 3 for a one-s | Is there a minimum sample size required for the t-test to be valid?
With all deference to him, he doesn't know what he's talking about. The t-test was designed for working with small samples. There isn't really a minimum (maybe you could say a minimum of 3 for a one-sample t-test, IDK), but you do have a concern regard... | Is there a minimum sample size required for the t-test to be valid?
With all deference to him, he doesn't know what he's talking about. The t-test was designed for working with small samples. There isn't really a minimum (maybe you could say a minimum of 3 for a one-s |
2,118 | Is there a minimum sample size required for the t-test to be valid? | As mentioned in existing answers, the main issue with a small sample size is low statistical power. There are various rules of thumb regarding what is acceptable statistical power. Some people say 80% statistical power is reasonable, but ultimately, more is better. There is also generally a trade-off between the cost o... | Is there a minimum sample size required for the t-test to be valid? | As mentioned in existing answers, the main issue with a small sample size is low statistical power. There are various rules of thumb regarding what is acceptable statistical power. Some people say 80% | Is there a minimum sample size required for the t-test to be valid?
As mentioned in existing answers, the main issue with a small sample size is low statistical power. There are various rules of thumb regarding what is acceptable statistical power. Some people say 80% statistical power is reasonable, but ultimately, mo... | Is there a minimum sample size required for the t-test to be valid?
As mentioned in existing answers, the main issue with a small sample size is low statistical power. There are various rules of thumb regarding what is acceptable statistical power. Some people say 80% |
2,119 | Is there a minimum sample size required for the t-test to be valid? | Consider the following from pp. 254-256 of Sauro, J., & Lewis, J. R. (2016). Quantifying the User Experience: Practical Statistics for User Research, 2nd Ed. Cambridge, MA: Morgan-Kaufmann (you can look inside at https://www.amazon.com/Quantifying-User-Experience-Second-Statistics/dp/0128023082/).
DO YOU NEED TO TE... | Is there a minimum sample size required for the t-test to be valid? | Consider the following from pp. 254-256 of Sauro, J., & Lewis, J. R. (2016). Quantifying the User Experience: Practical Statistics for User Research, 2nd Ed. Cambridge, MA: Morgan-Kaufmann (you can | Is there a minimum sample size required for the t-test to be valid?
Consider the following from pp. 254-256 of Sauro, J., & Lewis, J. R. (2016). Quantifying the User Experience: Practical Statistics for User Research, 2nd Ed. Cambridge, MA: Morgan-Kaufmann (you can look inside at https://www.amazon.com/Quantifying-U... | Is there a minimum sample size required for the t-test to be valid?
Consider the following from pp. 254-256 of Sauro, J., & Lewis, J. R. (2016). Quantifying the User Experience: Practical Statistics for User Research, 2nd Ed. Cambridge, MA: Morgan-Kaufmann (you can |
2,120 | Is there a minimum sample size required for the t-test to be valid? | The two-sample t-test is valid if the two samples are independent simple random samples from Normal distributions with the same variance and each of the sample sizes is at least two (so that the population variance can be estimated.) Considerations of power are irrelevant to the question of the validity of the test. De... | Is there a minimum sample size required for the t-test to be valid? | The two-sample t-test is valid if the two samples are independent simple random samples from Normal distributions with the same variance and each of the sample sizes is at least two (so that the popul | Is there a minimum sample size required for the t-test to be valid?
The two-sample t-test is valid if the two samples are independent simple random samples from Normal distributions with the same variance and each of the sample sizes is at least two (so that the population variance can be estimated.) Considerations of ... | Is there a minimum sample size required for the t-test to be valid?
The two-sample t-test is valid if the two samples are independent simple random samples from Normal distributions with the same variance and each of the sample sizes is at least two (so that the popul |
2,121 | Is there a minimum sample size required for the t-test to be valid? | While it is true that the t-distribution takes into account the small sample size, I would assume that your referee was thinking about the difficulty of establishing that the population is normally distributed, when the only information you have is a relatively small sample? This may not be a huge issue with a sample ... | Is there a minimum sample size required for the t-test to be valid? | While it is true that the t-distribution takes into account the small sample size, I would assume that your referee was thinking about the difficulty of establishing that the population is normally di | Is there a minimum sample size required for the t-test to be valid?
While it is true that the t-distribution takes into account the small sample size, I would assume that your referee was thinking about the difficulty of establishing that the population is normally distributed, when the only information you have is a r... | Is there a minimum sample size required for the t-test to be valid?
While it is true that the t-distribution takes into account the small sample size, I would assume that your referee was thinking about the difficulty of establishing that the population is normally di |
2,122 | Is there a minimum sample size required for the t-test to be valid? | There are two different ways to justify the use of the t-test.
Your data is normally distributed and you have at least two samples per group
You have large sample sizes in each group
If either of these cases hold, then the t-test is considered a valid test. So if you are willing to make the assumption that your dat... | Is there a minimum sample size required for the t-test to be valid? | There are two different ways to justify the use of the t-test.
Your data is normally distributed and you have at least two samples per group
You have large sample sizes in each group
If either of | Is there a minimum sample size required for the t-test to be valid?
There are two different ways to justify the use of the t-test.
Your data is normally distributed and you have at least two samples per group
You have large sample sizes in each group
If either of these cases hold, then the t-test is considered a va... | Is there a minimum sample size required for the t-test to be valid?
There are two different ways to justify the use of the t-test.
Your data is normally distributed and you have at least two samples per group
You have large sample sizes in each group
If either of |
2,123 | Is there a minimum sample size required for the t-test to be valid? | Czarina may find interesting to compare the results of her parametric t-test with the results obtained by a bootstrap t-test. The following code for Stata 13/1 mimics a fictitious example concerning a two-sample t-test with unequal variances (parametric t-test: p-value = 0.1493; bootstrap t-test: p-value = 0.1543).
se... | Is there a minimum sample size required for the t-test to be valid? | Czarina may find interesting to compare the results of her parametric t-test with the results obtained by a bootstrap t-test. The following code for Stata 13/1 mimics a fictitious example concerning a | Is there a minimum sample size required for the t-test to be valid?
Czarina may find interesting to compare the results of her parametric t-test with the results obtained by a bootstrap t-test. The following code for Stata 13/1 mimics a fictitious example concerning a two-sample t-test with unequal variances (parametri... | Is there a minimum sample size required for the t-test to be valid?
Czarina may find interesting to compare the results of her parametric t-test with the results obtained by a bootstrap t-test. The following code for Stata 13/1 mimics a fictitious example concerning a |
2,124 | Is there a minimum sample size required for the t-test to be valid? | I concur regarding the usefulness of a boostrapped t-test. I would also recommend, as a comparison, a look at the Bayesian method offered by Kruschke at http://www.indiana.edu/~kruschke/BEST/BEST.pdf. In general, questions of "How many subjects?" can't be answered unless you have in hand an idea of what a significant e... | Is there a minimum sample size required for the t-test to be valid? | I concur regarding the usefulness of a boostrapped t-test. I would also recommend, as a comparison, a look at the Bayesian method offered by Kruschke at http://www.indiana.edu/~kruschke/BEST/BEST.pdf. | Is there a minimum sample size required for the t-test to be valid?
I concur regarding the usefulness of a boostrapped t-test. I would also recommend, as a comparison, a look at the Bayesian method offered by Kruschke at http://www.indiana.edu/~kruschke/BEST/BEST.pdf. In general, questions of "How many subjects?" can't... | Is there a minimum sample size required for the t-test to be valid?
I concur regarding the usefulness of a boostrapped t-test. I would also recommend, as a comparison, a look at the Bayesian method offered by Kruschke at http://www.indiana.edu/~kruschke/BEST/BEST.pdf. |
2,125 | Is there a minimum sample size required for the t-test to be valid? | As far as assumptions go for the two sample case; it is that both samples are independent of each other and each sample consists of iid normal variables with the two samples having the same mean and a common unknown variance under the null hypothesis.
There is also the Welch t-test utilizing the Satterwaite Approxima... | Is there a minimum sample size required for the t-test to be valid? | As far as assumptions go for the two sample case; it is that both samples are independent of each other and each sample consists of iid normal variables with the two samples having the same mean and a | Is there a minimum sample size required for the t-test to be valid?
As far as assumptions go for the two sample case; it is that both samples are independent of each other and each sample consists of iid normal variables with the two samples having the same mean and a common unknown variance under the null hypothesis. ... | Is there a minimum sample size required for the t-test to be valid?
As far as assumptions go for the two sample case; it is that both samples are independent of each other and each sample consists of iid normal variables with the two samples having the same mean and a |
2,126 | Given the power of computers these days, is there ever a reason to do a chi-squared test rather than Fisher's exact test? | You can turn the question around. Since the ordinary Pearson $\chi^2$ test is almost always more accurate than Fisher's exact test and is much quicker to compute, why does anyone use Fisher's test?
Note that it is a fallacy that the expected cell frequencies have to exceed 5 for Pearson's $\chi^2$ to yield accurate $P... | Given the power of computers these days, is there ever a reason to do a chi-squared test rather than | You can turn the question around. Since the ordinary Pearson $\chi^2$ test is almost always more accurate than Fisher's exact test and is much quicker to compute, why does anyone use Fisher's test?
N | Given the power of computers these days, is there ever a reason to do a chi-squared test rather than Fisher's exact test?
You can turn the question around. Since the ordinary Pearson $\chi^2$ test is almost always more accurate than Fisher's exact test and is much quicker to compute, why does anyone use Fisher's test?... | Given the power of computers these days, is there ever a reason to do a chi-squared test rather than
You can turn the question around. Since the ordinary Pearson $\chi^2$ test is almost always more accurate than Fisher's exact test and is much quicker to compute, why does anyone use Fisher's test?
N |
2,127 | Given the power of computers these days, is there ever a reason to do a chi-squared test rather than Fisher's exact test? | This is a great question.
Fisher's exact test is one of the great examples of Fisher's clever use of experimental design, together with conditioning on data (basically on tables with the observed row and marginal totals) and his ingenuity at finding probability distributions (though this isn't the best example, for a b... | Given the power of computers these days, is there ever a reason to do a chi-squared test rather than | This is a great question.
Fisher's exact test is one of the great examples of Fisher's clever use of experimental design, together with conditioning on data (basically on tables with the observed row | Given the power of computers these days, is there ever a reason to do a chi-squared test rather than Fisher's exact test?
This is a great question.
Fisher's exact test is one of the great examples of Fisher's clever use of experimental design, together with conditioning on data (basically on tables with the observed ro... | Given the power of computers these days, is there ever a reason to do a chi-squared test rather than
This is a great question.
Fisher's exact test is one of the great examples of Fisher's clever use of experimental design, together with conditioning on data (basically on tables with the observed row |
2,128 | What is an "uninformative prior"? Can we ever have one with truly no information? | [Warning: as a card-carrying member of the Objective Bayes Section of ISBA, my views are not exactly representative of all Bayesian statisticians!, quite the opposite...]
In summary, there is no such thing as a prior with "truly no information".
Indeed, the concept of "uninformative" prior is sadly a misnomer. Any prio... | What is an "uninformative prior"? Can we ever have one with truly no information? | [Warning: as a card-carrying member of the Objective Bayes Section of ISBA, my views are not exactly representative of all Bayesian statisticians!, quite the opposite...]
In summary, there is no such | What is an "uninformative prior"? Can we ever have one with truly no information?
[Warning: as a card-carrying member of the Objective Bayes Section of ISBA, my views are not exactly representative of all Bayesian statisticians!, quite the opposite...]
In summary, there is no such thing as a prior with "truly no inform... | What is an "uninformative prior"? Can we ever have one with truly no information?
[Warning: as a card-carrying member of the Objective Bayes Section of ISBA, my views are not exactly representative of all Bayesian statisticians!, quite the opposite...]
In summary, there is no such |
2,129 | What is an "uninformative prior"? Can we ever have one with truly no information? | An appealing property of formal noninformative priors is the "frequentist-matching property" : it means that a posterior 95%-credibility interval is also (at least, approximately) a 95%-confidence interval in the frequentist sense.
This property holds for Bernardo's reference prior although the fundations of these non... | What is an "uninformative prior"? Can we ever have one with truly no information? | An appealing property of formal noninformative priors is the "frequentist-matching property" : it means that a posterior 95%-credibility interval is also (at least, approximately) a 95%-confidence int | What is an "uninformative prior"? Can we ever have one with truly no information?
An appealing property of formal noninformative priors is the "frequentist-matching property" : it means that a posterior 95%-credibility interval is also (at least, approximately) a 95%-confidence interval in the frequentist sense.
This ... | What is an "uninformative prior"? Can we ever have one with truly no information?
An appealing property of formal noninformative priors is the "frequentist-matching property" : it means that a posterior 95%-credibility interval is also (at least, approximately) a 95%-confidence int |
2,130 | What is an "uninformative prior"? Can we ever have one with truly no information? | Jeffreys distributions also suffer from inconsistencies: the Jeffreys priors for a variable over $(-\infty,\infty)$ or over $(0,\infty)$ are improper, which is not the case for the Jeffreys prior of a probability parameter $p$: the measure $\text{d}p/\sqrt{p(1-p)}$ has a mass of $\pi$ over $(0,1)$.
Renyi has shown that... | What is an "uninformative prior"? Can we ever have one with truly no information? | Jeffreys distributions also suffer from inconsistencies: the Jeffreys priors for a variable over $(-\infty,\infty)$ or over $(0,\infty)$ are improper, which is not the case for the Jeffreys prior of a | What is an "uninformative prior"? Can we ever have one with truly no information?
Jeffreys distributions also suffer from inconsistencies: the Jeffreys priors for a variable over $(-\infty,\infty)$ or over $(0,\infty)$ are improper, which is not the case for the Jeffreys prior of a probability parameter $p$: the measur... | What is an "uninformative prior"? Can we ever have one with truly no information?
Jeffreys distributions also suffer from inconsistencies: the Jeffreys priors for a variable over $(-\infty,\infty)$ or over $(0,\infty)$ are improper, which is not the case for the Jeffreys prior of a |
2,131 | What is an "uninformative prior"? Can we ever have one with truly no information? | I agree with the excellent answer by Xi'an, pointing out that there is no single prior that is "uninformative" in the sense of carrying no information. To expand on this topic, I wanted to point out that one alternative is to undertake Bayesian analysis within the imprecise probability framework (see esp. Walley 1991,... | What is an "uninformative prior"? Can we ever have one with truly no information? | I agree with the excellent answer by Xi'an, pointing out that there is no single prior that is "uninformative" in the sense of carrying no information. To expand on this topic, I wanted to point out | What is an "uninformative prior"? Can we ever have one with truly no information?
I agree with the excellent answer by Xi'an, pointing out that there is no single prior that is "uninformative" in the sense of carrying no information. To expand on this topic, I wanted to point out that one alternative is to undertake B... | What is an "uninformative prior"? Can we ever have one with truly no information?
I agree with the excellent answer by Xi'an, pointing out that there is no single prior that is "uninformative" in the sense of carrying no information. To expand on this topic, I wanted to point out |
2,132 | Essential data checking tests | It helps to understand how the data were recorded.
Let me share a story. Once, long ago, many datasets were stored only in fading hardcopy. In those dark days I contracted with an organization (of great pedigree and size; many of you probably own its stock) to computerize about 10^5 records of environmental monitorin... | Essential data checking tests | It helps to understand how the data were recorded.
Let me share a story. Once, long ago, many datasets were stored only in fading hardcopy. In those dark days I contracted with an organization (of g | Essential data checking tests
It helps to understand how the data were recorded.
Let me share a story. Once, long ago, many datasets were stored only in fading hardcopy. In those dark days I contracted with an organization (of great pedigree and size; many of you probably own its stock) to computerize about 10^5 reco... | Essential data checking tests
It helps to understand how the data were recorded.
Let me share a story. Once, long ago, many datasets were stored only in fading hardcopy. In those dark days I contracted with an organization (of g |
2,133 | Essential data checking tests | @whuber makes great suggestions; I would only add this: Plots, plots, plots, plots. Scatterplots, histograms, boxplots, lineplots, heatmaps and anything else you can think of. Of course, as you've found there are errors that won't be apparent on any plots but they're a good place to start. Just make sure you're clear o... | Essential data checking tests | @whuber makes great suggestions; I would only add this: Plots, plots, plots, plots. Scatterplots, histograms, boxplots, lineplots, heatmaps and anything else you can think of. Of course, as you've fou | Essential data checking tests
@whuber makes great suggestions; I would only add this: Plots, plots, plots, plots. Scatterplots, histograms, boxplots, lineplots, heatmaps and anything else you can think of. Of course, as you've found there are errors that won't be apparent on any plots but they're a good place to start.... | Essential data checking tests
@whuber makes great suggestions; I would only add this: Plots, plots, plots, plots. Scatterplots, histograms, boxplots, lineplots, heatmaps and anything else you can think of. Of course, as you've fou |
2,134 | Essential data checking tests | Big things I tend to check:
Variable type - to see that a number is numeric, and not factor/character (might indicate some problem with the data that was entered)
Consistent value levels - to see that a variable with the name "t1" didn't find it self again with the name "t1 " or "t 1"
Outliers - see that the ranges of... | Essential data checking tests | Big things I tend to check:
Variable type - to see that a number is numeric, and not factor/character (might indicate some problem with the data that was entered)
Consistent value levels - to see tha | Essential data checking tests
Big things I tend to check:
Variable type - to see that a number is numeric, and not factor/character (might indicate some problem with the data that was entered)
Consistent value levels - to see that a variable with the name "t1" didn't find it self again with the name "t1 " or "t 1"
Out... | Essential data checking tests
Big things I tend to check:
Variable type - to see that a number is numeric, and not factor/character (might indicate some problem with the data that was entered)
Consistent value levels - to see tha |
2,135 | Essential data checking tests | When you have measures along time ("longitudinal data") it is often useful to check the gradients as well as the marginal distributions. This gradient can be calculated at different scales. More generally you can do meaningful transformations on your data (fourier, wavelet) and check the distributions of the marginals ... | Essential data checking tests | When you have measures along time ("longitudinal data") it is often useful to check the gradients as well as the marginal distributions. This gradient can be calculated at different scales. More gener | Essential data checking tests
When you have measures along time ("longitudinal data") it is often useful to check the gradients as well as the marginal distributions. This gradient can be calculated at different scales. More generally you can do meaningful transformations on your data (fourier, wavelet) and check the d... | Essential data checking tests
When you have measures along time ("longitudinal data") it is often useful to check the gradients as well as the marginal distributions. This gradient can be calculated at different scales. More gener |
2,136 | Essential data checking tests | A few I always go through:
Are there the number of records there are supposed to be? For example, if you pulled your data from another source, or its a sub-set of someone elses data, do your numbers look reasonable. You'd think this would be covered, but you'd...be suprised.
Are all your variables there? Do the values... | Essential data checking tests | A few I always go through:
Are there the number of records there are supposed to be? For example, if you pulled your data from another source, or its a sub-set of someone elses data, do your numbers | Essential data checking tests
A few I always go through:
Are there the number of records there are supposed to be? For example, if you pulled your data from another source, or its a sub-set of someone elses data, do your numbers look reasonable. You'd think this would be covered, but you'd...be suprised.
Are all your ... | Essential data checking tests
A few I always go through:
Are there the number of records there are supposed to be? For example, if you pulled your data from another source, or its a sub-set of someone elses data, do your numbers |
2,137 | Essential data checking tests | I would use acceptance sampling method to each column ( it gives the cut-off number where you can draw the line between high quality and low quality) , there is an online calculator for that. | Essential data checking tests | I would use acceptance sampling method to each column ( it gives the cut-off number where you can draw the line between high quality and low quality) , there is an online calculator for that. | Essential data checking tests
I would use acceptance sampling method to each column ( it gives the cut-off number where you can draw the line between high quality and low quality) , there is an online calculator for that. | Essential data checking tests
I would use acceptance sampling method to each column ( it gives the cut-off number where you can draw the line between high quality and low quality) , there is an online calculator for that. |
2,138 | When to use an offset in a Poisson regression? [duplicate] | Here is an example of application.
Poisson regression is typically used to model count data. But, sometimes, it is more relevant to model rates instead of counts. This is relevant when, e.g., individuals are not followed the same amount of time. For example, six cases over 1 year should not amount to the same as six ca... | When to use an offset in a Poisson regression? [duplicate] | Here is an example of application.
Poisson regression is typically used to model count data. But, sometimes, it is more relevant to model rates instead of counts. This is relevant when, e.g., individu | When to use an offset in a Poisson regression? [duplicate]
Here is an example of application.
Poisson regression is typically used to model count data. But, sometimes, it is more relevant to model rates instead of counts. This is relevant when, e.g., individuals are not followed the same amount of time. For example, si... | When to use an offset in a Poisson regression? [duplicate]
Here is an example of application.
Poisson regression is typically used to model count data. But, sometimes, it is more relevant to model rates instead of counts. This is relevant when, e.g., individu |
2,139 | Why to optimize max log probability instead of probability | Gradient methods generally work better optimizing $\log p(x)$ than $p(x)$ because the gradient of $\log p(x)$ is generally more well-scaled. That is, it has a size that consistently and helpfully reflects the objective function's geometry, making it easier to select an appropriate step size and get to the optimum in fe... | Why to optimize max log probability instead of probability | Gradient methods generally work better optimizing $\log p(x)$ than $p(x)$ because the gradient of $\log p(x)$ is generally more well-scaled. That is, it has a size that consistently and helpfully refl | Why to optimize max log probability instead of probability
Gradient methods generally work better optimizing $\log p(x)$ than $p(x)$ because the gradient of $\log p(x)$ is generally more well-scaled. That is, it has a size that consistently and helpfully reflects the objective function's geometry, making it easier to s... | Why to optimize max log probability instead of probability
Gradient methods generally work better optimizing $\log p(x)$ than $p(x)$ because the gradient of $\log p(x)$ is generally more well-scaled. That is, it has a size that consistently and helpfully refl |
2,140 | Why to optimize max log probability instead of probability | Underflow
The computer uses a limited digit floating point representation of fractions, multiplying so many probabilities is guaranteed to be very very close to zero.
With $log$, we don't have this issue. | Why to optimize max log probability instead of probability | Underflow
The computer uses a limited digit floating point representation of fractions, multiplying so many probabilities is guaranteed to be very very close to zero.
With $log$, we don't have this is | Why to optimize max log probability instead of probability
Underflow
The computer uses a limited digit floating point representation of fractions, multiplying so many probabilities is guaranteed to be very very close to zero.
With $log$, we don't have this issue. | Why to optimize max log probability instead of probability
Underflow
The computer uses a limited digit floating point representation of fractions, multiplying so many probabilities is guaranteed to be very very close to zero.
With $log$, we don't have this is |
2,141 | Why to optimize max log probability instead of probability | The logarithm of the probability of multiple joint probabilities simplifies to the sum of the logarithms of the individual probabilities (and the sum rule is easier than the product rule for differentiation)
$\log \left(\prod_i P(x_i)\right) = \sum_i \log \left( P(x_i)\right)$
The logarithm of a member of the family of... | Why to optimize max log probability instead of probability | The logarithm of the probability of multiple joint probabilities simplifies to the sum of the logarithms of the individual probabilities (and the sum rule is easier than the product rule for different | Why to optimize max log probability instead of probability
The logarithm of the probability of multiple joint probabilities simplifies to the sum of the logarithms of the individual probabilities (and the sum rule is easier than the product rule for differentiation)
$\log \left(\prod_i P(x_i)\right) = \sum_i \log \left... | Why to optimize max log probability instead of probability
The logarithm of the probability of multiple joint probabilities simplifies to the sum of the logarithms of the individual probabilities (and the sum rule is easier than the product rule for different |
2,142 | Why to optimize max log probability instead of probability | It is much easier to take a derivative of sum of logarithms than to take a derivative of product, that contains, say, 100 multipliers. | Why to optimize max log probability instead of probability | It is much easier to take a derivative of sum of logarithms than to take a derivative of product, that contains, say, 100 multipliers. | Why to optimize max log probability instead of probability
It is much easier to take a derivative of sum of logarithms than to take a derivative of product, that contains, say, 100 multipliers. | Why to optimize max log probability instead of probability
It is much easier to take a derivative of sum of logarithms than to take a derivative of product, that contains, say, 100 multipliers. |
2,143 | Why to optimize max log probability instead of probability | As a general rule, the most basic and easy optimization problem is to optimize a quadratic function. You can easily find the optimum of such a function no matter where you start. How this manifests depends on the specific method but the closer your function to a quadratic, the better.
As noted by TemplateRex, in a wide... | Why to optimize max log probability instead of probability | As a general rule, the most basic and easy optimization problem is to optimize a quadratic function. You can easily find the optimum of such a function no matter where you start. How this manifests de | Why to optimize max log probability instead of probability
As a general rule, the most basic and easy optimization problem is to optimize a quadratic function. You can easily find the optimum of such a function no matter where you start. How this manifests depends on the specific method but the closer your function to ... | Why to optimize max log probability instead of probability
As a general rule, the most basic and easy optimization problem is to optimize a quadratic function. You can easily find the optimum of such a function no matter where you start. How this manifests de |
2,144 | Why to optimize max log probability instead of probability | By using the $\ln p$ we increase the dynamic range of the optimization algorithm. The $p$ in applications is usually a product of functions. For instance, in maximum likelihood estimation it's the product of the form $L(x|\theta)=\Pi_{i=1}^n f(x_i|\theta)$, where $f(.)$ is the density function, which can be greater or ... | Why to optimize max log probability instead of probability | By using the $\ln p$ we increase the dynamic range of the optimization algorithm. The $p$ in applications is usually a product of functions. For instance, in maximum likelihood estimation it's the pro | Why to optimize max log probability instead of probability
By using the $\ln p$ we increase the dynamic range of the optimization algorithm. The $p$ in applications is usually a product of functions. For instance, in maximum likelihood estimation it's the product of the form $L(x|\theta)=\Pi_{i=1}^n f(x_i|\theta)$, whe... | Why to optimize max log probability instead of probability
By using the $\ln p$ we increase the dynamic range of the optimization algorithm. The $p$ in applications is usually a product of functions. For instance, in maximum likelihood estimation it's the pro |
2,145 | Why to optimize max log probability instead of probability | Some nice answers have been given already. But I encountered recently a new one:
Often, you are given a huge training data set $\mathcal{X}$, and you define some probabilistic model $p(x|\theta)$, and you want to maximize the likelihood for $x \in \mathcal{X}$. They are assumed to be independent, i.e. you have
$$
p(\ma... | Why to optimize max log probability instead of probability | Some nice answers have been given already. But I encountered recently a new one:
Often, you are given a huge training data set $\mathcal{X}$, and you define some probabilistic model $p(x|\theta)$, and | Why to optimize max log probability instead of probability
Some nice answers have been given already. But I encountered recently a new one:
Often, you are given a huge training data set $\mathcal{X}$, and you define some probabilistic model $p(x|\theta)$, and you want to maximize the likelihood for $x \in \mathcal{X}$.... | Why to optimize max log probability instead of probability
Some nice answers have been given already. But I encountered recently a new one:
Often, you are given a huge training data set $\mathcal{X}$, and you define some probabilistic model $p(x|\theta)$, and |
2,146 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | There is a lot of information on this topic at the GLMM FAQ. However, in your particular case, I would suggest using
library(nlme)
m1 <- lme(value~status,random=~1|experiment,data=mydata)
anova(m1)
because you don't need any of the stuff that lmer offers (higher speed, handling of crossed random effects, GLMMs ...). l... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | There is a lot of information on this topic at the GLMM FAQ. However, in your particular case, I would suggest using
library(nlme)
m1 <- lme(value~status,random=~1|experiment,data=mydata)
anova(m1)
b | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
There is a lot of information on this topic at the GLMM FAQ. However, in your particular case, I would suggest using
library(nlme)
m1 <- lme(value~status,random=~1|experiment,data=mydata)
anova(m1)
because you don't need any of the stuf... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
There is a lot of information on this topic at the GLMM FAQ. However, in your particular case, I would suggest using
library(nlme)
m1 <- lme(value~status,random=~1|experiment,data=mydata)
anova(m1)
b |
2,147 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | You could use the package lmerTest. You just install/load it and the lmer models get extended. So e.g.
library(lmerTest)
lmm <- lmer(value~status+(1|experiment)))
summary(lmm)
anova(lmm)
would give you results with p-values. If p-values are the right indication is a little bit disputed, but if you want to have them, t... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | You could use the package lmerTest. You just install/load it and the lmer models get extended. So e.g.
library(lmerTest)
lmm <- lmer(value~status+(1|experiment)))
summary(lmm)
anova(lmm)
would give y | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
You could use the package lmerTest. You just install/load it and the lmer models get extended. So e.g.
library(lmerTest)
lmm <- lmer(value~status+(1|experiment)))
summary(lmm)
anova(lmm)
would give you results with p-values. If p-values... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
You could use the package lmerTest. You just install/load it and the lmer models get extended. So e.g.
library(lmerTest)
lmm <- lmer(value~status+(1|experiment)))
summary(lmm)
anova(lmm)
would give y |
2,148 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | If you can handle abandoning p-values (and you should), you can compute a likelihood ratio that would represent the weight of evidence for the effect of status via:
#compute a model where the effect of status is estimated
unrestricted_fit = lmer(
formula = value ~ (1|experiment) + status
, REML = F #because we ... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | If you can handle abandoning p-values (and you should), you can compute a likelihood ratio that would represent the weight of evidence for the effect of status via:
#compute a model where the effect o | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
If you can handle abandoning p-values (and you should), you can compute a likelihood ratio that would represent the weight of evidence for the effect of status via:
#compute a model where the effect of status is estimated
unrestricted_fi... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
If you can handle abandoning p-values (and you should), you can compute a likelihood ratio that would represent the weight of evidence for the effect of status via:
#compute a model where the effect o |
2,149 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | The issue is that the calculation of p-values for these models is not trivial, see dicussion here so the authors of the lme4 package have purposely chosen not to include p-values in the output. You may find a method of calculating these, but they will not necessarily be correct. | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | The issue is that the calculation of p-values for these models is not trivial, see dicussion here so the authors of the lme4 package have purposely chosen not to include p-values in the output. You ma | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
The issue is that the calculation of p-values for these models is not trivial, see dicussion here so the authors of the lme4 package have purposely chosen not to include p-values in the output. You may find a method of calculating these,... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
The issue is that the calculation of p-values for these models is not trivial, see dicussion here so the authors of the lme4 package have purposely chosen not to include p-values in the output. You ma |
2,150 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | Consider what you're asking. If you just want to know if the overall p-value for the effect of status passes some some sort of arbitrary cutoff value, like 0.05, then that's easy. First, you want to find out the overall effect. You could get that from anova.
m <- lmer(...) #just run your lmer command but save the mo... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | Consider what you're asking. If you just want to know if the overall p-value for the effect of status passes some some sort of arbitrary cutoff value, like 0.05, then that's easy. First, you want to | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
Consider what you're asking. If you just want to know if the overall p-value for the effect of status passes some some sort of arbitrary cutoff value, like 0.05, then that's easy. First, you want to find out the overall effect. You co... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
Consider what you're asking. If you just want to know if the overall p-value for the effect of status passes some some sort of arbitrary cutoff value, like 0.05, then that's easy. First, you want to |
2,151 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | Edit: This method is no longer supported in newer versions of lme4. Use the lmerTest package as suggested in this answer by pbx101.
There is a post on the R list by lme4's author for why p-values are not displayed. He suggests using MCMC samples instead, which you do using the pvals.fnc from the languageR package:
libr... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | Edit: This method is no longer supported in newer versions of lme4. Use the lmerTest package as suggested in this answer by pbx101.
There is a post on the R list by lme4's author for why p-values are | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
Edit: This method is no longer supported in newer versions of lme4. Use the lmerTest package as suggested in this answer by pbx101.
There is a post on the R list by lme4's author for why p-values are not displayed. He suggests using MCMC... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
Edit: This method is no longer supported in newer versions of lme4. Use the lmerTest package as suggested in this answer by pbx101.
There is a post on the R list by lme4's author for why p-values are |
2,152 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | Are you interested in knowing if the combined effect of status has a significant effect on value? If so, you can use the Anova function in the car package (not to be confused with the anova function in base R).
dat <- data.frame(
experiment = sample(c("A","B","C","D"), 264, replace=TRUE),
status = sample(c("D","R"... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | Are you interested in knowing if the combined effect of status has a significant effect on value? If so, you can use the Anova function in the car package (not to be confused with the anova function i | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
Are you interested in knowing if the combined effect of status has a significant effect on value? If so, you can use the Anova function in the car package (not to be confused with the anova function in base R).
dat <- data.frame(
exper... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
Are you interested in knowing if the combined effect of status has a significant effect on value? If so, you can use the Anova function in the car package (not to be confused with the anova function i |
2,153 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | Simply loading the afex package will print the p-values in the output of the lmer function from the lme4 package (you don't need to be using afex; just load it):
library(lme4) #for mixed model
library(afex) #for p-values
This will automatically add a p-value column to the output of the lmer(yourmodel) for the fixed ... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | Simply loading the afex package will print the p-values in the output of the lmer function from the lme4 package (you don't need to be using afex; just load it):
library(lme4) #for mixed model
librar | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
Simply loading the afex package will print the p-values in the output of the lmer function from the lme4 package (you don't need to be using afex; just load it):
library(lme4) #for mixed model
library(afex) #for p-values
This will aut... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
Simply loading the afex package will print the p-values in the output of the lmer function from the lme4 package (you don't need to be using afex; just load it):
library(lme4) #for mixed model
librar |
2,154 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | You could use parameters::p_value() to get the p-values. I found it to be very useful. | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | You could use parameters::p_value() to get the p-values. I found it to be very useful. | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
You could use parameters::p_value() to get the p-values. I found it to be very useful. | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
You could use parameters::p_value() to get the p-values. I found it to be very useful. |
2,155 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | The function pvals.fnc is not longer supported by lme4.
Using the package lmerTest package, it is possible to use other method to calculate the p-value, such as the Kenward-Roger's approximations
model=lmer(value~status+1|experiment)
anova(model, ddf="Kenward-Roger") | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | The function pvals.fnc is not longer supported by lme4.
Using the package lmerTest package, it is possible to use other method to calculate the p-value, such as the Kenward-Roger's approximations
mod | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
The function pvals.fnc is not longer supported by lme4.
Using the package lmerTest package, it is possible to use other method to calculate the p-value, such as the Kenward-Roger's approximations
model=lmer(value~status+1|experiment)
an... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
The function pvals.fnc is not longer supported by lme4.
Using the package lmerTest package, it is possible to use other method to calculate the p-value, such as the Kenward-Roger's approximations
mod |
2,156 | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | You can install the jtools package and use the summ function on the output model to get the p-value of the fixed effects.
library(lme4)
library(jtools)
model <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
summ(model)
FIXED EFFECTS:
---------------------------------------------------------
... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model? | You can install the jtools package and use the summ function on the output model to get the p-value of the fixed effects.
library(lme4)
library(jtools)
model <- lmer(Reaction ~ Days + (Days | Subject) | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
You can install the jtools package and use the summ function on the output model to get the p-value of the fixed effects.
library(lme4)
library(jtools)
model <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
summ(model)
FIXED EFFE... | How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
You can install the jtools package and use the summ function on the output model to get the p-value of the fixed effects.
library(lme4)
library(jtools)
model <- lmer(Reaction ~ Days + (Days | Subject) |
2,157 | What is "restricted maximum likelihood" and when should it be used? | As per ocram's answer, ML is biased for the estimation of variance components. But observe that the bias gets smaller for larger sample sizes. Hence in answer to your questions "...what are the advantages of REML vs ML ? Under what circumstances may REML be preferred over ML (or vice versa) when fitting a mixed effects... | What is "restricted maximum likelihood" and when should it be used? | As per ocram's answer, ML is biased for the estimation of variance components. But observe that the bias gets smaller for larger sample sizes. Hence in answer to your questions "...what are the advant | What is "restricted maximum likelihood" and when should it be used?
As per ocram's answer, ML is biased for the estimation of variance components. But observe that the bias gets smaller for larger sample sizes. Hence in answer to your questions "...what are the advantages of REML vs ML ? Under what circumstances may RE... | What is "restricted maximum likelihood" and when should it be used?
As per ocram's answer, ML is biased for the estimation of variance components. But observe that the bias gets smaller for larger sample sizes. Hence in answer to your questions "...what are the advant |
2,158 | What is "restricted maximum likelihood" and when should it be used? | Here is a quick answer...
Standard illustrative example
Let $y = (y_1, \dotsc, y_n)$ be a sample from a normal distribution $\mathrm{N}(\mu, \sigma^2$). Both $\mu$ and $\sigma^2$ are unknown. The maximum likelihood estimator of $\sigma^2$, obtained by taking the derivative of the log-likelihood with respect to $\sigma... | What is "restricted maximum likelihood" and when should it be used? | Here is a quick answer...
Standard illustrative example
Let $y = (y_1, \dotsc, y_n)$ be a sample from a normal distribution $\mathrm{N}(\mu, \sigma^2$). Both $\mu$ and $\sigma^2$ are unknown. The max | What is "restricted maximum likelihood" and when should it be used?
Here is a quick answer...
Standard illustrative example
Let $y = (y_1, \dotsc, y_n)$ be a sample from a normal distribution $\mathrm{N}(\mu, \sigma^2$). Both $\mu$ and $\sigma^2$ are unknown. The maximum likelihood estimator of $\sigma^2$, obtained by... | What is "restricted maximum likelihood" and when should it be used?
Here is a quick answer...
Standard illustrative example
Let $y = (y_1, \dotsc, y_n)$ be a sample from a normal distribution $\mathrm{N}(\mu, \sigma^2$). Both $\mu$ and $\sigma^2$ are unknown. The max |
2,159 | What is "restricted maximum likelihood" and when should it be used? | ML method underestimates the variance parameters because it assumes that the fixed parameters are known without uncertainty when estimating the variance parameters.
The REML method uses a mathematical trick to make the estimates for the variance parameters independent of the estimates for the fixed effects.
REML works... | What is "restricted maximum likelihood" and when should it be used? | ML method underestimates the variance parameters because it assumes that the fixed parameters are known without uncertainty when estimating the variance parameters.
The REML method uses a mathematica | What is "restricted maximum likelihood" and when should it be used?
ML method underestimates the variance parameters because it assumes that the fixed parameters are known without uncertainty when estimating the variance parameters.
The REML method uses a mathematical trick to make the estimates for the variance param... | What is "restricted maximum likelihood" and when should it be used?
ML method underestimates the variance parameters because it assumes that the fixed parameters are known without uncertainty when estimating the variance parameters.
The REML method uses a mathematica |
2,160 | When to use Fisher and Neyman-Pearson framework? | Let me start by defining the terms of the discussion as I see them. A p-value is the probability of getting a sample statistic (say, a sample mean) as far as, or further from some reference value than your sample statistic, if the reference value were the true population parameter. For example, a p-value answers the ... | When to use Fisher and Neyman-Pearson framework? | Let me start by defining the terms of the discussion as I see them. A p-value is the probability of getting a sample statistic (say, a sample mean) as far as, or further from some reference value tha | When to use Fisher and Neyman-Pearson framework?
Let me start by defining the terms of the discussion as I see them. A p-value is the probability of getting a sample statistic (say, a sample mean) as far as, or further from some reference value than your sample statistic, if the reference value were the true populatio... | When to use Fisher and Neyman-Pearson framework?
Let me start by defining the terms of the discussion as I see them. A p-value is the probability of getting a sample statistic (say, a sample mean) as far as, or further from some reference value tha |
2,161 | When to use Fisher and Neyman-Pearson framework? | Practicality is in the eye of the beholder, but;
Fisher's significance testing can be interpreted as a way of deciding whether or not the data suggests any interesting `signal'. We either reject the null hypothesis (which may be a Type I error) or don't say anything at all. For example, in lots of modern 'omics' appli... | When to use Fisher and Neyman-Pearson framework? | Practicality is in the eye of the beholder, but;
Fisher's significance testing can be interpreted as a way of deciding whether or not the data suggests any interesting `signal'. We either reject the | When to use Fisher and Neyman-Pearson framework?
Practicality is in the eye of the beholder, but;
Fisher's significance testing can be interpreted as a way of deciding whether or not the data suggests any interesting `signal'. We either reject the null hypothesis (which may be a Type I error) or don't say anything at ... | When to use Fisher and Neyman-Pearson framework?
Practicality is in the eye of the beholder, but;
Fisher's significance testing can be interpreted as a way of deciding whether or not the data suggests any interesting `signal'. We either reject the |
2,162 | When to use Fisher and Neyman-Pearson framework? | The whole point is that you cannot ignore the philosophical differences. A mathematical procedure in statistics doesn't just stand alone as something you apply without some underlying hypotheses, assumptions, theory... philosophy.
That said, if you insist on sticking with frequentist philosophies there might be a few ... | When to use Fisher and Neyman-Pearson framework? | The whole point is that you cannot ignore the philosophical differences. A mathematical procedure in statistics doesn't just stand alone as something you apply without some underlying hypotheses, ass | When to use Fisher and Neyman-Pearson framework?
The whole point is that you cannot ignore the philosophical differences. A mathematical procedure in statistics doesn't just stand alone as something you apply without some underlying hypotheses, assumptions, theory... philosophy.
That said, if you insist on sticking wi... | When to use Fisher and Neyman-Pearson framework?
The whole point is that you cannot ignore the philosophical differences. A mathematical procedure in statistics doesn't just stand alone as something you apply without some underlying hypotheses, ass |
2,163 | When to use Fisher and Neyman-Pearson framework? | My understanding is: p-value is to tell us what to believe (verifying a theory with sufficient data) while Neyman-Pearson approach is to tell us what to do (making best possible decisions even with limited data). So it looks to me that (small) p-value is more stringent while Neyman-Pearson approach is more pragmatic; T... | When to use Fisher and Neyman-Pearson framework? | My understanding is: p-value is to tell us what to believe (verifying a theory with sufficient data) while Neyman-Pearson approach is to tell us what to do (making best possible decisions even with li | When to use Fisher and Neyman-Pearson framework?
My understanding is: p-value is to tell us what to believe (verifying a theory with sufficient data) while Neyman-Pearson approach is to tell us what to do (making best possible decisions even with limited data). So it looks to me that (small) p-value is more stringent w... | When to use Fisher and Neyman-Pearson framework?
My understanding is: p-value is to tell us what to believe (verifying a theory with sufficient data) while Neyman-Pearson approach is to tell us what to do (making best possible decisions even with li |
2,164 | Kendall Tau or Spearman's rho? | I found that Spearman correlation is mostly used in place of usual linear correlation when working with integer valued scores on a measurement scale, when it has a moderate number of possible scores or when we don't want to make rely on assumptions about the bivariate relationships. As compared to Pearson coefficient, ... | Kendall Tau or Spearman's rho? | I found that Spearman correlation is mostly used in place of usual linear correlation when working with integer valued scores on a measurement scale, when it has a moderate number of possible scores o | Kendall Tau or Spearman's rho?
I found that Spearman correlation is mostly used in place of usual linear correlation when working with integer valued scores on a measurement scale, when it has a moderate number of possible scores or when we don't want to make rely on assumptions about the bivariate relationships. As co... | Kendall Tau or Spearman's rho?
I found that Spearman correlation is mostly used in place of usual linear correlation when working with integer valued scores on a measurement scale, when it has a moderate number of possible scores o |
2,165 | Kendall Tau or Spearman's rho? | I refer the honorable gentleman to my previous answer: "...confidence intervals for Spearman’s rS are less reliable and less interpretable than confidence intervals for Kendall’s τ-parameters", according to Kendall & Gibbons (1990). | Kendall Tau or Spearman's rho? | I refer the honorable gentleman to my previous answer: "...confidence intervals for Spearman’s rS are less reliable and less interpretable than confidence intervals for Kendall’s τ-parameters", accord | Kendall Tau or Spearman's rho?
I refer the honorable gentleman to my previous answer: "...confidence intervals for Spearman’s rS are less reliable and less interpretable than confidence intervals for Kendall’s τ-parameters", according to Kendall & Gibbons (1990). | Kendall Tau or Spearman's rho?
I refer the honorable gentleman to my previous answer: "...confidence intervals for Spearman’s rS are less reliable and less interpretable than confidence intervals for Kendall’s τ-parameters", accord |
2,166 | Kendall Tau or Spearman's rho? | Again somewhat philosophical answer; the basic difference is that Spearman's Rho is an attempt to extend R^2 (="variance explained") idea over nonlinear interactions, while Kendall's Tau is rather intended to be a test statistic for nonlinear correlation test.
So, Tau should be used for testing nonlinear correlations, ... | Kendall Tau or Spearman's rho? | Again somewhat philosophical answer; the basic difference is that Spearman's Rho is an attempt to extend R^2 (="variance explained") idea over nonlinear interactions, while Kendall's Tau is rather int | Kendall Tau or Spearman's rho?
Again somewhat philosophical answer; the basic difference is that Spearman's Rho is an attempt to extend R^2 (="variance explained") idea over nonlinear interactions, while Kendall's Tau is rather intended to be a test statistic for nonlinear correlation test.
So, Tau should be used for t... | Kendall Tau or Spearman's rho?
Again somewhat philosophical answer; the basic difference is that Spearman's Rho is an attempt to extend R^2 (="variance explained") idea over nonlinear interactions, while Kendall's Tau is rather int |
2,167 | Kendall Tau or Spearman's rho? | Here's a quote from Andrew Gilpin (1993) advocating Kendall's τ over Spearman's ρ for theoretical reasons:
"[Kendall's $τ$] approaches a normal distribution more rapidly than $ρ$, as $N$, the sample size, increases; and $τ$ is also more tractable mathematically, particularly when ties are present."
Reference
Gilpin,... | Kendall Tau or Spearman's rho? | Here's a quote from Andrew Gilpin (1993) advocating Kendall's τ over Spearman's ρ for theoretical reasons:
"[Kendall's $τ$] approaches a normal distribution more rapidly than $ρ$, as $N$, the sample | Kendall Tau or Spearman's rho?
Here's a quote from Andrew Gilpin (1993) advocating Kendall's τ over Spearman's ρ for theoretical reasons:
"[Kendall's $τ$] approaches a normal distribution more rapidly than $ρ$, as $N$, the sample size, increases; and $τ$ is also more tractable mathematically, particularly when ties a... | Kendall Tau or Spearman's rho?
Here's a quote from Andrew Gilpin (1993) advocating Kendall's τ over Spearman's ρ for theoretical reasons:
"[Kendall's $τ$] approaches a normal distribution more rapidly than $ρ$, as $N$, the sample |
2,168 | Kendall Tau or Spearman's rho? | FWIW, a quote from Myers & Well (research design and statistical analyses, second edition, 2003, p. 510). If you still care about the p-values;
Seigel and Castellan (1988, nonparametric statistics for the behavioral sciences) point out that, although $\tau$ and Spearman $\rho$ will generally have different values when... | Kendall Tau or Spearman's rho? | FWIW, a quote from Myers & Well (research design and statistical analyses, second edition, 2003, p. 510). If you still care about the p-values;
Seigel and Castellan (1988, nonparametric statistics fo | Kendall Tau or Spearman's rho?
FWIW, a quote from Myers & Well (research design and statistical analyses, second edition, 2003, p. 510). If you still care about the p-values;
Seigel and Castellan (1988, nonparametric statistics for the behavioral sciences) point out that, although $\tau$ and Spearman $\rho$ will gener... | Kendall Tau or Spearman's rho?
FWIW, a quote from Myers & Well (research design and statistical analyses, second edition, 2003, p. 510). If you still care about the p-values;
Seigel and Castellan (1988, nonparametric statistics fo |
2,169 | Why does k-means clustering algorithm use only Euclidean distance metric? | K-Means procedure - which is a vector quantization method often used as a clustering method - does not explicitly use pairwise distances between data points at all (in contrast to hierarchical and some other clusterings which allow for arbitrary proximity measure). It amounts to repeatedly assigning points to the close... | Why does k-means clustering algorithm use only Euclidean distance metric? | K-Means procedure - which is a vector quantization method often used as a clustering method - does not explicitly use pairwise distances between data points at all (in contrast to hierarchical and som | Why does k-means clustering algorithm use only Euclidean distance metric?
K-Means procedure - which is a vector quantization method often used as a clustering method - does not explicitly use pairwise distances between data points at all (in contrast to hierarchical and some other clusterings which allow for arbitrary ... | Why does k-means clustering algorithm use only Euclidean distance metric?
K-Means procedure - which is a vector quantization method often used as a clustering method - does not explicitly use pairwise distances between data points at all (in contrast to hierarchical and som |
2,170 | Why does k-means clustering algorithm use only Euclidean distance metric? | See also @ttnphns answer for an interpretation of k-means that actually involves pointwise Euclidean distances.
The way k-means is constructed is not based on distances.
K-means minimizes within-cluster variance. Now if you look at the definition of variance, it is identical to the sum of squared Euclidean distances fr... | Why does k-means clustering algorithm use only Euclidean distance metric? | See also @ttnphns answer for an interpretation of k-means that actually involves pointwise Euclidean distances.
The way k-means is constructed is not based on distances.
K-means minimizes within-clust | Why does k-means clustering algorithm use only Euclidean distance metric?
See also @ttnphns answer for an interpretation of k-means that actually involves pointwise Euclidean distances.
The way k-means is constructed is not based on distances.
K-means minimizes within-cluster variance. Now if you look at the definition... | Why does k-means clustering algorithm use only Euclidean distance metric?
See also @ttnphns answer for an interpretation of k-means that actually involves pointwise Euclidean distances.
The way k-means is constructed is not based on distances.
K-means minimizes within-clust |
2,171 | Why does k-means clustering algorithm use only Euclidean distance metric? | I might be a little pedantic here, but K-means is the name given to a particular algorithm that assigns labels to data points such that within cluster variances are minimized, and it is not the name for a "general technique".
K-means algorithm has been independently proposed from several fields, with strong interpret... | Why does k-means clustering algorithm use only Euclidean distance metric? | I might be a little pedantic here, but K-means is the name given to a particular algorithm that assigns labels to data points such that within cluster variances are minimized, and it is not the name | Why does k-means clustering algorithm use only Euclidean distance metric?
I might be a little pedantic here, but K-means is the name given to a particular algorithm that assigns labels to data points such that within cluster variances are minimized, and it is not the name for a "general technique".
K-means algorithm ... | Why does k-means clustering algorithm use only Euclidean distance metric?
I might be a little pedantic here, but K-means is the name given to a particular algorithm that assigns labels to data points such that within cluster variances are minimized, and it is not the name |
2,172 | Why does k-means clustering algorithm use only Euclidean distance metric? | Since this is apparently now a canonical question, and it hasn't been mentioned here yet:
One natural extension of k-means to use distance metrics other than the standard Euclidean distance on $\mathbb R^d$ is to use the kernel trick. This refers to the idea of implicitly mapping the inputs to a high-, or infinite-, di... | Why does k-means clustering algorithm use only Euclidean distance metric? | Since this is apparently now a canonical question, and it hasn't been mentioned here yet:
One natural extension of k-means to use distance metrics other than the standard Euclidean distance on $\mathb | Why does k-means clustering algorithm use only Euclidean distance metric?
Since this is apparently now a canonical question, and it hasn't been mentioned here yet:
One natural extension of k-means to use distance metrics other than the standard Euclidean distance on $\mathbb R^d$ is to use the kernel trick. This refers... | Why does k-means clustering algorithm use only Euclidean distance metric?
Since this is apparently now a canonical question, and it hasn't been mentioned here yet:
One natural extension of k-means to use distance metrics other than the standard Euclidean distance on $\mathb |
2,173 | Why does k-means clustering algorithm use only Euclidean distance metric? | I've read many interesting comments here, but let me add that Matlab's "personal" implementation of k-means supports 4 non-Euclidean distances [between data points and cluster centres]. The only comment from the documentation I can see about that is:
Distance measure, in p-dimensional space, used for minimization, spe... | Why does k-means clustering algorithm use only Euclidean distance metric? | I've read many interesting comments here, but let me add that Matlab's "personal" implementation of k-means supports 4 non-Euclidean distances [between data points and cluster centres]. The only comme | Why does k-means clustering algorithm use only Euclidean distance metric?
I've read many interesting comments here, but let me add that Matlab's "personal" implementation of k-means supports 4 non-Euclidean distances [between data points and cluster centres]. The only comment from the documentation I can see about that... | Why does k-means clustering algorithm use only Euclidean distance metric?
I've read many interesting comments here, but let me add that Matlab's "personal" implementation of k-means supports 4 non-Euclidean distances [between data points and cluster centres]. The only comme |
2,174 | Why does k-means clustering algorithm use only Euclidean distance metric? | From here:
Let us consider two documents A and B represented by the vectors in the above figure. The cosine treats both vectors as unit vectors by normalizing them, giving you a measure of the angle between the two vectors. It does provide an accurate measure of similarity but with no regard to magnitude. But magnitu... | Why does k-means clustering algorithm use only Euclidean distance metric? | From here:
Let us consider two documents A and B represented by the vectors in the above figure. The cosine treats both vectors as unit vectors by normalizing them, giving you a measure of the angle | Why does k-means clustering algorithm use only Euclidean distance metric?
From here:
Let us consider two documents A and B represented by the vectors in the above figure. The cosine treats both vectors as unit vectors by normalizing them, giving you a measure of the angle between the two vectors. It does provide an a... | Why does k-means clustering algorithm use only Euclidean distance metric?
From here:
Let us consider two documents A and B represented by the vectors in the above figure. The cosine treats both vectors as unit vectors by normalizing them, giving you a measure of the angle |
2,175 | How should outliers be dealt with in linear regression analysis? | Rather than exclude outliers, you can use a robust method of regression. In R, for example, the rlm() function from the MASS package can be used instead of the lm() function. The method of estimation can be tuned to be more or less robust to outliers. | How should outliers be dealt with in linear regression analysis? | Rather than exclude outliers, you can use a robust method of regression. In R, for example, the rlm() function from the MASS package can be used instead of the lm() function. The method of estimation | How should outliers be dealt with in linear regression analysis?
Rather than exclude outliers, you can use a robust method of regression. In R, for example, the rlm() function from the MASS package can be used instead of the lm() function. The method of estimation can be tuned to be more or less robust to outliers. | How should outliers be dealt with in linear regression analysis?
Rather than exclude outliers, you can use a robust method of regression. In R, for example, the rlm() function from the MASS package can be used instead of the lm() function. The method of estimation |
2,176 | How should outliers be dealt with in linear regression analysis? | Sometimes outliers are bad data, and should be excluded, such as typos. Sometimes they are Wayne Gretzky or Michael Jordan, and should be kept.
Outlier detection methods include:
Univariate -> boxplot. outside of 1.5 times inter-quartile range is an outlier.
Bivariate -> scatterplot with confidence ellipse. outside of... | How should outliers be dealt with in linear regression analysis? | Sometimes outliers are bad data, and should be excluded, such as typos. Sometimes they are Wayne Gretzky or Michael Jordan, and should be kept.
Outlier detection methods include:
Univariate -> boxplo | How should outliers be dealt with in linear regression analysis?
Sometimes outliers are bad data, and should be excluded, such as typos. Sometimes they are Wayne Gretzky or Michael Jordan, and should be kept.
Outlier detection methods include:
Univariate -> boxplot. outside of 1.5 times inter-quartile range is an outl... | How should outliers be dealt with in linear regression analysis?
Sometimes outliers are bad data, and should be excluded, such as typos. Sometimes they are Wayne Gretzky or Michael Jordan, and should be kept.
Outlier detection methods include:
Univariate -> boxplo |
2,177 | How should outliers be dealt with in linear regression analysis? | I do think there is something to be said for just excluding the outliers. A regression line is supposed to summarise the data. Because of leverage you can have a situation where 1% of your data points affects the slope by 50%.
It's only dangerous from a moral and scientific point of view if you don't tell anybody that ... | How should outliers be dealt with in linear regression analysis? | I do think there is something to be said for just excluding the outliers. A regression line is supposed to summarise the data. Because of leverage you can have a situation where 1% of your data points | How should outliers be dealt with in linear regression analysis?
I do think there is something to be said for just excluding the outliers. A regression line is supposed to summarise the data. Because of leverage you can have a situation where 1% of your data points affects the slope by 50%.
It's only dangerous from a m... | How should outliers be dealt with in linear regression analysis?
I do think there is something to be said for just excluding the outliers. A regression line is supposed to summarise the data. Because of leverage you can have a situation where 1% of your data points |
2,178 | How should outliers be dealt with in linear regression analysis? | Sharpie,
Taking your question literally, I would argue that there are no statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis (as opposed to determining whether or not a given observation is an outlier). This must come from subject-area knowledge.
I think the... | How should outliers be dealt with in linear regression analysis? | Sharpie,
Taking your question literally, I would argue that there are no statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis (as opposed to | How should outliers be dealt with in linear regression analysis?
Sharpie,
Taking your question literally, I would argue that there are no statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis (as opposed to determining whether or not a given observation is an o... | How should outliers be dealt with in linear regression analysis?
Sharpie,
Taking your question literally, I would argue that there are no statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis (as opposed to |
2,179 | How should outliers be dealt with in linear regression analysis? | I've published a method for identifying outliers in nonlinear regression, and it can be also used when fitting a linear model.
HJ Motulsky and RE Brown. Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate. BMC Bioinformatics 200... | How should outliers be dealt with in linear regression analysis? | I've published a method for identifying outliers in nonlinear regression, and it can be also used when fitting a linear model.
HJ Motulsky and RE Brown. Detecting outliers when fitting data with nonli | How should outliers be dealt with in linear regression analysis?
I've published a method for identifying outliers in nonlinear regression, and it can be also used when fitting a linear model.
HJ Motulsky and RE Brown. Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinea... | How should outliers be dealt with in linear regression analysis?
I've published a method for identifying outliers in nonlinear regression, and it can be also used when fitting a linear model.
HJ Motulsky and RE Brown. Detecting outliers when fitting data with nonli |
2,180 | How should outliers be dealt with in linear regression analysis? | There are two statistical distance measures that are specifically catered to detecting outliers and then considering whether such outliers should be removed from your linear regression.
The first one is Cook's distance. You can find a pretty good explanation of it at Wikipedia: http://en.wikipedia.org/wiki/Cook%27s_... | How should outliers be dealt with in linear regression analysis? | There are two statistical distance measures that are specifically catered to detecting outliers and then considering whether such outliers should be removed from your linear regression.
The first on | How should outliers be dealt with in linear regression analysis?
There are two statistical distance measures that are specifically catered to detecting outliers and then considering whether such outliers should be removed from your linear regression.
The first one is Cook's distance. You can find a pretty good expla... | How should outliers be dealt with in linear regression analysis?
There are two statistical distance measures that are specifically catered to detecting outliers and then considering whether such outliers should be removed from your linear regression.
The first on |
2,181 | How should outliers be dealt with in linear regression analysis? | Garbage in, garbage out....
Implicit in getting the full benefit of linear regression is that the noise follows a normal distribution. Ideally you have mostly data and a little noise.... not mostly noise and a little data. You can test for normality of residuals after the linear fit by looking at the residuals. You ... | How should outliers be dealt with in linear regression analysis? | Garbage in, garbage out....
Implicit in getting the full benefit of linear regression is that the noise follows a normal distribution. Ideally you have mostly data and a little noise.... not mostly n | How should outliers be dealt with in linear regression analysis?
Garbage in, garbage out....
Implicit in getting the full benefit of linear regression is that the noise follows a normal distribution. Ideally you have mostly data and a little noise.... not mostly noise and a little data. You can test for normality of ... | How should outliers be dealt with in linear regression analysis?
Garbage in, garbage out....
Implicit in getting the full benefit of linear regression is that the noise follows a normal distribution. Ideally you have mostly data and a little noise.... not mostly n |
2,182 | How should outliers be dealt with in linear regression analysis? | For a linear regression you could use a repeated median straight line fit. | How should outliers be dealt with in linear regression analysis? | For a linear regression you could use a repeated median straight line fit. | How should outliers be dealt with in linear regression analysis?
For a linear regression you could use a repeated median straight line fit. | How should outliers be dealt with in linear regression analysis?
For a linear regression you could use a repeated median straight line fit. |
2,183 | How should outliers be dealt with in linear regression analysis? | Statistical tests to be used as a basis for exclusion:
- standardised residuals
- leverage statistics
- Cook's distance, which is a combination of the two above.
From experience, exclusion should be limited to instances of incorrect data entry. Reweighting outliers in the linear regression model is a very good compromi... | How should outliers be dealt with in linear regression analysis? | Statistical tests to be used as a basis for exclusion:
- standardised residuals
- leverage statistics
- Cook's distance, which is a combination of the two above.
From experience, exclusion should be l | How should outliers be dealt with in linear regression analysis?
Statistical tests to be used as a basis for exclusion:
- standardised residuals
- leverage statistics
- Cook's distance, which is a combination of the two above.
From experience, exclusion should be limited to instances of incorrect data entry. Reweightin... | How should outliers be dealt with in linear regression analysis?
Statistical tests to be used as a basis for exclusion:
- standardised residuals
- leverage statistics
- Cook's distance, which is a combination of the two above.
From experience, exclusion should be l |
2,184 | How should outliers be dealt with in linear regression analysis? | in linear regression we can handle outlier using below steps:
Using training data find best hyperplane or line that best fit
Find points which are far away from the line or hyperplane
pointer which is very far away from hyperplane remove them considering those point as an outlier. i.e. D(train)=D(train)-outlier
retra... | How should outliers be dealt with in linear regression analysis? | in linear regression we can handle outlier using below steps:
Using training data find best hyperplane or line that best fit
Find points which are far away from the line or hyperplane
pointer which i | How should outliers be dealt with in linear regression analysis?
in linear regression we can handle outlier using below steps:
Using training data find best hyperplane or line that best fit
Find points which are far away from the line or hyperplane
pointer which is very far away from hyperplane remove them considering... | How should outliers be dealt with in linear regression analysis?
in linear regression we can handle outlier using below steps:
Using training data find best hyperplane or line that best fit
Find points which are far away from the line or hyperplane
pointer which i |
2,185 | How to interpret an inverse covariance or precision matrix? | There are basically two things to be said. The first is that if you look at the density for the multivariate normal distribution (with mean 0 here) it is proportional to
$$\exp\left(-\frac{1}{2}x^T P x\right)$$
where $P = \Sigma^{-1}$ is the inverse of the covariance matrix, also called the precision. This matrix is p... | How to interpret an inverse covariance or precision matrix? | There are basically two things to be said. The first is that if you look at the density for the multivariate normal distribution (with mean 0 here) it is proportional to
$$\exp\left(-\frac{1}{2}x^T P | How to interpret an inverse covariance or precision matrix?
There are basically two things to be said. The first is that if you look at the density for the multivariate normal distribution (with mean 0 here) it is proportional to
$$\exp\left(-\frac{1}{2}x^T P x\right)$$
where $P = \Sigma^{-1}$ is the inverse of the co... | How to interpret an inverse covariance or precision matrix?
There are basically two things to be said. The first is that if you look at the density for the multivariate normal distribution (with mean 0 here) it is proportional to
$$\exp\left(-\frac{1}{2}x^T P |
2,186 | How to interpret an inverse covariance or precision matrix? | I like this probabilistic graphical model to illustrate NRH's point that the partial correlation is zero if and only if X is conditionally independent from Y given Z, with the assumption that all involved variables are multivariate Gaussian (the property does not hold in the general case):
(the $y_i$ are Gaussian rand... | How to interpret an inverse covariance or precision matrix? | I like this probabilistic graphical model to illustrate NRH's point that the partial correlation is zero if and only if X is conditionally independent from Y given Z, with the assumption that all invo | How to interpret an inverse covariance or precision matrix?
I like this probabilistic graphical model to illustrate NRH's point that the partial correlation is zero if and only if X is conditionally independent from Y given Z, with the assumption that all involved variables are multivariate Gaussian (the property does ... | How to interpret an inverse covariance or precision matrix?
I like this probabilistic graphical model to illustrate NRH's point that the partial correlation is zero if and only if X is conditionally independent from Y given Z, with the assumption that all invo |
2,187 | How to interpret an inverse covariance or precision matrix? | The interpretation based on partial correlations is probably the most statistically useful, since it applies to all multivariate distributions. In the special case of the multivariate Normal distribution, zero partial correlation corresponds to conditional independence.
You can derive this interpretation by using the ... | How to interpret an inverse covariance or precision matrix? | The interpretation based on partial correlations is probably the most statistically useful, since it applies to all multivariate distributions. In the special case of the multivariate Normal distribu | How to interpret an inverse covariance or precision matrix?
The interpretation based on partial correlations is probably the most statistically useful, since it applies to all multivariate distributions. In the special case of the multivariate Normal distribution, zero partial correlation corresponds to conditional in... | How to interpret an inverse covariance or precision matrix?
The interpretation based on partial correlations is probably the most statistically useful, since it applies to all multivariate distributions. In the special case of the multivariate Normal distribu |
2,188 | How to interpret an inverse covariance or precision matrix? | Covariance matrix can represent relations between all variables while inverse covariance shows the relations of elements with their neighbors (as wikipedia said partial/pair wise relations).
I borrow the following example from here in 24:10. Imagine 5 masses are connected together and vowelling around with 6 springs. T... | How to interpret an inverse covariance or precision matrix? | Covariance matrix can represent relations between all variables while inverse covariance shows the relations of elements with their neighbors (as wikipedia said partial/pair wise relations).
I borrow | How to interpret an inverse covariance or precision matrix?
Covariance matrix can represent relations between all variables while inverse covariance shows the relations of elements with their neighbors (as wikipedia said partial/pair wise relations).
I borrow the following example from here in 24:10. Imagine 5 masses a... | How to interpret an inverse covariance or precision matrix?
Covariance matrix can represent relations between all variables while inverse covariance shows the relations of elements with their neighbors (as wikipedia said partial/pair wise relations).
I borrow |
2,189 | How to interpret an inverse covariance or precision matrix? | Bar-Shalom and Fortmann (1988) make mention of the inverse covariance in the context of Kalman filtering as follows:
...[T]here is a recursion for the inverse covariance (or information matrix)
$\mathbf{P}^{-1}(k+1|k+1) = \mathbf{P}^{-1}(k+1|k) + \mathbf{H}'(k+1) \mathbf{R}^{-1}(k+1)\mathbf{H}(k+1)$
...Indeed, a compl... | How to interpret an inverse covariance or precision matrix? | Bar-Shalom and Fortmann (1988) make mention of the inverse covariance in the context of Kalman filtering as follows:
...[T]here is a recursion for the inverse covariance (or information matrix)
$\mat | How to interpret an inverse covariance or precision matrix?
Bar-Shalom and Fortmann (1988) make mention of the inverse covariance in the context of Kalman filtering as follows:
...[T]here is a recursion for the inverse covariance (or information matrix)
$\mathbf{P}^{-1}(k+1|k+1) = \mathbf{P}^{-1}(k+1|k) + \mathbf{H}'(... | How to interpret an inverse covariance or precision matrix?
Bar-Shalom and Fortmann (1988) make mention of the inverse covariance in the context of Kalman filtering as follows:
...[T]here is a recursion for the inverse covariance (or information matrix)
$\mat |
2,190 | Euclidean distance is usually not good for sparse data (and more general case)? | Here is a simple toy example illustrating the effect of dimension in a discrimination problem e.g. the problem you face when you want to say if something is observed or if only random effect is observed (this problem is a classic in science).
Heuristic. The key issue here is that the Euclidian norm gives the same imp... | Euclidean distance is usually not good for sparse data (and more general case)? | Here is a simple toy example illustrating the effect of dimension in a discrimination problem e.g. the problem you face when you want to say if something is observed or if only random effect is observ | Euclidean distance is usually not good for sparse data (and more general case)?
Here is a simple toy example illustrating the effect of dimension in a discrimination problem e.g. the problem you face when you want to say if something is observed or if only random effect is observed (this problem is a classic in science... | Euclidean distance is usually not good for sparse data (and more general case)?
Here is a simple toy example illustrating the effect of dimension in a discrimination problem e.g. the problem you face when you want to say if something is observed or if only random effect is observ |
2,191 | Euclidean distance is usually not good for sparse data (and more general case)? | I believe it is not so much the sparsity, but the high dimensionality usually associated with sparse data. But maybe it is even worse when the data is very sparse. Because then the distance of any two objects will likely be a quadratic mean of their lengths, or
$$\lim_{dim\rightarrow\infty}d(x,y) = ||x-y|| \rightarrow_... | Euclidean distance is usually not good for sparse data (and more general case)? | I believe it is not so much the sparsity, but the high dimensionality usually associated with sparse data. But maybe it is even worse when the data is very sparse. Because then the distance of any two | Euclidean distance is usually not good for sparse data (and more general case)?
I believe it is not so much the sparsity, but the high dimensionality usually associated with sparse data. But maybe it is even worse when the data is very sparse. Because then the distance of any two objects will likely be a quadratic mean... | Euclidean distance is usually not good for sparse data (and more general case)?
I believe it is not so much the sparsity, but the high dimensionality usually associated with sparse data. But maybe it is even worse when the data is very sparse. Because then the distance of any two |
2,192 | Euclidean distance is usually not good for sparse data (and more general case)? | I'd suggest starting with
Cosine distance,
not Euclidean, for any data with most vectors nearly orthogonal,
$x \cdot y \approx$ 0.
To see why, look at
$|x - y|^2 = |x|^2 + |y|^2 - 2\ x \cdot y$.
If $x \cdot y \approx$ 0, this reduces to
$|x|^2 + |y|^2$:
a crummy measure of distance, as Anony-Mousse points out.
Cosine ... | Euclidean distance is usually not good for sparse data (and more general case)? | I'd suggest starting with
Cosine distance,
not Euclidean, for any data with most vectors nearly orthogonal,
$x \cdot y \approx$ 0.
To see why, look at
$|x - y|^2 = |x|^2 + |y|^2 - 2\ x \cdot y$.
If $x | Euclidean distance is usually not good for sparse data (and more general case)?
I'd suggest starting with
Cosine distance,
not Euclidean, for any data with most vectors nearly orthogonal,
$x \cdot y \approx$ 0.
To see why, look at
$|x - y|^2 = |x|^2 + |y|^2 - 2\ x \cdot y$.
If $x \cdot y \approx$ 0, this reduces to
$|x... | Euclidean distance is usually not good for sparse data (and more general case)?
I'd suggest starting with
Cosine distance,
not Euclidean, for any data with most vectors nearly orthogonal,
$x \cdot y \approx$ 0.
To see why, look at
$|x - y|^2 = |x|^2 + |y|^2 - 2\ x \cdot y$.
If $x |
2,193 | Euclidean distance is usually not good for sparse data (and more general case)? | Part of the curse of dimensionality is that data start to spread out away from the center. This is true for multivariate normal and even when the components are IID (spherical normal). But if you want to strictly speak about Euclidean distance even in low dimensional space if the data have a correlation structure Euc... | Euclidean distance is usually not good for sparse data (and more general case)? | Part of the curse of dimensionality is that data start to spread out away from the center. This is true for multivariate normal and even when the components are IID (spherical normal). But if you wa | Euclidean distance is usually not good for sparse data (and more general case)?
Part of the curse of dimensionality is that data start to spread out away from the center. This is true for multivariate normal and even when the components are IID (spherical normal). But if you want to strictly speak about Euclidean dis... | Euclidean distance is usually not good for sparse data (and more general case)?
Part of the curse of dimensionality is that data start to spread out away from the center. This is true for multivariate normal and even when the components are IID (spherical normal). But if you wa |
2,194 | Euclidean distance is usually not good for sparse data (and more general case)? | I believe this is related to the curse of dimensionality / concentration of measure but I can no longer find the discussion that motivates this remark. I believe there was a thread on metaoptimize but I failed to Google it...
For text data, normalizing the vectors using TF-IDF and then applying cosine similarity will p... | Euclidean distance is usually not good for sparse data (and more general case)? | I believe this is related to the curse of dimensionality / concentration of measure but I can no longer find the discussion that motivates this remark. I believe there was a thread on metaoptimize but | Euclidean distance is usually not good for sparse data (and more general case)?
I believe this is related to the curse of dimensionality / concentration of measure but I can no longer find the discussion that motivates this remark. I believe there was a thread on metaoptimize but I failed to Google it...
For text data,... | Euclidean distance is usually not good for sparse data (and more general case)?
I believe this is related to the curse of dimensionality / concentration of measure but I can no longer find the discussion that motivates this remark. I believe there was a thread on metaoptimize but |
2,195 | Euclidean distance is usually not good for sparse data (and more general case)? | An axiomatic measure of sparsity is the so-called $\ell_0$ count, that counts the (finite) number of non-zero entries in a vector. With this measure, vectors $(1,0,0,0)$ and $(0,21,0,0)$ possess the same sparsity. And absolutely not the same $\ell_2$ norm. And $(1,0,0,0)$ (very sparse) has the same $\ell_2$ norm as $\... | Euclidean distance is usually not good for sparse data (and more general case)? | An axiomatic measure of sparsity is the so-called $\ell_0$ count, that counts the (finite) number of non-zero entries in a vector. With this measure, vectors $(1,0,0,0)$ and $(0,21,0,0)$ possess the s | Euclidean distance is usually not good for sparse data (and more general case)?
An axiomatic measure of sparsity is the so-called $\ell_0$ count, that counts the (finite) number of non-zero entries in a vector. With this measure, vectors $(1,0,0,0)$ and $(0,21,0,0)$ possess the same sparsity. And absolutely not the sam... | Euclidean distance is usually not good for sparse data (and more general case)?
An axiomatic measure of sparsity is the so-called $\ell_0$ count, that counts the (finite) number of non-zero entries in a vector. With this measure, vectors $(1,0,0,0)$ and $(0,21,0,0)$ possess the s |
2,196 | Euclidean distance is usually not good for sparse data (and more general case)? | The paper On the surprising behavior of distance metrics in high dimensional space discusses the behaviour of distance metrics in high dimensional spaces.
They take on the $L_k$ norm and propose the manhattan $L_1$ norm as the most effective in high dimensional spaces for clustering purposes. They also introduce a frac... | Euclidean distance is usually not good for sparse data (and more general case)? | The paper On the surprising behavior of distance metrics in high dimensional space discusses the behaviour of distance metrics in high dimensional spaces.
They take on the $L_k$ norm and propose the m | Euclidean distance is usually not good for sparse data (and more general case)?
The paper On the surprising behavior of distance metrics in high dimensional space discusses the behaviour of distance metrics in high dimensional spaces.
They take on the $L_k$ norm and propose the manhattan $L_1$ norm as the most effectiv... | Euclidean distance is usually not good for sparse data (and more general case)?
The paper On the surprising behavior of distance metrics in high dimensional space discusses the behaviour of distance metrics in high dimensional spaces.
They take on the $L_k$ norm and propose the m |
2,197 | Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomization tests, and permutation tests | We can find different Resampling methods, or loosely called "simulation" methods, that depend upon resampling or shuffling of the samples. There might be differences in opinions with respect to proper terminology, but the following discussion tries to generalize and simplify what is available in the appropriate literat... | Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomiz | We can find different Resampling methods, or loosely called "simulation" methods, that depend upon resampling or shuffling of the samples. There might be differences in opinions with respect to proper | Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomization tests, and permutation tests
We can find different Resampling methods, or loosely called "simulation" methods, that depend upon resampling or shuffling of the samples. There might be differences in opinions with re... | Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomiz
We can find different Resampling methods, or loosely called "simulation" methods, that depend upon resampling or shuffling of the samples. There might be differences in opinions with respect to proper |
2,198 | Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomization tests, and permutation tests | Here's my contribution.
Data
Yvar <- c(8,9,10,13,12,
14,18,12,8,9,
1,3,2,3,4)
Xvar <- rep(LETTERS[1:3], each=5)
mydf <- data.frame(Yvar, Xvar)
Monte Carlo
I see Monte Carlo as a method to obtain a distribution of an (outcome) random variable, which is the result of a nontrivial function of other (i... | Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomiz | Here's my contribution.
Data
Yvar <- c(8,9,10,13,12,
14,18,12,8,9,
1,3,2,3,4)
Xvar <- rep(LETTERS[1:3], each=5)
mydf <- data.frame(Yvar, Xvar)
Monte Carlo
I see Monte Carlo as a m | Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomization tests, and permutation tests
Here's my contribution.
Data
Yvar <- c(8,9,10,13,12,
14,18,12,8,9,
1,3,2,3,4)
Xvar <- rep(LETTERS[1:3], each=5)
mydf <- data.frame(Yvar, Xvar)
Monte Carlo
I see Mon... | Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomiz
Here's my contribution.
Data
Yvar <- c(8,9,10,13,12,
14,18,12,8,9,
1,3,2,3,4)
Xvar <- rep(LETTERS[1:3], each=5)
mydf <- data.frame(Yvar, Xvar)
Monte Carlo
I see Monte Carlo as a m |
2,199 | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | A standard method is to generate three standard normals and construct a unit vector from them. That is, when $X_i \sim N(0,1)$ and $\lambda^2 = X_1^2 + X_2^2 + X_3^2$, then $(X_1/\lambda, X_2/\lambda, X_3/\lambda)$ is uniformly distributed on the sphere. This method works well for $d$-dimensional spheres, too.
In 3D ... | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | A standard method is to generate three standard normals and construct a unit vector from them. That is, when $X_i \sim N(0,1)$ and $\lambda^2 = X_1^2 + X_2^2 + X_3^2$, then $(X_1/\lambda, X_2/\lambda | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
A standard method is to generate three standard normals and construct a unit vector from them. That is, when $X_i \sim N(0,1)$ and $\lambda^2 = X_1^2 + X_2^2 + X_3^2$, then $(X_1/\lambda, X_2/\lambda, X_3/\lambda)$ is uniformly distrib... | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
A standard method is to generate three standard normals and construct a unit vector from them. That is, when $X_i \sim N(0,1)$ and $\lambda^2 = X_1^2 + X_2^2 + X_3^2$, then $(X_1/\lambda, X_2/\lambda |
2,200 | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | Here is some rather simple R code
n <- 100000 # large enough for meaningful tests
z <- 2*runif(n) - 1 # uniform on [-1, 1]
theta <- 2*pi*runif(n) - pi # uniform on [-pi, pi]
x <- sin(theta)*sqrt(1-z^2) # based on angle
y <- cos(theta)*sqrt(1-z^2)
It is very simple ... | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | Here is some rather simple R code
n <- 100000 # large enough for meaningful tests
z <- 2*runif(n) - 1 # uniform on [-1, 1]
theta <- 2*pi*runif(n) - pi # uniform | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
Here is some rather simple R code
n <- 100000 # large enough for meaningful tests
z <- 2*runif(n) - 1 # uniform on [-1, 1]
theta <- 2*pi*runif(n) - pi # uniform on [-pi, pi]
x <- sin(theta)*sq... | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
Here is some rather simple R code
n <- 100000 # large enough for meaningful tests
z <- 2*runif(n) - 1 # uniform on [-1, 1]
theta <- 2*pi*runif(n) - pi # uniform |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.