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 |
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55,301 | To what extent can statistics improve patient's treatment? | Never, NEVER, apply statistics on health matters of a single person. Not because of ethical reasons, but because the processes that drive our biology are still very poorly understood, especially in their interactions. They are so complex that the level of sophistication of our current statistical knowledge is a laugh c... | To what extent can statistics improve patient's treatment? | Never, NEVER, apply statistics on health matters of a single person. Not because of ethical reasons, but because the processes that drive our biology are still very poorly understood, especially in th | To what extent can statistics improve patient's treatment?
Never, NEVER, apply statistics on health matters of a single person. Not because of ethical reasons, but because the processes that drive our biology are still very poorly understood, especially in their interactions. They are so complex that the level of sophi... | To what extent can statistics improve patient's treatment?
Never, NEVER, apply statistics on health matters of a single person. Not because of ethical reasons, but because the processes that drive our biology are still very poorly understood, especially in th |
55,302 | To what extent can statistics improve patient's treatment? | I would say it is well worth the time of a person with a personal stake to investigate data on the individual level. What can be learned by comparing averages is severely limited, as was noted by Poisson et al when "numerical" methods were first being applied to medicine nearly 200 years ago:
In the field of statistic... | To what extent can statistics improve patient's treatment? | I would say it is well worth the time of a person with a personal stake to investigate data on the individual level. What can be learned by comparing averages is severely limited, as was noted by Pois | To what extent can statistics improve patient's treatment?
I would say it is well worth the time of a person with a personal stake to investigate data on the individual level. What can be learned by comparing averages is severely limited, as was noted by Poisson et al when "numerical" methods were first being applied t... | To what extent can statistics improve patient's treatment?
I would say it is well worth the time of a person with a personal stake to investigate data on the individual level. What can be learned by comparing averages is severely limited, as was noted by Pois |
55,303 | Decision tree and missing values | You will need to modify the algorithm slightly
The modification:
Building algo
suppose that we have some splitting test criterion $T$ and dataset $S$
information gain for splitting $S$ using $T$ is
$\Delta I (S, T) = I(S) - \sum_k \alpha_{T, k} \cdot I(S_k)$
let $S_0 \subseteq S$ for which we can't evaluate $T$ (... | Decision tree and missing values | You will need to modify the algorithm slightly
The modification:
Building algo
suppose that we have some splitting test criterion $T$ and dataset $S$
information gain for splitting $S$ using $T$ is | Decision tree and missing values
You will need to modify the algorithm slightly
The modification:
Building algo
suppose that we have some splitting test criterion $T$ and dataset $S$
information gain for splitting $S$ using $T$ is
$\Delta I (S, T) = I(S) - \sum_k \alpha_{T, k} \cdot I(S_k)$
let $S_0 \subseteq S$ ... | Decision tree and missing values
You will need to modify the algorithm slightly
The modification:
Building algo
suppose that we have some splitting test criterion $T$ and dataset $S$
information gain for splitting $S$ using $T$ is |
55,304 | Decision tree and missing values | In case of decision tree, such missing data inputation makes sense (especially, that here this huge number of days clearly makes sense, as it stand for "infinity"). You can also find this response:
Assigning values to missing data for use in binary logistic regression in SAS
usefull, as it concerns similar issue. | Decision tree and missing values | In case of decision tree, such missing data inputation makes sense (especially, that here this huge number of days clearly makes sense, as it stand for "infinity"). You can also find this response:
As | Decision tree and missing values
In case of decision tree, such missing data inputation makes sense (especially, that here this huge number of days clearly makes sense, as it stand for "infinity"). You can also find this response:
Assigning values to missing data for use in binary logistic regression in SAS
usefull, as... | Decision tree and missing values
In case of decision tree, such missing data inputation makes sense (especially, that here this huge number of days clearly makes sense, as it stand for "infinity"). You can also find this response:
As |
55,305 | Results of bootstrap reliable? | As explained by Nick Cox and an anonymous user, what you think of as instability is just what the mixture models do: they don't care about labels unless you make it very clear that you know what your modes look like, roughly.
In terms of what you can do about fixing the labels where you need them to be, you would want... | Results of bootstrap reliable? | As explained by Nick Cox and an anonymous user, what you think of as instability is just what the mixture models do: they don't care about labels unless you make it very clear that you know what your | Results of bootstrap reliable?
As explained by Nick Cox and an anonymous user, what you think of as instability is just what the mixture models do: they don't care about labels unless you make it very clear that you know what your modes look like, roughly.
In terms of what you can do about fixing the labels where you ... | Results of bootstrap reliable?
As explained by Nick Cox and an anonymous user, what you think of as instability is just what the mixture models do: they don't care about labels unless you make it very clear that you know what your |
55,306 | Results of bootstrap reliable? | My hunch is that your approach might not be reliable due to label switching, that is, each time you fit the mixture model, it's possible that the roles of the two normal distributions has been reversed.
That is, for different runs of the EM algorithm (mu1, sigma1) and (mu2, sigma2) might be switching roles.
It looks li... | Results of bootstrap reliable? | My hunch is that your approach might not be reliable due to label switching, that is, each time you fit the mixture model, it's possible that the roles of the two normal distributions has been reverse | Results of bootstrap reliable?
My hunch is that your approach might not be reliable due to label switching, that is, each time you fit the mixture model, it's possible that the roles of the two normal distributions has been reversed.
That is, for different runs of the EM algorithm (mu1, sigma1) and (mu2, sigma2) might ... | Results of bootstrap reliable?
My hunch is that your approach might not be reliable due to label switching, that is, each time you fit the mixture model, it's possible that the roles of the two normal distributions has been reverse |
55,307 | Which statistical topics to teach in European Studies study programme? | The scientific literature in (most areas of) experimental psychology is still choke-full of hypothesis tests with authors and reviewers alike expecting a p-value next to every number and very little interest for/understanding of effect sizes, modeling or anything else.
Consequently, a typical psychology graduate will h... | Which statistical topics to teach in European Studies study programme? | The scientific literature in (most areas of) experimental psychology is still choke-full of hypothesis tests with authors and reviewers alike expecting a p-value next to every number and very little i | Which statistical topics to teach in European Studies study programme?
The scientific literature in (most areas of) experimental psychology is still choke-full of hypothesis tests with authors and reviewers alike expecting a p-value next to every number and very little interest for/understanding of effect sizes, modeli... | Which statistical topics to teach in European Studies study programme?
The scientific literature in (most areas of) experimental psychology is still choke-full of hypothesis tests with authors and reviewers alike expecting a p-value next to every number and very little i |
55,308 | Which statistical topics to teach in European Studies study programme? | Thom Baguley, an outgoing editor of the British Journal of Mathematical and Statistical Psychology, wrote a good and effective book for the upper undergraduate level. It is very modern in many respects, including use of R and discussion of the advanced models such as multilevel stuff.
Another good book is "Mostly Harml... | Which statistical topics to teach in European Studies study programme? | Thom Baguley, an outgoing editor of the British Journal of Mathematical and Statistical Psychology, wrote a good and effective book for the upper undergraduate level. It is very modern in many respect | Which statistical topics to teach in European Studies study programme?
Thom Baguley, an outgoing editor of the British Journal of Mathematical and Statistical Psychology, wrote a good and effective book for the upper undergraduate level. It is very modern in many respects, including use of R and discussion of the advan... | Which statistical topics to teach in European Studies study programme?
Thom Baguley, an outgoing editor of the British Journal of Mathematical and Statistical Psychology, wrote a good and effective book for the upper undergraduate level. It is very modern in many respect |
55,309 | How to understand the label-bias problem in HMM? | Label-bias is not a problem for HMM,because input sequence is generated by the model. By global normalization, CRF model avoid this problem. | How to understand the label-bias problem in HMM? | Label-bias is not a problem for HMM,because input sequence is generated by the model. By global normalization, CRF model avoid this problem. | How to understand the label-bias problem in HMM?
Label-bias is not a problem for HMM,because input sequence is generated by the model. By global normalization, CRF model avoid this problem. | How to understand the label-bias problem in HMM?
Label-bias is not a problem for HMM,because input sequence is generated by the model. By global normalization, CRF model avoid this problem. |
55,310 | How to understand the label-bias problem in HMM? | Based on Section 2 of "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data" by Lafferty, J. et al,
I think it is "states with a single outgoing transition effectively ignores their observation. More generally, states with low-entropy next state distributions will take little notic... | How to understand the label-bias problem in HMM? | Based on Section 2 of "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data" by Lafferty, J. et al,
I think it is "states with a single outgoing transition effect | How to understand the label-bias problem in HMM?
Based on Section 2 of "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data" by Lafferty, J. et al,
I think it is "states with a single outgoing transition effectively ignores their observation. More generally, states with low-entrop... | How to understand the label-bias problem in HMM?
Based on Section 2 of "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data" by Lafferty, J. et al,
I think it is "states with a single outgoing transition effect |
55,311 | How to understand the label-bias problem in HMM? | Suppose a simple finite state machine which was developed for named entity recognition.
In those kinds of machines, the states with a single outgoing transition effectively ignore their observation. In other words, the states with a single transition simply have to move to the next state without considering their curr... | How to understand the label-bias problem in HMM? | Suppose a simple finite state machine which was developed for named entity recognition.
In those kinds of machines, the states with a single outgoing transition effectively ignore their observation. | How to understand the label-bias problem in HMM?
Suppose a simple finite state machine which was developed for named entity recognition.
In those kinds of machines, the states with a single outgoing transition effectively ignore their observation. In other words, the states with a single transition simply have to move... | How to understand the label-bias problem in HMM?
Suppose a simple finite state machine which was developed for named entity recognition.
In those kinds of machines, the states with a single outgoing transition effectively ignore their observation. |
55,312 | How to understand the label-bias problem in HMM? | CRF is a solution for MEMM and NOT for HMM.
In markov model Label-bias is not a problem ,because input sequence is generated by the model (Farhana Liza). in MEMM, while calculating the transition probabilities, from every position (AKA state), the probabilities sums up to 1.
So whats the problem?
Lets say we have a s... | How to understand the label-bias problem in HMM? | CRF is a solution for MEMM and NOT for HMM.
In markov model Label-bias is not a problem ,because input sequence is generated by the model (Farhana Liza). in MEMM, while calculating the transition prob | How to understand the label-bias problem in HMM?
CRF is a solution for MEMM and NOT for HMM.
In markov model Label-bias is not a problem ,because input sequence is generated by the model (Farhana Liza). in MEMM, while calculating the transition probabilities, from every position (AKA state), the probabilities sums up t... | How to understand the label-bias problem in HMM?
CRF is a solution for MEMM and NOT for HMM.
In markov model Label-bias is not a problem ,because input sequence is generated by the model (Farhana Liza). in MEMM, while calculating the transition prob |
55,313 | Regression with neural network | Unless you restrict the range of your inputs, the sigmoid may be giving you a problem. You won't even be able to learn the function $y=x$ if you have a sigmoid in the middle.
If you have a restricted range, then the input-hidden weights could scale the input values so that they hit the sigmoid at the part of the graph ... | Regression with neural network | Unless you restrict the range of your inputs, the sigmoid may be giving you a problem. You won't even be able to learn the function $y=x$ if you have a sigmoid in the middle.
If you have a restricted | Regression with neural network
Unless you restrict the range of your inputs, the sigmoid may be giving you a problem. You won't even be able to learn the function $y=x$ if you have a sigmoid in the middle.
If you have a restricted range, then the input-hidden weights could scale the input values so that they hit the si... | Regression with neural network
Unless you restrict the range of your inputs, the sigmoid may be giving you a problem. You won't even be able to learn the function $y=x$ if you have a sigmoid in the middle.
If you have a restricted |
55,314 | Regression with neural network | Assuming you wrote the implementation yourself, you may simply have a bug in your backpropagation algorithm. Some bugs can be quite subtle and leave the algorithm partially working with poor performance. You might try adding some gradient checking code to your implementation to verify the calculated gradients. Here's a... | Regression with neural network | Assuming you wrote the implementation yourself, you may simply have a bug in your backpropagation algorithm. Some bugs can be quite subtle and leave the algorithm partially working with poor performan | Regression with neural network
Assuming you wrote the implementation yourself, you may simply have a bug in your backpropagation algorithm. Some bugs can be quite subtle and leave the algorithm partially working with poor performance. You might try adding some gradient checking code to your implementation to verify the... | Regression with neural network
Assuming you wrote the implementation yourself, you may simply have a bug in your backpropagation algorithm. Some bugs can be quite subtle and leave the algorithm partially working with poor performan |
55,315 | Normalizing Term Frequency for document clustering | A common misunderstanding is the term "frequency". To some, it seems to be the count of objects. But usually, frequency is a relative value.
TF/IDF usually is a two-fold normalization.
First, each document is normalized to length 1, so there is no bias for longer or shorter documents. This equals taking the relative f... | Normalizing Term Frequency for document clustering | A common misunderstanding is the term "frequency". To some, it seems to be the count of objects. But usually, frequency is a relative value.
TF/IDF usually is a two-fold normalization.
First, each do | Normalizing Term Frequency for document clustering
A common misunderstanding is the term "frequency". To some, it seems to be the count of objects. But usually, frequency is a relative value.
TF/IDF usually is a two-fold normalization.
First, each document is normalized to length 1, so there is no bias for longer or s... | Normalizing Term Frequency for document clustering
A common misunderstanding is the term "frequency". To some, it seems to be the count of objects. But usually, frequency is a relative value.
TF/IDF usually is a two-fold normalization.
First, each do |
55,316 | How can reliability and validity of content analysis be quantified when there is only one person coding the data? | Usual reliability indices (Cronbach $\alpha$, Cohen $\kappa$, etc.) only ever quantify the influence of a single source of error. For $\alpha$ the relevant source of error is item-specific variance, for $\kappa$ and other measures of inter-rater argument, it is rater-specific error. In any measurement situation, there ... | How can reliability and validity of content analysis be quantified when there is only one person cod | Usual reliability indices (Cronbach $\alpha$, Cohen $\kappa$, etc.) only ever quantify the influence of a single source of error. For $\alpha$ the relevant source of error is item-specific variance, f | How can reliability and validity of content analysis be quantified when there is only one person coding the data?
Usual reliability indices (Cronbach $\alpha$, Cohen $\kappa$, etc.) only ever quantify the influence of a single source of error. For $\alpha$ the relevant source of error is item-specific variance, for $\k... | How can reliability and validity of content analysis be quantified when there is only one person cod
Usual reliability indices (Cronbach $\alpha$, Cohen $\kappa$, etc.) only ever quantify the influence of a single source of error. For $\alpha$ the relevant source of error is item-specific variance, f |
55,317 | Log likelihood improves with addition of a nonsignificant variable | Apparently the problem was that there was missing data in the predictor you added to your model, making the log-likelihoods on different scales and therefore not comparable. This is a very insidious problem sometimes because most software, by default, removes those cases silently and leaves you to figure out what happe... | Log likelihood improves with addition of a nonsignificant variable | Apparently the problem was that there was missing data in the predictor you added to your model, making the log-likelihoods on different scales and therefore not comparable. This is a very insidious p | Log likelihood improves with addition of a nonsignificant variable
Apparently the problem was that there was missing data in the predictor you added to your model, making the log-likelihoods on different scales and therefore not comparable. This is a very insidious problem sometimes because most software, by default, r... | Log likelihood improves with addition of a nonsignificant variable
Apparently the problem was that there was missing data in the predictor you added to your model, making the log-likelihoods on different scales and therefore not comparable. This is a very insidious p |
55,318 | What is "ANOVA"? | One-way and two-way ANOVA are just two simple versions, but I doubt two experts on the topic would agree exactly what is central to ANOVA, treated in moderate or extreme generality.
For evidence, see Speed, T.P. 1987. What is an analysis of variance?
Annals of Statistics 15: 885-910. Eleven discussions follow with a r... | What is "ANOVA"? | One-way and two-way ANOVA are just two simple versions, but I doubt two experts on the topic would agree exactly what is central to ANOVA, treated in moderate or extreme generality.
For evidence, see | What is "ANOVA"?
One-way and two-way ANOVA are just two simple versions, but I doubt two experts on the topic would agree exactly what is central to ANOVA, treated in moderate or extreme generality.
For evidence, see Speed, T.P. 1987. What is an analysis of variance?
Annals of Statistics 15: 885-910. Eleven discussion... | What is "ANOVA"?
One-way and two-way ANOVA are just two simple versions, but I doubt two experts on the topic would agree exactly what is central to ANOVA, treated in moderate or extreme generality.
For evidence, see |
55,319 | What is "ANOVA"? | ANOVA is a technique, not a model
Some sources refer to ANOVA as a "model" or a "collection of models" but in my view that is incorrect. The acronym ANOVA refers to the "analysis of variance", which is a statistical technique that can be applied to a variety of statistical models, rather than being a model itself. Th... | What is "ANOVA"? | ANOVA is a technique, not a model
Some sources refer to ANOVA as a "model" or a "collection of models" but in my view that is incorrect. The acronym ANOVA refers to the "analysis of variance", which | What is "ANOVA"?
ANOVA is a technique, not a model
Some sources refer to ANOVA as a "model" or a "collection of models" but in my view that is incorrect. The acronym ANOVA refers to the "analysis of variance", which is a statistical technique that can be applied to a variety of statistical models, rather than being a ... | What is "ANOVA"?
ANOVA is a technique, not a model
Some sources refer to ANOVA as a "model" or a "collection of models" but in my view that is incorrect. The acronym ANOVA refers to the "analysis of variance", which |
55,320 | How to derive the first order autocorrelation coefficient of an AR(1) process? | Find a mean and variance ($\gamma_0$) of $y_t$. How does the condition $|\Theta|<1$ help you to do that? Remember the assumptions of how residuals are distributed and similar.
Autocorrelation coefficient $\rho_1=\dfrac{\gamma_1}{\gamma_0}$, i.e. now you are missing only $\gamma_1$. Once you write down its expression th... | How to derive the first order autocorrelation coefficient of an AR(1) process? | Find a mean and variance ($\gamma_0$) of $y_t$. How does the condition $|\Theta|<1$ help you to do that? Remember the assumptions of how residuals are distributed and similar.
Autocorrelation coeffici | How to derive the first order autocorrelation coefficient of an AR(1) process?
Find a mean and variance ($\gamma_0$) of $y_t$. How does the condition $|\Theta|<1$ help you to do that? Remember the assumptions of how residuals are distributed and similar.
Autocorrelation coefficient $\rho_1=\dfrac{\gamma_1}{\gamma_0}$, ... | How to derive the first order autocorrelation coefficient of an AR(1) process?
Find a mean and variance ($\gamma_0$) of $y_t$. How does the condition $|\Theta|<1$ help you to do that? Remember the assumptions of how residuals are distributed and similar.
Autocorrelation coeffici |
55,321 | How to derive the first order autocorrelation coefficient of an AR(1) process? | Multiply both sides by $y_{t-1}$ and take expectation. Exploit the fact that $u_t$ and $y_{t-1}$ are not correlated.
To calculate variance simply square both sides and then take expectation. | How to derive the first order autocorrelation coefficient of an AR(1) process? | Multiply both sides by $y_{t-1}$ and take expectation. Exploit the fact that $u_t$ and $y_{t-1}$ are not correlated.
To calculate variance simply square both sides and then take expectation. | How to derive the first order autocorrelation coefficient of an AR(1) process?
Multiply both sides by $y_{t-1}$ and take expectation. Exploit the fact that $u_t$ and $y_{t-1}$ are not correlated.
To calculate variance simply square both sides and then take expectation. | How to derive the first order autocorrelation coefficient of an AR(1) process?
Multiply both sides by $y_{t-1}$ and take expectation. Exploit the fact that $u_t$ and $y_{t-1}$ are not correlated.
To calculate variance simply square both sides and then take expectation. |
55,322 | Correlation between discrete and continuous variables | The discreteness is not an issue, so much as the ordinal (ordered, graded) scale used for your assessment from normal to severe. That indeed implies something different from standard linear regression, namely some ordinal regression method such as ordered logit or ordered probit.
Note incidentally that multivariate re... | Correlation between discrete and continuous variables | The discreteness is not an issue, so much as the ordinal (ordered, graded) scale used for your assessment from normal to severe. That indeed implies something different from standard linear regression | Correlation between discrete and continuous variables
The discreteness is not an issue, so much as the ordinal (ordered, graded) scale used for your assessment from normal to severe. That indeed implies something different from standard linear regression, namely some ordinal regression method such as ordered logit or o... | Correlation between discrete and continuous variables
The discreteness is not an issue, so much as the ordinal (ordered, graded) scale used for your assessment from normal to severe. That indeed implies something different from standard linear regression |
55,323 | Does it make sense to run LDA on several principal components and not on all variables? | First of all, do you have an actual indication (external knowledge) that your data consists of a few variates that carry discriminatory information among noise-only variates? There is data that can be assumed to follow such a model (e.g. gene microarray data), while other types of data have the discriminatory informati... | Does it make sense to run LDA on several principal components and not on all variables? | First of all, do you have an actual indication (external knowledge) that your data consists of a few variates that carry discriminatory information among noise-only variates? There is data that can be | Does it make sense to run LDA on several principal components and not on all variables?
First of all, do you have an actual indication (external knowledge) that your data consists of a few variates that carry discriminatory information among noise-only variates? There is data that can be assumed to follow such a model ... | Does it make sense to run LDA on several principal components and not on all variables?
First of all, do you have an actual indication (external knowledge) that your data consists of a few variates that carry discriminatory information among noise-only variates? There is data that can be |
55,324 | Does it make sense to run LDA on several principal components and not on all variables? | One way of reducing the dimensionality of your samples might be the so-called "sparse PCA" (SPCA), but I don't know whether it is available for Stata. SPCA limits the number of variables with non-zero weight per component and thus allows you to select the variables much more tightly.
Alternatively, use the top N variab... | Does it make sense to run LDA on several principal components and not on all variables? | One way of reducing the dimensionality of your samples might be the so-called "sparse PCA" (SPCA), but I don't know whether it is available for Stata. SPCA limits the number of variables with non-zero | Does it make sense to run LDA on several principal components and not on all variables?
One way of reducing the dimensionality of your samples might be the so-called "sparse PCA" (SPCA), but I don't know whether it is available for Stata. SPCA limits the number of variables with non-zero weight per component and thus a... | Does it make sense to run LDA on several principal components and not on all variables?
One way of reducing the dimensionality of your samples might be the so-called "sparse PCA" (SPCA), but I don't know whether it is available for Stata. SPCA limits the number of variables with non-zero |
55,325 | is there any difference between taking more samples and a sample with more observations? | Suppose you take 10 samples of 50 and your friend takes one sample of 500. There is no difference in the amount of information you can extract versus your friend. In theory you are both under the same conditions because you have the same amount of data. Problems could arise if samples are not independent, but under ind... | is there any difference between taking more samples and a sample with more observations? | Suppose you take 10 samples of 50 and your friend takes one sample of 500. There is no difference in the amount of information you can extract versus your friend. In theory you are both under the same | is there any difference between taking more samples and a sample with more observations?
Suppose you take 10 samples of 50 and your friend takes one sample of 500. There is no difference in the amount of information you can extract versus your friend. In theory you are both under the same conditions because you have th... | is there any difference between taking more samples and a sample with more observations?
Suppose you take 10 samples of 50 and your friend takes one sample of 500. There is no difference in the amount of information you can extract versus your friend. In theory you are both under the same |
55,326 | is there any difference between taking more samples and a sample with more observations? | In some areas (e.g. analytical chemistry) the term sample means a piece (or quantity) of material that is to be analyzed (specimen). From a statistical point of view, you then have a nested/clustered/hierarchical structure of your sampling and the assumption of "independent random sampling" in @soakley's answer is not... | is there any difference between taking more samples and a sample with more observations? | In some areas (e.g. analytical chemistry) the term sample means a piece (or quantity) of material that is to be analyzed (specimen). From a statistical point of view, you then have a nested/clustered | is there any difference between taking more samples and a sample with more observations?
In some areas (e.g. analytical chemistry) the term sample means a piece (or quantity) of material that is to be analyzed (specimen). From a statistical point of view, you then have a nested/clustered/hierarchical structure of your... | is there any difference between taking more samples and a sample with more observations?
In some areas (e.g. analytical chemistry) the term sample means a piece (or quantity) of material that is to be analyzed (specimen). From a statistical point of view, you then have a nested/clustered |
55,327 | Not all Features Selected by GLMNET Considered Signficant by GLM (Logistic Regression) | First of all, it seems like glmnet is a reasonable tool for your problem - good choice!
If all you want is a predictive model, you don't need to worry about p-values. A simple way to assess the predictive accuracy of your model is to use cross validation. Glmnet will cross validate automatically for you (try cv.glmnet)... | Not all Features Selected by GLMNET Considered Signficant by GLM (Logistic Regression) | First of all, it seems like glmnet is a reasonable tool for your problem - good choice!
If all you want is a predictive model, you don't need to worry about p-values. A simple way to assess the predic | Not all Features Selected by GLMNET Considered Signficant by GLM (Logistic Regression)
First of all, it seems like glmnet is a reasonable tool for your problem - good choice!
If all you want is a predictive model, you don't need to worry about p-values. A simple way to assess the predictive accuracy of your model is to... | Not all Features Selected by GLMNET Considered Signficant by GLM (Logistic Regression)
First of all, it seems like glmnet is a reasonable tool for your problem - good choice!
If all you want is a predictive model, you don't need to worry about p-values. A simple way to assess the predic |
55,328 | Regression with non-normally distributed residuals | I wouldn't call that multinomial. Residuals are measured on a continuous scale, but they have multimodal distribution, rather than multinomial.
By the way, a kernel density plot makes the modality more difficult to judge. Some kind of strip plot or strip chart would be helpful.
Commenting on your model would be easie... | Regression with non-normally distributed residuals | I wouldn't call that multinomial. Residuals are measured on a continuous scale, but they have multimodal distribution, rather than multinomial.
By the way, a kernel density plot makes the modality mo | Regression with non-normally distributed residuals
I wouldn't call that multinomial. Residuals are measured on a continuous scale, but they have multimodal distribution, rather than multinomial.
By the way, a kernel density plot makes the modality more difficult to judge. Some kind of strip plot or strip chart would b... | Regression with non-normally distributed residuals
I wouldn't call that multinomial. Residuals are measured on a continuous scale, but they have multimodal distribution, rather than multinomial.
By the way, a kernel density plot makes the modality mo |
55,329 | Difference between cumulants and moments | Question asks: "is the $n$th cumulant equivalent to the $n$th central moment (i.e. about the mean)?"
Answer is: only for $n = 1, 2$ or $3$.
Here, for example, are the first 9 cumulants of the population in terms of central moments $\mu_i$ of the population:
using mathStatica's CumulantToCentral function.
More gener... | Difference between cumulants and moments | Question asks: "is the $n$th cumulant equivalent to the $n$th central moment (i.e. about the mean)?"
Answer is: only for $n = 1, 2$ or $3$.
Here, for example, are the first 9 cumulants of the popula | Difference between cumulants and moments
Question asks: "is the $n$th cumulant equivalent to the $n$th central moment (i.e. about the mean)?"
Answer is: only for $n = 1, 2$ or $3$.
Here, for example, are the first 9 cumulants of the population in terms of central moments $\mu_i$ of the population:
using mathStatica'... | Difference between cumulants and moments
Question asks: "is the $n$th cumulant equivalent to the $n$th central moment (i.e. about the mean)?"
Answer is: only for $n = 1, 2$ or $3$.
Here, for example, are the first 9 cumulants of the popula |
55,330 | Autocorrelation and partial autocorrelation interpretation | Neither the ACF nor the PACF are giving any reason to suppose an ARMA process, trend or seasonality: none of the correlations approach significance at conventional levels. Note that sixteen observations is very few to fit a time series model, so the only effects you might see would be very large ones.
The residuals of... | Autocorrelation and partial autocorrelation interpretation | Neither the ACF nor the PACF are giving any reason to suppose an ARMA process, trend or seasonality: none of the correlations approach significance at conventional levels. Note that sixteen observati | Autocorrelation and partial autocorrelation interpretation
Neither the ACF nor the PACF are giving any reason to suppose an ARMA process, trend or seasonality: none of the correlations approach significance at conventional levels. Note that sixteen observations is very few to fit a time series model, so the only effec... | Autocorrelation and partial autocorrelation interpretation
Neither the ACF nor the PACF are giving any reason to suppose an ARMA process, trend or seasonality: none of the correlations approach significance at conventional levels. Note that sixteen observati |
55,331 | Goodness of fit in a GLM with scaled deviance | I think when you allow for unknown dispersion, the GLM is no longer a maximum likelihood technique, but maximizes a "quasi" likelihood. Because of that, the deviance is fixed by using the sample dispersion as the model dispersion (as a consequence of maximizing quasi likelihood). By treating the dispersion as a paramet... | Goodness of fit in a GLM with scaled deviance | I think when you allow for unknown dispersion, the GLM is no longer a maximum likelihood technique, but maximizes a "quasi" likelihood. Because of that, the deviance is fixed by using the sample dispe | Goodness of fit in a GLM with scaled deviance
I think when you allow for unknown dispersion, the GLM is no longer a maximum likelihood technique, but maximizes a "quasi" likelihood. Because of that, the deviance is fixed by using the sample dispersion as the model dispersion (as a consequence of maximizing quasi likeli... | Goodness of fit in a GLM with scaled deviance
I think when you allow for unknown dispersion, the GLM is no longer a maximum likelihood technique, but maximizes a "quasi" likelihood. Because of that, the deviance is fixed by using the sample dispe |
55,332 | Re-check boxplot after outlier removal | If you have that many outliers, they aren't outliers; you have a non-normal distribution.
How are you going to be using the age variable? One possibility is that it is to be used as an independent variable in a regression. In this case, this distribution is not necessarily a problem - regression makes assumptions abou... | Re-check boxplot after outlier removal | If you have that many outliers, they aren't outliers; you have a non-normal distribution.
How are you going to be using the age variable? One possibility is that it is to be used as an independent va | Re-check boxplot after outlier removal
If you have that many outliers, they aren't outliers; you have a non-normal distribution.
How are you going to be using the age variable? One possibility is that it is to be used as an independent variable in a regression. In this case, this distribution is not necessarily a prob... | Re-check boxplot after outlier removal
If you have that many outliers, they aren't outliers; you have a non-normal distribution.
How are you going to be using the age variable? One possibility is that it is to be used as an independent va |
55,333 | Re-check boxplot after outlier removal | Answers 1: maybe, 2: depends. We need a little more information on why you want to remove these outliers. If you could provide a histogram, it might be possible to transform the data and eliminate some of the outliers, but it all depends on the research questions. Please tell us more about 1) your research questions... | Re-check boxplot after outlier removal | Answers 1: maybe, 2: depends. We need a little more information on why you want to remove these outliers. If you could provide a histogram, it might be possible to transform the data and eliminate s | Re-check boxplot after outlier removal
Answers 1: maybe, 2: depends. We need a little more information on why you want to remove these outliers. If you could provide a histogram, it might be possible to transform the data and eliminate some of the outliers, but it all depends on the research questions. Please tell u... | Re-check boxplot after outlier removal
Answers 1: maybe, 2: depends. We need a little more information on why you want to remove these outliers. If you could provide a histogram, it might be possible to transform the data and eliminate s |
55,334 | Re-check boxplot after outlier removal | when you remove outliers no of data changes thus its quantile changes means lower range and upper range changes thus it is again showing outliers
If you observe both box plot carefully your upper range for first in nearly 38
after removing outlier it become nearly 32 | Re-check boxplot after outlier removal | when you remove outliers no of data changes thus its quantile changes means lower range and upper range changes thus it is again showing outliers
If you observe both box plot carefully your upper rang | Re-check boxplot after outlier removal
when you remove outliers no of data changes thus its quantile changes means lower range and upper range changes thus it is again showing outliers
If you observe both box plot carefully your upper range for first in nearly 38
after removing outlier it become nearly 32 | Re-check boxplot after outlier removal
when you remove outliers no of data changes thus its quantile changes means lower range and upper range changes thus it is again showing outliers
If you observe both box plot carefully your upper rang |
55,335 | Re-check boxplot after outlier removal | Ok here is what I learned, It is enough to pick out the outliers once from your dataset. If you continue to do so IQR changes respectively which will keep giving you new outliers. If you do not want to see the outliers once you picked them out just add the code, "outline=F", to avoid seeing the new outliers. Hope this ... | Re-check boxplot after outlier removal | Ok here is what I learned, It is enough to pick out the outliers once from your dataset. If you continue to do so IQR changes respectively which will keep giving you new outliers. If you do not want t | Re-check boxplot after outlier removal
Ok here is what I learned, It is enough to pick out the outliers once from your dataset. If you continue to do so IQR changes respectively which will keep giving you new outliers. If you do not want to see the outliers once you picked them out just add the code, "outline=F", to av... | Re-check boxplot after outlier removal
Ok here is what I learned, It is enough to pick out the outliers once from your dataset. If you continue to do so IQR changes respectively which will keep giving you new outliers. If you do not want t |
55,336 | How can one set up a linear support vector machine in Excel? | Honestly, I am not sure why you want to do this in Excel. Nonetheless, ...
A linear SVM requires solving a quadratic program with several linear constraints. You can check this answer [1] to find out how the quadratic program is setup. Once you setup the quadratic program and find a solver that can help you solve it in... | How can one set up a linear support vector machine in Excel? | Honestly, I am not sure why you want to do this in Excel. Nonetheless, ...
A linear SVM requires solving a quadratic program with several linear constraints. You can check this answer [1] to find out | How can one set up a linear support vector machine in Excel?
Honestly, I am not sure why you want to do this in Excel. Nonetheless, ...
A linear SVM requires solving a quadratic program with several linear constraints. You can check this answer [1] to find out how the quadratic program is setup. Once you setup the quad... | How can one set up a linear support vector machine in Excel?
Honestly, I am not sure why you want to do this in Excel. Nonetheless, ...
A linear SVM requires solving a quadratic program with several linear constraints. You can check this answer [1] to find out |
55,337 | How can one set up a linear support vector machine in Excel? | This looks like a good tutorial, and has a downloadable Excel example:
http://people.revoledu.com/kardi/tutorial/Regression/KernelRegression/KernelRegression.htm | How can one set up a linear support vector machine in Excel? | This looks like a good tutorial, and has a downloadable Excel example:
http://people.revoledu.com/kardi/tutorial/Regression/KernelRegression/KernelRegression.htm | How can one set up a linear support vector machine in Excel?
This looks like a good tutorial, and has a downloadable Excel example:
http://people.revoledu.com/kardi/tutorial/Regression/KernelRegression/KernelRegression.htm | How can one set up a linear support vector machine in Excel?
This looks like a good tutorial, and has a downloadable Excel example:
http://people.revoledu.com/kardi/tutorial/Regression/KernelRegression/KernelRegression.htm |
55,338 | How can one set up a linear support vector machine in Excel? | You might try using Excel2SVM if you want to organize your data in an excel format. http://www.bioinformatics.org/Excel2SVM/ could be helpful | How can one set up a linear support vector machine in Excel? | You might try using Excel2SVM if you want to organize your data in an excel format. http://www.bioinformatics.org/Excel2SVM/ could be helpful | How can one set up a linear support vector machine in Excel?
You might try using Excel2SVM if you want to organize your data in an excel format. http://www.bioinformatics.org/Excel2SVM/ could be helpful | How can one set up a linear support vector machine in Excel?
You might try using Excel2SVM if you want to organize your data in an excel format. http://www.bioinformatics.org/Excel2SVM/ could be helpful |
55,339 | How can one set up a linear support vector machine in Excel? | You can find a tutorial here, it uses Excel (no macros) and explains everything in an intuitive way (beware: most parts are behind a paywall, but the price is reasonable):
http://people.revoledu.com/kardi/tutorial/SVM/index.html | How can one set up a linear support vector machine in Excel? | You can find a tutorial here, it uses Excel (no macros) and explains everything in an intuitive way (beware: most parts are behind a paywall, but the price is reasonable):
http://people.revoledu.com/k | How can one set up a linear support vector machine in Excel?
You can find a tutorial here, it uses Excel (no macros) and explains everything in an intuitive way (beware: most parts are behind a paywall, but the price is reasonable):
http://people.revoledu.com/kardi/tutorial/SVM/index.html | How can one set up a linear support vector machine in Excel?
You can find a tutorial here, it uses Excel (no macros) and explains everything in an intuitive way (beware: most parts are behind a paywall, but the price is reasonable):
http://people.revoledu.com/k |
55,340 | Can I repeat cross validation with a small dataset, and/or how can I improve my cross validation confidence? | It seems as if you are using an improper scoring rule, proportion correctly classified. Optimizing this measure will choose a bogus model.
You will need to repeat 10-fold cross-validation 100 times to get sufficient precision for validation estimates, and be sure to use a proper scoring rule (e.g., Brier score (quadra... | Can I repeat cross validation with a small dataset, and/or how can I improve my cross validation con | It seems as if you are using an improper scoring rule, proportion correctly classified. Optimizing this measure will choose a bogus model.
You will need to repeat 10-fold cross-validation 100 times t | Can I repeat cross validation with a small dataset, and/or how can I improve my cross validation confidence?
It seems as if you are using an improper scoring rule, proportion correctly classified. Optimizing this measure will choose a bogus model.
You will need to repeat 10-fold cross-validation 100 times to get suffi... | Can I repeat cross validation with a small dataset, and/or how can I improve my cross validation con
It seems as if you are using an improper scoring rule, proportion correctly classified. Optimizing this measure will choose a bogus model.
You will need to repeat 10-fold cross-validation 100 times t |
55,341 | How can I find $\text{Cov}(X_k,X_5)$ | The general situation you have here is described by a multinomial distribution. The Wikipedia article about the multinomial distribution explains:
For n independent trials each of which leads to a success for exactly one of k categories, with each category having a given fixed success probability [...]
In your case,... | How can I find $\text{Cov}(X_k,X_5)$ | The general situation you have here is described by a multinomial distribution. The Wikipedia article about the multinomial distribution explains:
For n independent trials each of which leads to a s | How can I find $\text{Cov}(X_k,X_5)$
The general situation you have here is described by a multinomial distribution. The Wikipedia article about the multinomial distribution explains:
For n independent trials each of which leads to a success for exactly one of k categories, with each category having a given fixed suc... | How can I find $\text{Cov}(X_k,X_5)$
The general situation you have here is described by a multinomial distribution. The Wikipedia article about the multinomial distribution explains:
For n independent trials each of which leads to a s |
55,342 | How can I find $\text{Cov}(X_k,X_5)$ | This is my first post on StackExchange, and I hope it is helpful. COOLSerdash gave a very complete answer already which I mostly agree with, but I will try to add a different perspective on the problem.
As you stated, $X_{k} \sim Binomial(n=100,p=\frac{1}{5}) \equiv 100 \times Bernoulli(\frac{1}{5})$
$\text{Cov}(X_5,X_... | How can I find $\text{Cov}(X_k,X_5)$ | This is my first post on StackExchange, and I hope it is helpful. COOLSerdash gave a very complete answer already which I mostly agree with, but I will try to add a different perspective on the proble | How can I find $\text{Cov}(X_k,X_5)$
This is my first post on StackExchange, and I hope it is helpful. COOLSerdash gave a very complete answer already which I mostly agree with, but I will try to add a different perspective on the problem.
As you stated, $X_{k} \sim Binomial(n=100,p=\frac{1}{5}) \equiv 100 \times Berno... | How can I find $\text{Cov}(X_k,X_5)$
This is my first post on StackExchange, and I hope it is helpful. COOLSerdash gave a very complete answer already which I mostly agree with, but I will try to add a different perspective on the proble |
55,343 | Cosine of a uniform random variable | Here's a mostly intuitive explanation of the general appearance of the result. Consider just the right half of the original $y$ range (the other half is symmetric about zero to what happens here).
Where do the values end up? Which values go close to 1? To 0?
Clearly, from inspection of the $\cos$ function, small $y$ v... | Cosine of a uniform random variable | Here's a mostly intuitive explanation of the general appearance of the result. Consider just the right half of the original $y$ range (the other half is symmetric about zero to what happens here).
Wh | Cosine of a uniform random variable
Here's a mostly intuitive explanation of the general appearance of the result. Consider just the right half of the original $y$ range (the other half is symmetric about zero to what happens here).
Where do the values end up? Which values go close to 1? To 0?
Clearly, from inspection... | Cosine of a uniform random variable
Here's a mostly intuitive explanation of the general appearance of the result. Consider just the right half of the original $y$ range (the other half is symmetric about zero to what happens here).
Wh |
55,344 | Why is the semi-partial correlation sometimes called the "part correlation"? | I'm afraid that my attempt at an answer is hardly more satisfying than gung's. Snedecor and Cochran's book discuss this briefly. That was an old statistics text, based out of a lot of agricultural work (and so much of the early work was) and in any case, takes us back, I think, to early work by Mordecai Ezekiel and B... | Why is the semi-partial correlation sometimes called the "part correlation"? | I'm afraid that my attempt at an answer is hardly more satisfying than gung's. Snedecor and Cochran's book discuss this briefly. That was an old statistics text, based out of a lot of agricultural w | Why is the semi-partial correlation sometimes called the "part correlation"?
I'm afraid that my attempt at an answer is hardly more satisfying than gung's. Snedecor and Cochran's book discuss this briefly. That was an old statistics text, based out of a lot of agricultural work (and so much of the early work was) and... | Why is the semi-partial correlation sometimes called the "part correlation"?
I'm afraid that my attempt at an answer is hardly more satisfying than gung's. Snedecor and Cochran's book discuss this briefly. That was an old statistics text, based out of a lot of agricultural w |
55,345 | Why is the semi-partial correlation sometimes called the "part correlation"? | I have no idea (and if you object, I can delete this answer), but I can tell you how I've tried to explain it to people so that they can get it.
Namely, the word "part" goes only half way through the word "partial". In the same sense, the part correlation partials the variable out of only one of X or Y, so it only g... | Why is the semi-partial correlation sometimes called the "part correlation"? | I have no idea (and if you object, I can delete this answer), but I can tell you how I've tried to explain it to people so that they can get it.
Namely, the word "part" goes only half way through th | Why is the semi-partial correlation sometimes called the "part correlation"?
I have no idea (and if you object, I can delete this answer), but I can tell you how I've tried to explain it to people so that they can get it.
Namely, the word "part" goes only half way through the word "partial". In the same sense, the p... | Why is the semi-partial correlation sometimes called the "part correlation"?
I have no idea (and if you object, I can delete this answer), but I can tell you how I've tried to explain it to people so that they can get it.
Namely, the word "part" goes only half way through th |
55,346 | Why is the semi-partial correlation sometimes called the "part correlation"? | According to Howell (2012, in the partial and semi-partial correlation), "part correlation" is due to McNemar (1969) Psychological statistics, Wiley. If someone can get a copy, maybe we could get further. | Why is the semi-partial correlation sometimes called the "part correlation"? | According to Howell (2012, in the partial and semi-partial correlation), "part correlation" is due to McNemar (1969) Psychological statistics, Wiley. If someone can get a copy, maybe we could get furt | Why is the semi-partial correlation sometimes called the "part correlation"?
According to Howell (2012, in the partial and semi-partial correlation), "part correlation" is due to McNemar (1969) Psychological statistics, Wiley. If someone can get a copy, maybe we could get further. | Why is the semi-partial correlation sometimes called the "part correlation"?
According to Howell (2012, in the partial and semi-partial correlation), "part correlation" is due to McNemar (1969) Psychological statistics, Wiley. If someone can get a copy, maybe we could get furt |
55,347 | Why is the CI for an odds ratio not always centered on the sample value? | Odds ratios are not distributed symmetrically - they can't be, because they can't go below zero, but they can go as high as infinity.
What is distributed symmetrically is the log of the odds ratio. Most stats packages give a choice of the regular regression coefficient (B), and the exponentiated regression coefficien... | Why is the CI for an odds ratio not always centered on the sample value? | Odds ratios are not distributed symmetrically - they can't be, because they can't go below zero, but they can go as high as infinity.
What is distributed symmetrically is the log of the odds ratio. | Why is the CI for an odds ratio not always centered on the sample value?
Odds ratios are not distributed symmetrically - they can't be, because they can't go below zero, but they can go as high as infinity.
What is distributed symmetrically is the log of the odds ratio. Most stats packages give a choice of the regula... | Why is the CI for an odds ratio not always centered on the sample value?
Odds ratios are not distributed symmetrically - they can't be, because they can't go below zero, but they can go as high as infinity.
What is distributed symmetrically is the log of the odds ratio. |
55,348 | Asymmetric S-shaped function mapping interval $[0, 1]$ to interval $[0, 1]$ | Yes.
And here is how you go about finding one.
For our purposes, convex means $F''(x)\ge 0$ and concave means $F''(x)\le 0$.
Ok, so let $F$ be such a function. If we also assume monotonicity, we have $F'(x)\ge 0$, and $F$ is a cumulative distribution function. Therefore, the convex and concave conditions are $f'(x)\ge ... | Asymmetric S-shaped function mapping interval $[0, 1]$ to interval $[0, 1]$ | Yes.
And here is how you go about finding one.
For our purposes, convex means $F''(x)\ge 0$ and concave means $F''(x)\le 0$.
Ok, so let $F$ be such a function. If we also assume monotonicity, we have | Asymmetric S-shaped function mapping interval $[0, 1]$ to interval $[0, 1]$
Yes.
And here is how you go about finding one.
For our purposes, convex means $F''(x)\ge 0$ and concave means $F''(x)\le 0$.
Ok, so let $F$ be such a function. If we also assume monotonicity, we have $F'(x)\ge 0$, and $F$ is a cumulative distri... | Asymmetric S-shaped function mapping interval $[0, 1]$ to interval $[0, 1]$
Yes.
And here is how you go about finding one.
For our purposes, convex means $F''(x)\ge 0$ and concave means $F''(x)\le 0$.
Ok, so let $F$ be such a function. If we also assume monotonicity, we have |
55,349 | Asymmetric S-shaped function mapping interval $[0, 1]$ to interval $[0, 1]$ | I would comment instead, but I don't have the score yet, so here's my two cents:
The only thing I could think of is an asymmetric tangent function, yet I couldn't find anything about them except for this part of the Proceedings of the Estonian Academy of Sciences, Engineering. See if it helps...? | Asymmetric S-shaped function mapping interval $[0, 1]$ to interval $[0, 1]$ | I would comment instead, but I don't have the score yet, so here's my two cents:
The only thing I could think of is an asymmetric tangent function, yet I couldn't find anything about them except for t | Asymmetric S-shaped function mapping interval $[0, 1]$ to interval $[0, 1]$
I would comment instead, but I don't have the score yet, so here's my two cents:
The only thing I could think of is an asymmetric tangent function, yet I couldn't find anything about them except for this part of the Proceedings of the Estonian ... | Asymmetric S-shaped function mapping interval $[0, 1]$ to interval $[0, 1]$
I would comment instead, but I don't have the score yet, so here's my two cents:
The only thing I could think of is an asymmetric tangent function, yet I couldn't find anything about them except for t |
55,350 | is the z-test for difference of proportions valid for massive samples with tiny proportions? | Whenever I have doubts about the performance of a particular method, I try to run a simulation study to examine how well the method works under similar conditions. Below is a simple example using R for the case you are describing. Note that I set the true proportions equal for the two groups and to a value that is some... | is the z-test for difference of proportions valid for massive samples with tiny proportions? | Whenever I have doubts about the performance of a particular method, I try to run a simulation study to examine how well the method works under similar conditions. Below is a simple example using R fo | is the z-test for difference of proportions valid for massive samples with tiny proportions?
Whenever I have doubts about the performance of a particular method, I try to run a simulation study to examine how well the method works under similar conditions. Below is a simple example using R for the case you are describi... | is the z-test for difference of proportions valid for massive samples with tiny proportions?
Whenever I have doubts about the performance of a particular method, I try to run a simulation study to examine how well the method works under similar conditions. Below is a simple example using R fo |
55,351 | How can I do a correlation between Likert scale and an ordinal categorical measure? | What about one of the Kendall's $\tau$s? They are a kind of rank correlation coefficient for ordinal data.
Here's an example with Stata and $\tau_{b}$. A value of $−1$ implies perfect negative association, and $+1$ indicates perfect agreement. Zero indicates the absence of association. Here we see a modest, though sig... | How can I do a correlation between Likert scale and an ordinal categorical measure? | What about one of the Kendall's $\tau$s? They are a kind of rank correlation coefficient for ordinal data.
Here's an example with Stata and $\tau_{b}$. A value of $−1$ implies perfect negative associ | How can I do a correlation between Likert scale and an ordinal categorical measure?
What about one of the Kendall's $\tau$s? They are a kind of rank correlation coefficient for ordinal data.
Here's an example with Stata and $\tau_{b}$. A value of $−1$ implies perfect negative association, and $+1$ indicates perfect ag... | How can I do a correlation between Likert scale and an ordinal categorical measure?
What about one of the Kendall's $\tau$s? They are a kind of rank correlation coefficient for ordinal data.
Here's an example with Stata and $\tau_{b}$. A value of $−1$ implies perfect negative associ |
55,352 | How can I do a correlation between Likert scale and an ordinal categorical measure? | Spearman rank correlation produces an interpretable measure of correlation when both measrues are ordinal. | How can I do a correlation between Likert scale and an ordinal categorical measure? | Spearman rank correlation produces an interpretable measure of correlation when both measrues are ordinal. | How can I do a correlation between Likert scale and an ordinal categorical measure?
Spearman rank correlation produces an interpretable measure of correlation when both measrues are ordinal. | How can I do a correlation between Likert scale and an ordinal categorical measure?
Spearman rank correlation produces an interpretable measure of correlation when both measrues are ordinal. |
55,353 | What is the name of this perceptron-like classifier? | The name ADALINE (ADaptive LInear NEuron) come from both the physical implementation of an early classifier, but it is also the name specific design.
See: http://en.wikipedia.org/wiki/ADALINE
Apparently McCulloch-Pitts perceptrons came first. ADALINE was a variation on this that used a linear response function, as opp... | What is the name of this perceptron-like classifier? | The name ADALINE (ADaptive LInear NEuron) come from both the physical implementation of an early classifier, but it is also the name specific design.
See: http://en.wikipedia.org/wiki/ADALINE
Apparent | What is the name of this perceptron-like classifier?
The name ADALINE (ADaptive LInear NEuron) come from both the physical implementation of an early classifier, but it is also the name specific design.
See: http://en.wikipedia.org/wiki/ADALINE
Apparently McCulloch-Pitts perceptrons came first. ADALINE was a variation... | What is the name of this perceptron-like classifier?
The name ADALINE (ADaptive LInear NEuron) come from both the physical implementation of an early classifier, but it is also the name specific design.
See: http://en.wikipedia.org/wiki/ADALINE
Apparent |
55,354 | What is the name of this perceptron-like classifier? | What you describe is essentially just logistic regression with a scaled output using squared loss rather than the usual log loss. Notice that $\tanh(x) = 2\sigma(x) - 1$ where
$$
\sigma(x) = \frac{1}{1 + e^{-x}}
$$
is the logistic function. The decision boundary will still be linear. | What is the name of this perceptron-like classifier? | What you describe is essentially just logistic regression with a scaled output using squared loss rather than the usual log loss. Notice that $\tanh(x) = 2\sigma(x) - 1$ where
$$
\sigma(x) = \frac{1}{ | What is the name of this perceptron-like classifier?
What you describe is essentially just logistic regression with a scaled output using squared loss rather than the usual log loss. Notice that $\tanh(x) = 2\sigma(x) - 1$ where
$$
\sigma(x) = \frac{1}{1 + e^{-x}}
$$
is the logistic function. The decision boundary will... | What is the name of this perceptron-like classifier?
What you describe is essentially just logistic regression with a scaled output using squared loss rather than the usual log loss. Notice that $\tanh(x) = 2\sigma(x) - 1$ where
$$
\sigma(x) = \frac{1}{ |
55,355 | Using Anselin Local Moran's I Values in Regression | There seems to be some confusion around what exactly the local Moran's I values are, so lets review what they are and then evaluate if they can be given any reasonable interpretation in a regression equation.
In ESRI's notation, I believe you are talking about putting the $z_{I_i}$ in the regression equation, or perhap... | Using Anselin Local Moran's I Values in Regression | There seems to be some confusion around what exactly the local Moran's I values are, so lets review what they are and then evaluate if they can be given any reasonable interpretation in a regression e | Using Anselin Local Moran's I Values in Regression
There seems to be some confusion around what exactly the local Moran's I values are, so lets review what they are and then evaluate if they can be given any reasonable interpretation in a regression equation.
In ESRI's notation, I believe you are talking about putting ... | Using Anselin Local Moran's I Values in Regression
There seems to be some confusion around what exactly the local Moran's I values are, so lets review what they are and then evaluate if they can be given any reasonable interpretation in a regression e |
55,356 | Using Anselin Local Moran's I Values in Regression | Why not just use a spatial regression model? That way you account for the dependency measured by Local Moran's I directly in the model. As an aside, I would not advise including the local I value in a model, nor would a reviewer, I trust. There is the topic of Moran Eigenvector filtering (http://hosho.ees.hokudai.ac.... | Using Anselin Local Moran's I Values in Regression | Why not just use a spatial regression model? That way you account for the dependency measured by Local Moran's I directly in the model. As an aside, I would not advise including the local I value in | Using Anselin Local Moran's I Values in Regression
Why not just use a spatial regression model? That way you account for the dependency measured by Local Moran's I directly in the model. As an aside, I would not advise including the local I value in a model, nor would a reviewer, I trust. There is the topic of Moran ... | Using Anselin Local Moran's I Values in Regression
Why not just use a spatial regression model? That way you account for the dependency measured by Local Moran's I directly in the model. As an aside, I would not advise including the local I value in |
55,357 | Intepretation of crossvalidation result - cv.glm() | Similar to what mambo said, the delta values are useful to compare this model with alternative models. You might, for example, plot the delta values of this vs. comparable models to see which produce the lowest MSE (delta). The first value of delta is the standard k-fold estimate and the second is bias corrected. | Intepretation of crossvalidation result - cv.glm() | Similar to what mambo said, the delta values are useful to compare this model with alternative models. You might, for example, plot the delta values of this vs. comparable models to see which produce | Intepretation of crossvalidation result - cv.glm()
Similar to what mambo said, the delta values are useful to compare this model with alternative models. You might, for example, plot the delta values of this vs. comparable models to see which produce the lowest MSE (delta). The first value of delta is the standard k-fo... | Intepretation of crossvalidation result - cv.glm()
Similar to what mambo said, the delta values are useful to compare this model with alternative models. You might, for example, plot the delta values of this vs. comparable models to see which produce |
55,358 | Intepretation of crossvalidation result - cv.glm() | I started digging through the code for the boot package and found the function cv.glm() at https://github.com/cran/boot/blob/5b1e0fea4d1ab1716f2226d673e981d669495b75/R/bootfuns.q#L825, as well as going through Introduction to Statistical Learning by James et al. I haven't gotten to the $K$-fold CV section yet, but here... | Intepretation of crossvalidation result - cv.glm() | I started digging through the code for the boot package and found the function cv.glm() at https://github.com/cran/boot/blob/5b1e0fea4d1ab1716f2226d673e981d669495b75/R/bootfuns.q#L825, as well as goin | Intepretation of crossvalidation result - cv.glm()
I started digging through the code for the boot package and found the function cv.glm() at https://github.com/cran/boot/blob/5b1e0fea4d1ab1716f2226d673e981d669495b75/R/bootfuns.q#L825, as well as going through Introduction to Statistical Learning by James et al. I have... | Intepretation of crossvalidation result - cv.glm()
I started digging through the code for the boot package and found the function cv.glm() at https://github.com/cran/boot/blob/5b1e0fea4d1ab1716f2226d673e981d669495b75/R/bootfuns.q#L825, as well as goin |
55,359 | Intepretation of crossvalidation result - cv.glm() | This might be useful for the understanding of prediction errors (delta):
R crossvalidation cv.glm: prediction error and confidence interval
This answer from AdamO was particularly helpful:
"Prediction errors are different from standard errors in two critical ways.
Prediction errors provide intervals for predicted valu... | Intepretation of crossvalidation result - cv.glm() | This might be useful for the understanding of prediction errors (delta):
R crossvalidation cv.glm: prediction error and confidence interval
This answer from AdamO was particularly helpful:
"Prediction | Intepretation of crossvalidation result - cv.glm()
This might be useful for the understanding of prediction errors (delta):
R crossvalidation cv.glm: prediction error and confidence interval
This answer from AdamO was particularly helpful:
"Prediction errors are different from standard errors in two critical ways.
Pre... | Intepretation of crossvalidation result - cv.glm()
This might be useful for the understanding of prediction errors (delta):
R crossvalidation cv.glm: prediction error and confidence interval
This answer from AdamO was particularly helpful:
"Prediction |
55,360 | Intepretation of crossvalidation result - cv.glm() | I found this online, which helps explain what delta is:
http://home.strw.leidenuniv.nl/~jarle/IAC/Tasks/IAC-lecture4-homework.pdf
It seems to me that that comparative values of delta between models are of importance rather than the absolute values. | Intepretation of crossvalidation result - cv.glm() | I found this online, which helps explain what delta is:
http://home.strw.leidenuniv.nl/~jarle/IAC/Tasks/IAC-lecture4-homework.pdf
It seems to me that that comparative values of delta between models ar | Intepretation of crossvalidation result - cv.glm()
I found this online, which helps explain what delta is:
http://home.strw.leidenuniv.nl/~jarle/IAC/Tasks/IAC-lecture4-homework.pdf
It seems to me that that comparative values of delta between models are of importance rather than the absolute values. | Intepretation of crossvalidation result - cv.glm()
I found this online, which helps explain what delta is:
http://home.strw.leidenuniv.nl/~jarle/IAC/Tasks/IAC-lecture4-homework.pdf
It seems to me that that comparative values of delta between models ar |
55,361 | Testing the race model inequality in R | UPDATE: All the custom versions of functions rewritten in R and used in this answer are added in the bottom for better clarity.
I guess. You probably knew where's gone wrong. As you said
it seems right, but I'm not 100% sure
Yes. This is the start of the differences between your data and Ulrich et al's.
cx <- c(24... | Testing the race model inequality in R | UPDATE: All the custom versions of functions rewritten in R and used in this answer are added in the bottom for better clarity.
I guess. You probably knew where's gone wrong. As you said
it seems ri | Testing the race model inequality in R
UPDATE: All the custom versions of functions rewritten in R and used in this answer are added in the bottom for better clarity.
I guess. You probably knew where's gone wrong. As you said
it seems right, but I'm not 100% sure
Yes. This is the start of the differences between you... | Testing the race model inequality in R
UPDATE: All the custom versions of functions rewritten in R and used in this answer are added in the bottom for better clarity.
I guess. You probably knew where's gone wrong. As you said
it seems ri |
55,362 | Testing the race model inequality in R | In short, I think that at the stage of b you find yourself combining RTs when what you should be combining are proportions. This is speculative, but I took the stab below to get things rolling. I do love me some race models and have been meaning to implement one form or another in R for a while, but never got around ... | Testing the race model inequality in R | In short, I think that at the stage of b you find yourself combining RTs when what you should be combining are proportions. This is speculative, but I took the stab below to get things rolling. I do | Testing the race model inequality in R
In short, I think that at the stage of b you find yourself combining RTs when what you should be combining are proportions. This is speculative, but I took the stab below to get things rolling. I do love me some race models and have been meaning to implement one form or another ... | Testing the race model inequality in R
In short, I think that at the stage of b you find yourself combining RTs when what you should be combining are proportions. This is speculative, but I took the stab below to get things rolling. I do |
55,363 | Sign of correlation of logged variables | No, without any additional assumption, knowing the (Pearson) correlation on $X$ and $Y$ does not give any clue on the (Pearson) correlation between $\log X$ and $\log Y$. See the following example in R:
x1 = c(10^-100, 1, 10^5)
x2 = c(1, 10^-100, 10^5)
cor(x1, x2) # = 1
cor(log(x1), log(x2)) # -0.4251781
(Here, $X_1$... | Sign of correlation of logged variables | No, without any additional assumption, knowing the (Pearson) correlation on $X$ and $Y$ does not give any clue on the (Pearson) correlation between $\log X$ and $\log Y$. See the following example in | Sign of correlation of logged variables
No, without any additional assumption, knowing the (Pearson) correlation on $X$ and $Y$ does not give any clue on the (Pearson) correlation between $\log X$ and $\log Y$. See the following example in R:
x1 = c(10^-100, 1, 10^5)
x2 = c(1, 10^-100, 10^5)
cor(x1, x2) # = 1
cor(log(... | Sign of correlation of logged variables
No, without any additional assumption, knowing the (Pearson) correlation on $X$ and $Y$ does not give any clue on the (Pearson) correlation between $\log X$ and $\log Y$. See the following example in |
55,364 | Sign of correlation of logged variables | We don't.
You can calculate an approximation via Taylor Series that should work fairly well for X and Y having a small coefficient of variation or being close to normal. | Sign of correlation of logged variables | We don't.
You can calculate an approximation via Taylor Series that should work fairly well for X and Y having a small coefficient of variation or being close to normal. | Sign of correlation of logged variables
We don't.
You can calculate an approximation via Taylor Series that should work fairly well for X and Y having a small coefficient of variation or being close to normal. | Sign of correlation of logged variables
We don't.
You can calculate an approximation via Taylor Series that should work fairly well for X and Y having a small coefficient of variation or being close to normal. |
55,365 | Minimizing variance of an estimator under sampling cost penalty | You are asking a question about the expected value of sample information.
First, I suggest that you not define your loss function in terms of variance, at least not without further examination. Instead, identify why you care about error in your estimation procedure. What are the different ways of being wrong? What are ... | Minimizing variance of an estimator under sampling cost penalty | You are asking a question about the expected value of sample information.
First, I suggest that you not define your loss function in terms of variance, at least not without further examination. Instea | Minimizing variance of an estimator under sampling cost penalty
You are asking a question about the expected value of sample information.
First, I suggest that you not define your loss function in terms of variance, at least not without further examination. Instead, identify why you care about error in your estimation ... | Minimizing variance of an estimator under sampling cost penalty
You are asking a question about the expected value of sample information.
First, I suggest that you not define your loss function in terms of variance, at least not without further examination. Instea |
55,366 | Minimizing variance of an estimator under sampling cost penalty | The cost vs. better precision/smaller variance trade-off has been studied in sampling literature to some extent. The first historical result dating back to 1930s is Neymann-Chuprow optimal allocation in stratified sampling, which is the problem of allocating the sample between strata with different variances and unit c... | Minimizing variance of an estimator under sampling cost penalty | The cost vs. better precision/smaller variance trade-off has been studied in sampling literature to some extent. The first historical result dating back to 1930s is Neymann-Chuprow optimal allocation | Minimizing variance of an estimator under sampling cost penalty
The cost vs. better precision/smaller variance trade-off has been studied in sampling literature to some extent. The first historical result dating back to 1930s is Neymann-Chuprow optimal allocation in stratified sampling, which is the problem of allocati... | Minimizing variance of an estimator under sampling cost penalty
The cost vs. better precision/smaller variance trade-off has been studied in sampling literature to some extent. The first historical result dating back to 1930s is Neymann-Chuprow optimal allocation |
55,367 | Basic Bayesian MCMC to estimate two parameters from binomial distributions given unknown number of trials | Model and Pseudocode
So I did some analysis in Python, though I used the pyMC library which hides all the MCMC mathy stuff. I'll show you how I modeled it in semi-pseudocode, and the results.
I set my observed data as $X=5, Y=10$.
X = 5
Y = 10
I assumed that $N$ has a Poisson prior, with the Poisson's rate a $EXP(1)... | Basic Bayesian MCMC to estimate two parameters from binomial distributions given unknown number of t | Model and Pseudocode
So I did some analysis in Python, though I used the pyMC library which hides all the MCMC mathy stuff. I'll show you how I modeled it in semi-pseudocode, and the results.
I set m | Basic Bayesian MCMC to estimate two parameters from binomial distributions given unknown number of trials
Model and Pseudocode
So I did some analysis in Python, though I used the pyMC library which hides all the MCMC mathy stuff. I'll show you how I modeled it in semi-pseudocode, and the results.
I set my observed dat... | Basic Bayesian MCMC to estimate two parameters from binomial distributions given unknown number of t
Model and Pseudocode
So I did some analysis in Python, though I used the pyMC library which hides all the MCMC mathy stuff. I'll show you how I modeled it in semi-pseudocode, and the results.
I set m |
55,368 | Increasing the sample size does not help the classification performance | Increasing training size does not neccessarily help the classifier and rather, may lead to a degradation in the generalization ability.
Regarding your own experiment, the factor of such unexpected degradation in performance given the increase in the training size could be one of the following:
1- Randomness:
Simply, if... | Increasing the sample size does not help the classification performance | Increasing training size does not neccessarily help the classifier and rather, may lead to a degradation in the generalization ability.
Regarding your own experiment, the factor of such unexpected deg | Increasing the sample size does not help the classification performance
Increasing training size does not neccessarily help the classifier and rather, may lead to a degradation in the generalization ability.
Regarding your own experiment, the factor of such unexpected degradation in performance given the increase in th... | Increasing the sample size does not help the classification performance
Increasing training size does not neccessarily help the classifier and rather, may lead to a degradation in the generalization ability.
Regarding your own experiment, the factor of such unexpected deg |
55,369 | Increasing the sample size does not help the classification performance | One possibility is that the data is not linearly separable, or the best linear separation doesn't give the best classifier. So, a common approach is to use a soft margin. The amount of slack should be increased with the size of the training set. If you aren't doing this, then you may get worse results with more data. | Increasing the sample size does not help the classification performance | One possibility is that the data is not linearly separable, or the best linear separation doesn't give the best classifier. So, a common approach is to use a soft margin. The amount of slack should be | Increasing the sample size does not help the classification performance
One possibility is that the data is not linearly separable, or the best linear separation doesn't give the best classifier. So, a common approach is to use a soft margin. The amount of slack should be increased with the size of the training set. If... | Increasing the sample size does not help the classification performance
One possibility is that the data is not linearly separable, or the best linear separation doesn't give the best classifier. So, a common approach is to use a soft margin. The amount of slack should be |
55,370 | Entropy-based methods in R | Well there is a package which implements entropy-based methods and it is called .... entropy.
More information: http://cran.r-project.org/web/packages/entropy/ | Entropy-based methods in R | Well there is a package which implements entropy-based methods and it is called .... entropy.
More information: http://cran.r-project.org/web/packages/entropy/ | Entropy-based methods in R
Well there is a package which implements entropy-based methods and it is called .... entropy.
More information: http://cran.r-project.org/web/packages/entropy/ | Entropy-based methods in R
Well there is a package which implements entropy-based methods and it is called .... entropy.
More information: http://cran.r-project.org/web/packages/entropy/ |
55,371 | Entropy-based methods in R | I haven't done this personally, but a very good reference for R packages is the R Graphical Manual
A search on that site of "empirical likelihood" gave several results, including EEF.profile from the boot package. There are also some packages that claim entropy methods (FNN), although I don't know precisely what you ar... | Entropy-based methods in R | I haven't done this personally, but a very good reference for R packages is the R Graphical Manual
A search on that site of "empirical likelihood" gave several results, including EEF.profile from the | Entropy-based methods in R
I haven't done this personally, but a very good reference for R packages is the R Graphical Manual
A search on that site of "empirical likelihood" gave several results, including EEF.profile from the boot package. There are also some packages that claim entropy methods (FNN), although I don't... | Entropy-based methods in R
I haven't done this personally, but a very good reference for R packages is the R Graphical Manual
A search on that site of "empirical likelihood" gave several results, including EEF.profile from the |
55,372 | Fisher overall p-value vs. pairwise comparisons | You are correct to be suspicious and you are correct that problems arise from some of the low cell counts in this case. However, there is nothing wrong with Fisher's test itself. We just need to be careful in interpreting its results.
Let's review the data:
0 1 Total
Site 1 7 2 | 9
Site 2 95 9 | ... | Fisher overall p-value vs. pairwise comparisons | You are correct to be suspicious and you are correct that problems arise from some of the low cell counts in this case. However, there is nothing wrong with Fisher's test itself. We just need to be | Fisher overall p-value vs. pairwise comparisons
You are correct to be suspicious and you are correct that problems arise from some of the low cell counts in this case. However, there is nothing wrong with Fisher's test itself. We just need to be careful in interpreting its results.
Let's review the data:
0 ... | Fisher overall p-value vs. pairwise comparisons
You are correct to be suspicious and you are correct that problems arise from some of the low cell counts in this case. However, there is nothing wrong with Fisher's test itself. We just need to be |
55,373 | Software for median polishing | Well R has medpolish built in, and it can deal with some level of missingness:
> a # some data
[,1] [,2] [,3] [,4]
[1,] 32.45884 29.50403 38.54330 30.06207
[2,] 27.92059 25.00838 NA 13.93309
[3,] 37.91911 23.98091 36.00139 27.73731
[4,] 29.20283 29.68059 18.41809 29.92471
[5,] N... | Software for median polishing | Well R has medpolish built in, and it can deal with some level of missingness:
> a # some data
[,1] [,2] [,3] [,4]
[1,] 32.45884 29.50403 38.54330 30.06207
[2,] 27.92059 25.0 | Software for median polishing
Well R has medpolish built in, and it can deal with some level of missingness:
> a # some data
[,1] [,2] [,3] [,4]
[1,] 32.45884 29.50403 38.54330 30.06207
[2,] 27.92059 25.00838 NA 13.93309
[3,] 37.91911 23.98091 36.00139 27.73731
[4,] 29.20283 29.68059 1... | Software for median polishing
Well R has medpolish built in, and it can deal with some level of missingness:
> a # some data
[,1] [,2] [,3] [,4]
[1,] 32.45884 29.50403 38.54330 30.06207
[2,] 27.92059 25.0 |
55,374 | How to specify in r spatial covariance structure similar to SAS sp(pow) in a marginal model? | The spatial power covariance structure is a generalization of the first-order autoregressive covariance structure. Where the first-order autoregressive structure assumes the time points are equally spaced, the spatial power structure can account for a continuous time point. In reality, we could just forget the first-... | How to specify in r spatial covariance structure similar to SAS sp(pow) in a marginal model? | The spatial power covariance structure is a generalization of the first-order autoregressive covariance structure. Where the first-order autoregressive structure assumes the time points are equally s | How to specify in r spatial covariance structure similar to SAS sp(pow) in a marginal model?
The spatial power covariance structure is a generalization of the first-order autoregressive covariance structure. Where the first-order autoregressive structure assumes the time points are equally spaced, the spatial power st... | How to specify in r spatial covariance structure similar to SAS sp(pow) in a marginal model?
The spatial power covariance structure is a generalization of the first-order autoregressive covariance structure. Where the first-order autoregressive structure assumes the time points are equally s |
55,375 | Basic reproduction number | I'm just guessing here but...
The basic reproduction number is the expected number of secondary infections over the lifetime of the initial infection. Let $S$ be the number of secondary infections over the lifetime of the initial infection and $L$ be the lifetime of the initial infection.
$S|L$ can be modeled as a Pois... | Basic reproduction number | I'm just guessing here but...
The basic reproduction number is the expected number of secondary infections over the lifetime of the initial infection. Let $S$ be the number of secondary infections ove | Basic reproduction number
I'm just guessing here but...
The basic reproduction number is the expected number of secondary infections over the lifetime of the initial infection. Let $S$ be the number of secondary infections over the lifetime of the initial infection and $L$ be the lifetime of the initial infection.
$S|L... | Basic reproduction number
I'm just guessing here but...
The basic reproduction number is the expected number of secondary infections over the lifetime of the initial infection. Let $S$ be the number of secondary infections ove |
55,376 | Basic reproduction number | The answer to this doesn't necessarily rely on the distribution, it can be thought of as a simple problem of incoming infections vs. outgoing infections.
If you are trying to fill a bathtub, the water level will only rise if the rate of incoming water outpaces the rate of outgoing water. The same principle is true of a... | Basic reproduction number | The answer to this doesn't necessarily rely on the distribution, it can be thought of as a simple problem of incoming infections vs. outgoing infections.
If you are trying to fill a bathtub, the water | Basic reproduction number
The answer to this doesn't necessarily rely on the distribution, it can be thought of as a simple problem of incoming infections vs. outgoing infections.
If you are trying to fill a bathtub, the water level will only rise if the rate of incoming water outpaces the rate of outgoing water. The s... | Basic reproduction number
The answer to this doesn't necessarily rely on the distribution, it can be thought of as a simple problem of incoming infections vs. outgoing infections.
If you are trying to fill a bathtub, the water |
55,377 | Fastest way to compare ROC curves | There is more to k-fold CV than you do. In essence, the idea of using those crazy splits instead of simply making a few random subsamples is that you can reconstruct the full decision and compare it with original just like you might have done with a predictions on a full train set.
So, sticking to a full k-fold CV me... | Fastest way to compare ROC curves | There is more to k-fold CV than you do. In essence, the idea of using those crazy splits instead of simply making a few random subsamples is that you can reconstruct the full decision and compare it w | Fastest way to compare ROC curves
There is more to k-fold CV than you do. In essence, the idea of using those crazy splits instead of simply making a few random subsamples is that you can reconstruct the full decision and compare it with original just like you might have done with a predictions on a full train set.
S... | Fastest way to compare ROC curves
There is more to k-fold CV than you do. In essence, the idea of using those crazy splits instead of simply making a few random subsamples is that you can reconstruct the full decision and compare it w |
55,378 | Fastest way to compare ROC curves | Just to chime in the @mbq's multiple testing: if you want to compare each of 120 models with each other, that is 7140 comparisons!
You may want to reduce the number of models beforehand by your expert knowledge of the problem. Or include a (few) models that give you baseline performance (constant prediction, random pr... | Fastest way to compare ROC curves | Just to chime in the @mbq's multiple testing: if you want to compare each of 120 models with each other, that is 7140 comparisons!
You may want to reduce the number of models beforehand by your exper | Fastest way to compare ROC curves
Just to chime in the @mbq's multiple testing: if you want to compare each of 120 models with each other, that is 7140 comparisons!
You may want to reduce the number of models beforehand by your expert knowledge of the problem. Or include a (few) models that give you baseline performan... | Fastest way to compare ROC curves
Just to chime in the @mbq's multiple testing: if you want to compare each of 120 models with each other, that is 7140 comparisons!
You may want to reduce the number of models beforehand by your exper |
55,379 | Can two or more splits in a binary decision tree be made on the same variable? | Yes, this is possible and happens frequently. Consider the tree page 4 of this tutorial, you'll see that multiple splits are made on both variables longitude and latitude. At each step of the CART algorithm, all predictors are tried and the best one (the one selected for splitting) is the one that maximizes the decreas... | Can two or more splits in a binary decision tree be made on the same variable? | Yes, this is possible and happens frequently. Consider the tree page 4 of this tutorial, you'll see that multiple splits are made on both variables longitude and latitude. At each step of the CART alg | Can two or more splits in a binary decision tree be made on the same variable?
Yes, this is possible and happens frequently. Consider the tree page 4 of this tutorial, you'll see that multiple splits are made on both variables longitude and latitude. At each step of the CART algorithm, all predictors are tried and the ... | Can two or more splits in a binary decision tree be made on the same variable?
Yes, this is possible and happens frequently. Consider the tree page 4 of this tutorial, you'll see that multiple splits are made on both variables longitude and latitude. At each step of the CART alg |
55,380 | Why do sampling distributions provide a major simplification on the route statistical inference? | Suppose that you want to know how many likely voters plan to vote for the incumbant in your cities race for mayor this year so you take a simple random sample of likely voters and ask them if they plan to vote for the incumbant or the challenger. The sampling distribution tells us the relationship between the proporti... | Why do sampling distributions provide a major simplification on the route statistical inference? | Suppose that you want to know how many likely voters plan to vote for the incumbant in your cities race for mayor this year so you take a simple random sample of likely voters and ask them if they pla | Why do sampling distributions provide a major simplification on the route statistical inference?
Suppose that you want to know how many likely voters plan to vote for the incumbant in your cities race for mayor this year so you take a simple random sample of likely voters and ask them if they plan to vote for the incum... | Why do sampling distributions provide a major simplification on the route statistical inference?
Suppose that you want to know how many likely voters plan to vote for the incumbant in your cities race for mayor this year so you take a simple random sample of likely voters and ask them if they pla |
55,381 | Why do sampling distributions provide a major simplification on the route statistical inference? | In parametric statistics, you usually start with a sample, let us say an iid sample, $X_1,\ldots,X_n$, distributed as
$$
\prod_{i=1}^n f_\theta(x_i),
$$
and you have to draw inference on $\theta$ using this distribution, which may be troublesome.
If, instead, for a reason or another, you decide to use only a specific t... | Why do sampling distributions provide a major simplification on the route statistical inference? | In parametric statistics, you usually start with a sample, let us say an iid sample, $X_1,\ldots,X_n$, distributed as
$$
\prod_{i=1}^n f_\theta(x_i),
$$
and you have to draw inference on $\theta$ usin | Why do sampling distributions provide a major simplification on the route statistical inference?
In parametric statistics, you usually start with a sample, let us say an iid sample, $X_1,\ldots,X_n$, distributed as
$$
\prod_{i=1}^n f_\theta(x_i),
$$
and you have to draw inference on $\theta$ using this distribution, wh... | Why do sampling distributions provide a major simplification on the route statistical inference?
In parametric statistics, you usually start with a sample, let us say an iid sample, $X_1,\ldots,X_n$, distributed as
$$
\prod_{i=1}^n f_\theta(x_i),
$$
and you have to draw inference on $\theta$ usin |
55,382 | Why do sampling distributions provide a major simplification on the route statistical inference? | It's because all of the information from the data given an assumed model is picked up by a multiple of the likelihood and that is all you need or many would argue should use in inference (tentatively taking the model as given). When that is just driven by some summary statistics, there is tremendous simplification.
Thi... | Why do sampling distributions provide a major simplification on the route statistical inference? | It's because all of the information from the data given an assumed model is picked up by a multiple of the likelihood and that is all you need or many would argue should use in inference (tentatively | Why do sampling distributions provide a major simplification on the route statistical inference?
It's because all of the information from the data given an assumed model is picked up by a multiple of the likelihood and that is all you need or many would argue should use in inference (tentatively taking the model as giv... | Why do sampling distributions provide a major simplification on the route statistical inference?
It's because all of the information from the data given an assumed model is picked up by a multiple of the likelihood and that is all you need or many would argue should use in inference (tentatively |
55,383 | Completely different results from lmer() and lme() | lmer uses Laplace approximation, when the whole normal distribution of the random effect is approximated at its mode. This approximation is known to produce the estimates of the variance components that are biased down. lme uses a more thorough approximation via Gaussian quadrature approximation, but I neither know the... | Completely different results from lmer() and lme() | lmer uses Laplace approximation, when the whole normal distribution of the random effect is approximated at its mode. This approximation is known to produce the estimates of the variance components th | Completely different results from lmer() and lme()
lmer uses Laplace approximation, when the whole normal distribution of the random effect is approximated at its mode. This approximation is known to produce the estimates of the variance components that are biased down. lme uses a more thorough approximation via Gaussi... | Completely different results from lmer() and lme()
lmer uses Laplace approximation, when the whole normal distribution of the random effect is approximated at its mode. This approximation is known to produce the estimates of the variance components th |
55,384 | Completely different results from lmer() and lme() | The log-likelihood is substantially higher for the lmer() result, which would suggest it's closer to the true optimum.
Since the lmer() fit is on the boundary of the parameter space, which lme() reparametrises off to infinity (the 'Log-Cholesky' parametrisation), it's plausible that lme just didn't find the better sol... | Completely different results from lmer() and lme() | The log-likelihood is substantially higher for the lmer() result, which would suggest it's closer to the true optimum.
Since the lmer() fit is on the boundary of the parameter space, which lme() repar | Completely different results from lmer() and lme()
The log-likelihood is substantially higher for the lmer() result, which would suggest it's closer to the true optimum.
Since the lmer() fit is on the boundary of the parameter space, which lme() reparametrises off to infinity (the 'Log-Cholesky' parametrisation), it's... | Completely different results from lmer() and lme()
The log-likelihood is substantially higher for the lmer() result, which would suggest it's closer to the true optimum.
Since the lmer() fit is on the boundary of the parameter space, which lme() repar |
55,385 | How to use triple exponential smoothing to forecast in Excel | This isn't an exact answer to your question, but... you are definitely best off spending a bit of time to do learn some R basics and use something like Rob Hyndman's forecast package to do this. This will let you try a number of robust forecasting procedures and choose appropriate parameters, all within a state of the... | How to use triple exponential smoothing to forecast in Excel | This isn't an exact answer to your question, but... you are definitely best off spending a bit of time to do learn some R basics and use something like Rob Hyndman's forecast package to do this. This | How to use triple exponential smoothing to forecast in Excel
This isn't an exact answer to your question, but... you are definitely best off spending a bit of time to do learn some R basics and use something like Rob Hyndman's forecast package to do this. This will let you try a number of robust forecasting procedures... | How to use triple exponential smoothing to forecast in Excel
This isn't an exact answer to your question, but... you are definitely best off spending a bit of time to do learn some R basics and use something like Rob Hyndman's forecast package to do this. This |
55,386 | How to use triple exponential smoothing to forecast in Excel | Your data can be easly modeled using a seaonal model of the form
Y(T) = 168.16
+[X1(T)][(+ 28.8257)] :PULSE 2012/ 3
+[X2(T)][(- 14.3322)] :PULSE 2010/ 13
+[X3(T)][(+ 15.0558)] ... | How to use triple exponential smoothing to forecast in Excel | Your data can be easly modeled using a seaonal model of the form
Y(T) = 168.16
+[X1(T)][(+ 28.8257)] : | How to use triple exponential smoothing to forecast in Excel
Your data can be easly modeled using a seaonal model of the form
Y(T) = 168.16
+[X1(T)][(+ 28.8257)] :PULSE 2012/ 3
+[X2(T)][(- 14.3322)] ... | How to use triple exponential smoothing to forecast in Excel
Your data can be easly modeled using a seaonal model of the form
Y(T) = 168.16
+[X1(T)][(+ 28.8257)] : |
55,387 | How to use triple exponential smoothing to forecast in Excel | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
http://www.calstatela.edu/faculty/hwarren/a503/forecas... | How to use triple exponential smoothing to forecast in Excel | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How to use triple exponential smoothing to forecast in Excel
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | How to use triple exponential smoothing to forecast in Excel
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
55,388 | Are discrimination parameters in two-parameter IRT models only specific to items? | @KH Kim, I believe there is some difficulty when any mathematical model meets the real world. In the IRT model, items and item parameters are invariant to the pool of individuals who answer those items - that's the theoretical building block of the model. This is quite a deep conversation in which you are immersing y... | Are discrimination parameters in two-parameter IRT models only specific to items? | @KH Kim, I believe there is some difficulty when any mathematical model meets the real world. In the IRT model, items and item parameters are invariant to the pool of individuals who answer those ite | Are discrimination parameters in two-parameter IRT models only specific to items?
@KH Kim, I believe there is some difficulty when any mathematical model meets the real world. In the IRT model, items and item parameters are invariant to the pool of individuals who answer those items - that's the theoretical building b... | Are discrimination parameters in two-parameter IRT models only specific to items?
@KH Kim, I believe there is some difficulty when any mathematical model meets the real world. In the IRT model, items and item parameters are invariant to the pool of individuals who answer those ite |
55,389 | Are discrimination parameters in two-parameter IRT models only specific to items? | The discrimination parameter is an item parameter because of how it is specified in the model
$resp_{ip} \sim \alpha_i ( \theta_p - \beta_i)$
where i is for item and p is for person. In this model, the person (represented only by $\theta$) has nothing to do with the discrimination parameter.
IRT models the interaction ... | Are discrimination parameters in two-parameter IRT models only specific to items? | The discrimination parameter is an item parameter because of how it is specified in the model
$resp_{ip} \sim \alpha_i ( \theta_p - \beta_i)$
where i is for item and p is for person. In this model, th | Are discrimination parameters in two-parameter IRT models only specific to items?
The discrimination parameter is an item parameter because of how it is specified in the model
$resp_{ip} \sim \alpha_i ( \theta_p - \beta_i)$
where i is for item and p is for person. In this model, the person (represented only by $\theta$... | Are discrimination parameters in two-parameter IRT models only specific to items?
The discrimination parameter is an item parameter because of how it is specified in the model
$resp_{ip} \sim \alpha_i ( \theta_p - \beta_i)$
where i is for item and p is for person. In this model, th |
55,390 | Are discrimination parameters in two-parameter IRT models only specific to items? | This is more a comment that grew too long and had to be edited than an answer buy anyway…
I don't know these models very well but it might help to remember that they were developed with personal attributes (traits like abilities, attitudes or personality) in mind. The model then describes responses to a particular ite... | Are discrimination parameters in two-parameter IRT models only specific to items? | This is more a comment that grew too long and had to be edited than an answer buy anyway…
I don't know these models very well but it might help to remember that they were developed with personal attr | Are discrimination parameters in two-parameter IRT models only specific to items?
This is more a comment that grew too long and had to be edited than an answer buy anyway…
I don't know these models very well but it might help to remember that they were developed with personal attributes (traits like abilities, attitud... | Are discrimination parameters in two-parameter IRT models only specific to items?
This is more a comment that grew too long and had to be edited than an answer buy anyway…
I don't know these models very well but it might help to remember that they were developed with personal attr |
55,391 | Are discrimination parameters in two-parameter IRT models only specific to items? | I found this article which tries a model with person specific discrimination parameter
An IRT Modeling Approach for Assessing Item and Person Discrimination in Binary Personality Responses
I should have tried first!
Related to this,
for polytomous data,
Wolfe and Firth(2002), Modelling subjective use of an ordinal res... | Are discrimination parameters in two-parameter IRT models only specific to items? | I found this article which tries a model with person specific discrimination parameter
An IRT Modeling Approach for Assessing Item and Person Discrimination in Binary Personality Responses
I should ha | Are discrimination parameters in two-parameter IRT models only specific to items?
I found this article which tries a model with person specific discrimination parameter
An IRT Modeling Approach for Assessing Item and Person Discrimination in Binary Personality Responses
I should have tried first!
Related to this,
for p... | Are discrimination parameters in two-parameter IRT models only specific to items?
I found this article which tries a model with person specific discrimination parameter
An IRT Modeling Approach for Assessing Item and Person Discrimination in Binary Personality Responses
I should ha |
55,392 | How to compare two related ordinal variables? | Welcome to the site, ellen.
You can conduct contingency table analysis, and that can be done in two ways.
First, you can tabulate the category against the occasion, which would give you a 2x4 table with 3 degrees of freedom, and test for independence of the counts. The test will tell you whether the marginal distributi... | How to compare two related ordinal variables? | Welcome to the site, ellen.
You can conduct contingency table analysis, and that can be done in two ways.
First, you can tabulate the category against the occasion, which would give you a 2x4 table wi | How to compare two related ordinal variables?
Welcome to the site, ellen.
You can conduct contingency table analysis, and that can be done in two ways.
First, you can tabulate the category against the occasion, which would give you a 2x4 table with 3 degrees of freedom, and test for independence of the counts. The test... | How to compare two related ordinal variables?
Welcome to the site, ellen.
You can conduct contingency table analysis, and that can be done in two ways.
First, you can tabulate the category against the occasion, which would give you a 2x4 table wi |
55,393 | How to compare two related ordinal variables? | Since the data are not continuous and certainly not close to being normally distributed a nonparametric paired test seems to be the answer. My suggestion would be the Wilcoxon signed rank test. | How to compare two related ordinal variables? | Since the data are not continuous and certainly not close to being normally distributed a nonparametric paired test seems to be the answer. My suggestion would be the Wilcoxon signed rank test. | How to compare two related ordinal variables?
Since the data are not continuous and certainly not close to being normally distributed a nonparametric paired test seems to be the answer. My suggestion would be the Wilcoxon signed rank test. | How to compare two related ordinal variables?
Since the data are not continuous and certainly not close to being normally distributed a nonparametric paired test seems to be the answer. My suggestion would be the Wilcoxon signed rank test. |
55,394 | How to compare two related ordinal variables? | To @StasK, I think the contingency table doesn't work here since the samples are paired. The contingency table cannot account for the dependence between the paired samples.
The Wilcoxon signed-rank test compares the difference between two paired samples when the response variable is on ordinal scale, and thus fits you... | How to compare two related ordinal variables? | To @StasK, I think the contingency table doesn't work here since the samples are paired. The contingency table cannot account for the dependence between the paired samples.
The Wilcoxon signed-rank t | How to compare two related ordinal variables?
To @StasK, I think the contingency table doesn't work here since the samples are paired. The contingency table cannot account for the dependence between the paired samples.
The Wilcoxon signed-rank test compares the difference between two paired samples when the response v... | How to compare two related ordinal variables?
To @StasK, I think the contingency table doesn't work here since the samples are paired. The contingency table cannot account for the dependence between the paired samples.
The Wilcoxon signed-rank t |
55,395 | Is there any criterion about choosing reference factor in multinomial logistic regression? | You are free to choose any of the categories as the reference. From the viewpoint of overall statistical quality of prediction by the model, the choice is arbitrary. In terms of interpretation of individual IV's effects, it makes difference. The multinomial logistic model is:
$log(\frac{Prob(category_i)}{Prob(category_... | Is there any criterion about choosing reference factor in multinomial logistic regression? | You are free to choose any of the categories as the reference. From the viewpoint of overall statistical quality of prediction by the model, the choice is arbitrary. In terms of interpretation of indi | Is there any criterion about choosing reference factor in multinomial logistic regression?
You are free to choose any of the categories as the reference. From the viewpoint of overall statistical quality of prediction by the model, the choice is arbitrary. In terms of interpretation of individual IV's effects, it makes... | Is there any criterion about choosing reference factor in multinomial logistic regression?
You are free to choose any of the categories as the reference. From the viewpoint of overall statistical quality of prediction by the model, the choice is arbitrary. In terms of interpretation of indi |
55,396 | An elementary question on binomial test: why should I take a sum? | Max's comment answers your question: The definition of the $p$-value is the probability that you get a value at least as extreme, and this includes all outcomes more lopsided than the one you observed. It's your choice whether to consider $10-40$ more lopsided than $31-19$, whether to use a two-tailed test or one-taile... | An elementary question on binomial test: why should I take a sum? | Max's comment answers your question: The definition of the $p$-value is the probability that you get a value at least as extreme, and this includes all outcomes more lopsided than the one you observed | An elementary question on binomial test: why should I take a sum?
Max's comment answers your question: The definition of the $p$-value is the probability that you get a value at least as extreme, and this includes all outcomes more lopsided than the one you observed. It's your choice whether to consider $10-40$ more lo... | An elementary question on binomial test: why should I take a sum?
Max's comment answers your question: The definition of the $p$-value is the probability that you get a value at least as extreme, and this includes all outcomes more lopsided than the one you observed |
55,397 | Data cleansing in regression analysis | Use a robust fit, such as lmrob in the robustbase package. This particular one can automatically detect and downweight up to 50% of the data if they appear to be outlying.
To see what can be accomplished, let's simulate a nasty dataset with plenty of outliers in both the $x$ and $y$ variables:
library(robustbase)
set.... | Data cleansing in regression analysis | Use a robust fit, such as lmrob in the robustbase package. This particular one can automatically detect and downweight up to 50% of the data if they appear to be outlying.
To see what can be accompli | Data cleansing in regression analysis
Use a robust fit, such as lmrob in the robustbase package. This particular one can automatically detect and downweight up to 50% of the data if they appear to be outlying.
To see what can be accomplished, let's simulate a nasty dataset with plenty of outliers in both the $x$ and $... | Data cleansing in regression analysis
Use a robust fit, such as lmrob in the robustbase package. This particular one can automatically detect and downweight up to 50% of the data if they appear to be outlying.
To see what can be accompli |
55,398 | Central moments of a gaussian mixture density? | It is simple because of linearity of integration (exchange order of integration and expectation).
$\mu=\omega_1 \mu_1 +\omega_2\mu_2 +\dots+\omega_k \mu_k$
The same is true for higher order moments and central moments with the $\mu_i$s replaced by the variances for variances etc. Now since each $\mu_i$ and $C_i$ deter... | Central moments of a gaussian mixture density? | It is simple because of linearity of integration (exchange order of integration and expectation).
$\mu=\omega_1 \mu_1 +\omega_2\mu_2 +\dots+\omega_k \mu_k$
The same is true for higher order moments an | Central moments of a gaussian mixture density?
It is simple because of linearity of integration (exchange order of integration and expectation).
$\mu=\omega_1 \mu_1 +\omega_2\mu_2 +\dots+\omega_k \mu_k$
The same is true for higher order moments and central moments with the $\mu_i$s replaced by the variances for varianc... | Central moments of a gaussian mixture density?
It is simple because of linearity of integration (exchange order of integration and expectation).
$\mu=\omega_1 \mu_1 +\omega_2\mu_2 +\dots+\omega_k \mu_k$
The same is true for higher order moments an |
55,399 | Central moments of a gaussian mixture density? | I illustrate the calculation with 2 components. The other cases are similar. The key results to use are
(a) E(E(X|Y))=E(X) and (b) V(X)= V(E(X|Y))+E(V(X|Y)).
Here Y denotes the component. So Y takes the values 1 and 2 with probabilities p and 1-p.
Let E(X|Y=i) = $\mu_i$ and V(X|Y=i) =$\sigma_i^2$
Now E(X)= p $\mu_1$ ... | Central moments of a gaussian mixture density? | I illustrate the calculation with 2 components. The other cases are similar. The key results to use are
(a) E(E(X|Y))=E(X) and (b) V(X)= V(E(X|Y))+E(V(X|Y)).
Here Y denotes the component. So Y takes | Central moments of a gaussian mixture density?
I illustrate the calculation with 2 components. The other cases are similar. The key results to use are
(a) E(E(X|Y))=E(X) and (b) V(X)= V(E(X|Y))+E(V(X|Y)).
Here Y denotes the component. So Y takes the values 1 and 2 with probabilities p and 1-p.
Let E(X|Y=i) = $\mu_i$ ... | Central moments of a gaussian mixture density?
I illustrate the calculation with 2 components. The other cases are similar. The key results to use are
(a) E(E(X|Y))=E(X) and (b) V(X)= V(E(X|Y))+E(V(X|Y)).
Here Y denotes the component. So Y takes |
55,400 | How to handle both text and numbers for PCA in R? | Check out the dudi.mix() function in the ade4 package: Ordination of Tables mixing quantitative variables and factors. Example:
library(ade4)
scatter.dudi(dudi.mix(iris,scannf=FALSE))
There are a couple other packages that do mixed correspondence analysis.
You can go ahead and fully dummy code your categorical variab... | How to handle both text and numbers for PCA in R? | Check out the dudi.mix() function in the ade4 package: Ordination of Tables mixing quantitative variables and factors. Example:
library(ade4)
scatter.dudi(dudi.mix(iris,scannf=FALSE))
There are a co | How to handle both text and numbers for PCA in R?
Check out the dudi.mix() function in the ade4 package: Ordination of Tables mixing quantitative variables and factors. Example:
library(ade4)
scatter.dudi(dudi.mix(iris,scannf=FALSE))
There are a couple other packages that do mixed correspondence analysis.
You can go ... | How to handle both text and numbers for PCA in R?
Check out the dudi.mix() function in the ade4 package: Ordination of Tables mixing quantitative variables and factors. Example:
library(ade4)
scatter.dudi(dudi.mix(iris,scannf=FALSE))
There are a co |
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