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 |
|---|---|---|---|---|---|---|
45,401 | simulate dirichlet process in R | With certainty, realizations of a Dirichlet Process are probability measures with countable support, as proved by D. Blackwell, The Annals of Statistics 1 (1973), no. 2, 356--358. You can sample realizations from a Dirichlet Process using the constructive stick-breaking representation introduced by J. Sethuraman, Stati... | simulate dirichlet process in R | With certainty, realizations of a Dirichlet Process are probability measures with countable support, as proved by D. Blackwell, The Annals of Statistics 1 (1973), no. 2, 356--358. You can sample reali | simulate dirichlet process in R
With certainty, realizations of a Dirichlet Process are probability measures with countable support, as proved by D. Blackwell, The Annals of Statistics 1 (1973), no. 2, 356--358. You can sample realizations from a Dirichlet Process using the constructive stick-breaking representation in... | simulate dirichlet process in R
With certainty, realizations of a Dirichlet Process are probability measures with countable support, as proved by D. Blackwell, The Annals of Statistics 1 (1973), no. 2, 356--358. You can sample reali |
45,402 | simulate dirichlet process in R | Check out the package DPackage in R. It has a lot of functionality for simulating from the Dirichlet Process. Here is a link to the documentation: DPackage. Zen's answer above is pretty good info as well. | simulate dirichlet process in R | Check out the package DPackage in R. It has a lot of functionality for simulating from the Dirichlet Process. Here is a link to the documentation: DPackage. Zen's answer above is pretty good info as w | simulate dirichlet process in R
Check out the package DPackage in R. It has a lot of functionality for simulating from the Dirichlet Process. Here is a link to the documentation: DPackage. Zen's answer above is pretty good info as well. | simulate dirichlet process in R
Check out the package DPackage in R. It has a lot of functionality for simulating from the Dirichlet Process. Here is a link to the documentation: DPackage. Zen's answer above is pretty good info as w |
45,403 | simulate dirichlet process in R | Not sure why sample(y, prob = p, replace = TRUE) from zen's answer is necessary.
library(tidyverse)
##concentration parameter
c <- 1000
##base distribution
G_0 <- function(n) rnorm(n, 0, 1)
##finite approximate realization of Dirichlet Process
n <- 1000
b <- rbeta(n, 1, c)
p <- numeric(n)
p[1] <- b[1]
p[2:n] <- sapply... | simulate dirichlet process in R | Not sure why sample(y, prob = p, replace = TRUE) from zen's answer is necessary.
library(tidyverse)
##concentration parameter
c <- 1000
##base distribution
G_0 <- function(n) rnorm(n, 0, 1)
##finite | simulate dirichlet process in R
Not sure why sample(y, prob = p, replace = TRUE) from zen's answer is necessary.
library(tidyverse)
##concentration parameter
c <- 1000
##base distribution
G_0 <- function(n) rnorm(n, 0, 1)
##finite approximate realization of Dirichlet Process
n <- 1000
b <- rbeta(n, 1, c)
p <- numeric(... | simulate dirichlet process in R
Not sure why sample(y, prob = p, replace = TRUE) from zen's answer is necessary.
library(tidyverse)
##concentration parameter
c <- 1000
##base distribution
G_0 <- function(n) rnorm(n, 0, 1)
##finite |
45,404 | Bayes Decision Boundary and classifier | Yes, Bayes Classifier is the one which produces the lowest possible test error rate. This is I think best illustrated through an example.
To simplify things a bit, let's say we have a simple two class classification problem. For example, we survey a group of students and collect their age, SAT scores and current GPA a... | Bayes Decision Boundary and classifier | Yes, Bayes Classifier is the one which produces the lowest possible test error rate. This is I think best illustrated through an example.
To simplify things a bit, let's say we have a simple two clas | Bayes Decision Boundary and classifier
Yes, Bayes Classifier is the one which produces the lowest possible test error rate. This is I think best illustrated through an example.
To simplify things a bit, let's say we have a simple two class classification problem. For example, we survey a group of students and collect ... | Bayes Decision Boundary and classifier
Yes, Bayes Classifier is the one which produces the lowest possible test error rate. This is I think best illustrated through an example.
To simplify things a bit, let's say we have a simple two clas |
45,405 | How to select tuning parameter for regularized regressions for interpretation? | I'd like to try answer your main question, here are two options:
Use the one-standard-error (1SE) rule
When cross-validating for selection purposes, it can help to use the 1SE rule. The standard error of the CV estimate is calculated for each fold. Instead of selecting the model corresponding to the minimum CV error... | How to select tuning parameter for regularized regressions for interpretation? | I'd like to try answer your main question, here are two options:
Use the one-standard-error (1SE) rule
When cross-validating for selection purposes, it can help to use the 1SE rule. The standard er | How to select tuning parameter for regularized regressions for interpretation?
I'd like to try answer your main question, here are two options:
Use the one-standard-error (1SE) rule
When cross-validating for selection purposes, it can help to use the 1SE rule. The standard error of the CV estimate is calculated for ... | How to select tuning parameter for regularized regressions for interpretation?
I'd like to try answer your main question, here are two options:
Use the one-standard-error (1SE) rule
When cross-validating for selection purposes, it can help to use the 1SE rule. The standard er |
45,406 | How to select tuning parameter for regularized regressions for interpretation? | Not sure whether this helps, but potentially you can create (using e.g. the caret package) a plot akin How to interpret the lasso selection plot , and subsequently choose a cross-validated tuning value that restricts the parameter space sufficiently to allow interpretability? | How to select tuning parameter for regularized regressions for interpretation? | Not sure whether this helps, but potentially you can create (using e.g. the caret package) a plot akin How to interpret the lasso selection plot , and subsequently choose a cross-validated tuning valu | How to select tuning parameter for regularized regressions for interpretation?
Not sure whether this helps, but potentially you can create (using e.g. the caret package) a plot akin How to interpret the lasso selection plot , and subsequently choose a cross-validated tuning value that restricts the parameter space suff... | How to select tuning parameter for regularized regressions for interpretation?
Not sure whether this helps, but potentially you can create (using e.g. the caret package) a plot akin How to interpret the lasso selection plot , and subsequently choose a cross-validated tuning valu |
45,407 | Can machine learning find all sort of crazy connections? | If you try a real thoroughly won't a computer find all sort of silly patterns?
Yes. (With emphasis on "silly") this is often referred to as overfitting.
If you have a huge (and growing amount) of information, would things start to match in a meaningless way?
The answer depends on how exactly the amount of informati... | Can machine learning find all sort of crazy connections? | If you try a real thoroughly won't a computer find all sort of silly patterns?
Yes. (With emphasis on "silly") this is often referred to as overfitting.
If you have a huge (and growing amount) of i | Can machine learning find all sort of crazy connections?
If you try a real thoroughly won't a computer find all sort of silly patterns?
Yes. (With emphasis on "silly") this is often referred to as overfitting.
If you have a huge (and growing amount) of information, would things start to match in a meaningless way?
... | Can machine learning find all sort of crazy connections?
If you try a real thoroughly won't a computer find all sort of silly patterns?
Yes. (With emphasis on "silly") this is often referred to as overfitting.
If you have a huge (and growing amount) of i |
45,408 | Can machine learning find all sort of crazy connections? | I would say there are two categories of safeguards.
Statistical Safeguards to give you a "fishing license" of sorts. Without them, you're sure to find spurious associations from chance alone as you sift through thousands of hypotheses. Here is general information on the Multiple Comparisons Problem. I prefer False Dis... | Can machine learning find all sort of crazy connections? | I would say there are two categories of safeguards.
Statistical Safeguards to give you a "fishing license" of sorts. Without them, you're sure to find spurious associations from chance alone as you s | Can machine learning find all sort of crazy connections?
I would say there are two categories of safeguards.
Statistical Safeguards to give you a "fishing license" of sorts. Without them, you're sure to find spurious associations from chance alone as you sift through thousands of hypotheses. Here is general informatio... | Can machine learning find all sort of crazy connections?
I would say there are two categories of safeguards.
Statistical Safeguards to give you a "fishing license" of sorts. Without them, you're sure to find spurious associations from chance alone as you s |
45,409 | Why are Random Forests splitted based on m random features? | Just as fitting each tree in the forest with a random subset sample (a bootstrap sample) of the available data adds stochasticity to improve the out-of-sample fit (by reducing the variance component of the error), so does selecting $m$ variables within which to search for each fit add stochasticity. The extra stochasti... | Why are Random Forests splitted based on m random features? | Just as fitting each tree in the forest with a random subset sample (a bootstrap sample) of the available data adds stochasticity to improve the out-of-sample fit (by reducing the variance component o | Why are Random Forests splitted based on m random features?
Just as fitting each tree in the forest with a random subset sample (a bootstrap sample) of the available data adds stochasticity to improve the out-of-sample fit (by reducing the variance component of the error), so does selecting $m$ variables within which t... | Why are Random Forests splitted based on m random features?
Just as fitting each tree in the forest with a random subset sample (a bootstrap sample) of the available data adds stochasticity to improve the out-of-sample fit (by reducing the variance component o |
45,410 | Why are Random Forests splitted based on m random features? | Actually, an earlier paper (predating the "random forest" terminology) from Leo Breiman considered all features as you suggest [Breiman 1996]. Below are some relevant excerpts from this article. First, some notation:
A learning set of $\mathcal{L}$ consists of data $\left\{ (y_n,x_n),n=1,...,N\right\}$ where the $y$'s... | Why are Random Forests splitted based on m random features? | Actually, an earlier paper (predating the "random forest" terminology) from Leo Breiman considered all features as you suggest [Breiman 1996]. Below are some relevant excerpts from this article. First | Why are Random Forests splitted based on m random features?
Actually, an earlier paper (predating the "random forest" terminology) from Leo Breiman considered all features as you suggest [Breiman 1996]. Below are some relevant excerpts from this article. First, some notation:
A learning set of $\mathcal{L}$ consists o... | Why are Random Forests splitted based on m random features?
Actually, an earlier paper (predating the "random forest" terminology) from Leo Breiman considered all features as you suggest [Breiman 1996]. Below are some relevant excerpts from this article. First |
45,411 | Difference estimate & confidence intervals for $\chi^2$ test between 2 proportions | Just for statistically intimidated people (wait, it should be 'intimidated by statistics') that can identify themselves with this joke:
A patient asks his surgeon what the odds are of him surviving an impending operation. The doctor replies that the odds are usually 50-50. "But there is no need to worry," the doctor e... | Difference estimate & confidence intervals for $\chi^2$ test between 2 proportions | Just for statistically intimidated people (wait, it should be 'intimidated by statistics') that can identify themselves with this joke:
A patient asks his surgeon what the odds are of him surviving a | Difference estimate & confidence intervals for $\chi^2$ test between 2 proportions
Just for statistically intimidated people (wait, it should be 'intimidated by statistics') that can identify themselves with this joke:
A patient asks his surgeon what the odds are of him surviving an impending operation. The doctor rep... | Difference estimate & confidence intervals for $\chi^2$ test between 2 proportions
Just for statistically intimidated people (wait, it should be 'intimidated by statistics') that can identify themselves with this joke:
A patient asks his surgeon what the odds are of him surviving a |
45,412 | Why do we use conditional expectation vs regular expectation in regression? | $E(Y)$ is just the mean of your responses: it's the same thing as a regression where all you have is an intercept. For all values of $X$, you are predicting the same value for the response. We use conditional expectation because we expect there to be a relationship between a predictor variable and the response variable... | Why do we use conditional expectation vs regular expectation in regression? | $E(Y)$ is just the mean of your responses: it's the same thing as a regression where all you have is an intercept. For all values of $X$, you are predicting the same value for the response. We use con | Why do we use conditional expectation vs regular expectation in regression?
$E(Y)$ is just the mean of your responses: it's the same thing as a regression where all you have is an intercept. For all values of $X$, you are predicting the same value for the response. We use conditional expectation because we expect there... | Why do we use conditional expectation vs regular expectation in regression?
$E(Y)$ is just the mean of your responses: it's the same thing as a regression where all you have is an intercept. For all values of $X$, you are predicting the same value for the response. We use con |
45,413 | Why do we use conditional expectation vs regular expectation in regression? | There are two basic versions of a regression specification, regarding the assumptions about the nature of the variables involved:
In the first, the regressors are assumed deterministic , and so the actual matrix contains elements that are not realizations of random variables. This comes from an "experimental design" ... | Why do we use conditional expectation vs regular expectation in regression? | There are two basic versions of a regression specification, regarding the assumptions about the nature of the variables involved:
In the first, the regressors are assumed deterministic , and so the | Why do we use conditional expectation vs regular expectation in regression?
There are two basic versions of a regression specification, regarding the assumptions about the nature of the variables involved:
In the first, the regressors are assumed deterministic , and so the actual matrix contains elements that are not... | Why do we use conditional expectation vs regular expectation in regression?
There are two basic versions of a regression specification, regarding the assumptions about the nature of the variables involved:
In the first, the regressors are assumed deterministic , and so the |
45,414 | Why do we use conditional expectation vs regular expectation in regression? | Let's say you observe a RV $X$ and you want to predict a second RV $Y$ where your predictor of $Y$ is $g(X)$.
Then you can prove that the "best" predictor of $Y$ is $g(X) = E[Y \mid X]$, where "best" is the function $g$ that minimizes $E[(Y-g(X))^2]$.
So $E[Y \mid X]$ will always be at least as good as, if not better t... | Why do we use conditional expectation vs regular expectation in regression? | Let's say you observe a RV $X$ and you want to predict a second RV $Y$ where your predictor of $Y$ is $g(X)$.
Then you can prove that the "best" predictor of $Y$ is $g(X) = E[Y \mid X]$, where "best" | Why do we use conditional expectation vs regular expectation in regression?
Let's say you observe a RV $X$ and you want to predict a second RV $Y$ where your predictor of $Y$ is $g(X)$.
Then you can prove that the "best" predictor of $Y$ is $g(X) = E[Y \mid X]$, where "best" is the function $g$ that minimizes $E[(Y-g(X... | Why do we use conditional expectation vs regular expectation in regression?
Let's say you observe a RV $X$ and you want to predict a second RV $Y$ where your predictor of $Y$ is $g(X)$.
Then you can prove that the "best" predictor of $Y$ is $g(X) = E[Y \mid X]$, where "best" |
45,415 | Is it ok to correlate before-and-after data? | None of those correlations you think aren't OK really aren't OK. The correlation is just a measure of linear relationship. Sometimes you need to know the extent of a relationship that you know exists, such as this one, or any of the others you listed. In this case they may want to know the amount of correlation for a v... | Is it ok to correlate before-and-after data? | None of those correlations you think aren't OK really aren't OK. The correlation is just a measure of linear relationship. Sometimes you need to know the extent of a relationship that you know exists, | Is it ok to correlate before-and-after data?
None of those correlations you think aren't OK really aren't OK. The correlation is just a measure of linear relationship. Sometimes you need to know the extent of a relationship that you know exists, such as this one, or any of the others you listed. In this case they may w... | Is it ok to correlate before-and-after data?
None of those correlations you think aren't OK really aren't OK. The correlation is just a measure of linear relationship. Sometimes you need to know the extent of a relationship that you know exists, |
45,416 | Is it ok to correlate before-and-after data? | This is perfectly fine. You are considering two different variables each measured once per subject. One contains the 'pre' values, the other the 'post' values. I think you are mixing up independence between observations (subjects) and independence of variables.
Please note that in your situation, you might want to anal... | Is it ok to correlate before-and-after data? | This is perfectly fine. You are considering two different variables each measured once per subject. One contains the 'pre' values, the other the 'post' values. I think you are mixing up independence b | Is it ok to correlate before-and-after data?
This is perfectly fine. You are considering two different variables each measured once per subject. One contains the 'pre' values, the other the 'post' values. I think you are mixing up independence between observations (subjects) and independence of variables.
Please note t... | Is it ok to correlate before-and-after data?
This is perfectly fine. You are considering two different variables each measured once per subject. One contains the 'pre' values, the other the 'post' values. I think you are mixing up independence b |
45,417 | Is it ok to correlate before-and-after data? | I think it depends on what you are trying to do with your data. Technically, it is okay to correlate repeated measures from the same subject in the sense that it is mathematically possible. But if you trying to draw some kind of inference (for example, causality) from your data, simply correlated two observations that ... | Is it ok to correlate before-and-after data? | I think it depends on what you are trying to do with your data. Technically, it is okay to correlate repeated measures from the same subject in the sense that it is mathematically possible. But if you | Is it ok to correlate before-and-after data?
I think it depends on what you are trying to do with your data. Technically, it is okay to correlate repeated measures from the same subject in the sense that it is mathematically possible. But if you trying to draw some kind of inference (for example, causality) from your d... | Is it ok to correlate before-and-after data?
I think it depends on what you are trying to do with your data. Technically, it is okay to correlate repeated measures from the same subject in the sense that it is mathematically possible. But if you |
45,418 | Correlation between vegetation and erosion | I agree with @John's answer but would also suggest simply plotting boxplots of erosion for each vegetation category. If the vegetation variable actually has a ordinal interpretation then we could draw some exploratory conclusions about correlations between vegetation and erosion. For example, consider the following f... | Correlation between vegetation and erosion | I agree with @John's answer but would also suggest simply plotting boxplots of erosion for each vegetation category. If the vegetation variable actually has a ordinal interpretation then we could dra | Correlation between vegetation and erosion
I agree with @John's answer but would also suggest simply plotting boxplots of erosion for each vegetation category. If the vegetation variable actually has a ordinal interpretation then we could draw some exploratory conclusions about correlations between vegetation and eros... | Correlation between vegetation and erosion
I agree with @John's answer but would also suggest simply plotting boxplots of erosion for each vegetation category. If the vegetation variable actually has a ordinal interpretation then we could dra |
45,419 | Correlation between vegetation and erosion | You could calculate a $\chi^2$ and the $\Phi$ coefficient measure as a substitute for correlation. $\Phi$ would have a similar interpretation to a Pearson correlation coefficient but should really only be used in 2x2 designs. In your case you should probably go with a Cramer's V which is standardized but doesn't quite ... | Correlation between vegetation and erosion | You could calculate a $\chi^2$ and the $\Phi$ coefficient measure as a substitute for correlation. $\Phi$ would have a similar interpretation to a Pearson correlation coefficient but should really onl | Correlation between vegetation and erosion
You could calculate a $\chi^2$ and the $\Phi$ coefficient measure as a substitute for correlation. $\Phi$ would have a similar interpretation to a Pearson correlation coefficient but should really only be used in 2x2 designs. In your case you should probably go with a Cramer's... | Correlation between vegetation and erosion
You could calculate a $\chi^2$ and the $\Phi$ coefficient measure as a substitute for correlation. $\Phi$ would have a similar interpretation to a Pearson correlation coefficient but should really onl |
45,420 | Maximum Likelihood Curve/Model Fitting in Python | Here's some pseudocode to do it. Of course, it depends on the error structure you choose. You don't need the stats models to do it, because Scipy has an minimizer built-in. The minimizer probably doesn't give you CIs though, like mle2 will. There may be another minimizer that will profile your parameters, but I don't k... | Maximum Likelihood Curve/Model Fitting in Python | Here's some pseudocode to do it. Of course, it depends on the error structure you choose. You don't need the stats models to do it, because Scipy has an minimizer built-in. The minimizer probably does | Maximum Likelihood Curve/Model Fitting in Python
Here's some pseudocode to do it. Of course, it depends on the error structure you choose. You don't need the stats models to do it, because Scipy has an minimizer built-in. The minimizer probably doesn't give you CIs though, like mle2 will. There may be another minimizer... | Maximum Likelihood Curve/Model Fitting in Python
Here's some pseudocode to do it. Of course, it depends on the error structure you choose. You don't need the stats models to do it, because Scipy has an minimizer built-in. The minimizer probably does |
45,421 | How do you report percentage accuracy for glmnet logistic regression? | glmnet is designed around a proper accuracy score, the (penalized) deviance. Summaries of predictive discrimination should use proper scores, not arbitrary classifications that are at odds with costs of false positives and false negatives. Consider a couple of accepted proper scoring rules: Brier (quadratic) score an... | How do you report percentage accuracy for glmnet logistic regression? | glmnet is designed around a proper accuracy score, the (penalized) deviance. Summaries of predictive discrimination should use proper scores, not arbitrary classifications that are at odds with costs | How do you report percentage accuracy for glmnet logistic regression?
glmnet is designed around a proper accuracy score, the (penalized) deviance. Summaries of predictive discrimination should use proper scores, not arbitrary classifications that are at odds with costs of false positives and false negatives. Consider... | How do you report percentage accuracy for glmnet logistic regression?
glmnet is designed around a proper accuracy score, the (penalized) deviance. Summaries of predictive discrimination should use proper scores, not arbitrary classifications that are at odds with costs |
45,422 | How do you report percentage accuracy for glmnet logistic regression? | The predict function for glmnet offers a "class" type that will predict the class rather than the response for binomial logistic regression, eliminating the need for your conditionals. You could also do the cv.glmnet using the type.measure parameter value "auc" or "class" to produce some validation accuracy measures p... | How do you report percentage accuracy for glmnet logistic regression? | The predict function for glmnet offers a "class" type that will predict the class rather than the response for binomial logistic regression, eliminating the need for your conditionals. You could also | How do you report percentage accuracy for glmnet logistic regression?
The predict function for glmnet offers a "class" type that will predict the class rather than the response for binomial logistic regression, eliminating the need for your conditionals. You could also do the cv.glmnet using the type.measure parameter... | How do you report percentage accuracy for glmnet logistic regression?
The predict function for glmnet offers a "class" type that will predict the class rather than the response for binomial logistic regression, eliminating the need for your conditionals. You could also |
45,423 | How do you report percentage accuracy for glmnet logistic regression? | A much simpler way of doing this is by using the predict function and finding the mean error:
mean(predicted_y!=Yactual) | How do you report percentage accuracy for glmnet logistic regression? | A much simpler way of doing this is by using the predict function and finding the mean error:
mean(predicted_y!=Yactual) | How do you report percentage accuracy for glmnet logistic regression?
A much simpler way of doing this is by using the predict function and finding the mean error:
mean(predicted_y!=Yactual) | How do you report percentage accuracy for glmnet logistic regression?
A much simpler way of doing this is by using the predict function and finding the mean error:
mean(predicted_y!=Yactual) |
45,424 | Order of variables in R lm model | Updated:
There is only one intercept in the equation. The intercept consists of the observations related to factor A and year 1985 (which is the case for model 3). However, in your first case, you are omitting factor A (and thus it acts as base), where as in the model 2 you are using year 1985 as the base. So the coef... | Order of variables in R lm model | Updated:
There is only one intercept in the equation. The intercept consists of the observations related to factor A and year 1985 (which is the case for model 3). However, in your first case, you ar | Order of variables in R lm model
Updated:
There is only one intercept in the equation. The intercept consists of the observations related to factor A and year 1985 (which is the case for model 3). However, in your first case, you are omitting factor A (and thus it acts as base), where as in the model 2 you are using y... | Order of variables in R lm model
Updated:
There is only one intercept in the equation. The intercept consists of the observations related to factor A and year 1985 (which is the case for model 3). However, in your first case, you ar |
45,425 | Order of variables in R lm model | # install.packages("tidyverse")
library(dplyr)
library(tibble)
set.seed(0)
d <- tibble(
fm = c(rep("A", 5), rep("B", 5), rep("C", 5), rep("D", 5)),
yr = rep(c(1985, 1986, 1987, 1988, 1989), 4),
y = rnorm(length(yr)),
x = rnorm(length(yr))
) %>%
mutate(shock = ifelse(yr == 1988, 1, 0))
In light of more r... | Order of variables in R lm model | # install.packages("tidyverse")
library(dplyr)
library(tibble)
set.seed(0)
d <- tibble(
fm = c(rep("A", 5), rep("B", 5), rep("C", 5), rep("D", 5)),
yr = rep(c(1985, 1986, 1987, 1988, 1989), 4),
| Order of variables in R lm model
# install.packages("tidyverse")
library(dplyr)
library(tibble)
set.seed(0)
d <- tibble(
fm = c(rep("A", 5), rep("B", 5), rep("C", 5), rep("D", 5)),
yr = rep(c(1985, 1986, 1987, 1988, 1989), 4),
y = rnorm(length(yr)),
x = rnorm(length(yr))
) %>%
mutate(shock = ifelse(yr ==... | Order of variables in R lm model
# install.packages("tidyverse")
library(dplyr)
library(tibble)
set.seed(0)
d <- tibble(
fm = c(rep("A", 5), rep("B", 5), rep("C", 5), rep("D", 5)),
yr = rep(c(1985, 1986, 1987, 1988, 1989), 4),
|
45,426 | Validity of normality assumption in the case of multiple independent data sets with small sample size | This may help:
DR Cox, PJ Solomon. 1986.
Analysis of variability with large numbers of small samples.
Biometrika 73: 543-554.
Abstract: Procedures are discussed for the detailed analysis of distributional form, based on
many samples of size r, where especially r= 2, 3, 4. The possibility of discriminating between ... | Validity of normality assumption in the case of multiple independent data sets with small sample siz | This may help:
DR Cox, PJ Solomon. 1986.
Analysis of variability with large numbers of small samples.
Biometrika 73: 543-554.
Abstract: Procedures are discussed for the detailed analysis of distri | Validity of normality assumption in the case of multiple independent data sets with small sample size
This may help:
DR Cox, PJ Solomon. 1986.
Analysis of variability with large numbers of small samples.
Biometrika 73: 543-554.
Abstract: Procedures are discussed for the detailed analysis of distributional form, bas... | Validity of normality assumption in the case of multiple independent data sets with small sample siz
This may help:
DR Cox, PJ Solomon. 1986.
Analysis of variability with large numbers of small samples.
Biometrika 73: 543-554.
Abstract: Procedures are discussed for the detailed analysis of distri |
45,427 | Validity of normality assumption in the case of multiple independent data sets with small sample size | Due to limitations in experimental setup, I only have small data sets with n=3. Despite the low df the difference between treated and control is large enough to generate a significant p-value.
The problem is that with small sample sizes doing a t-test becomes more sensitive to the assumption that the data are drawn fro... | Validity of normality assumption in the case of multiple independent data sets with small sample siz | Due to limitations in experimental setup, I only have small data sets with n=3. Despite the low df the difference between treated and control is large enough to generate a significant p-value.
The pro | Validity of normality assumption in the case of multiple independent data sets with small sample size
Due to limitations in experimental setup, I only have small data sets with n=3. Despite the low df the difference between treated and control is large enough to generate a significant p-value.
The problem is that with ... | Validity of normality assumption in the case of multiple independent data sets with small sample siz
Due to limitations in experimental setup, I only have small data sets with n=3. Despite the low df the difference between treated and control is large enough to generate a significant p-value.
The pro |
45,428 | Validity of normality assumption in the case of multiple independent data sets with small sample size | There are certainly alternative statistics. You could do permutation tests, for example. You could also do nonparametric tests, such as Wilcoxon. | Validity of normality assumption in the case of multiple independent data sets with small sample siz | There are certainly alternative statistics. You could do permutation tests, for example. You could also do nonparametric tests, such as Wilcoxon. | Validity of normality assumption in the case of multiple independent data sets with small sample size
There are certainly alternative statistics. You could do permutation tests, for example. You could also do nonparametric tests, such as Wilcoxon. | Validity of normality assumption in the case of multiple independent data sets with small sample siz
There are certainly alternative statistics. You could do permutation tests, for example. You could also do nonparametric tests, such as Wilcoxon. |
45,429 | Gauss-Markov assumptions | The LS-Estimator is:$$b=\beta + (X'X)^{-1}X'e$$
The estimator is unbiased if $(X'X)^{-1}X'e$ converges to zero, and this is the case, if the designmatrix $X$ is not correlated with the error $e$.
So, the necessary assumption is: $$E[X_{t,k}*e_t]=0$$ | Gauss-Markov assumptions | The LS-Estimator is:$$b=\beta + (X'X)^{-1}X'e$$
The estimator is unbiased if $(X'X)^{-1}X'e$ converges to zero, and this is the case, if the designmatrix $X$ is not correlated with the error $e$.
So, | Gauss-Markov assumptions
The LS-Estimator is:$$b=\beta + (X'X)^{-1}X'e$$
The estimator is unbiased if $(X'X)^{-1}X'e$ converges to zero, and this is the case, if the designmatrix $X$ is not correlated with the error $e$.
So, the necessary assumption is: $$E[X_{t,k}*e_t]=0$$ | Gauss-Markov assumptions
The LS-Estimator is:$$b=\beta + (X'X)^{-1}X'e$$
The estimator is unbiased if $(X'X)^{-1}X'e$ converges to zero, and this is the case, if the designmatrix $X$ is not correlated with the error $e$.
So, |
45,430 | Gauss-Markov assumptions | The Gauss-Markov Theorem is actually telling us that in a regression model, where the expected value of our error terms is zero, $E(\epsilon_{i}) = 0$ and variance of the error terms is constant and finite $\sigma^{2}(\epsilon_{i}) = \sigma^{2} < \infty$ and $\epsilon_{i}$ and $\epsilon_{j}$ are uncorrelated for all i ... | Gauss-Markov assumptions | The Gauss-Markov Theorem is actually telling us that in a regression model, where the expected value of our error terms is zero, $E(\epsilon_{i}) = 0$ and variance of the error terms is constant and f | Gauss-Markov assumptions
The Gauss-Markov Theorem is actually telling us that in a regression model, where the expected value of our error terms is zero, $E(\epsilon_{i}) = 0$ and variance of the error terms is constant and finite $\sigma^{2}(\epsilon_{i}) = \sigma^{2} < \infty$ and $\epsilon_{i}$ and $\epsilon_{j}$ ar... | Gauss-Markov assumptions
The Gauss-Markov Theorem is actually telling us that in a regression model, where the expected value of our error terms is zero, $E(\epsilon_{i}) = 0$ and variance of the error terms is constant and f |
45,431 | Gauss-Markov assumptions | $$\hat{\beta} = ([inv(X'X)]X')(X\beta + \epsilon)$$
$$\hat{\beta} = \beta + ([inv(X'X)]X')\epsilon$$
$\hat{\beta}$ is an unbiased estimator of $\beta$ under two conditions:
$X$ is non-stochastic $$E(\hat{\beta}) = \beta + E[([inv(X'X)]X')\epsilon]$$ if $X$ is deterministic, this would reduce to:
$$E(\hat{\beta}) = \... | Gauss-Markov assumptions | $$\hat{\beta} = ([inv(X'X)]X')(X\beta + \epsilon)$$
$$\hat{\beta} = \beta + ([inv(X'X)]X')\epsilon$$
$\hat{\beta}$ is an unbiased estimator of $\beta$ under two conditions:
$X$ is non-stochastic $$E( | Gauss-Markov assumptions
$$\hat{\beta} = ([inv(X'X)]X')(X\beta + \epsilon)$$
$$\hat{\beta} = \beta + ([inv(X'X)]X')\epsilon$$
$\hat{\beta}$ is an unbiased estimator of $\beta$ under two conditions:
$X$ is non-stochastic $$E(\hat{\beta}) = \beta + E[([inv(X'X)]X')\epsilon]$$ if $X$ is deterministic, this would reduce ... | Gauss-Markov assumptions
$$\hat{\beta} = ([inv(X'X)]X')(X\beta + \epsilon)$$
$$\hat{\beta} = \beta + ([inv(X'X)]X')\epsilon$$
$\hat{\beta}$ is an unbiased estimator of $\beta$ under two conditions:
$X$ is non-stochastic $$E( |
45,432 | Distribution of function of variable having a Gaussian distribution | I think you are talking about change of variables.
If X is a continuous r.v. with pdf $f_X(x)$ and sample space S, if $g: S \rightarrow T$ is an invertible transformation with differentiable inverse $h = g^{-1}$, and $Y = g(X)$, then Y is a continuous r.v. with pdf $f_Y(y)$ defined by
$f_Y(y) = f_X(h(y)) \cdot |h'(y)|$... | Distribution of function of variable having a Gaussian distribution | I think you are talking about change of variables.
If X is a continuous r.v. with pdf $f_X(x)$ and sample space S, if $g: S \rightarrow T$ is an invertible transformation with differentiable inverse $ | Distribution of function of variable having a Gaussian distribution
I think you are talking about change of variables.
If X is a continuous r.v. with pdf $f_X(x)$ and sample space S, if $g: S \rightarrow T$ is an invertible transformation with differentiable inverse $h = g^{-1}$, and $Y = g(X)$, then Y is a continuous ... | Distribution of function of variable having a Gaussian distribution
I think you are talking about change of variables.
If X is a continuous r.v. with pdf $f_X(x)$ and sample space S, if $g: S \rightarrow T$ is an invertible transformation with differentiable inverse $ |
45,433 | Distribution of function of variable having a Gaussian distribution | Addressing the last question in particular --
1) Consider $X$ being standard Gaussian (mean 0, variance 1), and $U = \Phi(X)$ where $\Phi()$ is the standard normal cdf. Does $U$ have a Gaussian distribution? Let's simulate (in R in this case):
u <- pnorm(rnorm(100000L))
hist(u,n=300)
... nope.
In fact you can work... | Distribution of function of variable having a Gaussian distribution | Addressing the last question in particular --
1) Consider $X$ being standard Gaussian (mean 0, variance 1), and $U = \Phi(X)$ where $\Phi()$ is the standard normal cdf. Does $U$ have a Gaussian distr | Distribution of function of variable having a Gaussian distribution
Addressing the last question in particular --
1) Consider $X$ being standard Gaussian (mean 0, variance 1), and $U = \Phi(X)$ where $\Phi()$ is the standard normal cdf. Does $U$ have a Gaussian distribution? Let's simulate (in R in this case):
u <- p... | Distribution of function of variable having a Gaussian distribution
Addressing the last question in particular --
1) Consider $X$ being standard Gaussian (mean 0, variance 1), and $U = \Phi(X)$ where $\Phi()$ is the standard normal cdf. Does $U$ have a Gaussian distr |
45,434 | Minimum no. of observations required for statistical distribution fitting! | Well, in the special case of loss data I think most appropriate is the second source, but I give you some others too:
http://imash.leeds.ac.uk/dicode/wp4/Stats2-Forum/view_topic.php?id=10079
http://www.mathwave.com/articles/distribution-fitting-preliminary.html
a pdf:
http://www.vanbelle.org/chapters/webchapter2.pdf
... | Minimum no. of observations required for statistical distribution fitting! | Well, in the special case of loss data I think most appropriate is the second source, but I give you some others too:
http://imash.leeds.ac.uk/dicode/wp4/Stats2-Forum/view_topic.php?id=10079
http:// | Minimum no. of observations required for statistical distribution fitting!
Well, in the special case of loss data I think most appropriate is the second source, but I give you some others too:
http://imash.leeds.ac.uk/dicode/wp4/Stats2-Forum/view_topic.php?id=10079
http://www.mathwave.com/articles/distribution-fittin... | Minimum no. of observations required for statistical distribution fitting!
Well, in the special case of loss data I think most appropriate is the second source, but I give you some others too:
http://imash.leeds.ac.uk/dicode/wp4/Stats2-Forum/view_topic.php?id=10079
http:// |
45,435 | What is behind JAGS (Just Another Gibbs Sampler)? | There are several tools used by JAGS and/or BUGS.
Where conjugate distributions are used, straight Gibbs sampling is done. When that's not the case, adaptive rejection, slice sampling, or Metropolis-Hastings might be used (this is the case with BUGS at least; I believe it would be the case for JAGS as well).
You can ex... | What is behind JAGS (Just Another Gibbs Sampler)? | There are several tools used by JAGS and/or BUGS.
Where conjugate distributions are used, straight Gibbs sampling is done. When that's not the case, adaptive rejection, slice sampling, or Metropolis-H | What is behind JAGS (Just Another Gibbs Sampler)?
There are several tools used by JAGS and/or BUGS.
Where conjugate distributions are used, straight Gibbs sampling is done. When that's not the case, adaptive rejection, slice sampling, or Metropolis-Hastings might be used (this is the case with BUGS at least; I believe ... | What is behind JAGS (Just Another Gibbs Sampler)?
There are several tools used by JAGS and/or BUGS.
Where conjugate distributions are used, straight Gibbs sampling is done. When that's not the case, adaptive rejection, slice sampling, or Metropolis-H |
45,436 | What is behind JAGS (Just Another Gibbs Sampler)? | According to the JAGS documentation,
A report on the samplers chosen by the model, and the stochastic nodes they act on, can be generated using the “SAMPLERS TO” command.
It seems that the user can't control the choice of sampler, which is automatically chosen by JAGS, as described in the manual. I'd say that typica... | What is behind JAGS (Just Another Gibbs Sampler)? | According to the JAGS documentation,
A report on the samplers chosen by the model, and the stochastic nodes they act on, can be generated using the “SAMPLERS TO” command.
It seems that the user can | What is behind JAGS (Just Another Gibbs Sampler)?
According to the JAGS documentation,
A report on the samplers chosen by the model, and the stochastic nodes they act on, can be generated using the “SAMPLERS TO” command.
It seems that the user can't control the choice of sampler, which is automatically chosen by JAG... | What is behind JAGS (Just Another Gibbs Sampler)?
According to the JAGS documentation,
A report on the samplers chosen by the model, and the stochastic nodes they act on, can be generated using the “SAMPLERS TO” command.
It seems that the user can |
45,437 | Acceptance Rate of Statistical Journals | Well, you can usually obtain these statistics in the annual reports, which are available if you have a subscription to elsevier or otherwise. For statistics per se, it is difficult to find a comprised table, as it is usually applied in a interdisciplinary/multidisciplinary context.
However, you fill easily find lists ... | Acceptance Rate of Statistical Journals | Well, you can usually obtain these statistics in the annual reports, which are available if you have a subscription to elsevier or otherwise. For statistics per se, it is difficult to find a comprised | Acceptance Rate of Statistical Journals
Well, you can usually obtain these statistics in the annual reports, which are available if you have a subscription to elsevier or otherwise. For statistics per se, it is difficult to find a comprised table, as it is usually applied in a interdisciplinary/multidisciplinary contex... | Acceptance Rate of Statistical Journals
Well, you can usually obtain these statistics in the annual reports, which are available if you have a subscription to elsevier or otherwise. For statistics per se, it is difficult to find a comprised |
45,438 | contrapositive of probability | No, $P(A\mid B) = 0.95$ does not tell you very much about $P(B^c\mid A^c)$.
For example, suppose that $P(B) = 0.5$ and $P(A\cap B) = 0.475$ so that
$$P(A\mid B) = \frac{P(A\cap B)}{P(B)} = \frac{0.475}{0.5} = 0.95.$$
We also have that $P(A^c\cap B) = P(B) - P(A\cap B) = 0.5-0.475 = 0.025$.
Now consider two possibilitie... | contrapositive of probability | No, $P(A\mid B) = 0.95$ does not tell you very much about $P(B^c\mid A^c)$.
For example, suppose that $P(B) = 0.5$ and $P(A\cap B) = 0.475$ so that
$$P(A\mid B) = \frac{P(A\cap B)}{P(B)} = \frac{0.475 | contrapositive of probability
No, $P(A\mid B) = 0.95$ does not tell you very much about $P(B^c\mid A^c)$.
For example, suppose that $P(B) = 0.5$ and $P(A\cap B) = 0.475$ so that
$$P(A\mid B) = \frac{P(A\cap B)}{P(B)} = \frac{0.475}{0.5} = 0.95.$$
We also have that $P(A^c\cap B) = P(B) - P(A\cap B) = 0.5-0.475 = 0.025$.... | contrapositive of probability
No, $P(A\mid B) = 0.95$ does not tell you very much about $P(B^c\mid A^c)$.
For example, suppose that $P(B) = 0.5$ and $P(A\cap B) = 0.475$ so that
$$P(A\mid B) = \frac{P(A\cap B)}{P(B)} = \frac{0.475 |
45,439 | contrapositive of probability | To approach it conceptually, I'd say that the contrapositive is only implied in the case of absolute conditionals. A->B means "A always implies B (i.e., P(B|A) = 1). It does not mean "this conditional is considered correct for any case in which A and B are true." If P(B|A) = .95 then there are cases that contradict the... | contrapositive of probability | To approach it conceptually, I'd say that the contrapositive is only implied in the case of absolute conditionals. A->B means "A always implies B (i.e., P(B|A) = 1). It does not mean "this conditional | contrapositive of probability
To approach it conceptually, I'd say that the contrapositive is only implied in the case of absolute conditionals. A->B means "A always implies B (i.e., P(B|A) = 1). It does not mean "this conditional is considered correct for any case in which A and B are true." If P(B|A) = .95 then there... | contrapositive of probability
To approach it conceptually, I'd say that the contrapositive is only implied in the case of absolute conditionals. A->B means "A always implies B (i.e., P(B|A) = 1). It does not mean "this conditional |
45,440 | contrapositive of probability | A couple of points in addition to the good answers you see already.
You use the phrase "null hypothesis" which is generally used in frequentist statistics. In frequentist statistics the null hypothesis is a fixed fact not subject to probability so saying something like the "null hypothesis is wrong with 95% chance" is... | contrapositive of probability | A couple of points in addition to the good answers you see already.
You use the phrase "null hypothesis" which is generally used in frequentist statistics. In frequentist statistics the null hypothes | contrapositive of probability
A couple of points in addition to the good answers you see already.
You use the phrase "null hypothesis" which is generally used in frequentist statistics. In frequentist statistics the null hypothesis is a fixed fact not subject to probability so saying something like the "null hypothesi... | contrapositive of probability
A couple of points in addition to the good answers you see already.
You use the phrase "null hypothesis" which is generally used in frequentist statistics. In frequentist statistics the null hypothes |
45,441 | Interpreting p-value significance from Pearson correlation | You have interpreted these results correctly according to the conventional textbook scheme.
Personally, I am often not a fan of the standard way of thinking about p-values. (Mounting soapbox...) Firstly, it's worth considering that there are several valid ways to look at p-values. Fisher thought of them as a conti... | Interpreting p-value significance from Pearson correlation | You have interpreted these results correctly according to the conventional textbook scheme.
Personally, I am often not a fan of the standard way of thinking about p-values. (Mounting soapbox...) F | Interpreting p-value significance from Pearson correlation
You have interpreted these results correctly according to the conventional textbook scheme.
Personally, I am often not a fan of the standard way of thinking about p-values. (Mounting soapbox...) Firstly, it's worth considering that there are several valid w... | Interpreting p-value significance from Pearson correlation
You have interpreted these results correctly according to the conventional textbook scheme.
Personally, I am often not a fan of the standard way of thinking about p-values. (Mounting soapbox...) F |
45,442 | Interpreting p-value significance from Pearson correlation | Consistent with @gung, I believe that hypothesis testing is very problematic, especially in this particular correlation assessment setting. It is far more useful, and will get us into less trouble, to think of this as an estimation problem: we are estimating the unitness strength of association of two variables. We c... | Interpreting p-value significance from Pearson correlation | Consistent with @gung, I believe that hypothesis testing is very problematic, especially in this particular correlation assessment setting. It is far more useful, and will get us into less trouble, t | Interpreting p-value significance from Pearson correlation
Consistent with @gung, I believe that hypothesis testing is very problematic, especially in this particular correlation assessment setting. It is far more useful, and will get us into less trouble, to think of this as an estimation problem: we are estimating t... | Interpreting p-value significance from Pearson correlation
Consistent with @gung, I believe that hypothesis testing is very problematic, especially in this particular correlation assessment setting. It is far more useful, and will get us into less trouble, t |
45,443 | Calculating the probability of a rare event | Well, 60 pedestrians in a year would be a lot, that's right. But you only have a sample of one month, so it would be more appropriate to scale the yearly average number of deaths down, not the observation up:
ppois(5,3/12,lower.tail=FALSE)
[1] 2.738136e-07
Still unusual, but far less so than 60 over an entire year.
An... | Calculating the probability of a rare event | Well, 60 pedestrians in a year would be a lot, that's right. But you only have a sample of one month, so it would be more appropriate to scale the yearly average number of deaths down, not the observa | Calculating the probability of a rare event
Well, 60 pedestrians in a year would be a lot, that's right. But you only have a sample of one month, so it would be more appropriate to scale the yearly average number of deaths down, not the observation up:
ppois(5,3/12,lower.tail=FALSE)
[1] 2.738136e-07
Still unusual, but... | Calculating the probability of a rare event
Well, 60 pedestrians in a year would be a lot, that's right. But you only have a sample of one month, so it would be more appropriate to scale the yearly average number of deaths down, not the observa |
45,444 | Breusch–Pagan test for heteroscedasticity contradicts White's test? | The Breusch-Pagan test only checks for the linear form of heteroskedasticity i.e. it models the error variance as $\sigma_i^2 = \sigma^2h(z_i'\alpha)$ where $z_i$ is a vector of your independent variables. It tests $H_0: \alpha = 0$ versus $H_a: \alpha \neq 0$.
The White test on the other hand is more generic. It relie... | Breusch–Pagan test for heteroscedasticity contradicts White's test? | The Breusch-Pagan test only checks for the linear form of heteroskedasticity i.e. it models the error variance as $\sigma_i^2 = \sigma^2h(z_i'\alpha)$ where $z_i$ is a vector of your independent varia | Breusch–Pagan test for heteroscedasticity contradicts White's test?
The Breusch-Pagan test only checks for the linear form of heteroskedasticity i.e. it models the error variance as $\sigma_i^2 = \sigma^2h(z_i'\alpha)$ where $z_i$ is a vector of your independent variables. It tests $H_0: \alpha = 0$ versus $H_a: \alpha... | Breusch–Pagan test for heteroscedasticity contradicts White's test?
The Breusch-Pagan test only checks for the linear form of heteroskedasticity i.e. it models the error variance as $\sigma_i^2 = \sigma^2h(z_i'\alpha)$ where $z_i$ is a vector of your independent varia |
45,445 | Breusch–Pagan test for heteroscedasticity contradicts White's test? | Breusch-Pagan / Cook-Weisberg tests the null hypothesis that the error variances are all equal
versus the alternative that the error variances are a multiplicative function of one or more
variables. For example, in the default form of the hettest command shown above, the
alternative hypothesis states that the error ... | Breusch–Pagan test for heteroscedasticity contradicts White's test? | Breusch-Pagan / Cook-Weisberg tests the null hypothesis that the error variances are all equal
versus the alternative that the error variances are a multiplicative function of one or more
variables. | Breusch–Pagan test for heteroscedasticity contradicts White's test?
Breusch-Pagan / Cook-Weisberg tests the null hypothesis that the error variances are all equal
versus the alternative that the error variances are a multiplicative function of one or more
variables. For example, in the default form of the hettest com... | Breusch–Pagan test for heteroscedasticity contradicts White's test?
Breusch-Pagan / Cook-Weisberg tests the null hypothesis that the error variances are all equal
versus the alternative that the error variances are a multiplicative function of one or more
variables. |
45,446 | LibSVM cost weights for unbalanced data doesn't work | I just know of two methods to deal with unbalanced sets with SVMs:
Use bagging: you create bootstrap samples of your data, so that you a a big number of well-balanced problems. You train a SVM on each of them, and then use majority voting on the resulting ensemble of classifiers.
If you are using C-SVM, then you can ... | LibSVM cost weights for unbalanced data doesn't work | I just know of two methods to deal with unbalanced sets with SVMs:
Use bagging: you create bootstrap samples of your data, so that you a a big number of well-balanced problems. You train a SVM on eac | LibSVM cost weights for unbalanced data doesn't work
I just know of two methods to deal with unbalanced sets with SVMs:
Use bagging: you create bootstrap samples of your data, so that you a a big number of well-balanced problems. You train a SVM on each of them, and then use majority voting on the resulting ensemble o... | LibSVM cost weights for unbalanced data doesn't work
I just know of two methods to deal with unbalanced sets with SVMs:
Use bagging: you create bootstrap samples of your data, so that you a a big number of well-balanced problems. You train a SVM on eac |
45,447 | LibSVM cost weights for unbalanced data doesn't work | ANSWER: remove -b 1 or make it -b 0
-b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
I encountered the same problem and came across this post from googling. Apparently it doesn't work with probability estimates on. | LibSVM cost weights for unbalanced data doesn't work | ANSWER: remove -b 1 or make it -b 0
-b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
I encountered the same problem and came across this post | LibSVM cost weights for unbalanced data doesn't work
ANSWER: remove -b 1 or make it -b 0
-b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
I encountered the same problem and came across this post from googling. Apparently it doesn't work with probability estimat... | LibSVM cost weights for unbalanced data doesn't work
ANSWER: remove -b 1 or make it -b 0
-b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
I encountered the same problem and came across this post |
45,448 | LibSVM cost weights for unbalanced data doesn't work | If you're performing a ranking task, it might make more sense to evaluate your system in terms of area under the ROC curve! Accuracy, for ranking tasks, isn't necessarily what you want to optimize your system for, in my view.
More to your question, how skewed is your data? There's been quite a bit of work on dealing w... | LibSVM cost weights for unbalanced data doesn't work | If you're performing a ranking task, it might make more sense to evaluate your system in terms of area under the ROC curve! Accuracy, for ranking tasks, isn't necessarily what you want to optimize you | LibSVM cost weights for unbalanced data doesn't work
If you're performing a ranking task, it might make more sense to evaluate your system in terms of area under the ROC curve! Accuracy, for ranking tasks, isn't necessarily what you want to optimize your system for, in my view.
More to your question, how skewed is you... | LibSVM cost weights for unbalanced data doesn't work
If you're performing a ranking task, it might make more sense to evaluate your system in terms of area under the ROC curve! Accuracy, for ranking tasks, isn't necessarily what you want to optimize you |
45,449 | What is the preferred way to give asymmetric uncertainties? | Assuming your paper only reports one or a few such values, there is nothing wrong with being wordy so no one can get confused:
"The mean is 4.1 and the 95% confidence interval ranges from 2.6 to 6.2."
(Of course, replace "mean" with "slope" or whatever parameter you are actually reporting.) | What is the preferred way to give asymmetric uncertainties? | Assuming your paper only reports one or a few such values, there is nothing wrong with being wordy so no one can get confused:
"The mean is 4.1 and the 95% confidence interval ranges from 2.6 to 6.2 | What is the preferred way to give asymmetric uncertainties?
Assuming your paper only reports one or a few such values, there is nothing wrong with being wordy so no one can get confused:
"The mean is 4.1 and the 95% confidence interval ranges from 2.6 to 6.2."
(Of course, replace "mean" with "slope" or whatever para... | What is the preferred way to give asymmetric uncertainties?
Assuming your paper only reports one or a few such values, there is nothing wrong with being wordy so no one can get confused:
"The mean is 4.1 and the 95% confidence interval ranges from 2.6 to 6.2 |
45,450 | What is the preferred way to give asymmetric uncertainties? | It would depend on the field and the journal. In the social sciences, it would be mean = 4.1, 95% CI = ???
I like specifying that it's a CI, because the +- notation could be standard deviation, standard errors, or who knows what. | What is the preferred way to give asymmetric uncertainties? | It would depend on the field and the journal. In the social sciences, it would be mean = 4.1, 95% CI = ???
I like specifying that it's a CI, because the +- notation could be standard deviation, stan | What is the preferred way to give asymmetric uncertainties?
It would depend on the field and the journal. In the social sciences, it would be mean = 4.1, 95% CI = ???
I like specifying that it's a CI, because the +- notation could be standard deviation, standard errors, or who knows what. | What is the preferred way to give asymmetric uncertainties?
It would depend on the field and the journal. In the social sciences, it would be mean = 4.1, 95% CI = ???
I like specifying that it's a CI, because the +- notation could be standard deviation, stan |
45,451 | What is the preferred way to give asymmetric uncertainties? | Astrophysics commonly use $4.1^{+2.1}_{-1.5}$ style, for example :
http://iopscience.iop.org/0004-637X/765/1/47
Also see wikipedia using the same style:
http://en.wikipedia.org/wiki/R136a1 | What is the preferred way to give asymmetric uncertainties? | Astrophysics commonly use $4.1^{+2.1}_{-1.5}$ style, for example :
http://iopscience.iop.org/0004-637X/765/1/47
Also see wikipedia using the same style:
http://en.wikipedia.org/wiki/R136a1 | What is the preferred way to give asymmetric uncertainties?
Astrophysics commonly use $4.1^{+2.1}_{-1.5}$ style, for example :
http://iopscience.iop.org/0004-637X/765/1/47
Also see wikipedia using the same style:
http://en.wikipedia.org/wiki/R136a1 | What is the preferred way to give asymmetric uncertainties?
Astrophysics commonly use $4.1^{+2.1}_{-1.5}$ style, for example :
http://iopscience.iop.org/0004-637X/765/1/47
Also see wikipedia using the same style:
http://en.wikipedia.org/wiki/R136a1 |
45,452 | Spectral clustering of graph | The problem of finding the correct number of classes is unsolved and there are many approaches that deal with this problem. For general approaches, you can have a look at the problem of finding k in a k-means.
When performing spectral analysis, you can use the eigengap method to find a good approximation of the number ... | Spectral clustering of graph | The problem of finding the correct number of classes is unsolved and there are many approaches that deal with this problem. For general approaches, you can have a look at the problem of finding k in a | Spectral clustering of graph
The problem of finding the correct number of classes is unsolved and there are many approaches that deal with this problem. For general approaches, you can have a look at the problem of finding k in a k-means.
When performing spectral analysis, you can use the eigengap method to find a good... | Spectral clustering of graph
The problem of finding the correct number of classes is unsolved and there are many approaches that deal with this problem. For general approaches, you can have a look at the problem of finding k in a |
45,453 | Spectral clustering of graph | If you're using R or Python (or even C), you can have a look at the excellent igraph package. Especially, look at the various community detection algorithms that this package implements. What you discuss is closely related to the leading eigenvector algorithm of Newman (2006). Here is the paper introducing this algorit... | Spectral clustering of graph | If you're using R or Python (or even C), you can have a look at the excellent igraph package. Especially, look at the various community detection algorithms that this package implements. What you disc | Spectral clustering of graph
If you're using R or Python (or even C), you can have a look at the excellent igraph package. Especially, look at the various community detection algorithms that this package implements. What you discuss is closely related to the leading eigenvector algorithm of Newman (2006). Here is the p... | Spectral clustering of graph
If you're using R or Python (or even C), you can have a look at the excellent igraph package. Especially, look at the various community detection algorithms that this package implements. What you disc |
45,454 | What is crisp logic (in the area of classification)? | Crisp vs Fuzzy Logic
As far as I remember, crisp logic is the same as boolean logic. Either a statement is true or it is not, meanwhile fuzzy logic captures the degree to which something is true.
Consider the statement: "The agreed to met at 12 o'clock but Ben was not punctual."
Crisp logic: If Ben showed up precisle... | What is crisp logic (in the area of classification)? | Crisp vs Fuzzy Logic
As far as I remember, crisp logic is the same as boolean logic. Either a statement is true or it is not, meanwhile fuzzy logic captures the degree to which something is true.
Cons | What is crisp logic (in the area of classification)?
Crisp vs Fuzzy Logic
As far as I remember, crisp logic is the same as boolean logic. Either a statement is true or it is not, meanwhile fuzzy logic captures the degree to which something is true.
Consider the statement: "The agreed to met at 12 o'clock but Ben was no... | What is crisp logic (in the area of classification)?
Crisp vs Fuzzy Logic
As far as I remember, crisp logic is the same as boolean logic. Either a statement is true or it is not, meanwhile fuzzy logic captures the degree to which something is true.
Cons |
45,455 | What is crisp logic (in the area of classification)? | crisp / fuzzy is used in fuzzy logic
hard / soft is sometimes used for continuous classifier scores in [0, 1] as well, e.g. in the remote sensing community.
Interpretation of continuous [0, 1] scores varies:
mixtures of pure, hard/crisp classes that are not resolved by the measurement
cases that are truly in between... | What is crisp logic (in the area of classification)? | crisp / fuzzy is used in fuzzy logic
hard / soft is sometimes used for continuous classifier scores in [0, 1] as well, e.g. in the remote sensing community.
Interpretation of continuous [0, 1] score | What is crisp logic (in the area of classification)?
crisp / fuzzy is used in fuzzy logic
hard / soft is sometimes used for continuous classifier scores in [0, 1] as well, e.g. in the remote sensing community.
Interpretation of continuous [0, 1] scores varies:
mixtures of pure, hard/crisp classes that are not resolv... | What is crisp logic (in the area of classification)?
crisp / fuzzy is used in fuzzy logic
hard / soft is sometimes used for continuous classifier scores in [0, 1] as well, e.g. in the remote sensing community.
Interpretation of continuous [0, 1] score |
45,456 | What is crisp logic (in the area of classification)? | The statement which is either true or false but not both is called a proportion is denoted by an upper case letter of alphabets , a simple proportion is also known as an atom, in order to represent complex information one has to build a sequence of proportion link using connectives or operators
There are five major op... | What is crisp logic (in the area of classification)? | The statement which is either true or false but not both is called a proportion is denoted by an upper case letter of alphabets , a simple proportion is also known as an atom, in order to represent co | What is crisp logic (in the area of classification)?
The statement which is either true or false but not both is called a proportion is denoted by an upper case letter of alphabets , a simple proportion is also known as an atom, in order to represent complex information one has to build a sequence of proportion link us... | What is crisp logic (in the area of classification)?
The statement which is either true or false but not both is called a proportion is denoted by an upper case letter of alphabets , a simple proportion is also known as an atom, in order to represent co |
45,457 | Summarizing k-fold cross validation results | box and whisker plots are commonly used to visually compare and summarize
cross validation results.
Here is an example, taken from the cvTools package in R.
library(cvTools)
## set up folds for cross-validation
folds <- cvFolds(nrow(coleman), K = 5, R = 50)
## compare LS, MM and LTS regression
# perform cross-validat... | Summarizing k-fold cross validation results | box and whisker plots are commonly used to visually compare and summarize
cross validation results.
Here is an example, taken from the cvTools package in R.
library(cvTools)
## set up folds for cros | Summarizing k-fold cross validation results
box and whisker plots are commonly used to visually compare and summarize
cross validation results.
Here is an example, taken from the cvTools package in R.
library(cvTools)
## set up folds for cross-validation
folds <- cvFolds(nrow(coleman), K = 5, R = 50)
## compare LS, M... | Summarizing k-fold cross validation results
box and whisker plots are commonly used to visually compare and summarize
cross validation results.
Here is an example, taken from the cvTools package in R.
library(cvTools)
## set up folds for cros |
45,458 | Initialize ARIMA simulations with different time-series | You can "fit" the model to different data and then simulate:
m2 <- Arima(z,model=m1)
simulate.Arima(m2,future=TRUE,bootstrap=TRUE)
m2 will have the same parameters as m1 (they are not re-estimated), but the residuals, etc., are computed on the new data.
However, I am concerned with your model. Seasonal models are for ... | Initialize ARIMA simulations with different time-series | You can "fit" the model to different data and then simulate:
m2 <- Arima(z,model=m1)
simulate.Arima(m2,future=TRUE,bootstrap=TRUE)
m2 will have the same parameters as m1 (they are not re-estimated), | Initialize ARIMA simulations with different time-series
You can "fit" the model to different data and then simulate:
m2 <- Arima(z,model=m1)
simulate.Arima(m2,future=TRUE,bootstrap=TRUE)
m2 will have the same parameters as m1 (they are not re-estimated), but the residuals, etc., are computed on the new data.
However, ... | Initialize ARIMA simulations with different time-series
You can "fit" the model to different data and then simulate:
m2 <- Arima(z,model=m1)
simulate.Arima(m2,future=TRUE,bootstrap=TRUE)
m2 will have the same parameters as m1 (they are not re-estimated), |
45,459 | Initialize ARIMA simulations with different time-series | On a related note, you can accomplish the same objective if your ARIMA model has external regressors. This has been helpful for me on occasion.
For instance, say your first model was created as follows:
fit.arimax <- Arima(response, order=c(1, 0, 1), xreg=xreg)
Then suppose that after creating your model, you observe ... | Initialize ARIMA simulations with different time-series | On a related note, you can accomplish the same objective if your ARIMA model has external regressors. This has been helpful for me on occasion.
For instance, say your first model was created as follow | Initialize ARIMA simulations with different time-series
On a related note, you can accomplish the same objective if your ARIMA model has external regressors. This has been helpful for me on occasion.
For instance, say your first model was created as follows:
fit.arimax <- Arima(response, order=c(1, 0, 1), xreg=xreg)
T... | Initialize ARIMA simulations with different time-series
On a related note, you can accomplish the same objective if your ARIMA model has external regressors. This has been helpful for me on occasion.
For instance, say your first model was created as follow |
45,460 | Is it true that in high dimensions, data is easier to separate linearly? | Trivially, if you have $N$ data points, they will be linearly separable in $N-1$ dimensions. Any structure in the data may reduce the required dimensionality for linear separation further. You might say that (a projection of) a data set either is or is not completely linearly separable, in which using any (projection i... | Is it true that in high dimensions, data is easier to separate linearly? | Trivially, if you have $N$ data points, they will be linearly separable in $N-1$ dimensions. Any structure in the data may reduce the required dimensionality for linear separation further. You might s | Is it true that in high dimensions, data is easier to separate linearly?
Trivially, if you have $N$ data points, they will be linearly separable in $N-1$ dimensions. Any structure in the data may reduce the required dimensionality for linear separation further. You might say that (a projection of) a data set either is ... | Is it true that in high dimensions, data is easier to separate linearly?
Trivially, if you have $N$ data points, they will be linearly separable in $N-1$ dimensions. Any structure in the data may reduce the required dimensionality for linear separation further. You might s |
45,461 | Is it true that in high dimensions, data is easier to separate linearly? | I'm not sure if it matters whether the data actually has a high dimensionality or whether data is projected into a higher dimension. In the latter case, it is true that it's easier to linearly separate something projected into a higher dimension, hence the whole idea of kernel methods. (See Cover's Theorem, etc.)
My ty... | Is it true that in high dimensions, data is easier to separate linearly? | I'm not sure if it matters whether the data actually has a high dimensionality or whether data is projected into a higher dimension. In the latter case, it is true that it's easier to linearly separat | Is it true that in high dimensions, data is easier to separate linearly?
I'm not sure if it matters whether the data actually has a high dimensionality or whether data is projected into a higher dimension. In the latter case, it is true that it's easier to linearly separate something projected into a higher dimension, ... | Is it true that in high dimensions, data is easier to separate linearly?
I'm not sure if it matters whether the data actually has a high dimensionality or whether data is projected into a higher dimension. In the latter case, it is true that it's easier to linearly separat |
45,462 | Is it true that in high dimensions, data is easier to separate linearly? | I think what you might be asking about is the use of kernels to make a data set more compatible with linear techniques. A short piece about this is available here: http://ldtopology.wordpress.com/2012/05/27/making-linear-data-algorithms-less-linear-kernels/. | Is it true that in high dimensions, data is easier to separate linearly? | I think what you might be asking about is the use of kernels to make a data set more compatible with linear techniques. A short piece about this is available here: http://ldtopology.wordpress.com/2012 | Is it true that in high dimensions, data is easier to separate linearly?
I think what you might be asking about is the use of kernels to make a data set more compatible with linear techniques. A short piece about this is available here: http://ldtopology.wordpress.com/2012/05/27/making-linear-data-algorithms-less-linea... | Is it true that in high dimensions, data is easier to separate linearly?
I think what you might be asking about is the use of kernels to make a data set more compatible with linear techniques. A short piece about this is available here: http://ldtopology.wordpress.com/2012 |
45,463 | Are support vector regression and kernel ridge regression used for the same type of problems? | Yes, they refer to the same class of problems, aka. fitting a linear function to data in possibly transformed space, but they don't solve the same optimization problem (they have different loss functions), and will give different results.
To see the exact difference you may want to see chapters $3.4.1$ and $12.3.6$ of ... | Are support vector regression and kernel ridge regression used for the same type of problems? | Yes, they refer to the same class of problems, aka. fitting a linear function to data in possibly transformed space, but they don't solve the same optimization problem (they have different loss functi | Are support vector regression and kernel ridge regression used for the same type of problems?
Yes, they refer to the same class of problems, aka. fitting a linear function to data in possibly transformed space, but they don't solve the same optimization problem (they have different loss functions), and will give differ... | Are support vector regression and kernel ridge regression used for the same type of problems?
Yes, they refer to the same class of problems, aka. fitting a linear function to data in possibly transformed space, but they don't solve the same optimization problem (they have different loss functi |
45,464 | How does a generalized linear mixed model estimate means and how does this differ from calculating means by hand? | Here are the short answers to your questions:
The regression model allows you use the structure of the model to estimate the mean at predictor values by plugging the value into the fitted model equation. You can even do this for predictor values you didn't directly observe in the data set (more about this below).
Th... | How does a generalized linear mixed model estimate means and how does this differ from calculating m | Here are the short answers to your questions:
The regression model allows you use the structure of the model to estimate the mean at predictor values by plugging the value into the fitted model equa | How does a generalized linear mixed model estimate means and how does this differ from calculating means by hand?
Here are the short answers to your questions:
The regression model allows you use the structure of the model to estimate the mean at predictor values by plugging the value into the fitted model equation. ... | How does a generalized linear mixed model estimate means and how does this differ from calculating m
Here are the short answers to your questions:
The regression model allows you use the structure of the model to estimate the mean at predictor values by plugging the value into the fitted model equa |
45,465 | Fuzzy K-means - Cluster Sizes | K-means and also fuzzy k-means (emphasized by your "the winner takes it all" strategy) assume that clusters have the same spatial extend.
This is best explained by looking at an object $o$ almost half-way between cluster centers $c_i$ and $c_j$. If it is slightly closer to $c_i$, it will go into cluster $i$, if it is s... | Fuzzy K-means - Cluster Sizes | K-means and also fuzzy k-means (emphasized by your "the winner takes it all" strategy) assume that clusters have the same spatial extend.
This is best explained by looking at an object $o$ almost half | Fuzzy K-means - Cluster Sizes
K-means and also fuzzy k-means (emphasized by your "the winner takes it all" strategy) assume that clusters have the same spatial extend.
This is best explained by looking at an object $o$ almost half-way between cluster centers $c_i$ and $c_j$. If it is slightly closer to $c_i$, it will g... | Fuzzy K-means - Cluster Sizes
K-means and also fuzzy k-means (emphasized by your "the winner takes it all" strategy) assume that clusters have the same spatial extend.
This is best explained by looking at an object $o$ almost half |
45,466 | Fuzzy K-means - Cluster Sizes | Why not using fanny() from the cluster package? You can find the details of the algorithm in the details section of the help.
Here is the example code from ?fanny
## generate 10+15 objects in two clusters, plus 3 objects lying
## between those clusters.
x <- rbind(cbind(rnorm(10, 0, 0.5), rnorm(10, 0, 0.5)),
... | Fuzzy K-means - Cluster Sizes | Why not using fanny() from the cluster package? You can find the details of the algorithm in the details section of the help.
Here is the example code from ?fanny
## generate 10+15 objects in two clu | Fuzzy K-means - Cluster Sizes
Why not using fanny() from the cluster package? You can find the details of the algorithm in the details section of the help.
Here is the example code from ?fanny
## generate 10+15 objects in two clusters, plus 3 objects lying
## between those clusters.
x <- rbind(cbind(rnorm(10, 0, 0.5),... | Fuzzy K-means - Cluster Sizes
Why not using fanny() from the cluster package? You can find the details of the algorithm in the details section of the help.
Here is the example code from ?fanny
## generate 10+15 objects in two clu |
45,467 | How to decide if to do dimensionality reduction before clustering? | You do dimensionality reduction if it improves results.
You don't do dimensionality reduction if the results become worse.
There is no one size fits all in data mining. You have to do multiple iterations of preprocessing, data mining, evaluating, retry, until your results work for you. Different data sets have differen... | How to decide if to do dimensionality reduction before clustering? | You do dimensionality reduction if it improves results.
You don't do dimensionality reduction if the results become worse.
There is no one size fits all in data mining. You have to do multiple iterati | How to decide if to do dimensionality reduction before clustering?
You do dimensionality reduction if it improves results.
You don't do dimensionality reduction if the results become worse.
There is no one size fits all in data mining. You have to do multiple iterations of preprocessing, data mining, evaluating, retry,... | How to decide if to do dimensionality reduction before clustering?
You do dimensionality reduction if it improves results.
You don't do dimensionality reduction if the results become worse.
There is no one size fits all in data mining. You have to do multiple iterati |
45,468 | Logistic regression: Is it valid to code NA as 0? | I would say "no". If you have this as a numeric variable, then 0 would be less than 1, indicating an even more frequent visitor; indeed, 0 has a sensible interpretation as "visited the site today". | Logistic regression: Is it valid to code NA as 0? | I would say "no". If you have this as a numeric variable, then 0 would be less than 1, indicating an even more frequent visitor; indeed, 0 has a sensible interpretation as "visited the site today". | Logistic regression: Is it valid to code NA as 0?
I would say "no". If you have this as a numeric variable, then 0 would be less than 1, indicating an even more frequent visitor; indeed, 0 has a sensible interpretation as "visited the site today". | Logistic regression: Is it valid to code NA as 0?
I would say "no". If you have this as a numeric variable, then 0 would be less than 1, indicating an even more frequent visitor; indeed, 0 has a sensible interpretation as "visited the site today". |
45,469 | Logistic regression: Is it valid to code NA as 0? | To answer the direct question, no. As @PeterFlom points out, 0 has the interpretation "visited today." You likely already have a lot of 0's in your data from people who did just that.
I don't know that I would call this data "missing". The data is all there, it's just that the interpretation is a little difficult. ... | Logistic regression: Is it valid to code NA as 0? | To answer the direct question, no. As @PeterFlom points out, 0 has the interpretation "visited today." You likely already have a lot of 0's in your data from people who did just that.
I don't know t | Logistic regression: Is it valid to code NA as 0?
To answer the direct question, no. As @PeterFlom points out, 0 has the interpretation "visited today." You likely already have a lot of 0's in your data from people who did just that.
I don't know that I would call this data "missing". The data is all there, it's jus... | Logistic regression: Is it valid to code NA as 0?
To answer the direct question, no. As @PeterFlom points out, 0 has the interpretation "visited today." You likely already have a lot of 0's in your data from people who did just that.
I don't know t |
45,470 | Logistic regression: Is it valid to code NA as 0? | If the outcome of interest is number of days since last visit, then first-time users don't provide any information about that outcome, so it seems like leaving out first time visitors is the only thing to do.
In any case, coding first time users as 0 reflects a belief that first time users are equivalent to a person w... | Logistic regression: Is it valid to code NA as 0? | If the outcome of interest is number of days since last visit, then first-time users don't provide any information about that outcome, so it seems like leaving out first time visitors is the only thin | Logistic regression: Is it valid to code NA as 0?
If the outcome of interest is number of days since last visit, then first-time users don't provide any information about that outcome, so it seems like leaving out first time visitors is the only thing to do.
In any case, coding first time users as 0 reflects a belief ... | Logistic regression: Is it valid to code NA as 0?
If the outcome of interest is number of days since last visit, then first-time users don't provide any information about that outcome, so it seems like leaving out first time visitors is the only thin |
45,471 | Logistic regression: Is it valid to code NA as 0? | My answer is neither. You should code them as missing, a simple dot for missing numerical data if you use SAS. The use of 0 is bad because the software will treat 0 as a number when the data point is actually missing. The use of NA is no good because you have a numerical variable and you are mixing character and nu... | Logistic regression: Is it valid to code NA as 0? | My answer is neither. You should code them as missing, a simple dot for missing numerical data if you use SAS. The use of 0 is bad because the software will treat 0 as a number when the data point | Logistic regression: Is it valid to code NA as 0?
My answer is neither. You should code them as missing, a simple dot for missing numerical data if you use SAS. The use of 0 is bad because the software will treat 0 as a number when the data point is actually missing. The use of NA is no good because you have a nume... | Logistic regression: Is it valid to code NA as 0?
My answer is neither. You should code them as missing, a simple dot for missing numerical data if you use SAS. The use of 0 is bad because the software will treat 0 as a number when the data point |
45,472 | How to verify that simulated data is normally distributed? | The issues arise with the idea of 'small' amounts of non-normality and 'some' autocorrelation. Until it's clear how to operationalise these then you're stuck with tests of normality (not near normality). There is, as you imply, quite a conceptual difference between an insensitive test of normality and a sensitive tes... | How to verify that simulated data is normally distributed? | The issues arise with the idea of 'small' amounts of non-normality and 'some' autocorrelation. Until it's clear how to operationalise these then you're stuck with tests of normality (not near normali | How to verify that simulated data is normally distributed?
The issues arise with the idea of 'small' amounts of non-normality and 'some' autocorrelation. Until it's clear how to operationalise these then you're stuck with tests of normality (not near normality). There is, as you imply, quite a conceptual difference b... | How to verify that simulated data is normally distributed?
The issues arise with the idea of 'small' amounts of non-normality and 'some' autocorrelation. Until it's clear how to operationalise these then you're stuck with tests of normality (not near normali |
45,473 | How to verify that simulated data is normally distributed? | If point two is your primary concern you could 'lag' the data one observation and then regress the 'raw' data on the 'lagged' data. Do this for a lag each way and decide based on the p value whether the data is sufficiently random. | How to verify that simulated data is normally distributed? | If point two is your primary concern you could 'lag' the data one observation and then regress the 'raw' data on the 'lagged' data. Do this for a lag each way and decide based on the p value whether t | How to verify that simulated data is normally distributed?
If point two is your primary concern you could 'lag' the data one observation and then regress the 'raw' data on the 'lagged' data. Do this for a lag each way and decide based on the p value whether the data is sufficiently random. | How to verify that simulated data is normally distributed?
If point two is your primary concern you could 'lag' the data one observation and then regress the 'raw' data on the 'lagged' data. Do this for a lag each way and decide based on the p value whether t |
45,474 | How to verify that simulated data is normally distributed? | Another suggestion would be to compute the Kullback-Leiber divergence or Hellinger distance between your generated data and the normal distribution. That gives you a measure of how non-normal your data is (and hopefully you can determine what a small deviation from normality is). | How to verify that simulated data is normally distributed? | Another suggestion would be to compute the Kullback-Leiber divergence or Hellinger distance between your generated data and the normal distribution. That gives you a measure of how non-normal your dat | How to verify that simulated data is normally distributed?
Another suggestion would be to compute the Kullback-Leiber divergence or Hellinger distance between your generated data and the normal distribution. That gives you a measure of how non-normal your data is (and hopefully you can determine what a small deviation ... | How to verify that simulated data is normally distributed?
Another suggestion would be to compute the Kullback-Leiber divergence or Hellinger distance between your generated data and the normal distribution. That gives you a measure of how non-normal your dat |
45,475 | How to verify that simulated data is normally distributed? | The best test that I can think of for near-normality is the visual test in:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for exploratory
data analysis and model diagnostics Phil. Trans. R. Soc. A 2009
367, 4361-4383 doi: 10.1098/rsta.2009.0120
... | How to verify that simulated data is normally distributed? | The best test that I can think of for near-normality is the visual test in:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for ex | How to verify that simulated data is normally distributed?
The best test that I can think of for near-normality is the visual test in:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for exploratory
data analysis and model diagnostics Phil. Trans. R... | How to verify that simulated data is normally distributed?
The best test that I can think of for near-normality is the visual test in:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for ex |
45,476 | Is there a difference between the "maximum probability" and the "mode" of a parameter? | The answer to the (now edited) question is no, there is no difference: maximum posterior probability for a given value $\hat{\theta}_{MAP}$ (the so-called maximum a-posteriori estimate of $\theta$) is equivalent to the mode of the posterior distribution of your parameter. Mathematically, you can express this in the con... | Is there a difference between the "maximum probability" and the "mode" of a parameter? | The answer to the (now edited) question is no, there is no difference: maximum posterior probability for a given value $\hat{\theta}_{MAP}$ (the so-called maximum a-posteriori estimate of $\theta$) is | Is there a difference between the "maximum probability" and the "mode" of a parameter?
The answer to the (now edited) question is no, there is no difference: maximum posterior probability for a given value $\hat{\theta}_{MAP}$ (the so-called maximum a-posteriori estimate of $\theta$) is equivalent to the mode of the po... | Is there a difference between the "maximum probability" and the "mode" of a parameter?
The answer to the (now edited) question is no, there is no difference: maximum posterior probability for a given value $\hat{\theta}_{MAP}$ (the so-called maximum a-posteriori estimate of $\theta$) is |
45,477 | Mean and variance of log-binomial distribution | We can use an entirely analogous technique to the one typically used to calculate the moments of a lognormal.
In particular, note that if $\newcommand{\E}{\mathbb E}X \sim \mathrm{Bin}(n,p)$ and $Y = e^X$, then $Y^k = e^{k X}$. But, $\E e^{kX} = m_X(k)$ where $m_X(t)$ is the moment-generating function of $X$ evaluated ... | Mean and variance of log-binomial distribution | We can use an entirely analogous technique to the one typically used to calculate the moments of a lognormal.
In particular, note that if $\newcommand{\E}{\mathbb E}X \sim \mathrm{Bin}(n,p)$ and $Y = | Mean and variance of log-binomial distribution
We can use an entirely analogous technique to the one typically used to calculate the moments of a lognormal.
In particular, note that if $\newcommand{\E}{\mathbb E}X \sim \mathrm{Bin}(n,p)$ and $Y = e^X$, then $Y^k = e^{k X}$. But, $\E e^{kX} = m_X(k)$ where $m_X(t)$ is t... | Mean and variance of log-binomial distribution
We can use an entirely analogous technique to the one typically used to calculate the moments of a lognormal.
In particular, note that if $\newcommand{\E}{\mathbb E}X \sim \mathrm{Bin}(n,p)$ and $Y = |
45,478 | Mean and variance of log-binomial distribution | 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.
See:
Parameter Estimation and Goodness-of-Fit in Log ... | Mean and variance of log-binomial distribution | 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.
| Mean and variance of log-binomial distribution
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.
See:
P... | Mean and variance of log-binomial distribution
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.
|
45,479 | Variance explained by random effects using lme4 | Such a value doesn't exists for a GLMM. The model you show that does have a Residual component is a LMM not a GLMM. In a GLMM there is a known mean-variance relationship and there isn't a parameter $\sigma$ to estimate. You can compute the residual deviance but this doesn't fit into the scheme of being a variance param... | Variance explained by random effects using lme4 | Such a value doesn't exists for a GLMM. The model you show that does have a Residual component is a LMM not a GLMM. In a GLMM there is a known mean-variance relationship and there isn't a parameter $\ | Variance explained by random effects using lme4
Such a value doesn't exists for a GLMM. The model you show that does have a Residual component is a LMM not a GLMM. In a GLMM there is a known mean-variance relationship and there isn't a parameter $\sigma$ to estimate. You can compute the residual deviance but this doesn... | Variance explained by random effects using lme4
Such a value doesn't exists for a GLMM. The model you show that does have a Residual component is a LMM not a GLMM. In a GLMM there is a known mean-variance relationship and there isn't a parameter $\ |
45,480 | How to sample natural numbers, such that the sum is equal to a constant? | I believe what you are looking for is something like the Dirichlet Process [DP] which is a distribution on distributions. It is not an easy concept to understand, but the base measure you will use is the discrete distribution of cluster sizes you started with. The parameter $\alpha$ controls how 'close' to the original... | How to sample natural numbers, such that the sum is equal to a constant? | I believe what you are looking for is something like the Dirichlet Process [DP] which is a distribution on distributions. It is not an easy concept to understand, but the base measure you will use is | How to sample natural numbers, such that the sum is equal to a constant?
I believe what you are looking for is something like the Dirichlet Process [DP] which is a distribution on distributions. It is not an easy concept to understand, but the base measure you will use is the discrete distribution of cluster sizes you ... | How to sample natural numbers, such that the sum is equal to a constant?
I believe what you are looking for is something like the Dirichlet Process [DP] which is a distribution on distributions. It is not an easy concept to understand, but the base measure you will use is |
45,481 | How to sample natural numbers, such that the sum is equal to a constant? | One easy way to achieve your goal is to permute the labels. Say you had 10 objects, with memberships defined as $\{1, 2, 3, 4 ,5, 6\}$, $\{7, 8\}$, $\{9\}$ and $\{10\}$. You take a random permutation $\sigma=(3, 7, 2, 5, 1, 8, 10, 9, 6, 4)$, and then your new clusters are $\{\sigma(1), \sigma(2), \sigma(3), \sigma(4), ... | How to sample natural numbers, such that the sum is equal to a constant? | One easy way to achieve your goal is to permute the labels. Say you had 10 objects, with memberships defined as $\{1, 2, 3, 4 ,5, 6\}$, $\{7, 8\}$, $\{9\}$ and $\{10\}$. You take a random permutation | How to sample natural numbers, such that the sum is equal to a constant?
One easy way to achieve your goal is to permute the labels. Say you had 10 objects, with memberships defined as $\{1, 2, 3, 4 ,5, 6\}$, $\{7, 8\}$, $\{9\}$ and $\{10\}$. You take a random permutation $\sigma=(3, 7, 2, 5, 1, 8, 10, 9, 6, 4)$, and t... | How to sample natural numbers, such that the sum is equal to a constant?
One easy way to achieve your goal is to permute the labels. Say you had 10 objects, with memberships defined as $\{1, 2, 3, 4 ,5, 6\}$, $\{7, 8\}$, $\{9\}$ and $\{10\}$. You take a random permutation |
45,482 | How to sample natural numbers, such that the sum is equal to a constant? | Can you think of this as $n$ balls being distributed among $k$ urns? That seems to fit your description of clusters (where you have $k$ clusters and $n$ numbers). If you need at least one ball in each urn, then first put 1 ball in each urn, then randomly select the urn for each of the remaining $n-k$ balls. Here is ... | How to sample natural numbers, such that the sum is equal to a constant? | Can you think of this as $n$ balls being distributed among $k$ urns? That seems to fit your description of clusters (where you have $k$ clusters and $n$ numbers). If you need at least one ball in ea | How to sample natural numbers, such that the sum is equal to a constant?
Can you think of this as $n$ balls being distributed among $k$ urns? That seems to fit your description of clusters (where you have $k$ clusters and $n$ numbers). If you need at least one ball in each urn, then first put 1 ball in each urn, then... | How to sample natural numbers, such that the sum is equal to a constant?
Can you think of this as $n$ balls being distributed among $k$ urns? That seems to fit your description of clusters (where you have $k$ clusters and $n$ numbers). If you need at least one ball in ea |
45,483 | How to choose the most appropriate distribution for a given vector in R? | When deciding on a distribution, the science is more important than the tests. Think about what lead to the data, what values are possible, likely, and meaningful. The formal tests can find obvious differences, but often cannot rule out distributions that are similar (and note that the chi-squared distribution is a ... | How to choose the most appropriate distribution for a given vector in R? | When deciding on a distribution, the science is more important than the tests. Think about what lead to the data, what values are possible, likely, and meaningful. The formal tests can find obvious | How to choose the most appropriate distribution for a given vector in R?
When deciding on a distribution, the science is more important than the tests. Think about what lead to the data, what values are possible, likely, and meaningful. The formal tests can find obvious differences, but often cannot rule out distrib... | How to choose the most appropriate distribution for a given vector in R?
When deciding on a distribution, the science is more important than the tests. Think about what lead to the data, what values are possible, likely, and meaningful. The formal tests can find obvious |
45,484 | How to choose the most appropriate distribution for a given vector in R? | There is no reason one of the "official" distributions would fit your data. The most relevant statistical test for checking fit to a distribution is the Kolmogorov-Smirnov test. E.g.,
> x=rnorm(133)
> ks.test(x,"pnorm",mean(x),sd(x))
One-sample Kolmogorov-Smirnov test
data: x
D = 0.0388, p-value = 0.9882
... | How to choose the most appropriate distribution for a given vector in R? | There is no reason one of the "official" distributions would fit your data. The most relevant statistical test for checking fit to a distribution is the Kolmogorov-Smirnov test. E.g.,
> x=rnorm(133) | How to choose the most appropriate distribution for a given vector in R?
There is no reason one of the "official" distributions would fit your data. The most relevant statistical test for checking fit to a distribution is the Kolmogorov-Smirnov test. E.g.,
> x=rnorm(133)
> ks.test(x,"pnorm",mean(x),sd(x))
On... | How to choose the most appropriate distribution for a given vector in R?
There is no reason one of the "official" distributions would fit your data. The most relevant statistical test for checking fit to a distribution is the Kolmogorov-Smirnov test. E.g.,
> x=rnorm(133) |
45,485 | Identifying outlier data in high-dimensional settings | (classical) Mahalanobis distances cannot be used to find outliers in data because the Mahalanobis distance themselves are sensitive to outliers (i.e., they will always by construction sum to $(n-1)\times p$, the product of the dimensions of your dataset).
The recommended solution depends on what you mean by 'high dime... | Identifying outlier data in high-dimensional settings | (classical) Mahalanobis distances cannot be used to find outliers in data because the Mahalanobis distance themselves are sensitive to outliers (i.e., they will always by construction sum to $(n-1)\t | Identifying outlier data in high-dimensional settings
(classical) Mahalanobis distances cannot be used to find outliers in data because the Mahalanobis distance themselves are sensitive to outliers (i.e., they will always by construction sum to $(n-1)\times p$, the product of the dimensions of your dataset).
The recom... | Identifying outlier data in high-dimensional settings
(classical) Mahalanobis distances cannot be used to find outliers in data because the Mahalanobis distance themselves are sensitive to outliers (i.e., they will always by construction sum to $(n-1)\t |
45,486 | Identifying outlier data in high-dimensional settings | A basic approach is to use Mahalanobis distance, and look for data that are more extreme than you would expect. Mahalanobis distance is a multidimensional measure that takes into account the pattern of intercorrelations amongst your variables. One issue to bear in mind, is that this assumes your data are continuous; ... | Identifying outlier data in high-dimensional settings | A basic approach is to use Mahalanobis distance, and look for data that are more extreme than you would expect. Mahalanobis distance is a multidimensional measure that takes into account the pattern | Identifying outlier data in high-dimensional settings
A basic approach is to use Mahalanobis distance, and look for data that are more extreme than you would expect. Mahalanobis distance is a multidimensional measure that takes into account the pattern of intercorrelations amongst your variables. One issue to bear in... | Identifying outlier data in high-dimensional settings
A basic approach is to use Mahalanobis distance, and look for data that are more extreme than you would expect. Mahalanobis distance is a multidimensional measure that takes into account the pattern |
45,487 | Identifying outlier data in high-dimensional settings | It might be worth looking at one-class-SVM, which attempts to construct the smallest hypersphere that encloses as large a proportion of the data as possible, which has been used successfully in the machine learning community for novelty detection, so its use for outlier detection seems reasonable. I suspect there are ... | Identifying outlier data in high-dimensional settings | It might be worth looking at one-class-SVM, which attempts to construct the smallest hypersphere that encloses as large a proportion of the data as possible, which has been used successfully in the ma | Identifying outlier data in high-dimensional settings
It might be worth looking at one-class-SVM, which attempts to construct the smallest hypersphere that encloses as large a proportion of the data as possible, which has been used successfully in the machine learning community for novelty detection, so its use for out... | Identifying outlier data in high-dimensional settings
It might be worth looking at one-class-SVM, which attempts to construct the smallest hypersphere that encloses as large a proportion of the data as possible, which has been used successfully in the ma |
45,488 | Is there a fast algorithm to check for AR(p) stationarity? | The Schur-Cohn algorithm has $d=2$; this is what I learned in a computational statistics class at Berkeley some years ago. | Is there a fast algorithm to check for AR(p) stationarity? | The Schur-Cohn algorithm has $d=2$; this is what I learned in a computational statistics class at Berkeley some years ago. | Is there a fast algorithm to check for AR(p) stationarity?
The Schur-Cohn algorithm has $d=2$; this is what I learned in a computational statistics class at Berkeley some years ago. | Is there a fast algorithm to check for AR(p) stationarity?
The Schur-Cohn algorithm has $d=2$; this is what I learned in a computational statistics class at Berkeley some years ago. |
45,489 | Is there a fast algorithm to check for AR(p) stationarity? | It is unnecessary to find the $p$ complex roots as far as
these are not to be used for themselves. Moreover, most (if not all)
root finding processes can fail for large $p$.
Another solution is as follows.
The $\mathrm{AR}(p)$ model can be reparametrised thanks to its $p$
partial autocorrelations (PACs) $\zeta_k$ for $... | Is there a fast algorithm to check for AR(p) stationarity? | It is unnecessary to find the $p$ complex roots as far as
these are not to be used for themselves. Moreover, most (if not all)
root finding processes can fail for large $p$.
Another solution is as fol | Is there a fast algorithm to check for AR(p) stationarity?
It is unnecessary to find the $p$ complex roots as far as
these are not to be used for themselves. Moreover, most (if not all)
root finding processes can fail for large $p$.
Another solution is as follows.
The $\mathrm{AR}(p)$ model can be reparametrised thanks... | Is there a fast algorithm to check for AR(p) stationarity?
It is unnecessary to find the $p$ complex roots as far as
these are not to be used for themselves. Moreover, most (if not all)
root finding processes can fail for large $p$.
Another solution is as fol |
45,490 | Is it possible to compute a covariance matrix with unequal sample sizes? | A covariance matrix relies on the idea that, in the case of two vectors, the observations are paired - each observation in vector $x$ corresponds in some logical manner to an observation in vector $y$ and vice versa. If there's no pairing of elements, you can't construct a covariance matrix.
If there is a pairing of e... | Is it possible to compute a covariance matrix with unequal sample sizes? | A covariance matrix relies on the idea that, in the case of two vectors, the observations are paired - each observation in vector $x$ corresponds in some logical manner to an observation in vector $y$ | Is it possible to compute a covariance matrix with unequal sample sizes?
A covariance matrix relies on the idea that, in the case of two vectors, the observations are paired - each observation in vector $x$ corresponds in some logical manner to an observation in vector $y$ and vice versa. If there's no pairing of elem... | Is it possible to compute a covariance matrix with unequal sample sizes?
A covariance matrix relies on the idea that, in the case of two vectors, the observations are paired - each observation in vector $x$ corresponds in some logical manner to an observation in vector $y$ |
45,491 | One component in PCA is always the mean vector in two-dimensions but not three [duplicate] | The answer is elaboration of my comment, because you asked. The core action of PCA is singular-value decomposition of data matrix X. It is the same thing as eigen-decomposition of square symmetrical matrix X'X: both actions will leave you the same matrix of eigenvectors (which within SVD you call V matrix). This matrix... | One component in PCA is always the mean vector in two-dimensions but not three [duplicate] | The answer is elaboration of my comment, because you asked. The core action of PCA is singular-value decomposition of data matrix X. It is the same thing as eigen-decomposition of square symmetrical m | One component in PCA is always the mean vector in two-dimensions but not three [duplicate]
The answer is elaboration of my comment, because you asked. The core action of PCA is singular-value decomposition of data matrix X. It is the same thing as eigen-decomposition of square symmetrical matrix X'X: both actions will ... | One component in PCA is always the mean vector in two-dimensions but not three [duplicate]
The answer is elaboration of my comment, because you asked. The core action of PCA is singular-value decomposition of data matrix X. It is the same thing as eigen-decomposition of square symmetrical m |
45,492 | One component in PCA is always the mean vector in two-dimensions but not three [duplicate] | Your $\bf{}X^TX$ matrix is a (multiple of a) correlation matrix when the inputs have been standardised. So we can do this analysis for a correlation matrix.
2 dimensions
Take a $2\times 2$ correlation matrix:
$$R_2=\begin{pmatrix} 1 & r \\ r & 1\end{pmatrix}$$
The characteristic polynomial is given by:
$$p(\lambda)=de... | One component in PCA is always the mean vector in two-dimensions but not three [duplicate] | Your $\bf{}X^TX$ matrix is a (multiple of a) correlation matrix when the inputs have been standardised. So we can do this analysis for a correlation matrix.
2 dimensions
Take a $2\times 2$ correlatio | One component in PCA is always the mean vector in two-dimensions but not three [duplicate]
Your $\bf{}X^TX$ matrix is a (multiple of a) correlation matrix when the inputs have been standardised. So we can do this analysis for a correlation matrix.
2 dimensions
Take a $2\times 2$ correlation matrix:
$$R_2=\begin{pmatri... | One component in PCA is always the mean vector in two-dimensions but not three [duplicate]
Your $\bf{}X^TX$ matrix is a (multiple of a) correlation matrix when the inputs have been standardised. So we can do this analysis for a correlation matrix.
2 dimensions
Take a $2\times 2$ correlatio |
45,493 | How should I deal with the consequences of proportional hazards violations in log-rank (and related) tests? | The log-rank test is valid whatever the true situation with the hazards is. You are correct that only its power is affected. So if it rejects, then the hazards are not equal. If it does not reject, then you have to worry about the proportionality of hazards and power.
The principled approach would be trying to estimate... | How should I deal with the consequences of proportional hazards violations in log-rank (and related) | The log-rank test is valid whatever the true situation with the hazards is. You are correct that only its power is affected. So if it rejects, then the hazards are not equal. If it does not reject, th | How should I deal with the consequences of proportional hazards violations in log-rank (and related) tests?
The log-rank test is valid whatever the true situation with the hazards is. You are correct that only its power is affected. So if it rejects, then the hazards are not equal. If it does not reject, then you have ... | How should I deal with the consequences of proportional hazards violations in log-rank (and related)
The log-rank test is valid whatever the true situation with the hazards is. You are correct that only its power is affected. So if it rejects, then the hazards are not equal. If it does not reject, th |
45,494 | How should I deal with the consequences of proportional hazards violations in log-rank (and related) tests? | Indeed- only power will be affected when the log-rank test is used without proportional hazards. However, if one chooses a new test/model post-hoc (i.e. after having determined that the hazards are not proportional), the results may not be valid. This paper shows how type-I error rates could be inflated:
http://onlin... | How should I deal with the consequences of proportional hazards violations in log-rank (and related) | Indeed- only power will be affected when the log-rank test is used without proportional hazards. However, if one chooses a new test/model post-hoc (i.e. after having determined that the hazards are n | How should I deal with the consequences of proportional hazards violations in log-rank (and related) tests?
Indeed- only power will be affected when the log-rank test is used without proportional hazards. However, if one chooses a new test/model post-hoc (i.e. after having determined that the hazards are not proportio... | How should I deal with the consequences of proportional hazards violations in log-rank (and related)
Indeed- only power will be affected when the log-rank test is used without proportional hazards. However, if one chooses a new test/model post-hoc (i.e. after having determined that the hazards are n |
45,495 | What is an elegant way of visualising two time series with many data points? | Partial transparency ("alpha") might help you here, e.g.:
> temp11=runif(100)
> temp22=runif(100)
> plot(temp11, type="p", col=rgb(0.2, 0.2, 1, 0.6), pch=19, xlab="Time", ylab="price")
> lines(temp11, lwd=3, col=rgb(0.2, 0.2, 1, 0.3))
> points(temp22, pch=19, col=rgb(1, 0.2, 0.2, 0.6))
> lines(temp22, lwd=3, col=rgb(1,... | What is an elegant way of visualising two time series with many data points? | Partial transparency ("alpha") might help you here, e.g.:
> temp11=runif(100)
> temp22=runif(100)
> plot(temp11, type="p", col=rgb(0.2, 0.2, 1, 0.6), pch=19, xlab="Time", ylab="price")
> lines(temp11, | What is an elegant way of visualising two time series with many data points?
Partial transparency ("alpha") might help you here, e.g.:
> temp11=runif(100)
> temp22=runif(100)
> plot(temp11, type="p", col=rgb(0.2, 0.2, 1, 0.6), pch=19, xlab="Time", ylab="price")
> lines(temp11, lwd=3, col=rgb(0.2, 0.2, 1, 0.3))
> points... | What is an elegant way of visualising two time series with many data points?
Partial transparency ("alpha") might help you here, e.g.:
> temp11=runif(100)
> temp22=runif(100)
> plot(temp11, type="p", col=rgb(0.2, 0.2, 1, 0.6), pch=19, xlab="Time", ylab="price")
> lines(temp11, |
45,496 | What is an elegant way of visualising two time series with many data points? | Here's what I was trying to say in the comment, with a little bit more detail (though this might not be the answer to your question). First, get some data.
x <- arima.sim(200, model = list(ar = 0.6)) + 3
y <- arima.sim(200, model = list(ar = -0.7)) - 3
A plot with two columns would look something like this:
par(mfrow... | What is an elegant way of visualising two time series with many data points? | Here's what I was trying to say in the comment, with a little bit more detail (though this might not be the answer to your question). First, get some data.
x <- arima.sim(200, model = list(ar = 0.6)) | What is an elegant way of visualising two time series with many data points?
Here's what I was trying to say in the comment, with a little bit more detail (though this might not be the answer to your question). First, get some data.
x <- arima.sim(200, model = list(ar = 0.6)) + 3
y <- arima.sim(200, model = list(ar = ... | What is an elegant way of visualising two time series with many data points?
Here's what I was trying to say in the comment, with a little bit more detail (though this might not be the answer to your question). First, get some data.
x <- arima.sim(200, model = list(ar = 0.6)) |
45,497 | How to specify labels per facet in ggplot2? [closed] | The answer is no. However, as you saw in the link you posted, there is grid.arrange::gridExtra. Another option is to use something that's originally from the ggplot2 book (I think that's the source, but examples abound on the internet)
p1 <- ggplot(diamonds, aes(depth, carat)) + geom_point()
p2 <- ggplot(diamonds, aes(... | How to specify labels per facet in ggplot2? [closed] | The answer is no. However, as you saw in the link you posted, there is grid.arrange::gridExtra. Another option is to use something that's originally from the ggplot2 book (I think that's the source, b | How to specify labels per facet in ggplot2? [closed]
The answer is no. However, as you saw in the link you posted, there is grid.arrange::gridExtra. Another option is to use something that's originally from the ggplot2 book (I think that's the source, but examples abound on the internet)
p1 <- ggplot(diamonds, aes(dept... | How to specify labels per facet in ggplot2? [closed]
The answer is no. However, as you saw in the link you posted, there is grid.arrange::gridExtra. Another option is to use something that's originally from the ggplot2 book (I think that's the source, b |
45,498 | How to specify labels per facet in ggplot2? [closed] | You can use labeller option to specify labels for each facet.
See this link
Question | How to specify labels per facet in ggplot2? [closed] | You can use labeller option to specify labels for each facet.
See this link
Question | How to specify labels per facet in ggplot2? [closed]
You can use labeller option to specify labels for each facet.
See this link
Question | How to specify labels per facet in ggplot2? [closed]
You can use labeller option to specify labels for each facet.
See this link
Question |
45,499 | How is Google+ population estimated? | Two assumptions are made: (1) the rate of US citizens to all people is the same within the Google+ population as in the global population, and (2) for US citizens, the rate of people with any surname to all US citizens is (on average) the same within the Google+ population as in the global population.
So: you take, say... | How is Google+ population estimated? | Two assumptions are made: (1) the rate of US citizens to all people is the same within the Google+ population as in the global population, and (2) for US citizens, the rate of people with any surname | How is Google+ population estimated?
Two assumptions are made: (1) the rate of US citizens to all people is the same within the Google+ population as in the global population, and (2) for US citizens, the rate of people with any surname to all US citizens is (on average) the same within the Google+ population as in the... | How is Google+ population estimated?
Two assumptions are made: (1) the rate of US citizens to all people is the same within the Google+ population as in the global population, and (2) for US citizens, the rate of people with any surname |
45,500 | How is Google+ population estimated? | This exercise will be pretty useless unless the sample of the surnames is statistically sound, i.e., a random sample with known probabilities of selection. Otherwise, you are estimating the number of female drivers by first picking a color (say yellow), counting the fraction of female drivers in the yellow cars, and th... | How is Google+ population estimated? | This exercise will be pretty useless unless the sample of the surnames is statistically sound, i.e., a random sample with known probabilities of selection. Otherwise, you are estimating the number of | How is Google+ population estimated?
This exercise will be pretty useless unless the sample of the surnames is statistically sound, i.e., a random sample with known probabilities of selection. Otherwise, you are estimating the number of female drivers by first picking a color (say yellow), counting the fraction of fema... | How is Google+ population estimated?
This exercise will be pretty useless unless the sample of the surnames is statistically sound, i.e., a random sample with known probabilities of selection. Otherwise, you are estimating the number of |
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