idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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42,801 | How to summarize knowledge about importance of variables? | In case you are interested in a linear model, here's direction that hasn't been mentioned in the comments / other answers:
Let $x \in R^{30}$ be the feature vector, and you want to learn a linear mapping $y = w\cdot x$ that maps $x$ to a value (that can be transformed into a probability using logistic regression, from ... | How to summarize knowledge about importance of variables? | In case you are interested in a linear model, here's direction that hasn't been mentioned in the comments / other answers:
Let $x \in R^{30}$ be the feature vector, and you want to learn a linear mapp | How to summarize knowledge about importance of variables?
In case you are interested in a linear model, here's direction that hasn't been mentioned in the comments / other answers:
Let $x \in R^{30}$ be the feature vector, and you want to learn a linear mapping $y = w\cdot x$ that maps $x$ to a value (that can be trans... | How to summarize knowledge about importance of variables?
In case you are interested in a linear model, here's direction that hasn't been mentioned in the comments / other answers:
Let $x \in R^{30}$ be the feature vector, and you want to learn a linear mapp |
42,802 | How to summarize knowledge about importance of variables? | Whenever I approach problem of optimization function that I cannot define, but I can calculate, I use some heuristic method.
As your searched space is big (but not BIG big, 30 features is not a lot), I would go with some genetic algorithm. This could be quite trivial, but representation of your problem may just be bina... | How to summarize knowledge about importance of variables? | Whenever I approach problem of optimization function that I cannot define, but I can calculate, I use some heuristic method.
As your searched space is big (but not BIG big, 30 features is not a lot), | How to summarize knowledge about importance of variables?
Whenever I approach problem of optimization function that I cannot define, but I can calculate, I use some heuristic method.
As your searched space is big (but not BIG big, 30 features is not a lot), I would go with some genetic algorithm. This could be quite tr... | How to summarize knowledge about importance of variables?
Whenever I approach problem of optimization function that I cannot define, but I can calculate, I use some heuristic method.
As your searched space is big (but not BIG big, 30 features is not a lot), |
42,803 | Are these data underdispersed? If so, what mechanisms may explain this? | This kind of underdispersion can happen if the subject needs to calibrate themselves to the task on any given day. This implies that the probability of success is not constant but changes according to a fixed pattern. The following sequence of success probabilities (one for each of the 20 trials, to be used every day... | Are these data underdispersed? If so, what mechanisms may explain this? | This kind of underdispersion can happen if the subject needs to calibrate themselves to the task on any given day. This implies that the probability of success is not constant but changes according t | Are these data underdispersed? If so, what mechanisms may explain this?
This kind of underdispersion can happen if the subject needs to calibrate themselves to the task on any given day. This implies that the probability of success is not constant but changes according to a fixed pattern. The following sequence of su... | Are these data underdispersed? If so, what mechanisms may explain this?
This kind of underdispersion can happen if the subject needs to calibrate themselves to the task on any given day. This implies that the probability of success is not constant but changes according t |
42,804 | Managing 'prefer not to says' in sensitive questionnaires | Answering your specific technical question -- the theory of inference for partially identified distributions has been developed in the works of Charles Manski. In your example, the confidence interval would be (60% minus 1.96 times whatever standard error corresponds to the 60% assumption, 70% plus 1.96 times whatever ... | Managing 'prefer not to says' in sensitive questionnaires | Answering your specific technical question -- the theory of inference for partially identified distributions has been developed in the works of Charles Manski. In your example, the confidence interval | Managing 'prefer not to says' in sensitive questionnaires
Answering your specific technical question -- the theory of inference for partially identified distributions has been developed in the works of Charles Manski. In your example, the confidence interval would be (60% minus 1.96 times whatever standard error corres... | Managing 'prefer not to says' in sensitive questionnaires
Answering your specific technical question -- the theory of inference for partially identified distributions has been developed in the works of Charles Manski. In your example, the confidence interval |
42,805 | Managing 'prefer not to says' in sensitive questionnaires | You can't get there from here, in my view. If there are very few prefer-not-to-say's, you can omit them without much bias. However, if you have lots in that category (say 5% or more), then I think you need a different design. Like ... don't even ask them if they "prefer not to say"!
There are ways of asking sensitive q... | Managing 'prefer not to says' in sensitive questionnaires | You can't get there from here, in my view. If there are very few prefer-not-to-say's, you can omit them without much bias. However, if you have lots in that category (say 5% or more), then I think you | Managing 'prefer not to says' in sensitive questionnaires
You can't get there from here, in my view. If there are very few prefer-not-to-say's, you can omit them without much bias. However, if you have lots in that category (say 5% or more), then I think you need a different design. Like ... don't even ask them if they... | Managing 'prefer not to says' in sensitive questionnaires
You can't get there from here, in my view. If there are very few prefer-not-to-say's, you can omit them without much bias. However, if you have lots in that category (say 5% or more), then I think you |
42,806 | Managing 'prefer not to says' in sensitive questionnaires | (add a statistical answer complementary to my political comment on top)
We have reasonable suspicion that the 'prefer not to says' are not missing at random. We think that the probability of an individual selecting 'prefer not to say' will be higher for individuals who are homosexuals, bisexuals and other(s)
Here one w... | Managing 'prefer not to says' in sensitive questionnaires | (add a statistical answer complementary to my political comment on top)
We have reasonable suspicion that the 'prefer not to says' are not missing at random. We think that the probability of an indivi | Managing 'prefer not to says' in sensitive questionnaires
(add a statistical answer complementary to my political comment on top)
We have reasonable suspicion that the 'prefer not to says' are not missing at random. We think that the probability of an individual selecting 'prefer not to say' will be higher for individu... | Managing 'prefer not to says' in sensitive questionnaires
(add a statistical answer complementary to my political comment on top)
We have reasonable suspicion that the 'prefer not to says' are not missing at random. We think that the probability of an indivi |
42,807 | Feature Selection: Information Gain VS Mutual Information | They are identically the same.
If $X$ is a nominal feature with $k$ different values and $C$ is your target class with $m$ classes.
\begin{align}
\mbox{MI} &= \sum_{i=1}^k\sum_{j=1}^m P(x_i,c_j)\log \frac{P(x_i,c_j)}{P(x_i)P(c_j)}\\
&=-\sum_{i=1}^k\sum_{j=1}^m P(x_i,c_j)\log P(c_j) + \sum_{i=1}^k\sum_{j=1}^m P(c_j|x_i... | Feature Selection: Information Gain VS Mutual Information | They are identically the same.
If $X$ is a nominal feature with $k$ different values and $C$ is your target class with $m$ classes.
\begin{align}
\mbox{MI} &= \sum_{i=1}^k\sum_{j=1}^m P(x_i,c_j)\log \ | Feature Selection: Information Gain VS Mutual Information
They are identically the same.
If $X$ is a nominal feature with $k$ different values and $C$ is your target class with $m$ classes.
\begin{align}
\mbox{MI} &= \sum_{i=1}^k\sum_{j=1}^m P(x_i,c_j)\log \frac{P(x_i,c_j)}{P(x_i)P(c_j)}\\
&=-\sum_{i=1}^k\sum_{j=1}^m P... | Feature Selection: Information Gain VS Mutual Information
They are identically the same.
If $X$ is a nominal feature with $k$ different values and $C$ is your target class with $m$ classes.
\begin{align}
\mbox{MI} &= \sum_{i=1}^k\sum_{j=1}^m P(x_i,c_j)\log \ |
42,808 | In neural networks, how to tell the feature which contributes the most to the output value? | This is more of a statistics question than a specific programming one. If you have your heart set on using neural nets an example using feature selection with Garson's algorithm is here. Below I have provided the code that you can try. Hopefully this can give you something to start with.
But please note this is only... | In neural networks, how to tell the feature which contributes the most to the output value? | This is more of a statistics question than a specific programming one. If you have your heart set on using neural nets an example using feature selection with Garson's algorithm is here. Below I hav | In neural networks, how to tell the feature which contributes the most to the output value?
This is more of a statistics question than a specific programming one. If you have your heart set on using neural nets an example using feature selection with Garson's algorithm is here. Below I have provided the code that you... | In neural networks, how to tell the feature which contributes the most to the output value?
This is more of a statistics question than a specific programming one. If you have your heart set on using neural nets an example using feature selection with Garson's algorithm is here. Below I hav |
42,809 | In neural networks, how to tell the feature which contributes the most to the output value? | caret package offers variable importance for neural network models along with others. You can refer to this here which says:
Neural Networks: The method used here is based on Gevrey et al (2003), which uses combinations of the absolute values of the weights. For classification models, the class-specific importances wi... | In neural networks, how to tell the feature which contributes the most to the output value? | caret package offers variable importance for neural network models along with others. You can refer to this here which says:
Neural Networks: The method used here is based on Gevrey et al (2003), whi | In neural networks, how to tell the feature which contributes the most to the output value?
caret package offers variable importance for neural network models along with others. You can refer to this here which says:
Neural Networks: The method used here is based on Gevrey et al (2003), which uses combinations of the ... | In neural networks, how to tell the feature which contributes the most to the output value?
caret package offers variable importance for neural network models along with others. You can refer to this here which says:
Neural Networks: The method used here is based on Gevrey et al (2003), whi |
42,810 | Comparison between MAD and SD | Let $X$ be a $N(\mu, \sigma^2)$ random variable, and we have a size-$n$ sample of i.i.d. realizations from it. Then for the random variable
$$\tilde X_i= X_i - \bar X_n = \left(1-\frac 1n\right)X_i - \frac1n\sum_{j\neq i}X_j$$
we have that it is also normal, and that
$$E(\tilde X_i) = \frac {n-1}n\mu - \frac {n-1}n\mu ... | Comparison between MAD and SD | Let $X$ be a $N(\mu, \sigma^2)$ random variable, and we have a size-$n$ sample of i.i.d. realizations from it. Then for the random variable
$$\tilde X_i= X_i - \bar X_n = \left(1-\frac 1n\right)X_i - | Comparison between MAD and SD
Let $X$ be a $N(\mu, \sigma^2)$ random variable, and we have a size-$n$ sample of i.i.d. realizations from it. Then for the random variable
$$\tilde X_i= X_i - \bar X_n = \left(1-\frac 1n\right)X_i - \frac1n\sum_{j\neq i}X_j$$
we have that it is also normal, and that
$$E(\tilde X_i) = \fra... | Comparison between MAD and SD
Let $X$ be a $N(\mu, \sigma^2)$ random variable, and we have a size-$n$ sample of i.i.d. realizations from it. Then for the random variable
$$\tilde X_i= X_i - \bar X_n = \left(1-\frac 1n\right)X_i - |
42,811 | Intervention Analysis - Pulse over several periods | Lets say you have a time series data 42 observations. Observation 7 to 13 have step(level shift) intervention and observations 29 to 35 have step intervention of 5 units. The remainder observations are 0. As an example, see below chart for simulated data with actual and actual + error.
If you would like to model this... | Intervention Analysis - Pulse over several periods | Lets say you have a time series data 42 observations. Observation 7 to 13 have step(level shift) intervention and observations 29 to 35 have step intervention of 5 units. The remainder observations ar | Intervention Analysis - Pulse over several periods
Lets say you have a time series data 42 observations. Observation 7 to 13 have step(level shift) intervention and observations 29 to 35 have step intervention of 5 units. The remainder observations are 0. As an example, see below chart for simulated data with actual a... | Intervention Analysis - Pulse over several periods
Lets say you have a time series data 42 observations. Observation 7 to 13 have step(level shift) intervention and observations 29 to 35 have step intervention of 5 units. The remainder observations ar |
42,812 | Quasi maximum likelihood estimation versus pseudo MLE | Quasi-likelihood and Pseudo-likelihood mean different things. If the probability model is possibly misspecified, then the likelihood function is called a quasi-likelihood function (see White 1982 econometrica for example). In the special case where the probability model is correctly specified then the quasi-maximum lik... | Quasi maximum likelihood estimation versus pseudo MLE | Quasi-likelihood and Pseudo-likelihood mean different things. If the probability model is possibly misspecified, then the likelihood function is called a quasi-likelihood function (see White 1982 econ | Quasi maximum likelihood estimation versus pseudo MLE
Quasi-likelihood and Pseudo-likelihood mean different things. If the probability model is possibly misspecified, then the likelihood function is called a quasi-likelihood function (see White 1982 econometrica for example). In the special case where the probability m... | Quasi maximum likelihood estimation versus pseudo MLE
Quasi-likelihood and Pseudo-likelihood mean different things. If the probability model is possibly misspecified, then the likelihood function is called a quasi-likelihood function (see White 1982 econ |
42,813 | Quasi maximum likelihood estimation versus pseudo MLE | The two names denote the same concept. See f.e. Hayashi's Econometrics. Concerning the second part I am not sure, but, normally, it is explained as the estimation if the model is misspecified (generally) - "in ways that do not affect the consistency of the estimator" (Davidson, Mackinnon - Econometric Theory and Method... | Quasi maximum likelihood estimation versus pseudo MLE | The two names denote the same concept. See f.e. Hayashi's Econometrics. Concerning the second part I am not sure, but, normally, it is explained as the estimation if the model is misspecified (general | Quasi maximum likelihood estimation versus pseudo MLE
The two names denote the same concept. See f.e. Hayashi's Econometrics. Concerning the second part I am not sure, but, normally, it is explained as the estimation if the model is misspecified (generally) - "in ways that do not affect the consistency of the estimator... | Quasi maximum likelihood estimation versus pseudo MLE
The two names denote the same concept. See f.e. Hayashi's Econometrics. Concerning the second part I am not sure, but, normally, it is explained as the estimation if the model is misspecified (general |
42,814 | Elastic net package for mixed effects models? | Maybe look at the MMS package. The lassop function seems to only be for lasso analysis, yet is clearly built on the glmnet package and includes some of its parameters such as alpha. If nothing else it is likely a good package to modify for your purposes.
https://cran.r-project.org/web/packages/MMS/MMS.pdf | Elastic net package for mixed effects models? | Maybe look at the MMS package. The lassop function seems to only be for lasso analysis, yet is clearly built on the glmnet package and includes some of its parameters such as alpha. If nothing else it | Elastic net package for mixed effects models?
Maybe look at the MMS package. The lassop function seems to only be for lasso analysis, yet is clearly built on the glmnet package and includes some of its parameters such as alpha. If nothing else it is likely a good package to modify for your purposes.
https://cran.r-proj... | Elastic net package for mixed effects models?
Maybe look at the MMS package. The lassop function seems to only be for lasso analysis, yet is clearly built on the glmnet package and includes some of its parameters such as alpha. If nothing else it |
42,815 | Elastic net package for mixed effects models? | It has been a while, but if anyone is still searching: https://github.com/debbiemarkslab/GELMMnet | Elastic net package for mixed effects models? | It has been a while, but if anyone is still searching: https://github.com/debbiemarkslab/GELMMnet | Elastic net package for mixed effects models?
It has been a while, but if anyone is still searching: https://github.com/debbiemarkslab/GELMMnet | Elastic net package for mixed effects models?
It has been a while, but if anyone is still searching: https://github.com/debbiemarkslab/GELMMnet |
42,816 | What regression model to use when independent variables are percentages to predict % outcome? | Your response is binary and so you probably want to look at something like a binomial GLM for that, such as logistic regression.
Having a group of $k$ predictors that add to 1 (e.g. the $k=3$ body proportion predictors) would imply that at most you can have $k-1$ of them in the model because of the multicollinearity is... | What regression model to use when independent variables are percentages to predict % outcome? | Your response is binary and so you probably want to look at something like a binomial GLM for that, such as logistic regression.
Having a group of $k$ predictors that add to 1 (e.g. the $k=3$ body pro | What regression model to use when independent variables are percentages to predict % outcome?
Your response is binary and so you probably want to look at something like a binomial GLM for that, such as logistic regression.
Having a group of $k$ predictors that add to 1 (e.g. the $k=3$ body proportion predictors) would ... | What regression model to use when independent variables are percentages to predict % outcome?
Your response is binary and so you probably want to look at something like a binomial GLM for that, such as logistic regression.
Having a group of $k$ predictors that add to 1 (e.g. the $k=3$ body pro |
42,817 | What regression model to use when independent variables are percentages to predict % outcome? | I'd also go for logistic regression since you don't mention that you have a time variable specifying the time from inclusion in the study until start of smoking or censoring (end of study); in that case a Cox regression would be better.
I doubt that there is any difference in using percentages as predictors as compared... | What regression model to use when independent variables are percentages to predict % outcome? | I'd also go for logistic regression since you don't mention that you have a time variable specifying the time from inclusion in the study until start of smoking or censoring (end of study); in that ca | What regression model to use when independent variables are percentages to predict % outcome?
I'd also go for logistic regression since you don't mention that you have a time variable specifying the time from inclusion in the study until start of smoking or censoring (end of study); in that case a Cox regression would ... | What regression model to use when independent variables are percentages to predict % outcome?
I'd also go for logistic regression since you don't mention that you have a time variable specifying the time from inclusion in the study until start of smoking or censoring (end of study); in that ca |
42,818 | recommendations to analyze a survey of the entire sample frame with a 20% response rate | Non-response is a frequent problem in surveys, to be sure. Any use of these data would need to include some language about the non-response rate, and the results judged with caution.
As to your question about R usage, the code looks fine. As a detail, instead of using weights in the svydesign part, I would use fpc=~rep... | recommendations to analyze a survey of the entire sample frame with a 20% response rate | Non-response is a frequent problem in surveys, to be sure. Any use of these data would need to include some language about the non-response rate, and the results judged with caution.
As to your questi | recommendations to analyze a survey of the entire sample frame with a 20% response rate
Non-response is a frequent problem in surveys, to be sure. Any use of these data would need to include some language about the non-response rate, and the results judged with caution.
As to your question about R usage, the code looks... | recommendations to analyze a survey of the entire sample frame with a 20% response rate
Non-response is a frequent problem in surveys, to be sure. Any use of these data would need to include some language about the non-response rate, and the results judged with caution.
As to your questi |
42,819 | recommendations to analyze a survey of the entire sample frame with a 20% response rate | What do you mean by "there were no sampling probabilities" ? Did all units have the same inclusion probability (simple random sampling) ? Or is it because you tried to contact every unit in your population (in which case all initial weights are equal to 1) ?
Anyway, your results can clearly be used (even though your re... | recommendations to analyze a survey of the entire sample frame with a 20% response rate | What do you mean by "there were no sampling probabilities" ? Did all units have the same inclusion probability (simple random sampling) ? Or is it because you tried to contact every unit in your popul | recommendations to analyze a survey of the entire sample frame with a 20% response rate
What do you mean by "there were no sampling probabilities" ? Did all units have the same inclusion probability (simple random sampling) ? Or is it because you tried to contact every unit in your population (in which case all initial... | recommendations to analyze a survey of the entire sample frame with a 20% response rate
What do you mean by "there were no sampling probabilities" ? Did all units have the same inclusion probability (simple random sampling) ? Or is it because you tried to contact every unit in your popul |
42,820 | Logistic regression assumptions for a model with many binary independent variables | If all your regressor variables are binary, then the linearity assumption is vacuous! so can be ignored. But you say you have variables like age, which is not binary. Then you can consider using a spline of age instead of age directly, which leads to GAMs (generalized additive models) or the use of regression splines... | Logistic regression assumptions for a model with many binary independent variables | If all your regressor variables are binary, then the linearity assumption is vacuous! so can be ignored. But you say you have variables like age, which is not binary. Then you can consider using a s | Logistic regression assumptions for a model with many binary independent variables
If all your regressor variables are binary, then the linearity assumption is vacuous! so can be ignored. But you say you have variables like age, which is not binary. Then you can consider using a spline of age instead of age directly,... | Logistic regression assumptions for a model with many binary independent variables
If all your regressor variables are binary, then the linearity assumption is vacuous! so can be ignored. But you say you have variables like age, which is not binary. Then you can consider using a s |
42,821 | Logistic regression assumptions for a model with many binary independent variables | Here are some thoughts:
If you only want to identify relevant variables (e.g., you don't want to test pre-existing hypotheses, and you aren't trying to build the optimal model for predicting favorability later), you might try either CART, or combining logistic regression with LASSO penalization.
There is no assump... | Logistic regression assumptions for a model with many binary independent variables | Here are some thoughts:
If you only want to identify relevant variables (e.g., you don't want to test pre-existing hypotheses, and you aren't trying to build the optimal model for predicting favora | Logistic regression assumptions for a model with many binary independent variables
Here are some thoughts:
If you only want to identify relevant variables (e.g., you don't want to test pre-existing hypotheses, and you aren't trying to build the optimal model for predicting favorability later), you might try either C... | Logistic regression assumptions for a model with many binary independent variables
Here are some thoughts:
If you only want to identify relevant variables (e.g., you don't want to test pre-existing hypotheses, and you aren't trying to build the optimal model for predicting favora |
42,822 | Reduce Random Forest model memory size | I used this function to reduce my default caret-output from 137 MB to 3 MB. You can still use this model for prediction with $finalModel
## Clean Model to Save Memory
## http://stats.stackexchange.com/questions/102667/reduce-random-forest-model-memory-size
stripRF <- function(cm) {
cm$finalModel$predicted <- NULL
... | Reduce Random Forest model memory size | I used this function to reduce my default caret-output from 137 MB to 3 MB. You can still use this model for prediction with $finalModel
## Clean Model to Save Memory
## http://stats.stackexchange.co | Reduce Random Forest model memory size
I used this function to reduce my default caret-output from 137 MB to 3 MB. You can still use this model for prediction with $finalModel
## Clean Model to Save Memory
## http://stats.stackexchange.com/questions/102667/reduce-random-forest-model-memory-size
stripRF <- function(cm)... | Reduce Random Forest model memory size
I used this function to reduce my default caret-output from 137 MB to 3 MB. You can still use this model for prediction with $finalModel
## Clean Model to Save Memory
## http://stats.stackexchange.co |
42,823 | Inconsistency in Two stage Maximum Likelihood Estimation | This seems analogous to a profile likelihood approach. If no matter what $\beta$ is, you will always get the same MLE for $(\alpha_1, \alpha_2)$, then you can maximize with respect to $(\alpha_1, \alpha_2)$ first, then maximize with respect to $\beta$ conditional on$(\hat{\alpha_1}, \hat{\alpha_2})$ .
If that's not ... | Inconsistency in Two stage Maximum Likelihood Estimation | This seems analogous to a profile likelihood approach. If no matter what $\beta$ is, you will always get the same MLE for $(\alpha_1, \alpha_2)$, then you can maximize with respect to $(\alpha_1, \al | Inconsistency in Two stage Maximum Likelihood Estimation
This seems analogous to a profile likelihood approach. If no matter what $\beta$ is, you will always get the same MLE for $(\alpha_1, \alpha_2)$, then you can maximize with respect to $(\alpha_1, \alpha_2)$ first, then maximize with respect to $\beta$ conditiona... | Inconsistency in Two stage Maximum Likelihood Estimation
This seems analogous to a profile likelihood approach. If no matter what $\beta$ is, you will always get the same MLE for $(\alpha_1, \alpha_2)$, then you can maximize with respect to $(\alpha_1, \al |
42,824 | Power when using binomial data versus continuous data | A Bernoulli (binary) random variable has one bit of information. This is the lowest amount of information something can have short of no information at all. A continuous response that is integer valued 0-100 has 6 bits (binary digits) of information. Assuming that not all of the 6 bits are pure noise, the non-binary... | Power when using binomial data versus continuous data | A Bernoulli (binary) random variable has one bit of information. This is the lowest amount of information something can have short of no information at all. A continuous response that is integer val | Power when using binomial data versus continuous data
A Bernoulli (binary) random variable has one bit of information. This is the lowest amount of information something can have short of no information at all. A continuous response that is integer valued 0-100 has 6 bits (binary digits) of information. Assuming tha... | Power when using binomial data versus continuous data
A Bernoulli (binary) random variable has one bit of information. This is the lowest amount of information something can have short of no information at all. A continuous response that is integer val |
42,825 | Item reliability or fit in IRT-based adaptive tests? | Item fit is a pretty active area of research in IRT literature. There are specific models (i.e., Rasch models) that have their own specific item fit statistics, such as the infit and outfit (which may relate to your case, within each packet of equally discriminating items), and more general ones that don't rely on the ... | Item reliability or fit in IRT-based adaptive tests? | Item fit is a pretty active area of research in IRT literature. There are specific models (i.e., Rasch models) that have their own specific item fit statistics, such as the infit and outfit (which may | Item reliability or fit in IRT-based adaptive tests?
Item fit is a pretty active area of research in IRT literature. There are specific models (i.e., Rasch models) that have their own specific item fit statistics, such as the infit and outfit (which may relate to your case, within each packet of equally discriminating ... | Item reliability or fit in IRT-based adaptive tests?
Item fit is a pretty active area of research in IRT literature. There are specific models (i.e., Rasch models) that have their own specific item fit statistics, such as the infit and outfit (which may |
42,826 | Guide / tutorial for design & analysis of experiments in retail settings | Great question!
Though I think that you will find most books on the principles of experimental design helpful, here is an HBR piece that serves as a step-by-step guide for business experiments. | Guide / tutorial for design & analysis of experiments in retail settings | Great question!
Though I think that you will find most books on the principles of experimental design helpful, here is an HBR piece that serves as a step-by-step guide for business experiments. | Guide / tutorial for design & analysis of experiments in retail settings
Great question!
Though I think that you will find most books on the principles of experimental design helpful, here is an HBR piece that serves as a step-by-step guide for business experiments. | Guide / tutorial for design & analysis of experiments in retail settings
Great question!
Though I think that you will find most books on the principles of experimental design helpful, here is an HBR piece that serves as a step-by-step guide for business experiments. |
42,827 | How to derive the Projected normal distribution | There is a trick for calculating the pdf of 2-d projected normal, which can be found on pp. 52 of Mardia (1972).
After change of variable $x_1 = r\, \cos\theta$ and $x_2 = r\,\sin\theta$, we have
$f(\theta)= \int_{0}^{\infty}\frac{1}{\sqrt{2\pi}\sigma_1\sigma_2\sqrt{1-\rho^2} }r\,\mbox{exp}\{-\frac{\sigma_2^2(r\cos\th... | How to derive the Projected normal distribution | There is a trick for calculating the pdf of 2-d projected normal, which can be found on pp. 52 of Mardia (1972).
After change of variable $x_1 = r\, \cos\theta$ and $x_2 = r\,\sin\theta$, we have
$f( | How to derive the Projected normal distribution
There is a trick for calculating the pdf of 2-d projected normal, which can be found on pp. 52 of Mardia (1972).
After change of variable $x_1 = r\, \cos\theta$ and $x_2 = r\,\sin\theta$, we have
$f(\theta)= \int_{0}^{\infty}\frac{1}{\sqrt{2\pi}\sigma_1\sigma_2\sqrt{1-\r... | How to derive the Projected normal distribution
There is a trick for calculating the pdf of 2-d projected normal, which can be found on pp. 52 of Mardia (1972).
After change of variable $x_1 = r\, \cos\theta$ and $x_2 = r\,\sin\theta$, we have
$f( |
42,828 | Comparing ISOMAP residual variance to PCA explained variance | The original Isomap paper defined "residual variance" as follows (reference 42):
$$\text{residual variance} = 1 - R^2(\hat D_M, D_y)$$
where $R$ is the Pearson correlation coefficient over all entries of $\hat D_M$ and $D_Y$. $\hat D_M$ is the euclidean distance matrix for PCA and the geodesic distance matrix for Isom... | Comparing ISOMAP residual variance to PCA explained variance | The original Isomap paper defined "residual variance" as follows (reference 42):
$$\text{residual variance} = 1 - R^2(\hat D_M, D_y)$$
where $R$ is the Pearson correlation coefficient over all entrie | Comparing ISOMAP residual variance to PCA explained variance
The original Isomap paper defined "residual variance" as follows (reference 42):
$$\text{residual variance} = 1 - R^2(\hat D_M, D_y)$$
where $R$ is the Pearson correlation coefficient over all entries of $\hat D_M$ and $D_Y$. $\hat D_M$ is the euclidean dist... | Comparing ISOMAP residual variance to PCA explained variance
The original Isomap paper defined "residual variance" as follows (reference 42):
$$\text{residual variance} = 1 - R^2(\hat D_M, D_y)$$
where $R$ is the Pearson correlation coefficient over all entrie |
42,829 | Finding the cluster centers in kernel k-means clustering | I think I found an answer. All you need to do in Kernel K means is to compute
$$
C^{(t+1)}(i) = argmin_k \{K(x_i,x_i) -\frac{2}{N_k}{\Sigma_{l\epsilon C^{t}_k}}K(x_i,x_l) +\frac{1}{N_k^2} {\Sigma_{{l,{l`}}\epsilon C^{t}_k} }K(x_l,x_{l`})\} ...(1)
$$
So this is the only operation that needs to be done. One need not t... | Finding the cluster centers in kernel k-means clustering | I think I found an answer. All you need to do in Kernel K means is to compute
$$
C^{(t+1)}(i) = argmin_k \{K(x_i,x_i) -\frac{2}{N_k}{\Sigma_{l\epsilon C^{t}_k}}K(x_i,x_l) +\frac{1}{N_k^2} {\Sigma_{{l | Finding the cluster centers in kernel k-means clustering
I think I found an answer. All you need to do in Kernel K means is to compute
$$
C^{(t+1)}(i) = argmin_k \{K(x_i,x_i) -\frac{2}{N_k}{\Sigma_{l\epsilon C^{t}_k}}K(x_i,x_l) +\frac{1}{N_k^2} {\Sigma_{{l,{l`}}\epsilon C^{t}_k} }K(x_l,x_{l`})\} ...(1)
$$
So this is... | Finding the cluster centers in kernel k-means clustering
I think I found an answer. All you need to do in Kernel K means is to compute
$$
C^{(t+1)}(i) = argmin_k \{K(x_i,x_i) -\frac{2}{N_k}{\Sigma_{l\epsilon C^{t}_k}}K(x_i,x_l) +\frac{1}{N_k^2} {\Sigma_{{l |
42,830 | Finding the cluster centers in kernel k-means clustering | You can't compute the cluster centers. If we let $\Phi\::\: \mathbb{R}^p \to F$ be the map into the unknown feature space, then the center for a cluster $C_m$ is $$ \mu_m^\Phi = \frac{1}{n_m}\sum_{x_i \in C_m} \Phi(x_i),$$
where $n_m$ is the number of observations in cluster $C_m$. But, we don't know what $\Phi$ is (wh... | Finding the cluster centers in kernel k-means clustering | You can't compute the cluster centers. If we let $\Phi\::\: \mathbb{R}^p \to F$ be the map into the unknown feature space, then the center for a cluster $C_m$ is $$ \mu_m^\Phi = \frac{1}{n_m}\sum_{x_i | Finding the cluster centers in kernel k-means clustering
You can't compute the cluster centers. If we let $\Phi\::\: \mathbb{R}^p \to F$ be the map into the unknown feature space, then the center for a cluster $C_m$ is $$ \mu_m^\Phi = \frac{1}{n_m}\sum_{x_i \in C_m} \Phi(x_i),$$
where $n_m$ is the number of observation... | Finding the cluster centers in kernel k-means clustering
You can't compute the cluster centers. If we let $\Phi\::\: \mathbb{R}^p \to F$ be the map into the unknown feature space, then the center for a cluster $C_m$ is $$ \mu_m^\Phi = \frac{1}{n_m}\sum_{x_i |
42,831 | Can the curse of dimensionality be solved by changing the metric tensor? | Yes indeed, that is the case of the Isomap algorithm and Laplacians eigenmaps, for example. There are quite a few approaches listed in this article and also here. Some of them are implemented in scikit-learn. | Can the curse of dimensionality be solved by changing the metric tensor? | Yes indeed, that is the case of the Isomap algorithm and Laplacians eigenmaps, for example. There are quite a few approaches listed in this article and also here. Some of them are implemented in sciki | Can the curse of dimensionality be solved by changing the metric tensor?
Yes indeed, that is the case of the Isomap algorithm and Laplacians eigenmaps, for example. There are quite a few approaches listed in this article and also here. Some of them are implemented in scikit-learn. | Can the curse of dimensionality be solved by changing the metric tensor?
Yes indeed, that is the case of the Isomap algorithm and Laplacians eigenmaps, for example. There are quite a few approaches listed in this article and also here. Some of them are implemented in sciki |
42,832 | Is the value of $\alpha$ the same for all support vectors (SV) in the dual and what is the reason for it if they do or don't? | The cost function of SVM is:
$$\min_{\alpha,\xi,b} \frac{1}{2}\|\mathbf{w}\|^2 + C\sum_{i=1}^n \xi_i$$
where $\xi$ are the slack variables (0 for hard margin) and
$$
||\mathbf{w}||^2 =\mathbf{w}^T\mathbf{w} = \sum_{i\in \mathcal{S}}\sum_{j\in \mathcal{S}} \alpha_i \alpha_j y_i y_j \kappa(\mathbf{x}_i,\mathbf{x}_j)
$$
A... | Is the value of $\alpha$ the same for all support vectors (SV) in the dual and what is the reason fo | The cost function of SVM is:
$$\min_{\alpha,\xi,b} \frac{1}{2}\|\mathbf{w}\|^2 + C\sum_{i=1}^n \xi_i$$
where $\xi$ are the slack variables (0 for hard margin) and
$$
||\mathbf{w}||^2 =\mathbf{w}^T\mat | Is the value of $\alpha$ the same for all support vectors (SV) in the dual and what is the reason for it if they do or don't?
The cost function of SVM is:
$$\min_{\alpha,\xi,b} \frac{1}{2}\|\mathbf{w}\|^2 + C\sum_{i=1}^n \xi_i$$
where $\xi$ are the slack variables (0 for hard margin) and
$$
||\mathbf{w}||^2 =\mathbf{w}... | Is the value of $\alpha$ the same for all support vectors (SV) in the dual and what is the reason fo
The cost function of SVM is:
$$\min_{\alpha,\xi,b} \frac{1}{2}\|\mathbf{w}\|^2 + C\sum_{i=1}^n \xi_i$$
where $\xi$ are the slack variables (0 for hard margin) and
$$
||\mathbf{w}||^2 =\mathbf{w}^T\mat |
42,833 | Bayesian hurdle model and predictions | Let's call the posterior predictive distribution $\tilde{Y}$.
$$\begin{align}\tilde{Y}_{\pi} &\sim\text{Bernoulli}(\pi)\\
\tilde{Y}_{\lambda} &\sim\text{Poisson}(\lambda)\\
\tilde{Y} &\sim\tilde{Y}_{\pi}*\tilde{Y}_{\lambda}\end{align}$$
You would need to truncate $\tilde{Y}_{\lambda}$ to be non-zero. Since you would th... | Bayesian hurdle model and predictions | Let's call the posterior predictive distribution $\tilde{Y}$.
$$\begin{align}\tilde{Y}_{\pi} &\sim\text{Bernoulli}(\pi)\\
\tilde{Y}_{\lambda} &\sim\text{Poisson}(\lambda)\\
\tilde{Y} &\sim\tilde{Y}_{\ | Bayesian hurdle model and predictions
Let's call the posterior predictive distribution $\tilde{Y}$.
$$\begin{align}\tilde{Y}_{\pi} &\sim\text{Bernoulli}(\pi)\\
\tilde{Y}_{\lambda} &\sim\text{Poisson}(\lambda)\\
\tilde{Y} &\sim\tilde{Y}_{\pi}*\tilde{Y}_{\lambda}\end{align}$$
You would need to truncate $\tilde{Y}_{\lambd... | Bayesian hurdle model and predictions
Let's call the posterior predictive distribution $\tilde{Y}$.
$$\begin{align}\tilde{Y}_{\pi} &\sim\text{Bernoulli}(\pi)\\
\tilde{Y}_{\lambda} &\sim\text{Poisson}(\lambda)\\
\tilde{Y} &\sim\tilde{Y}_{\ |
42,834 | How to generate random points in the volume of a sphere with uniform nearest neighbour distances | I wouldn't expect the distribution of the nearest neighbour distances to be uniform under spatial randomness.
According to Wikipedia (http://en.wikipedia.org/wiki/Complete_spatial_randomness), the distance of the first neighbour in your case has the following distribution:
$P_1(r) = 3\lambda r^2\exp(-\lambda r^3)$
wher... | How to generate random points in the volume of a sphere with uniform nearest neighbour distances | I wouldn't expect the distribution of the nearest neighbour distances to be uniform under spatial randomness.
According to Wikipedia (http://en.wikipedia.org/wiki/Complete_spatial_randomness), the dis | How to generate random points in the volume of a sphere with uniform nearest neighbour distances
I wouldn't expect the distribution of the nearest neighbour distances to be uniform under spatial randomness.
According to Wikipedia (http://en.wikipedia.org/wiki/Complete_spatial_randomness), the distance of the first neig... | How to generate random points in the volume of a sphere with uniform nearest neighbour distances
I wouldn't expect the distribution of the nearest neighbour distances to be uniform under spatial randomness.
According to Wikipedia (http://en.wikipedia.org/wiki/Complete_spatial_randomness), the dis |
42,835 | How to generate random points in the volume of a sphere with uniform nearest neighbour distances | Consider a uniform 1 dimensional $U[0;1]$ distribution.
Probably the simplest distribution we can find, right?
The distribution of distances will not be uniform.
Instead (if I'm not mistaken; might only hold for the central area), it should be a Beta $B(k,n+1-k)$ distribution. If you are talking about the 1 nearest nei... | How to generate random points in the volume of a sphere with uniform nearest neighbour distances | Consider a uniform 1 dimensional $U[0;1]$ distribution.
Probably the simplest distribution we can find, right?
The distribution of distances will not be uniform.
Instead (if I'm not mistaken; might on | How to generate random points in the volume of a sphere with uniform nearest neighbour distances
Consider a uniform 1 dimensional $U[0;1]$ distribution.
Probably the simplest distribution we can find, right?
The distribution of distances will not be uniform.
Instead (if I'm not mistaken; might only hold for the central... | How to generate random points in the volume of a sphere with uniform nearest neighbour distances
Consider a uniform 1 dimensional $U[0;1]$ distribution.
Probably the simplest distribution we can find, right?
The distribution of distances will not be uniform.
Instead (if I'm not mistaken; might on |
42,836 | use of t-test to compare performance of algorithms | I suggest using a paired t-test because the accuracies on different data sets should not be compared directly. Each data set you test on should form a pair in your t test.
Based on your example, you would be doing something like this in R to compare SIFT and SURF:
SIFT <- c(0.90, 0.84, 0.90, 0.45)
SURF <- c(0.84, 0.67,... | use of t-test to compare performance of algorithms | I suggest using a paired t-test because the accuracies on different data sets should not be compared directly. Each data set you test on should form a pair in your t test.
Based on your example, you w | use of t-test to compare performance of algorithms
I suggest using a paired t-test because the accuracies on different data sets should not be compared directly. Each data set you test on should form a pair in your t test.
Based on your example, you would be doing something like this in R to compare SIFT and SURF:
SIFT... | use of t-test to compare performance of algorithms
I suggest using a paired t-test because the accuracies on different data sets should not be compared directly. Each data set you test on should form a pair in your t test.
Based on your example, you w |
42,837 | use of t-test to compare performance of algorithms | The t-test is for comparing 2 groups (or one group to a theoretical value). With 3 groups (tests) you would need ANOVA and since there is blocking (the generalization of pairing) due to the different datasets you would be using randomized block ANOVA or a mixed effects model.
However, these methods depend on approxima... | use of t-test to compare performance of algorithms | The t-test is for comparing 2 groups (or one group to a theoretical value). With 3 groups (tests) you would need ANOVA and since there is blocking (the generalization of pairing) due to the different | use of t-test to compare performance of algorithms
The t-test is for comparing 2 groups (or one group to a theoretical value). With 3 groups (tests) you would need ANOVA and since there is blocking (the generalization of pairing) due to the different datasets you would be using randomized block ANOVA or a mixed effect... | use of t-test to compare performance of algorithms
The t-test is for comparing 2 groups (or one group to a theoretical value). With 3 groups (tests) you would need ANOVA and since there is blocking (the generalization of pairing) due to the different |
42,838 | use of t-test to compare performance of algorithms | Usually you don't summarise since performance of specific algorithm is related to the characteristics of the specific dataset. In literature you see phrases like "Algorithm X won in 5 out of 10 datasets".
However, if numbers are correct, in your case there is a clear winner and that's SIFT: it beats all other algorithm... | use of t-test to compare performance of algorithms | Usually you don't summarise since performance of specific algorithm is related to the characteristics of the specific dataset. In literature you see phrases like "Algorithm X won in 5 out of 10 datase | use of t-test to compare performance of algorithms
Usually you don't summarise since performance of specific algorithm is related to the characteristics of the specific dataset. In literature you see phrases like "Algorithm X won in 5 out of 10 datasets".
However, if numbers are correct, in your case there is a clear w... | use of t-test to compare performance of algorithms
Usually you don't summarise since performance of specific algorithm is related to the characteristics of the specific dataset. In literature you see phrases like "Algorithm X won in 5 out of 10 datase |
42,839 | Evaluating goodness of fit for distributions (e.g. LogNorm, Gamma, ...) with estimated parameters using KS tests (and R) | The paper from Clauset et al. warns (Section 4.2) against small sample sizes (< 100) which are much easier to fit. You may want to consider using the direct comparisons of models.
While the p-value of the KS statistic with estimated parameters is an overestimate, the bootstrapping procedure you described is able to ta... | Evaluating goodness of fit for distributions (e.g. LogNorm, Gamma, ...) with estimated parameters us | The paper from Clauset et al. warns (Section 4.2) against small sample sizes (< 100) which are much easier to fit. You may want to consider using the direct comparisons of models.
While the p-value o | Evaluating goodness of fit for distributions (e.g. LogNorm, Gamma, ...) with estimated parameters using KS tests (and R)
The paper from Clauset et al. warns (Section 4.2) against small sample sizes (< 100) which are much easier to fit. You may want to consider using the direct comparisons of models.
While the p-value ... | Evaluating goodness of fit for distributions (e.g. LogNorm, Gamma, ...) with estimated parameters us
The paper from Clauset et al. warns (Section 4.2) against small sample sizes (< 100) which are much easier to fit. You may want to consider using the direct comparisons of models.
While the p-value o |
42,840 | Enforcing orthogonality of inputs for multiple linear regression | There are plenty of examples of orthogonal designs for continuous predictors in the experimental design literature. A simple one is the design matrix (using centred predictors)
$$\boldsymbol{X}=(\boldsymbol{I},\boldsymbol{x}_1,\boldsymbol{x}_2)=\left(\begin{matrix}
1 & -1 & -1\\
1 & -1 & 0\\
1 & -1 & 1\\
1 & 0 & -1\\
1... | Enforcing orthogonality of inputs for multiple linear regression | There are plenty of examples of orthogonal designs for continuous predictors in the experimental design literature. A simple one is the design matrix (using centred predictors)
$$\boldsymbol{X}=(\bold | Enforcing orthogonality of inputs for multiple linear regression
There are plenty of examples of orthogonal designs for continuous predictors in the experimental design literature. A simple one is the design matrix (using centred predictors)
$$\boldsymbol{X}=(\boldsymbol{I},\boldsymbol{x}_1,\boldsymbol{x}_2)=\left(\beg... | Enforcing orthogonality of inputs for multiple linear regression
There are plenty of examples of orthogonal designs for continuous predictors in the experimental design literature. A simple one is the design matrix (using centred predictors)
$$\boldsymbol{X}=(\bold |
42,841 | Estimating successes while obtaining Bernoulli samples | Given that I've already seen k successes on n samples of a Bernoulli
distribution with unknown parameter p, what is the probability that,
if I keep sampling from the same distribution, after N observations
(say N=100), I'll see at least K successes (say K=10).
A Bayesian answer to this question is:
$$
\Theta\... | Estimating successes while obtaining Bernoulli samples | Given that I've already seen k successes on n samples of a Bernoulli
distribution with unknown parameter p, what is the probability that,
if I keep sampling from the same distribution, after N obs | Estimating successes while obtaining Bernoulli samples
Given that I've already seen k successes on n samples of a Bernoulli
distribution with unknown parameter p, what is the probability that,
if I keep sampling from the same distribution, after N observations
(say N=100), I'll see at least K successes (say K=10)... | Estimating successes while obtaining Bernoulli samples
Given that I've already seen k successes on n samples of a Bernoulli
distribution with unknown parameter p, what is the probability that,
if I keep sampling from the same distribution, after N obs |
42,842 | Estimating successes while obtaining Bernoulli samples | This sounds like the perfect job for Bayesian parameter estimation. So the model you have is:
$$ k \sim \text{Binom}(p,n) $$
And what you want to know is the probability of $p > 0.1$. Using Bayesian parameter estimation there is nothing stopping you from estimating $\text{prob}(p > 0.1)$ at $n=1, n=2, \dots$ and stoppi... | Estimating successes while obtaining Bernoulli samples | This sounds like the perfect job for Bayesian parameter estimation. So the model you have is:
$$ k \sim \text{Binom}(p,n) $$
And what you want to know is the probability of $p > 0.1$. Using Bayesian p | Estimating successes while obtaining Bernoulli samples
This sounds like the perfect job for Bayesian parameter estimation. So the model you have is:
$$ k \sim \text{Binom}(p,n) $$
And what you want to know is the probability of $p > 0.1$. Using Bayesian parameter estimation there is nothing stopping you from estimating... | Estimating successes while obtaining Bernoulli samples
This sounds like the perfect job for Bayesian parameter estimation. So the model you have is:
$$ k \sim \text{Binom}(p,n) $$
And what you want to know is the probability of $p > 0.1$. Using Bayesian p |
42,843 | Ordinal regression: proportional odds assumption | First off, are your two independent variables being adjusted as factors or numerically coded responses and is there an interaction term for the two? The reason I ask is because the test of proportional odds grows very sensitive with small cell counts. For this reason, I often find it justifiable to adjust input variabl... | Ordinal regression: proportional odds assumption | First off, are your two independent variables being adjusted as factors or numerically coded responses and is there an interaction term for the two? The reason I ask is because the test of proportiona | Ordinal regression: proportional odds assumption
First off, are your two independent variables being adjusted as factors or numerically coded responses and is there an interaction term for the two? The reason I ask is because the test of proportional odds grows very sensitive with small cell counts. For this reason, I ... | Ordinal regression: proportional odds assumption
First off, are your two independent variables being adjusted as factors or numerically coded responses and is there an interaction term for the two? The reason I ask is because the test of proportiona |
42,844 | Relation between autocorrelation function and periodogram in time series analysis | The relation between the autocovariance (or autocorrelation) and the spectral density (for which the periodogram is an estimator) is given by the Fourier transform. The two form a so-called Fourier-transform pair meaning the two are time(or space)-domain vs. frequency-domain representations of the same thing. Specifica... | Relation between autocorrelation function and periodogram in time series analysis | The relation between the autocovariance (or autocorrelation) and the spectral density (for which the periodogram is an estimator) is given by the Fourier transform. The two form a so-called Fourier-tr | Relation between autocorrelation function and periodogram in time series analysis
The relation between the autocovariance (or autocorrelation) and the spectral density (for which the periodogram is an estimator) is given by the Fourier transform. The two form a so-called Fourier-transform pair meaning the two are time(... | Relation between autocorrelation function and periodogram in time series analysis
The relation between the autocovariance (or autocorrelation) and the spectral density (for which the periodogram is an estimator) is given by the Fourier transform. The two form a so-called Fourier-tr |
42,845 | Average over two variables: Why do standard error of mean and error propagation differ and what does that mean? | If you take the standard deviation of your two values (first way you suggested), this does not include the error within each measurement, only the error between the two methods (heating vs. cooling). That is why the first way shows surprisingly low standard deviation- it's only incorporating one of the sources of error... | Average over two variables: Why do standard error of mean and error propagation differ and what does | If you take the standard deviation of your two values (first way you suggested), this does not include the error within each measurement, only the error between the two methods (heating vs. cooling). | Average over two variables: Why do standard error of mean and error propagation differ and what does that mean?
If you take the standard deviation of your two values (first way you suggested), this does not include the error within each measurement, only the error between the two methods (heating vs. cooling). That is ... | Average over two variables: Why do standard error of mean and error propagation differ and what does
If you take the standard deviation of your two values (first way you suggested), this does not include the error within each measurement, only the error between the two methods (heating vs. cooling). |
42,846 | What is a "factor" in factor analysis? | The usual factor analysis model is
$$\mathbf{Y} = \mathbf{\mu}+ \mathbf{\Phi}\mathbf{L} + \mathbf{\eta},$$
where $\mathbf{Y}$ represents a collection of $n$ observations of $k$ random variables; i.e. it is a matrix of $n \times k$ size. $\mathbf{\mu} = \mathbf{1}_n^{\prime} (\mu_1, \mu_2, \ldots, \mu_k)$
is also an... | What is a "factor" in factor analysis? | The usual factor analysis model is
$$\mathbf{Y} = \mathbf{\mu}+ \mathbf{\Phi}\mathbf{L} + \mathbf{\eta},$$
where $\mathbf{Y}$ represents a collection of $n$ observations of $k$ random variables; i.e. | What is a "factor" in factor analysis?
The usual factor analysis model is
$$\mathbf{Y} = \mathbf{\mu}+ \mathbf{\Phi}\mathbf{L} + \mathbf{\eta},$$
where $\mathbf{Y}$ represents a collection of $n$ observations of $k$ random variables; i.e. it is a matrix of $n \times k$ size. $\mathbf{\mu} = \mathbf{1}_n^{\prime} (\mu_... | What is a "factor" in factor analysis?
The usual factor analysis model is
$$\mathbf{Y} = \mathbf{\mu}+ \mathbf{\Phi}\mathbf{L} + \mathbf{\eta},$$
where $\mathbf{Y}$ represents a collection of $n$ observations of $k$ random variables; i.e. |
42,847 | What is a "factor" in factor analysis? | Factor is a vector. The set of factors give you a coordinate system, a basis. Factor loadings are sets of coordinates in this basis.
Let's say you have a $T\times n$ matrix $X=x_{ti}$. Imagine that it's a path of particle in n-dimensional space, where $t$ is time, and $i$ is the dimension.
What factor analysis does is... | What is a "factor" in factor analysis? | Factor is a vector. The set of factors give you a coordinate system, a basis. Factor loadings are sets of coordinates in this basis.
Let's say you have a $T\times n$ matrix $X=x_{ti}$. Imagine that it | What is a "factor" in factor analysis?
Factor is a vector. The set of factors give you a coordinate system, a basis. Factor loadings are sets of coordinates in this basis.
Let's say you have a $T\times n$ matrix $X=x_{ti}$. Imagine that it's a path of particle in n-dimensional space, where $t$ is time, and $i$ is the d... | What is a "factor" in factor analysis?
Factor is a vector. The set of factors give you a coordinate system, a basis. Factor loadings are sets of coordinates in this basis.
Let's say you have a $T\times n$ matrix $X=x_{ti}$. Imagine that it |
42,848 | Time series dynamic poisson regression | It looks like a zero-inflated Poisson or negative binomial dynamic model would be adequate. If you are using R, you might want to look at package ZIM. | Time series dynamic poisson regression | It looks like a zero-inflated Poisson or negative binomial dynamic model would be adequate. If you are using R, you might want to look at package ZIM. | Time series dynamic poisson regression
It looks like a zero-inflated Poisson or negative binomial dynamic model would be adequate. If you are using R, you might want to look at package ZIM. | Time series dynamic poisson regression
It looks like a zero-inflated Poisson or negative binomial dynamic model would be adequate. If you are using R, you might want to look at package ZIM. |
42,849 | Degenerate random variable | NO. Let $X$ be any variable and $Y$ independent such that $Y=0$ with probability 1. Then $XY$ is degenerate, but $X$ need not be.
This was already answered in comments:
No. Only one of them needs to be. Let $X$ be zero with probability 1 and let $Y$ be any finite-valued random variable. – Macro
To complete @Macro's ... | Degenerate random variable | NO. Let $X$ be any variable and $Y$ independent such that $Y=0$ with probability 1. Then $XY$ is degenerate, but $X$ need not be.
This was already answered in comments:
No. Only one of them needs to | Degenerate random variable
NO. Let $X$ be any variable and $Y$ independent such that $Y=0$ with probability 1. Then $XY$ is degenerate, but $X$ need not be.
This was already answered in comments:
No. Only one of them needs to be. Let $X$ be zero with probability 1 and let $Y$ be any finite-valued random variable. – M... | Degenerate random variable
NO. Let $X$ be any variable and $Y$ independent such that $Y=0$ with probability 1. Then $XY$ is degenerate, but $X$ need not be.
This was already answered in comments:
No. Only one of them needs to |
42,850 | Create 3 correlated variables with pre-specified correlations [duplicate] | You could do something like this:
library(mvtnorm)
x = 3.3
sig = matrix(c(30,x,x,x,x,1,.7,.7,x,.7,1,.7,x,.7,.7,1),nrow=4)
X = rmvnorm(100,mean=rep(0,4),sigma=sig,method="svd")
round(cor(X),2)
Y = rmvnorm(10000,mean=rep(0,4),sigma=sig,method="svd")
round(cor(Y),2)
Not the structure of the covariance matrix:
> sig
... | Create 3 correlated variables with pre-specified correlations [duplicate] | You could do something like this:
library(mvtnorm)
x = 3.3
sig = matrix(c(30,x,x,x,x,1,.7,.7,x,.7,1,.7,x,.7,.7,1),nrow=4)
X = rmvnorm(100,mean=rep(0,4),sigma=sig,method="svd")
round(cor(X),2)
Y = r | Create 3 correlated variables with pre-specified correlations [duplicate]
You could do something like this:
library(mvtnorm)
x = 3.3
sig = matrix(c(30,x,x,x,x,1,.7,.7,x,.7,1,.7,x,.7,.7,1),nrow=4)
X = rmvnorm(100,mean=rep(0,4),sigma=sig,method="svd")
round(cor(X),2)
Y = rmvnorm(10000,mean=rep(0,4),sigma=sig,method="s... | Create 3 correlated variables with pre-specified correlations [duplicate]
You could do something like this:
library(mvtnorm)
x = 3.3
sig = matrix(c(30,x,x,x,x,1,.7,.7,x,.7,1,.7,x,.7,.7,1),nrow=4)
X = rmvnorm(100,mean=rep(0,4),sigma=sig,method="svd")
round(cor(X),2)
Y = r |
42,851 | Create 3 correlated variables with pre-specified correlations [duplicate] | I don't know R, so here are verbal directions.
Step 1. Let $U$ be the upper Cholesky factor of the correlation matrix.
For the correlation matrix you give, $U$ is
1 .6 .6 .6
0 .8 .425 .425
0 0 .677772 .235145
0 0 0 .635674
Step 2. Let $X$ be an $n \times 4$ matrix in which the first column ... | Create 3 correlated variables with pre-specified correlations [duplicate] | I don't know R, so here are verbal directions.
Step 1. Let $U$ be the upper Cholesky factor of the correlation matrix.
For the correlation matrix you give, $U$ is
1 .6 .6 .6
0 .8 .425 .4 | Create 3 correlated variables with pre-specified correlations [duplicate]
I don't know R, so here are verbal directions.
Step 1. Let $U$ be the upper Cholesky factor of the correlation matrix.
For the correlation matrix you give, $U$ is
1 .6 .6 .6
0 .8 .425 .425
0 0 .677772 .235145
0 0 0 .63... | Create 3 correlated variables with pre-specified correlations [duplicate]
I don't know R, so here are verbal directions.
Step 1. Let $U$ be the upper Cholesky factor of the correlation matrix.
For the correlation matrix you give, $U$ is
1 .6 .6 .6
0 .8 .425 .4 |
42,852 | Is it OK to use correlated variables for cluster analysis? | Correlation can cause problems with many clustering algorithms by giving extra weight on these attributes. For k-means it seems to be a best practise to whiten the data first, for example.
However, there exist correlation clustering algorithms that are meant to process data containing multiple correlations, and cluster... | Is it OK to use correlated variables for cluster analysis? | Correlation can cause problems with many clustering algorithms by giving extra weight on these attributes. For k-means it seems to be a best practise to whiten the data first, for example.
However, th | Is it OK to use correlated variables for cluster analysis?
Correlation can cause problems with many clustering algorithms by giving extra weight on these attributes. For k-means it seems to be a best practise to whiten the data first, for example.
However, there exist correlation clustering algorithms that are meant to... | Is it OK to use correlated variables for cluster analysis?
Correlation can cause problems with many clustering algorithms by giving extra weight on these attributes. For k-means it seems to be a best practise to whiten the data first, for example.
However, th |
42,853 | Kernel regression with monotonicity constraints | Shape restrictions on the kernel regression function can be imposed using various approach.
One general method was developed in Du, Parmeter and Racine (2013). The basic idea is to impose the constraints (here monotonicity) through weights in a generalized kernel estimator in the spirit of Hall and Huang (2001). The pr... | Kernel regression with monotonicity constraints | Shape restrictions on the kernel regression function can be imposed using various approach.
One general method was developed in Du, Parmeter and Racine (2013). The basic idea is to impose the constrai | Kernel regression with monotonicity constraints
Shape restrictions on the kernel regression function can be imposed using various approach.
One general method was developed in Du, Parmeter and Racine (2013). The basic idea is to impose the constraints (here monotonicity) through weights in a generalized kernel estimato... | Kernel regression with monotonicity constraints
Shape restrictions on the kernel regression function can be imposed using various approach.
One general method was developed in Du, Parmeter and Racine (2013). The basic idea is to impose the constrai |
42,854 | How to specify the Wishart distribution scale matrix | The author of rjags has mentioned, that the Wishhart distribution can only be used, when it appears as a conjugate distribution in a strong sense:
http://sourceforge.net/p/mcmc-jags/discussion/610037/thread/eab372de
There is a way around this though (I have to give credit to the authors of the R package bamdit though, ... | How to specify the Wishart distribution scale matrix | The author of rjags has mentioned, that the Wishhart distribution can only be used, when it appears as a conjugate distribution in a strong sense:
http://sourceforge.net/p/mcmc-jags/discussion/610037/ | How to specify the Wishart distribution scale matrix
The author of rjags has mentioned, that the Wishhart distribution can only be used, when it appears as a conjugate distribution in a strong sense:
http://sourceforge.net/p/mcmc-jags/discussion/610037/thread/eab372de
There is a way around this though (I have to give c... | How to specify the Wishart distribution scale matrix
The author of rjags has mentioned, that the Wishhart distribution can only be used, when it appears as a conjugate distribution in a strong sense:
http://sourceforge.net/p/mcmc-jags/discussion/610037/ |
42,855 | How to transform factor scores of a PCA for a regression, in SPSS? | simply put:
$
factorscore = loading_1*X_1+loading_2*X_2+\ldots+loading_k*X_k
$
You may need to standardize your variables beforehand if they do not share the same metric. If you do this with your data, your self-computed factor score should correlate above .9 with SPSS' factor score (at least if you do not have cross-l... | How to transform factor scores of a PCA for a regression, in SPSS? | simply put:
$
factorscore = loading_1*X_1+loading_2*X_2+\ldots+loading_k*X_k
$
You may need to standardize your variables beforehand if they do not share the same metric. If you do this with your data | How to transform factor scores of a PCA for a regression, in SPSS?
simply put:
$
factorscore = loading_1*X_1+loading_2*X_2+\ldots+loading_k*X_k
$
You may need to standardize your variables beforehand if they do not share the same metric. If you do this with your data, your self-computed factor score should correlate ab... | How to transform factor scores of a PCA for a regression, in SPSS?
simply put:
$
factorscore = loading_1*X_1+loading_2*X_2+\ldots+loading_k*X_k
$
You may need to standardize your variables beforehand if they do not share the same metric. If you do this with your data |
42,856 | How to transform factor scores of a PCA for a regression, in SPSS? | i think this will help:
https://pdfs.semanticscholar.org/f7d6/bdbd63f784ef9badb087e9235f62fb74a5e9.pdf
Me, myself was troubled on the link between MLR and PCA or FA and it was so hard to find an answer on this very hot issue: it gives FA or PCA a new whole perspective and added utility.
However, having (hopefully) ans... | How to transform factor scores of a PCA for a regression, in SPSS? | i think this will help:
https://pdfs.semanticscholar.org/f7d6/bdbd63f784ef9badb087e9235f62fb74a5e9.pdf
Me, myself was troubled on the link between MLR and PCA or FA and it was so hard to find an answe | How to transform factor scores of a PCA for a regression, in SPSS?
i think this will help:
https://pdfs.semanticscholar.org/f7d6/bdbd63f784ef9badb087e9235f62fb74a5e9.pdf
Me, myself was troubled on the link between MLR and PCA or FA and it was so hard to find an answer on this very hot issue: it gives FA or PCA a new wh... | How to transform factor scores of a PCA for a regression, in SPSS?
i think this will help:
https://pdfs.semanticscholar.org/f7d6/bdbd63f784ef9badb087e9235f62fb74a5e9.pdf
Me, myself was troubled on the link between MLR and PCA or FA and it was so hard to find an answe |
42,857 | How to test the effect of variable on spatial distribution of points in R | Moran's I is able to test if spatial correlation is present in data. In other words, it can answer three kinds of hypotheses:
1:
Are points with similar values (birds with similar personality) close to each other?
In this case is p-value<0.05 and Moran.I$observed will be
positive number
2:
Are points with similar v... | How to test the effect of variable on spatial distribution of points in R | Moran's I is able to test if spatial correlation is present in data. In other words, it can answer three kinds of hypotheses:
1:
Are points with similar values (birds with similar personality) close t | How to test the effect of variable on spatial distribution of points in R
Moran's I is able to test if spatial correlation is present in data. In other words, it can answer three kinds of hypotheses:
1:
Are points with similar values (birds with similar personality) close to each other?
In this case is p-value<0.05 an... | How to test the effect of variable on spatial distribution of points in R
Moran's I is able to test if spatial correlation is present in data. In other words, it can answer three kinds of hypotheses:
1:
Are points with similar values (birds with similar personality) close t |
42,858 | Distribution of deviances from logistic regression | If your individual binomial denominators $n_i$ are sufficiently large, then the deviance residuals are approximately normally distributed. This is the basis of the common assumption that the residual deviance $D$ has a chi-square distribution with df equal to the residual df of the model. Basically, the theory underlyi... | Distribution of deviances from logistic regression | If your individual binomial denominators $n_i$ are sufficiently large, then the deviance residuals are approximately normally distributed. This is the basis of the common assumption that the residual | Distribution of deviances from logistic regression
If your individual binomial denominators $n_i$ are sufficiently large, then the deviance residuals are approximately normally distributed. This is the basis of the common assumption that the residual deviance $D$ has a chi-square distribution with df equal to the resid... | Distribution of deviances from logistic regression
If your individual binomial denominators $n_i$ are sufficiently large, then the deviance residuals are approximately normally distributed. This is the basis of the common assumption that the residual |
42,859 | Can we calculate the standard error of prediction just based on simple linear regression output? | The question is to calculate the following statistic from the above regression output:
$$s.e.(\hat\mu|x_j)=\hat\sigma\sqrt{1/n+(x_j-\bar{x})^2/\Sigma{(x_i-\bar{x})^2}}.$$
The answer is inspired by @whuber:
get $\hat\sigma$ from $\hat\sigma^2=SS_{Residual}/(n-p-1)$,
where $p=1$;
$n$ and $x_j$ are known;
obtain $\bar{x}... | Can we calculate the standard error of prediction just based on simple linear regression output? | The question is to calculate the following statistic from the above regression output:
$$s.e.(\hat\mu|x_j)=\hat\sigma\sqrt{1/n+(x_j-\bar{x})^2/\Sigma{(x_i-\bar{x})^2}}.$$
The answer is inspired by @wh | Can we calculate the standard error of prediction just based on simple linear regression output?
The question is to calculate the following statistic from the above regression output:
$$s.e.(\hat\mu|x_j)=\hat\sigma\sqrt{1/n+(x_j-\bar{x})^2/\Sigma{(x_i-\bar{x})^2}}.$$
The answer is inspired by @whuber:
get $\hat\sigma$... | Can we calculate the standard error of prediction just based on simple linear regression output?
The question is to calculate the following statistic from the above regression output:
$$s.e.(\hat\mu|x_j)=\hat\sigma\sqrt{1/n+(x_j-\bar{x})^2/\Sigma{(x_i-\bar{x})^2}}.$$
The answer is inspired by @wh |
42,860 | Can we calculate the standard error of prediction just based on simple linear regression output? | For simplicity, we are working with the following model:
$$y=\beta_0 + \beta_1x + \varepsilon,$$
where $\varepsilon\sim N(0, \sigma^2)$.
Now suppose that for $x=5$, we would like to predict $E(y|x=5) = \beta_0 + 5\beta_1$, denoted by $pre$.
We note that $pre$ is just a value if we know $\beta_0$ and $\beta_1$. However,... | Can we calculate the standard error of prediction just based on simple linear regression output? | For simplicity, we are working with the following model:
$$y=\beta_0 + \beta_1x + \varepsilon,$$
where $\varepsilon\sim N(0, \sigma^2)$.
Now suppose that for $x=5$, we would like to predict $E(y|x=5) | Can we calculate the standard error of prediction just based on simple linear regression output?
For simplicity, we are working with the following model:
$$y=\beta_0 + \beta_1x + \varepsilon,$$
where $\varepsilon\sim N(0, \sigma^2)$.
Now suppose that for $x=5$, we would like to predict $E(y|x=5) = \beta_0 + 5\beta_1$, ... | Can we calculate the standard error of prediction just based on simple linear regression output?
For simplicity, we are working with the following model:
$$y=\beta_0 + \beta_1x + \varepsilon,$$
where $\varepsilon\sim N(0, \sigma^2)$.
Now suppose that for $x=5$, we would like to predict $E(y|x=5) |
42,861 | Can we calculate the standard error of prediction just based on simple linear regression output? | See Section 6-4a of Wooldridge (2020), Introductory Econometrics: A Modern Approach, 7ed, Cengage.
Variance of prediction error
It's not $\sigma^2 [ 1/n + (x_j-\bar{x})^2 / \sum_i (x_i - \bar{x})^2 ]$.
For the correct expression, let me use vector notations. The model is $y=x\beta + u$, where $x=(1,pop)$. Let $x_0 = (1... | Can we calculate the standard error of prediction just based on simple linear regression output? | See Section 6-4a of Wooldridge (2020), Introductory Econometrics: A Modern Approach, 7ed, Cengage.
Variance of prediction error
It's not $\sigma^2 [ 1/n + (x_j-\bar{x})^2 / \sum_i (x_i - \bar{x})^2 ]$ | Can we calculate the standard error of prediction just based on simple linear regression output?
See Section 6-4a of Wooldridge (2020), Introductory Econometrics: A Modern Approach, 7ed, Cengage.
Variance of prediction error
It's not $\sigma^2 [ 1/n + (x_j-\bar{x})^2 / \sum_i (x_i - \bar{x})^2 ]$.
For the correct expre... | Can we calculate the standard error of prediction just based on simple linear regression output?
See Section 6-4a of Wooldridge (2020), Introductory Econometrics: A Modern Approach, 7ed, Cengage.
Variance of prediction error
It's not $\sigma^2 [ 1/n + (x_j-\bar{x})^2 / \sum_i (x_i - \bar{x})^2 ]$ |
42,862 | Post hoc tests for robust mixed design ANOVA using R | The robust mixed-design ANOVA function is now called rmanova instead tsplit and unfortunately the WRS2 package does not yet contain a corresponding post-hoc test for it. (mcp2atm is solely for t2way). In case you need to conduct a post-hoc test, you must use the original WRS package for now.
This package contains the f... | Post hoc tests for robust mixed design ANOVA using R | The robust mixed-design ANOVA function is now called rmanova instead tsplit and unfortunately the WRS2 package does not yet contain a corresponding post-hoc test for it. (mcp2atm is solely for t2way). | Post hoc tests for robust mixed design ANOVA using R
The robust mixed-design ANOVA function is now called rmanova instead tsplit and unfortunately the WRS2 package does not yet contain a corresponding post-hoc test for it. (mcp2atm is solely for t2way). In case you need to conduct a post-hoc test, you must use the orig... | Post hoc tests for robust mixed design ANOVA using R
The robust mixed-design ANOVA function is now called rmanova instead tsplit and unfortunately the WRS2 package does not yet contain a corresponding post-hoc test for it. (mcp2atm is solely for t2way). |
42,863 | Post hoc tests for robust mixed design ANOVA using R | You can use the WRS2 package and run the following posthoc tests: sppbb (within effects p), sppba (between effects), and sppbi (interaction effects).
You can read more about it here: https://link.springer.com/article/10.3758/s13428-019-01246-w | Post hoc tests for robust mixed design ANOVA using R | You can use the WRS2 package and run the following posthoc tests: sppbb (within effects p), sppba (between effects), and sppbi (interaction effects).
You can read more about it here: https://link.spri | Post hoc tests for robust mixed design ANOVA using R
You can use the WRS2 package and run the following posthoc tests: sppbb (within effects p), sppba (between effects), and sppbi (interaction effects).
You can read more about it here: https://link.springer.com/article/10.3758/s13428-019-01246-w | Post hoc tests for robust mixed design ANOVA using R
You can use the WRS2 package and run the following posthoc tests: sppbb (within effects p), sppba (between effects), and sppbi (interaction effects).
You can read more about it here: https://link.spri |
42,864 | Appropriate regression model when dependent variable is between 0 and 1? | Do you have any values of the response that are exactly 0 or 1? (those will cause problems with a logit transform)
Have you tried plotting your data? What exploratory techniques have you used? What have other researchers in the area done?
You could try simulating some data that fits with a logit transform or a beta r... | Appropriate regression model when dependent variable is between 0 and 1? | Do you have any values of the response that are exactly 0 or 1? (those will cause problems with a logit transform)
Have you tried plotting your data? What exploratory techniques have you used? What | Appropriate regression model when dependent variable is between 0 and 1?
Do you have any values of the response that are exactly 0 or 1? (those will cause problems with a logit transform)
Have you tried plotting your data? What exploratory techniques have you used? What have other researchers in the area done?
You co... | Appropriate regression model when dependent variable is between 0 and 1?
Do you have any values of the response that are exactly 0 or 1? (those will cause problems with a logit transform)
Have you tried plotting your data? What exploratory techniques have you used? What |
42,865 | Appropriate regression model when dependent variable is between 0 and 1? | You know $k$ but you used dependent variable $1/k$. Do not divide, but use values of $k$ as dependent variable. As you say $k$ is number of classes, so you should see the regression with categorical dependent variable. For reference you should look
here
and I think you should avoid $1/k$ if you used any other regressi... | Appropriate regression model when dependent variable is between 0 and 1? | You know $k$ but you used dependent variable $1/k$. Do not divide, but use values of $k$ as dependent variable. As you say $k$ is number of classes, so you should see the regression with categorical d | Appropriate regression model when dependent variable is between 0 and 1?
You know $k$ but you used dependent variable $1/k$. Do not divide, but use values of $k$ as dependent variable. As you say $k$ is number of classes, so you should see the regression with categorical dependent variable. For reference you should loo... | Appropriate regression model when dependent variable is between 0 and 1?
You know $k$ but you used dependent variable $1/k$. Do not divide, but use values of $k$ as dependent variable. As you say $k$ is number of classes, so you should see the regression with categorical d |
42,866 | Random Forest and cluster-level bootstrapping | Your question seems to be addressed in the paper "An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF++" available here.
Those authors addressed the question with "subject-level bootstrapping". They wrote their own software which I have not used (so I... | Random Forest and cluster-level bootstrapping | Your question seems to be addressed in the paper "An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF++" available here.
Those auth | Random Forest and cluster-level bootstrapping
Your question seems to be addressed in the paper "An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF++" available here.
Those authors addressed the question with "subject-level bootstrapping". They wrote ... | Random Forest and cluster-level bootstrapping
Your question seems to be addressed in the paper "An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF++" available here.
Those auth |
42,867 | Random Forest and cluster-level bootstrapping | Have you looked into using the strata and sampsize parameters from the R package 'random forest'? You could indicate household in the strata parameter.
This is copied from the R documentation on the randomForest package:
strata: A (factor) variable that is used for stratified sampling.
sampsize: Size(s) of sample to d... | Random Forest and cluster-level bootstrapping | Have you looked into using the strata and sampsize parameters from the R package 'random forest'? You could indicate household in the strata parameter.
This is copied from the R documentation on the | Random Forest and cluster-level bootstrapping
Have you looked into using the strata and sampsize parameters from the R package 'random forest'? You could indicate household in the strata parameter.
This is copied from the R documentation on the randomForest package:
strata: A (factor) variable that is used for stratif... | Random Forest and cluster-level bootstrapping
Have you looked into using the strata and sampsize parameters from the R package 'random forest'? You could indicate household in the strata parameter.
This is copied from the R documentation on the |
42,868 | Are crossed factorial design and full factorial design the same concept? | (Crossed) factorial design is not always equal to Full factorial design.
In this context, the word "crossed" is a synonym of "factorial" (see here). Both indicates the presence of interactions between factors.
A factorial design can be either:
Full (all possible interactions) or,
Fractional (without all interaction... | Are crossed factorial design and full factorial design the same concept? | (Crossed) factorial design is not always equal to Full factorial design.
In this context, the word "crossed" is a synonym of "factorial" (see here). Both indicates the presence of interactions between | Are crossed factorial design and full factorial design the same concept?
(Crossed) factorial design is not always equal to Full factorial design.
In this context, the word "crossed" is a synonym of "factorial" (see here). Both indicates the presence of interactions between factors.
A factorial design can be either:
... | Are crossed factorial design and full factorial design the same concept?
(Crossed) factorial design is not always equal to Full factorial design.
In this context, the word "crossed" is a synonym of "factorial" (see here). Both indicates the presence of interactions between |
42,869 | K-means as limit of Soft K-means algorithm | When I look at the case where we have a single point and two clusters, it seems to me that both clusters in soft k-means will eventually be centered around the single point.
In the soft k-means, there is no such thing as an "un-assigned" cluster, so each cluster that would've been unassigned in the hard k-means will c... | K-means as limit of Soft K-means algorithm | When I look at the case where we have a single point and two clusters, it seems to me that both clusters in soft k-means will eventually be centered around the single point.
In the soft k-means, ther | K-means as limit of Soft K-means algorithm
When I look at the case where we have a single point and two clusters, it seems to me that both clusters in soft k-means will eventually be centered around the single point.
In the soft k-means, there is no such thing as an "un-assigned" cluster, so each cluster that would've... | K-means as limit of Soft K-means algorithm
When I look at the case where we have a single point and two clusters, it seems to me that both clusters in soft k-means will eventually be centered around the single point.
In the soft k-means, ther |
42,870 | K-means as limit of Soft K-means algorithm | Try to find a probabilistic interpretation for each $r$. Then try to express the new position as an expectation over the r's. | K-means as limit of Soft K-means algorithm | Try to find a probabilistic interpretation for each $r$. Then try to express the new position as an expectation over the r's. | K-means as limit of Soft K-means algorithm
Try to find a probabilistic interpretation for each $r$. Then try to express the new position as an expectation over the r's. | K-means as limit of Soft K-means algorithm
Try to find a probabilistic interpretation for each $r$. Then try to express the new position as an expectation over the r's. |
42,871 | (Why) should bootstrap sampling distribution for logistic regression slope be conditional on $S=\sum Y_j$? | Davison & Hinkley, I now believe, would argue that, for this problem, their proposed method is preferable.
Example 4.1 comes near the start of a chapter in which the authors first develop a big-picture overview of several approaches to hypothesis testing and only then introduce bootstrap hypothesis tests. In discussing... | (Why) should bootstrap sampling distribution for logistic regression slope be conditional on $S=\sum | Davison & Hinkley, I now believe, would argue that, for this problem, their proposed method is preferable.
Example 4.1 comes near the start of a chapter in which the authors first develop a big-pictur | (Why) should bootstrap sampling distribution for logistic regression slope be conditional on $S=\sum Y_j$?
Davison & Hinkley, I now believe, would argue that, for this problem, their proposed method is preferable.
Example 4.1 comes near the start of a chapter in which the authors first develop a big-picture overview of... | (Why) should bootstrap sampling distribution for logistic regression slope be conditional on $S=\sum
Davison & Hinkley, I now believe, would argue that, for this problem, their proposed method is preferable.
Example 4.1 comes near the start of a chapter in which the authors first develop a big-pictur |
42,872 | How to Test the Difference between 2 Sets of Pearson Correlations? | Simply do a t-test of the transformed correlations, exactly as you would test any two sets of data to compare their means. The test technically is a comparison of the mean transformed correlations, but for most purposes that's not a problem. (How meaningful would an arithmetic mean of correlations be in the first pla... | How to Test the Difference between 2 Sets of Pearson Correlations? | Simply do a t-test of the transformed correlations, exactly as you would test any two sets of data to compare their means. The test technically is a comparison of the mean transformed correlations, b | How to Test the Difference between 2 Sets of Pearson Correlations?
Simply do a t-test of the transformed correlations, exactly as you would test any two sets of data to compare their means. The test technically is a comparison of the mean transformed correlations, but for most purposes that's not a problem. (How mean... | How to Test the Difference between 2 Sets of Pearson Correlations?
Simply do a t-test of the transformed correlations, exactly as you would test any two sets of data to compare their means. The test technically is a comparison of the mean transformed correlations, b |
42,873 | How to Test the Difference between 2 Sets of Pearson Correlations? | The problem with using a 2 sample t-test is, presumably, that the correlations are not normally distributed. So, you can use a non-parametric test such as Wilcoxon. Or you could do a permutation test. | How to Test the Difference between 2 Sets of Pearson Correlations? | The problem with using a 2 sample t-test is, presumably, that the correlations are not normally distributed. So, you can use a non-parametric test such as Wilcoxon. Or you could do a permutation test. | How to Test the Difference between 2 Sets of Pearson Correlations?
The problem with using a 2 sample t-test is, presumably, that the correlations are not normally distributed. So, you can use a non-parametric test such as Wilcoxon. Or you could do a permutation test. | How to Test the Difference between 2 Sets of Pearson Correlations?
The problem with using a 2 sample t-test is, presumably, that the correlations are not normally distributed. So, you can use a non-parametric test such as Wilcoxon. Or you could do a permutation test. |
42,874 | Libsvm one-class svm: how to consider all data to be in-class | If you set the nu parameter to very small (-n 0.001) and set the gamma to small as well (-g 0.001) you will get almost all your training data being in your class. | Libsvm one-class svm: how to consider all data to be in-class | If you set the nu parameter to very small (-n 0.001) and set the gamma to small as well (-g 0.001) you will get almost all your training data being in your class. | Libsvm one-class svm: how to consider all data to be in-class
If you set the nu parameter to very small (-n 0.001) and set the gamma to small as well (-g 0.001) you will get almost all your training data being in your class. | Libsvm one-class svm: how to consider all data to be in-class
If you set the nu parameter to very small (-n 0.001) and set the gamma to small as well (-g 0.001) you will get almost all your training data being in your class. |
42,875 | Libsvm one-class svm: how to consider all data to be in-class | I tried your code. For one-class-SVM, the penalty determined by a parameter nu. The default value is 0.5. That is what gives you this plot. If you set it to be 1, all the points are classified as one class. Setting C does not affect the output because C is not relevant to one-class-SVM. | Libsvm one-class svm: how to consider all data to be in-class | I tried your code. For one-class-SVM, the penalty determined by a parameter nu. The default value is 0.5. That is what gives you this plot. If you set it to be 1, all the points are classified as one | Libsvm one-class svm: how to consider all data to be in-class
I tried your code. For one-class-SVM, the penalty determined by a parameter nu. The default value is 0.5. That is what gives you this plot. If you set it to be 1, all the points are classified as one class. Setting C does not affect the output because C is n... | Libsvm one-class svm: how to consider all data to be in-class
I tried your code. For one-class-SVM, the penalty determined by a parameter nu. The default value is 0.5. That is what gives you this plot. If you set it to be 1, all the points are classified as one |
42,876 | Libsvm one-class svm: how to consider all data to be in-class | I know it's a bit late but anyway my contribution to your question is this:
You are using a gaussian kernel to train your one-class svm. This may mislead you for making deductions about the support vectors presence. If you just modify your example and use a linear one-class svm (always better explained in linear case) ... | Libsvm one-class svm: how to consider all data to be in-class | I know it's a bit late but anyway my contribution to your question is this:
You are using a gaussian kernel to train your one-class svm. This may mislead you for making deductions about the support ve | Libsvm one-class svm: how to consider all data to be in-class
I know it's a bit late but anyway my contribution to your question is this:
You are using a gaussian kernel to train your one-class svm. This may mislead you for making deductions about the support vectors presence. If you just modify your example and use a ... | Libsvm one-class svm: how to consider all data to be in-class
I know it's a bit late but anyway my contribution to your question is this:
You are using a gaussian kernel to train your one-class svm. This may mislead you for making deductions about the support ve |
42,877 | Libsvm one-class svm: how to consider all data to be in-class | I changed the colors:
green -> training set,
red-> SV,
blue->in class data
Also I added the nu value into svmtrain parameters
model = svmtrain(labels, data, '-s 2 -t 2 -nu 0.1');
optimization finished, #iter = 484
obj = 723.098765, rho = 15.666491
nSV = 112, nBSV = 88
Accuracy = 90% (900/1000) (classification) | Libsvm one-class svm: how to consider all data to be in-class | I changed the colors:
green -> training set,
red-> SV,
blue->in class data
Also I added the nu value into svmtrain parameters
model = svmtrain(labels, data, '-s 2 -t 2 -nu 0.1');
optimization finis | Libsvm one-class svm: how to consider all data to be in-class
I changed the colors:
green -> training set,
red-> SV,
blue->in class data
Also I added the nu value into svmtrain parameters
model = svmtrain(labels, data, '-s 2 -t 2 -nu 0.1');
optimization finished, #iter = 484
obj = 723.098765, rho = 15.666491
nSV = 1... | Libsvm one-class svm: how to consider all data to be in-class
I changed the colors:
green -> training set,
red-> SV,
blue->in class data
Also I added the nu value into svmtrain parameters
model = svmtrain(labels, data, '-s 2 -t 2 -nu 0.1');
optimization finis |
42,878 | Should my test set be balanced or imbalanced? | I would say neither of the options you suggested: use all the data you have and try to ensure the distributions of training and test set match. This will give you the most realistic assessment of the model's performance. If your classification algorithm has trouble dealing with imbalanced data, use something else.
So i... | Should my test set be balanced or imbalanced? | I would say neither of the options you suggested: use all the data you have and try to ensure the distributions of training and test set match. This will give you the most realistic assessment of the | Should my test set be balanced or imbalanced?
I would say neither of the options you suggested: use all the data you have and try to ensure the distributions of training and test set match. This will give you the most realistic assessment of the model's performance. If your classification algorithm has trouble dealing ... | Should my test set be balanced or imbalanced?
I would say neither of the options you suggested: use all the data you have and try to ensure the distributions of training and test set match. This will give you the most realistic assessment of the |
42,879 | Confidence intervals for glmer() from lme4 | the code then calculates the confidence intervals it says prediction +/- 2*sqrt(pvar1). Shouldn't it be the t.crit value or 1.96 for a normal distribution?
This is correct. 1.96 is a more accurate critical value, however, statistical inference in mixed models is plagued with problems due to the presence of the random ... | Confidence intervals for glmer() from lme4 | the code then calculates the confidence intervals it says prediction +/- 2*sqrt(pvar1). Shouldn't it be the t.crit value or 1.96 for a normal distribution?
This is correct. 1.96 is a more accurate cr | Confidence intervals for glmer() from lme4
the code then calculates the confidence intervals it says prediction +/- 2*sqrt(pvar1). Shouldn't it be the t.crit value or 1.96 for a normal distribution?
This is correct. 1.96 is a more accurate critical value, however, statistical inference in mixed models is plagued with ... | Confidence intervals for glmer() from lme4
the code then calculates the confidence intervals it says prediction +/- 2*sqrt(pvar1). Shouldn't it be the t.crit value or 1.96 for a normal distribution?
This is correct. 1.96 is a more accurate cr |
42,880 | Ratios of means - statistical comparison test using Fieller's theorem? | Although the general solution may be difficult, for this situation the solution is simple if analyses are performed in the log scale. I would argue that working in the log scale would make sense for this type of data even if ratios weren't being compared.
The experiments and desired comparisons described only make sens... | Ratios of means - statistical comparison test using Fieller's theorem? | Although the general solution may be difficult, for this situation the solution is simple if analyses are performed in the log scale. I would argue that working in the log scale would make sense for t | Ratios of means - statistical comparison test using Fieller's theorem?
Although the general solution may be difficult, for this situation the solution is simple if analyses are performed in the log scale. I would argue that working in the log scale would make sense for this type of data even if ratios weren't being com... | Ratios of means - statistical comparison test using Fieller's theorem?
Although the general solution may be difficult, for this situation the solution is simple if analyses are performed in the log scale. I would argue that working in the log scale would make sense for t |
42,881 | Are the posteriors "different"? How does one discuss the result? | I think the most appropriate summary for this application is just to simply state that the years were 15% different, ± some uncertainty. Here's why: first, the concept of statistical significance per se doesn't fit naturally into the Bayesian framework; second, in this application it's not really plausible that the par... | Are the posteriors "different"? How does one discuss the result? | I think the most appropriate summary for this application is just to simply state that the years were 15% different, ± some uncertainty. Here's why: first, the concept of statistical significance per | Are the posteriors "different"? How does one discuss the result?
I think the most appropriate summary for this application is just to simply state that the years were 15% different, ± some uncertainty. Here's why: first, the concept of statistical significance per se doesn't fit naturally into the Bayesian framework; s... | Are the posteriors "different"? How does one discuss the result?
I think the most appropriate summary for this application is just to simply state that the years were 15% different, ± some uncertainty. Here's why: first, the concept of statistical significance per |
42,882 | Variations in time series | You asked for a general explanation of the concept. Your comments about the current status of forecasting at three levels of aggregation is dead on ! My answer may not precisely deal with some of your specific interests as you have focused on some distractions but I thought that I would share the follwing with you. I ... | Variations in time series | You asked for a general explanation of the concept. Your comments about the current status of forecasting at three levels of aggregation is dead on ! My answer may not precisely deal with some of you | Variations in time series
You asked for a general explanation of the concept. Your comments about the current status of forecasting at three levels of aggregation is dead on ! My answer may not precisely deal with some of your specific interests as you have focused on some distractions but I thought that I would share... | Variations in time series
You asked for a general explanation of the concept. Your comments about the current status of forecasting at three levels of aggregation is dead on ! My answer may not precisely deal with some of you |
42,883 | Variations in time series | It will depend on your research question.
I'm interested that there is no answer to this question so far; does it mean there is no standard solution? Seasonal adjustment seems to be extremely ad hoc...
If the company you are looking at is focused on "working days" (eg they are a firm that only produces on those days) ... | Variations in time series | It will depend on your research question.
I'm interested that there is no answer to this question so far; does it mean there is no standard solution? Seasonal adjustment seems to be extremely ad hoc. | Variations in time series
It will depend on your research question.
I'm interested that there is no answer to this question so far; does it mean there is no standard solution? Seasonal adjustment seems to be extremely ad hoc...
If the company you are looking at is focused on "working days" (eg they are a firm that onl... | Variations in time series
It will depend on your research question.
I'm interested that there is no answer to this question so far; does it mean there is no standard solution? Seasonal adjustment seems to be extremely ad hoc. |
42,884 | equivalence testing for a chi-squared test | An appropriate test statistic used to measure the degree of association between binomial paired variables is the log odds ratio.
The odds ratio expresses how much more likely there is to be a success in one case given a success in the other. for more info on odds ratios see Grimes & Schulz 2008
the important thing is t... | equivalence testing for a chi-squared test | An appropriate test statistic used to measure the degree of association between binomial paired variables is the log odds ratio.
The odds ratio expresses how much more likely there is to be a success | equivalence testing for a chi-squared test
An appropriate test statistic used to measure the degree of association between binomial paired variables is the log odds ratio.
The odds ratio expresses how much more likely there is to be a success in one case given a success in the other. for more info on odds ratios see Gr... | equivalence testing for a chi-squared test
An appropriate test statistic used to measure the degree of association between binomial paired variables is the log odds ratio.
The odds ratio expresses how much more likely there is to be a success |
42,885 | Time series prediction with non-constant sampling interval | Broadly speaking, I can think of three approaches:
Stay in your native space: Design a meaningful distance function $d(S_1, S_2)$ between samples. For instance, you could do something like summing $t_1 * t_2$ over every t in $S_1$ and $S_2$ and weighting by the distance between the two. Once you have this function, yo... | Time series prediction with non-constant sampling interval | Broadly speaking, I can think of three approaches:
Stay in your native space: Design a meaningful distance function $d(S_1, S_2)$ between samples. For instance, you could do something like summing $t | Time series prediction with non-constant sampling interval
Broadly speaking, I can think of three approaches:
Stay in your native space: Design a meaningful distance function $d(S_1, S_2)$ between samples. For instance, you could do something like summing $t_1 * t_2$ over every t in $S_1$ and $S_2$ and weighting by th... | Time series prediction with non-constant sampling interval
Broadly speaking, I can think of three approaches:
Stay in your native space: Design a meaningful distance function $d(S_1, S_2)$ between samples. For instance, you could do something like summing $t |
42,886 | Time series prediction with non-constant sampling interval | If I understand correctly, this is just standard sequential binary prediction. You have an alphabet $\Sigma = \{-1,1\}$, and samples drawn from $\Sigma^*$ (the set of all finite length strings over your alphabet) Given a string $s_{1:t-1} \in \Sigma^*$ (the observations from time $1$ to $t-1$) you want to predict $s_{t... | Time series prediction with non-constant sampling interval | If I understand correctly, this is just standard sequential binary prediction. You have an alphabet $\Sigma = \{-1,1\}$, and samples drawn from $\Sigma^*$ (the set of all finite length strings over yo | Time series prediction with non-constant sampling interval
If I understand correctly, this is just standard sequential binary prediction. You have an alphabet $\Sigma = \{-1,1\}$, and samples drawn from $\Sigma^*$ (the set of all finite length strings over your alphabet) Given a string $s_{1:t-1} \in \Sigma^*$ (the obs... | Time series prediction with non-constant sampling interval
If I understand correctly, this is just standard sequential binary prediction. You have an alphabet $\Sigma = \{-1,1\}$, and samples drawn from $\Sigma^*$ (the set of all finite length strings over yo |
42,887 | Markov Chain process - Missing values | This is best and most transparently done in bayesian modelling. Bayesian inference works using MCMC (Markov Chain Monte Carlo) simulations, and that's exactly what you need. Example model code in bugs (not tested!):
dataset[1] ~ dbern(P1) # you must somehow solve the first element
for (i in 2:n) { # markov chain - defi... | Markov Chain process - Missing values | This is best and most transparently done in bayesian modelling. Bayesian inference works using MCMC (Markov Chain Monte Carlo) simulations, and that's exactly what you need. Example model code in bugs | Markov Chain process - Missing values
This is best and most transparently done in bayesian modelling. Bayesian inference works using MCMC (Markov Chain Monte Carlo) simulations, and that's exactly what you need. Example model code in bugs (not tested!):
dataset[1] ~ dbern(P1) # you must somehow solve the first element
... | Markov Chain process - Missing values
This is best and most transparently done in bayesian modelling. Bayesian inference works using MCMC (Markov Chain Monte Carlo) simulations, and that's exactly what you need. Example model code in bugs |
42,888 | Deep belief network performs worse than a simple MLP | My guess is that your training procedure is simply unable to find good parameters for such a big model. It is quite hard to get an MLP with more than three layers to work well on image classification problems, even when pretraining with a DBN. Most papers on image classification I have seen use three layers or even sho... | Deep belief network performs worse than a simple MLP | My guess is that your training procedure is simply unable to find good parameters for such a big model. It is quite hard to get an MLP with more than three layers to work well on image classification | Deep belief network performs worse than a simple MLP
My guess is that your training procedure is simply unable to find good parameters for such a big model. It is quite hard to get an MLP with more than three layers to work well on image classification problems, even when pretraining with a DBN. Most papers on image cl... | Deep belief network performs worse than a simple MLP
My guess is that your training procedure is simply unable to find good parameters for such a big model. It is quite hard to get an MLP with more than three layers to work well on image classification |
42,889 | Deep belief network performs worse than a simple MLP | I'm seeing similar results but with a much simpler network using only a single hidden layer. I'm doing some tests on the CIFAR-10 image database. My network is 768-64-1, a binary classifier. If I use a RBM to pre-train layer 768-64 + backprop I get an F1 score of about 0.38 on validation/test. Where as using random wei... | Deep belief network performs worse than a simple MLP | I'm seeing similar results but with a much simpler network using only a single hidden layer. I'm doing some tests on the CIFAR-10 image database. My network is 768-64-1, a binary classifier. If I use | Deep belief network performs worse than a simple MLP
I'm seeing similar results but with a much simpler network using only a single hidden layer. I'm doing some tests on the CIFAR-10 image database. My network is 768-64-1, a binary classifier. If I use a RBM to pre-train layer 768-64 + backprop I get an F1 score of abo... | Deep belief network performs worse than a simple MLP
I'm seeing similar results but with a much simpler network using only a single hidden layer. I'm doing some tests on the CIFAR-10 image database. My network is 768-64-1, a binary classifier. If I use |
42,890 | What is the standard error for the distribution of the difference in proportions (for hypothesis testing)? | The proportions $p_1$ and $p_2$ you use in the variance should be the true proportions under the null hypothesis, where $p_1=p_2+c$. Obviously you don't know what these are (other than $c$) so you have to estimate them from your data. Either of the formulae you have reasonable estimates, but it is possible to combine... | What is the standard error for the distribution of the difference in proportions (for hypothesis tes | The proportions $p_1$ and $p_2$ you use in the variance should be the true proportions under the null hypothesis, where $p_1=p_2+c$. Obviously you don't know what these are (other than $c$) so you ha | What is the standard error for the distribution of the difference in proportions (for hypothesis testing)?
The proportions $p_1$ and $p_2$ you use in the variance should be the true proportions under the null hypothesis, where $p_1=p_2+c$. Obviously you don't know what these are (other than $c$) so you have to estimat... | What is the standard error for the distribution of the difference in proportions (for hypothesis tes
The proportions $p_1$ and $p_2$ you use in the variance should be the true proportions under the null hypothesis, where $p_1=p_2+c$. Obviously you don't know what these are (other than $c$) so you ha |
42,891 | Joint likelihood of two models | So it turns out the solution is trivial, and is simply the sum of the two sub-models. | Joint likelihood of two models | So it turns out the solution is trivial, and is simply the sum of the two sub-models. | Joint likelihood of two models
So it turns out the solution is trivial, and is simply the sum of the two sub-models. | Joint likelihood of two models
So it turns out the solution is trivial, and is simply the sum of the two sub-models. |
42,892 | Bayesian Updating Without Conjugate Prior | One approach is to fit a density estimator to your first posterior distribution, then use the estimated density as the prior in your update. One option for a density estimator to use is logspline densities, see the logspline package in R for one way to fit these.
It depends on what tool you are using to do your gibbs ... | Bayesian Updating Without Conjugate Prior | One approach is to fit a density estimator to your first posterior distribution, then use the estimated density as the prior in your update. One option for a density estimator to use is logspline den | Bayesian Updating Without Conjugate Prior
One approach is to fit a density estimator to your first posterior distribution, then use the estimated density as the prior in your update. One option for a density estimator to use is logspline densities, see the logspline package in R for one way to fit these.
It depends on... | Bayesian Updating Without Conjugate Prior
One approach is to fit a density estimator to your first posterior distribution, then use the estimated density as the prior in your update. One option for a density estimator to use is logspline den |
42,893 | Reference on examples with R codes for Bayesian simulation based methods of posterior approximation | Online information would suggest
Christian Robert, George Casella (2009). Introducing Monte Carlo Methods with R. Springer-Verlag, New York.
would at least be a start and the code is available in the mcsm package here.
Perhaps someone who has actually read it might be able to provide further comments. | Reference on examples with R codes for Bayesian simulation based methods of posterior approximation | Online information would suggest
Christian Robert, George Casella (2009). Introducing Monte Carlo Methods with R. Springer-Verlag, New York.
would at least be a start and the code is available in t | Reference on examples with R codes for Bayesian simulation based methods of posterior approximation
Online information would suggest
Christian Robert, George Casella (2009). Introducing Monte Carlo Methods with R. Springer-Verlag, New York.
would at least be a start and the code is available in the mcsm package here... | Reference on examples with R codes for Bayesian simulation based methods of posterior approximation
Online information would suggest
Christian Robert, George Casella (2009). Introducing Monte Carlo Methods with R. Springer-Verlag, New York.
would at least be a start and the code is available in t |
42,894 | Creating a recommender system for shoe sizing | Depends what assumptions you're willing to make about the sizing of shoes. For instance, I think it's safe to assume the difference between a size 9 to 10 Redwings Iron Ranger is similar to the difference between a 10 and 11 Adidas Samba. If a 9 IR wears a 10 Samba, then we'd know a 10IR would wear an 11 Samba.
It als... | Creating a recommender system for shoe sizing | Depends what assumptions you're willing to make about the sizing of shoes. For instance, I think it's safe to assume the difference between a size 9 to 10 Redwings Iron Ranger is similar to the differ | Creating a recommender system for shoe sizing
Depends what assumptions you're willing to make about the sizing of shoes. For instance, I think it's safe to assume the difference between a size 9 to 10 Redwings Iron Ranger is similar to the difference between a 10 and 11 Adidas Samba. If a 9 IR wears a 10 Samba, then we... | Creating a recommender system for shoe sizing
Depends what assumptions you're willing to make about the sizing of shoes. For instance, I think it's safe to assume the difference between a size 9 to 10 Redwings Iron Ranger is similar to the differ |
42,895 | How to calculate chi squared from mauchly of R's ezANOVA output? | Using this paper on Mauchly Test (switched the $W$ to $\log(W)$),
I can get an approximate value for $\chi^2$:
library(reshape2); library(ez)
dat <- data.frame(id=as.factor(1:10), matrix(rpois(90, 10), ncol=9))
dat2 <- melt(dat)
mod <- ezANOVA(data=dat2, dv = .(value), wid=.(id), within=.(variable),
detailed=TRUE,... | How to calculate chi squared from mauchly of R's ezANOVA output? | Using this paper on Mauchly Test (switched the $W$ to $\log(W)$),
I can get an approximate value for $\chi^2$:
library(reshape2); library(ez)
dat <- data.frame(id=as.factor(1:10), matrix(rpois(90, 10) | How to calculate chi squared from mauchly of R's ezANOVA output?
Using this paper on Mauchly Test (switched the $W$ to $\log(W)$),
I can get an approximate value for $\chi^2$:
library(reshape2); library(ez)
dat <- data.frame(id=as.factor(1:10), matrix(rpois(90, 10), ncol=9))
dat2 <- melt(dat)
mod <- ezANOVA(data=dat2, ... | How to calculate chi squared from mauchly of R's ezANOVA output?
Using this paper on Mauchly Test (switched the $W$ to $\log(W)$),
I can get an approximate value for $\chi^2$:
library(reshape2); library(ez)
dat <- data.frame(id=as.factor(1:10), matrix(rpois(90, 10) |
42,896 | Understanding the random effect in linear mixed models (lme4, R) [duplicate] | To answer your question (or at least part of it), why do all tutorials seem to favor write ups like (1+P$fgrupp|P$lweek) rather than (P$grupp|P$lweek)... I think the answer is that they are simply trying to be more clear in their notation. (1+P$fgrupp|P$lweek) and (P$grupp|P$lweek) do yield the same results. However, ... | Understanding the random effect in linear mixed models (lme4, R) [duplicate] | To answer your question (or at least part of it), why do all tutorials seem to favor write ups like (1+P$fgrupp|P$lweek) rather than (P$grupp|P$lweek)... I think the answer is that they are simply try | Understanding the random effect in linear mixed models (lme4, R) [duplicate]
To answer your question (or at least part of it), why do all tutorials seem to favor write ups like (1+P$fgrupp|P$lweek) rather than (P$grupp|P$lweek)... I think the answer is that they are simply trying to be more clear in their notation. (1... | Understanding the random effect in linear mixed models (lme4, R) [duplicate]
To answer your question (or at least part of it), why do all tutorials seem to favor write ups like (1+P$fgrupp|P$lweek) rather than (P$grupp|P$lweek)... I think the answer is that they are simply try |
42,897 | Can this be considered close to normally distributed? | I think that you are mixing together a couple of different concepts. You ask about normality, using t-tests, and deciding if something is large.
First, no, the data shown is not considered close to normal, but if you are interested in using t-tests then that is not the important question. The population and sample di... | Can this be considered close to normally distributed? | I think that you are mixing together a couple of different concepts. You ask about normality, using t-tests, and deciding if something is large.
First, no, the data shown is not considered close to n | Can this be considered close to normally distributed?
I think that you are mixing together a couple of different concepts. You ask about normality, using t-tests, and deciding if something is large.
First, no, the data shown is not considered close to normal, but if you are interested in using t-tests then that is not... | Can this be considered close to normally distributed?
I think that you are mixing together a couple of different concepts. You ask about normality, using t-tests, and deciding if something is large.
First, no, the data shown is not considered close to n |
42,898 | How to combine the responses of two sensors? | You may want to have a look on Dempster-Shafer theory. This framework has been investigated for merging information coming from sensors, for instance you can have a look at the following papers:
Information fusion and evidential grammars for object class
segmentation
Information Fusion on Oversegmented Images:
An App... | How to combine the responses of two sensors? | You may want to have a look on Dempster-Shafer theory. This framework has been investigated for merging information coming from sensors, for instance you can have a look at the following papers:
Inf | How to combine the responses of two sensors?
You may want to have a look on Dempster-Shafer theory. This framework has been investigated for merging information coming from sensors, for instance you can have a look at the following papers:
Information fusion and evidential grammars for object class
segmentation
Infor... | How to combine the responses of two sensors?
You may want to have a look on Dempster-Shafer theory. This framework has been investigated for merging information coming from sensors, for instance you can have a look at the following papers:
Inf |
42,899 | How to combine the responses of two sensors? | Being sensed is a binary outcome. The sensors' distance from target is a covariate that determine the outcomes. Logistic regression is one way to model this. The model would look like a smooth 2-dimensional surface over the x-y plane where x and y represent the respective sensor's distance from the target, with the h... | How to combine the responses of two sensors? | Being sensed is a binary outcome. The sensors' distance from target is a covariate that determine the outcomes. Logistic regression is one way to model this. The model would look like a smooth 2-dim | How to combine the responses of two sensors?
Being sensed is a binary outcome. The sensors' distance from target is a covariate that determine the outcomes. Logistic regression is one way to model this. The model would look like a smooth 2-dimensional surface over the x-y plane where x and y represent the respective ... | How to combine the responses of two sensors?
Being sensed is a binary outcome. The sensors' distance from target is a covariate that determine the outcomes. Logistic regression is one way to model this. The model would look like a smooth 2-dim |
42,900 | Model broken stick model in R where one line has a constant gradient? | I may have found a solution to my problem, but could someone please confirm what I've done?
I slightly modified the above functions b1 and b2. I have called them b3 and b4:
b3 <- function(x, bp) ifelse(x < bp, bp - x, 0) #Y = mx + c
b4 <- function(x, bp) ifelse(x < bp, 0, 1) #Y = C
The mixed model is similar,... | Model broken stick model in R where one line has a constant gradient? | I may have found a solution to my problem, but could someone please confirm what I've done?
I slightly modified the above functions b1 and b2. I have called them b3 and b4:
b3 <- function(x, bp) ifels | Model broken stick model in R where one line has a constant gradient?
I may have found a solution to my problem, but could someone please confirm what I've done?
I slightly modified the above functions b1 and b2. I have called them b3 and b4:
b3 <- function(x, bp) ifelse(x < bp, bp - x, 0) #Y = mx + c
b4 <- function(... | Model broken stick model in R where one line has a constant gradient?
I may have found a solution to my problem, but could someone please confirm what I've done?
I slightly modified the above functions b1 and b2. I have called them b3 and b4:
b3 <- function(x, bp) ifels |
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