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I use YJ transformation for predictor variables and try to plot the model between predicted and observed values. Could you please help me to do this?
I am looking for a r code for inverse of Yeo-Johnson transformation.
There's an answer to this in the comments for this question over at Cross Validated. Here's the documentation for VGAM::yeo.johnson (use inverse = TRUE).
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When using the glm function: which is the best distribution for multinomial regression --- is it "binomial"? If the glm function doesn't have an appropriate distribution for multinomial regression, which r package has a better function? Thank you!
You may try the nnet::multinom()
You can find the documentation at here.
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Is there a library that compute precision and recall of a fitted model in R.
Perhaps the caret package might help.
https://topepo.github.io/caret/measuring-performance.html
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I would like to simulate in R, N Bernoulli/Binary random variables with a prescribed correlation. I have been using the package (MultiOrd) with the function
generate.binary.
However, it is too slow. Is there a computationally fast way to do this in existing packages? Thanks.
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I would like to estimate mean and standard deviation from a series of censored normal distributions in my data in R. Do you suggest any particular package in order to do it?
Thank you very much for your help!
Check this package from the University of Pennsylvania:
http://finzi.psych.upenn.edu/R/library/EnvStats/html/enparCensored.html
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I am aware of the http://cran.r-project.org/web/packages/glmnet/index.html and http://cran.r-project.org/web/packages/penalized/index.html packages, but neither of them seems to support Gamma GLMs.
I'd like to utilize elastic net for gamma GLMs in R, what is the easiest way to do it?
(meta: also debating whether this should go on Cross Validated for better responses?)
You can use the HDtweedie package. Gamma is a special case of the Tweedie distribution with p = 2. It's a relatively new package, so expect some teething problems.