I want to compute an effect size for my linear models in R with the package MBESS.
I installed the package and then wanted to use effect.size (lm) but then R tells me that it can´t find "effect.size".... I thought this was included in the package?
Related
The catboost documentation contains instructions for setting model scale and bias in the Python package (here) and the command line implementation (here), but not the R package.
Can anyone show how it is done in R?
I am trying to run a simple binary logistic regression, but one of my cells has a 0. Because of this, I wanted to run exact logistic regression but I get that package elrm is not available for R 3.6.2.
I researched online and found you could install from archive, but I have never done that before.
I tried the following, but still got the message that "ERROR: dependency 'coda' is not available for package 'elrm'
elrmurl <- "https://cran.r-project.org/src/contrib/Archive/elrm/elrm_1.2.3.tar.gz"
install.packages(elrmurl, repos=NULL, type="source")
I'm very out of my depth with this one but would appreciate any guidance on either 1) how to properly install the elrm package from archive (https://cran.r-project.org/src/contrib/Archive/elrm/) or 2) potential alternative packages that might allow me to do exact logistic regression in R.
Try :
install.packages("devtools")
library(devtools)
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = "true")
install_github("cran/elrm")
You probably already thought about it, but you could also collapse or delete levels in order to have enough observations to do a regular logistic regression with glm
In the 'pracma' package manual there is function fmincon to Minimize Nonlinear Constrained Multivariable Function (page 116). However I've installed this package, but there is no such function.
Has it been removed from this package?
Could anyone indicate any other R ackage or function with such funcionality (Minimize Nonlinear Constrained Multivariable Function) ?
What version of pracma do you have installed? The manual you linked to is from version 2.2.5, and that's also the version on CRAN, and it does have fmincon.
Perhaps you forgot
library(pracma)
to attach it? Installing isn't enough to make the contents visible.
I am working in R, and using the nnet package to train a neural network model. I have been experimenting with the nnetpredint package to compute prediction intervals around the model output. How can I modify the nnetpredint package so that it can handle a linear transfer function at the output node, i.e., when I use the linout=T parameter in the nnet() function?
I have a random forest model built using the bigrfc() function of the bigrf package in R. I would like to use that model with the prediction function of randomForest package (the predict.randomForest() function).
Is there any way to directly use or translate the generated model with the randomForest's functions?
Thanks.