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
How can I suppress the "divergence or singularity in samples" message in the validate function of the rms package. I have read the documentation, searched Harrell's 2015 book, and searched internet without success. Thank you very much. pbm
I'm trying out Naive Bayes classification using stock dataset in R. Now for displaying the final results I've seen a function mmetric taking testing data and prediction model as arguments. But while I'm trying the same I'm facing the error could not find function "mmetric". I have installed the rminer and list packages which are quotes as reference packages for these. But still I could not resolve it.
Any Suggestions to resolve the error?
I got same error but i updated my Rcpp package that will be needed by rminer to load on the system, hope this helps.
I am working with the R package 'zoib' for performing beta regression in R. I am trying to replicate the example included on page 41 in the paper the package authors published in The R Journal:
Lui F and Kong Y. 2015. zoib: An R Package for Bayesian Inference for Beta Regression and Zero/One Inflated Beta Regression. The R Journal 7(2)
I believe I am using the exact same data and code that they use:
library(zoib)
data("GasolineYield", package="zoib")
GasolineYield$batch <- as.factor(GasolineYield$batch)
d <- GasolineYield
eg1.fixed <- zoib(yield ~ temp + as.factor(batch) | 1, data=GasolineYield, joint=FALSE,
random=0, EUID=1:nrow(d), zero.inflation=F, one.inflation=F,
n.iter=1050, n.thin=5, n.burn=50)
sample1 <- eg1$coeff
traceplot(sample1)
autocorr.plot(sample1)
gelman.diag(sample1)
However, I am getting an error when I try to do the diagnostic plots on the samples. This is the error message:
Error in ts(seq(from = start(x), to = end(x), by = thin(x)), start = start(x), :
invalid time series parameters specified
I cannot understand why the code isn't working or what I can do to fix the problem. I can trace the error to the time function which is called by zoib, and it seems like maybe it is a problem that the sample object does not have a tsp attribute, but the zoib package authors make it clear that their model output is meant to be used with coda, so I am very confused. I don't have much experience working with MCMC or time series objects, so maybe I am just missing something obvious. Can anyone explain why the example provided by the package authors is failing, and what the solution is?
I e-mailed the package author (Fang Liu) and she informed me that there was in fact a bug in the version of the package I have, but that the bug is fixed in the most recent version of zoib (Version 1.4.2). Using the most recent version, the code now works.