Is there any way to perform Ordinal Logistic Regression (OLR) with mlr?
One common implementation is the polr function from the MASS library.
See:
https://www.analyticsvidhya.com/blog/2016/02/multinomial-ordinal-logistic-regression/
or
Ordinal Logistic Regression In R
This isn't supported at the moment and won't be added to mlr (though possibly to mlr3 at some point). You can implement your own learner though.
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I am using regsubsets method for linear regression and came across step() method for selecting columns for logistic regression methods.
I am not sure whether we can use regsubsets or steps for Poisson regression. It will be helpful if there is a method to find the best subsets for Poisson regression in R programming.
From here it looks like
step() (base R: works on glm objects -> includes Poisson regression models)
bestglm package
glmulti package
Possibly others.
Be prepared for the GLM (Poisson etc.) case to be much slower than the analogous problem for Gaussian responses (OLS/lm).
I was checking tidymodels for multivariate regression and saw this example here:
https://www.tidymodels.org/learn/models/pls/
This covers multivariate for Partial Least Squares model.
Is there a page that states what models currently support multivariate regression?
I believe the current models that support multivariate (more than one outcome) regression are:
single layer neural network: mlp()
multivariate adaptive regression splines: mars()
good old linear regression: linear_reg()
This list was made by looking for which models use the maybe_multivariate() internal helper, but we should document this better somehow.
I had a statistic class where a Logistic Regression was set up on some ecotoxicological data (dose-response curve) and the nlstools package was used for this purpose. So far when I came across the Logistic Regression I only used the glm and I am not sure what is the difference between these two approaches. When applying both approaches on the same data there was a clear difference between the fitted curves.
I already tried to look for answers but what I've read so far is that the nlstools package is used for nonlinear Regressions. From what I've read the Logistic Regression is not really a nonlinear Regression but a gerneralized linear model. Why can you use the nlstools package then? And I am a little bit confused how I should classify the Logistic Regression. Thanks for your help!
I'm trying to learn about MARS/Earth models for classification and am using "classif.earth" in the MLR package in R. My issue is that the MLR documentation says that "classif.earth" performs flexible discriminant analysis using the earth algorithm.
However, when I look at the code:
(https://github.com/mlr-org/mlr/blob/master/R/RLearner_classif_earth.R)
I don't see a call to fda in the mda package, rather it directs earth to fit a glm with a default logit link.
So tell me if I'm wrong, but it seems to me that "classif.earth" is not doing flexible discriminant analysis but rather fitting a logistic regression on the earth model.
The implementation uses MARS to perform the FDA, where the MARS model determines the different groups. You can find more information in this paper; I quote from the abstract:
Linear discriminant analysis is equivalent to multiresponse linear regression [...] to represent the groups.
I can see how cv.glm work with a glm object, but what about fitted survival models?
I have a bunch of models (Weibull, Gompertz, lognormal, etc). I want to assess the prediction error using cross validation. Which package/function can do this in R?
SuperLearner can do V-fold cross-validation for a large library of underlying machine learning algorithms, not sure that it includes survival models. Alternatively, take a look at the cvTools package, which is designed to help do cross-validation of any prediction algorithm you give it.