I'm really confused about regression models and functions in R. Here is my problem. I'm using the PLS package to make a model like Y~x. To do that I have to use 'plsr':
model=plsr(Y~X,ncomp=10,data=df1,center=TRUE, scale=TRUE, validation="LOO")
I couldn't find the source of the 'plsr' in the PLS source code but in the help document it says it refers to 'mvr{pls}' which I could find it. first is 'plsr' a function or model.. in the R terminology? is it built in R? and how does it refer to 'mvr' function in pls package?
Thanks
Related
I used Caret to create a regression model of a dataset in R, and I wish to find this equation for usage in other websites (e.g. Desmos). I am unable to find info anywhere on how to do this, so if anyone has answers, that would be much appreciated! :D
I want to build a Bagged Logistic Regression Model in R. My dataset is really biased and has 0.007% of positive occurrences.
My thoughts to solve this was to use Bagged Logistic Regression. I came across the hybridEnsemble package in R. Does anyone have an example of how this package can be used? I searched online, but unfortunately did not find any examples.
Any help will be appreciated.
The way that I would try to solve this is use the h2o.stackedEnsemble() function in the h2o R package. You can automatically create more balanced classifiers by using the balance_classes = TRUE option in all of the base learners. More information about how to use this function to create ensembles is located in the Stacked Ensemble H2O docs.
Also, using H2O will be way faster than anything that's written in native R.
In the caret package, which ensemble models can be used for multi class classification?
Also on trying some of the functions mentioned in http://topepo.github.io/caret/Ensemble_Model.html it is giving:
Not in caret's built-in library.
There are no suggestions of relevant packages for many functions on Google either. Could anyone kindly help me out with both these questions?
Most of them can (assuming that they are not solely regression models). We've listed the exclusions here
Here you can see an overview that also lists packages needed.
I'm searching for a package or a way to use the algorithm Adaboost.R2 (adaboost for regression) in R. I can use it in ROOT and change my loss function in "quadratic" and I want to get the same thing in R. Someone can help me? Thanks.
I'm trying to fit a mixed model in R.
Actually I have a Stata code and want to do the same thing using R.
The Stata code is
xtmixed laz c.x1##i.j4alloc_n c.x2##i.j4alloc_n ||childuid:age_m, cov(uns) var
I can't find how to specify 'unstructured' correlation structure in R.
In the R help file, there is no 'unstructured'
https://stat.ethz.ch/R-manual/R-devel/library/nlme/html/corClasses.html