Has anyone tried using package dlmodeler which seems to provide an equivalent of PROC UCM (SAS). Using structTS function is equal to running univariate PROC UCM. Can anyone tell me how to do a multivariate analysis using the same.
Built a package which runs unobserved components model in R which depends on the KFAS package.
The package is available on github.
Related
A vendor that we work with is attempting to replicate my KM estimates/curves, including 95%CIs. I code in R while they code in SAS. I am using the survminer package in R and the surv_median function to derive my 95%CIs. They will be utilizing the PROC LIFETEST procedure.
I tried searching through the package documentation (link: survminer documenation) but couldn't find anything useful.
Any assistance determining the CI estimation methodology is greatly appreciated.
I'd like to simulate a\ periodic ARMA(1,1) using R. I found the R package perARMA but I don't understand how to use it.
There is the function makeparma that permit to simulated the parma(1,1). But I don't understand the input parameters and the model used to simulate the periodic stuff.
This is the source code provided by the package trying to simulate a parma(2,1):
T=12
nlen=480
p=1
a=matrix(0,T,p)
q=1
b=matrix(0,T,q)
a[1,1]=.8
a[2,1]=.3
phia<-ab2phth(a)
phi0=phia$phi
phi0=as.matrix(phi0)
b[1,1]=-.7
b[2,1]=-.6
thetab<-ab2phth(b)
theta0=thetab$phi
theta0=as.matrix(theta0)
del0=matrix(1,T,1)
PARMA21<-makeparma(nlen,phi0,theta0,del0)
parma<-PARMA21$y
I don't understand why we should specify two beta value. And why del0 is a matrix.
I solved using the R package sarima and for the simulation I used the function prepareSimSarima
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.
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.
I was wondering if the functionality given by Weka of building Model trees like M5P which has regression models in the leaves is possible in R. I know there is a way to handle it using the RWeka package. What was somehow strange to me is that the functionality does not exist in other R packages like rpart. The only way to get a "Model Tree" is using the Rweka package?
Thanks for clarification.
Please check cubist and CORElearn packages.