I want to conduct LCA to predict latent class membership through GAM(generalized additive model)
but I can't find method to handle two or more categorical indicator variable in flexmix package.
I want to use one step method, not three step.
Is it impossible to conduct LCA in flexmix package? not poLCA
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I am performing a spatial analysis of student grades according to their city of origin using R. I have several covariates such as poverty, education and socio-cultural indices. So far I have fitted univariate models such as: linear regression, weighted linear regression and CAR (conditional autoregressive).
Now, I am reading "Hierarchical Modeling and Analysis for Spatial Data" from Banerjee, Carlin and Gelfand. I am interested in applying multivariate models, in particular a MCAR (Multivariate Conditional Autoregressive) model.
However, I have not found any code in R (or Python) that has it implemented. The most possible has been the "spatialreg" library that includes univariate CAR and SAR models.
Is there any library that you know of that includes them? Thanks in advance
I have found "CARBayes" package. This works perfectly for fitting MCAR model.
Does anyone have experience with obtaining latent trait scores from a repeated measure design. Currently, my data is in long format (where baseline data is stacked on post-treatment data, similar to the way you would fix the dataset for lmer). With this dataset in long format, I attempted to use the mixedmirt function from the mirt package and regressed the latent trait on Time; however, I am unsure if mirt recognized that my design is repeated. It seemed to model the correlation and covariance in the latent abilities. Does anyone have experience with mirt or can recommend another R package for my analysis?
I would like to perform a model-based clustering using a mixture of ordinal logistic regressions (for outcome, not as concomitant model)
Does some one know if it implemented in R? For example, can I manage to use ordinal regression instead of multinomial in flexmix package?
Thanks a lot!
I recently fitted a Graded Response Model to my data using R's latent trait modelling package. I tried to add my fitted Graded response model in mice package to impute missing data but i am failing to access the mice algorithm to edit. My question is does it make any difference if i use the polr() function in mice or if i use my graded response model that i fitted using latent trait modelling package to impute data since both these functions are derived from polr() function in R's mass package
Within R's "rpart" package for classification/regression trees, is it possible to specify prior weights for the predictor variables? Alternatively, is this a possibility with the BART (Bayesian Additive Regression Trees) package, random forests, or any other package in R?
Based on expert opinion, I would like to force certain predictor variables to be included. Thanks