# Generalized Supervised Principal Component Regression

## Documentation for package ‘gspcr’ version 0.9.5

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

CFA_data | CFA example data
compute_sc | Compute the GLM systematic component.
cp_AIC | Compute Akaike's information criterion
cp_BIC | Compute bayesian information criterion
cp_F | Compute F statistic
cp_gR2 | Compute generalized R-squared
cp_LRT | Compute likelihood ratio test
cp_thrs_LLS | Compute threshold values based on Log-likelihood values
cp_thrs_NOR | Compute normalized association measure
cp_thrs_PR2 | Compute threshold values based on the pseudo R2
cp_validation_fit | Compute fit measure(s) on the validation data set
cv_average | Average fit measures computed in the K-fold cross-validation procedure
cv_choose | Cross-validation choice
cv_gspcr | Cross-validation of Generalized Principal Component Regression
est_gspcr | Estimate Generalized Principal Component Regression
est_univ_mods | Estimate simple GLM models
GSPCRexdata | GSPCR example data
LL_baseline | Baseline category logistic regression log-likelihood
LL_binomial | Binomial log-likelihood
LL_cumulative | Proportional odds model log-likelihood
LL_gaussian | Gaussian log-likelihood
LL_newdata | Log-Likelihood for new data
LL_poisson | Poisson regression log-likelihood
pca_mix | PCA of a mixture of numerical and categorical data
plot.gspcrcv | Plot the cross-validation solution path for the GSPCR algorithm
predict.gspcrout | Predict GSPCR model dependent variable scores