Generalized Supervised Principal Component Regression

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Documentation for package ‘gspcr’ version 0.9.5

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

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