Man pages for gspcr
Generalized Supervised Principal Component Regression
| 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... |
| cv_choose | Cross-validation choice |
| cv_gspcr | Cross-validation of Generalized Principal Component... |
| est_gspcr | Estimate Generalized Principal Component Regression |
| est_univ_mods | Estimate simple GLM models |
| GSPCRexdata | GSPCR example data |
| gspcr-package | gspcr: Generalized Supervised Principal Component Regression |
| 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... |
| predict.gspcrout | Predict GSPCR model dependent variable scores |
