Man pages for cayek/MatrixFactorizationR
Latent Factor Mixed Models
| compute_P | Compute the matrix used to reduce correlation with X |
| compute_pvalue_from_tscore | score are assume to follow student distibution with df degre... |
| compute_pvalue_from_zscore | score are assume to follow normal distibution |
| Dat | Class which store data |
| effect_size | Direct effect sizes estimated from latent factor models |
| example.data | Genetic and phenotypic data for Arabidopsis thaliana |
| forward_test | Forward inclusion tests with latent factor mixed models |
| glm_test | GLM tests with latent factor mixed models |
| hypothesis_testing_lm | Hypothesis testing with lm |
| left.out.kfold | return a list of train/test indices |
| lfmm | R package with matrix factorization algorithms |
| lfmm_CV | Cross validation |
| LfmmDat | Class which store data |
| lfmm_fit | Fit the model |
| lfmm_fit_knowing_loadings | Fit the model when latent factor loadings are known |
| lfmm_fit_knowing_loadings.ridgeLFMM | Fit assuming V and B |
| lfmm_impute | Impute Y with a fitted model. |
| lfmm_lasso | LFMM least-squares estimates with lasso penalty |
| lfmm_residual_error2 | Compute the residual error |
| lfmm_ridge | LFMM least-squares estimates with ridge penalty |
| lfmm_ridge_CV | Cross validation of LFMM estimates with ridge penalty |
| lfmm_sampler | LFMM generative data sampler |
| lfmm_test | Statistical tests with latent factor mixed models (linear... |
| predict_lfmm | Predict polygenic scores from latent factor models |
| SimulatedLfmmDat | Class which store data |
| skin.exposure | Simulated (and real) methylation levels for sun exposed... |
