| median_normalization() |
Column wise median normalization of the data matrix |
| proDA() |
Main function to fit the probabilistic dropout model |
| dist_approx() dist_approx() dist_approx() |
Distance method for 'proDAFit' object |
| test_diff() result_names() |
Identify differentially abundant proteins |
| Accessor methods Methods to access values of the fit |
|
| abundances() design() hyper_parameters() feature_parameters() coefficients() coefficient_variance_matrices() reference_level() convergence() |
Get different features and elements of the 'proDAFit' object |
| .DollarNames() `$`() `$<-`() |
Fluent use of accessor methods |
| Quick start functions |
|
| pd_row_t_test() pd_row_f_test() |
Row-wise tests of difference using the probabilistic dropout model |
| generate_synthetic_data() |
Generate a dataset according to the probabilistic dropout model |
| Low-level statistics functions |
|
| pd_lm() |
Fit a single linear probabilistic dropout model |
| invprobit() |
Inverse probit function |
| predict() |
Predict the parameters or values of additional proteins |
| Utility function |
|
| mply_dbl() msply_dbl() |
apply function that always returns a numeric matrix |
| Class and Package Information |
|
| proDAFit-class |
proDA Class Definition |
| proDA_package |
proDA: Identify differentially abundant proteins in label-free mass spectrometry |
| Generic functions Function definitions for S4 Generics |
|
| abundances() |
Get the abundance matrix |
| coefficients() |
Get the coefficients |
| coefficient_variance_matrices() |
Get the coefficients |
| convergence() |
Get the convergence information |
| dist_approx() |
Calculate an approximate distance for 'object' |
| feature_parameters() |
Get the feature parameters |
| hyper_parameters() |
Get the hyper parameters |
| reference_level() |
Get the reference level |
| result_names() |
Get the result_names |
| All functions |
|
| abundances() |
Get the abundance matrix |
| abundances() design() hyper_parameters() feature_parameters() coefficients() coefficient_variance_matrices() reference_level() convergence() |
Get different features and elements of the 'proDAFit' object |
| .DollarNames() `$`() `$<-`() |
Fluent use of accessor methods |
| coefficients() |
Get the coefficients |
| coefficient_variance_matrices() |
Get the coefficients |
| convergence() |
Get the convergence information |
| dist_approx() dist_approx() dist_approx() |
Distance method for 'proDAFit' object |
| dist_approx() |
Calculate an approximate distance for 'object' |
| feature_parameters() |
Get the feature parameters |
| generate_synthetic_data() |
Generate a dataset according to the probabilistic dropout model |
| hyper_parameters() |
Get the hyper parameters |
| invprobit() |
Inverse probit function |
| median_normalization() |
Column wise median normalization of the data matrix |
| mply_dbl() msply_dbl() |
apply function that always returns a numeric matrix |
| pd_lm() |
Fit a single linear probabilistic dropout model |
| pd_row_t_test() pd_row_f_test() |
Row-wise tests of difference using the probabilistic dropout model |
| predict() |
Predict the parameters or values of additional proteins |
| proDA() |
Main function to fit the probabilistic dropout model |
| proDAFit-class |
proDA Class Definition |
| proDA_package |
proDA: Identify differentially abundant proteins in label-free mass spectrometry |
| reference_level() |
Get the reference level |
| test_diff() result_names() |
Identify differentially abundant proteins |