Function reference • rMVPA

All functions

binary_classification_result(): Classification results for binary outcome

classification_result(): Create a classification_result instance

combine_prediction_tables(): Combine prediction tables

compute_performance(): Compute Performance Metrics for Classification/Regression Results

create_dist(): Create Distance Function Object

bootstrap_blocked_cross_validation() blocked_cross_validation() sequential_blocked_cross_validation() custom_cross_validation(): bootstrap_blocked_cross_validation

crossv_block(): Block Cross-Validation Data Preparation

crossv_k(): K-fold Cross-Validation Data Preparation

crossval_samples(): crossval_samples

custom_performance(): Apply Custom Performance Metric to Prediction Result

data_sample(): Extract Sample from Dataset

cordist() mahadist() eucdist() robustmahadist() pcadist(): Distance Function Constructors

external_crossval(): External Cross-Validation

feature_rsa_iterate(): feature_rsa_iterate

feature_selector(): Create a feature selection specification

fit_model(): Fit Model

fit_model(<mvpa_model>): Fit an MVPA model

gen_sample_dataset(): gen_sample_dataset

get_samples(): Get Multiple Data Samples

get_searchlight(): Generate Searchlight Iterator

group_means(): Compute Group Means of a Matrix

has_test_set(): Test Set Availability

kfold_cross_validation(): kfold_cross_validation

list_model(): a list of model fits

load_model(): load_model

manova_design(): Create a MANOVA Design

manova_iterate(): MANOVA Iteration for Voxel Sets

manova_model(): Create a MANOVA Model

merge_predictions(): Merge predictions from multiple models

merge_results(): Merge Multiple Classification/Regression Results

merge_results(<regional_mvpa_result>): Merge regional MVPA results

multiway_classification_result(): Create a Multiway Classification Result Object

mvpa_dataset(): mvpa_dataset

mvpa_design(): Create an MVPA Design Object

mvpa_iterate(): MVPA Iteration for Voxel Sets with Parallelization

mvpa_model(): Create an MVPA Model

mvpa_surface_dataset(): mvpa_surface_dataset

nobs(): Get Number of Observations

nresponses(): Number of response categories

pairwise_dist(<cordist>): Compute Pairwise Correlation Distances

pairwise_dist(<euclidean>): Compute Pairwise Euclidean Distances

pairwise_dist(<mahalanobis>): Compute Pairwise Mahalanobis Distances

pairwise_dist(<robustmahadist>): Compute Pairwise Robust Mahalanobis Distances

performance(): Compute Performance Metrics for Classification/Regression Results

performance(<regression_result>): Calculate Performance Metrics for Regression Result

predict(<class_model_fit>): This function predicts class labels and probabilities for new data using a fitted model.

predict(<regression_model_fit>): Predict continuous values for a new dataset using a regression model

predicted_class(): Calculate the Predicted Class from Probability Matrix

prep_regional(): Prepare regional data for MVPA analysis

prob_observed(): Probability of observed class

rMVPA: rMVPA: A package for multi-voxel pattern analysis (MVPA)

regional_mvpa_result(): Create a regional_mvpa_result instance

regression_result(): Create a Regression Result Object

rsa_design(): Construct a design for an RSA (Representational Similarity Analysis) model

rsa_iterate(): rsa_iterate

rsa_model(): Construct an RSA (Representational Similarity Analysis) model

rsa_model_mat(): Construct a model matrix for an RSA (Representational Similarity Analysis) design

run_regional(): Region of interest based MVPA analysis

run_regional(<mvpa_model>): Run regional MVPA analysis on a specified MVPA model

run_regional(<rsa_model>): Run regional RSA analysis on a specified RSA model

run_searchlight(): run_searchlight

second_order_similarity(): Compute Second-Order Similarity Scores

select_features(<FTest>): Perform feature selection using the F-test method

select_features(): Select Features

select_features(<catscore>): Perform feature selection using the CATSCORE method

sub_result(): Extract Row-wise Subset of a Result Object

sub_result(<binary_classification_result>): Subset Binary Classification Result

sub_result(<multiway_classification_result>): Subset Multiway Classification Result

test_design(): Test Design Extraction

train_model(): Train Model

train_model(<feature_rsa_model>): Train an RSA Model

train_model(<manova_model>): Train a MANOVA Model

train_model(<mvpa_model>): Train an MVPA Model

train_model(<rsa_model>): Train an RSA Model

train_model(<vector_rsa_model>): Train a vector RSA model

tune_grid(): Tune Grid Extraction

twofold_blocked_cross_validation(): twofold_blocked_cross_validation

vector_rsa_design(): Construct a design for a vectorized RSA model

vector_rsa_model(): Create a vectorized RSA model

y_test(): Test Labels/Response Extraction

y_train(): Training Labels/Response Extraction