Package index • quadVAR

All functions

block_cv(): Use Block Cross-Validation to Evaluate Models

compare_4_emo(): Compare estimated model with true model for 4-emotion model

find_index(): Find index of data that satisfies certain conditions

get_adj_mat(): Extract the adjacency matrix from a quadVAR object.

linear_quadVAR_network() plot(<linear_quadVAR_network>): Linearize a quadVAR object to produce a network.

partial_plot(): Make a partial plot of a variable in a model This function takes a quadVAR model as input, and returns a plot of the partial effect of a variable on the dependent variable (controlling all other variables and the intercept), for higher and lower levels of the moderator variable split by the median.

predict(<quadVAR>): Predict the values of the dependent variables using the quadVAR model

quadVAR() print(<quadVAR>) summary(<quadVAR>) coef(<quadVAR>) print(<coef_quadVAR>) plot(<quadVAR>): Estimate lag-1 quadratic vector autoregression models

quadVAR_to_dyn_eqns(): Transform a quadVAR object to a list of dynamic equations.

sim_4_emo(): Simulate a 4-emotion model

true_model_4_emo() coef(<true_model_4_emo>) print(<true_model_4_emo>): True model for 4-emotion model

tune.fit(): Using the glmnet and ncvreg packages, fits a Generalized Linear Model or Cox Proportional Hazards Model using various methods for choosing the regularization parameter \(\lambda\)