cat_lmm_tune {catalytic} | R Documentation |
Catalytic Linear Mixed Model (LMM) Fitting Function by Tuning tau from a Sequence of tau Values
Description
This function tunes a catalytic linear mixed model by performing cross-validation
to estimate the optimal value of the tuning parameter tau. It finally uses the optimal tau value
in the lmer
function from the lme4
package for model fitting. (Only consider one random effect variance)
Usage
cat_lmm_tune(cat_init, tau_seq = NULL, cross_validation_fold_num = 5)
Arguments
cat_init |
A list generated from |
tau_seq |
A numeric vector specifying the sequence of tau values to be tested. If NULL, a default sequence is generated based on the number of predictors. |
cross_validation_fold_num |
An integer representing the number of folds for cross-validation. Defaults to 5. |
Value
A list containing the values of all the arguments and the following components:
tau |
The optimal tau value determined from cross-validation. |
model |
The fitted lmer model object by using the optimal tau value. |
coefficients |
Coefficients of the fitted model by using the optimal tau value. |
risk_estimate_list |
Average prediction errors for each tau value. |
Examples
data(mtcars)
cat_init <- cat_lmm_initialization(
formula = mpg ~ wt + (1 | cyl), # formula for simple model
data = mtcars,
x_cols = c("wt"), # Fixed effects
y_col = "mpg", # Response variable
z_cols = c("disp", "hp", "drat", "qsec", "vs", "am", "gear", "carb"), # Random effects
group_col = "cyl", # Grouping column
syn_size = 100, # Synthetic data size
resample_by_group = FALSE, # Resampling option
resample_only = FALSE, # Resampling method
na_replace = mean # NA replacement method
)
cat_model <- cat_lmm_tune(
cat_init = cat_init, # Only accept object generated from cat_lmm_initialization
tau_seq = c(1, 2), # Vector of weights for synthetic data
cross_validation_fold_num = 3 # number of folds for cross-validation
)
cat_model