cv.galasso: Cross Validated Multiple Imputation Grouped Adaptive LASSO
In miselect: Variable Selection for Multiply Imputed Data

Description Usage Arguments Details Value References Examples

Does k-fold cross-validation for galasso, and returns an optimal value for lambda.

cv.galasso(
  x,
  y,
  pf,
  adWeight,
  family = c("gaussian", "binomial"),
  nlambda = 100,
  lambda.min.ratio = 1e-04,
  lambda = NULL,
  nfolds = 5,
  foldid = NULL,
  maxit = 10000,
  eps = 1e-05
)

`x`	A length `m` list of `n * p` numeric matrices. No matrix should contain an intercept, or any missing values
`y`	A length `m` list of length `n` numeric response vectors. No vector should contain missing values
`pf`	Penalty factor. Can be used to differentially penalize certain variables
`adWeight`	Numeric vector of length p representing the adaptive weights for the L1 penalty
`family`	The type of response. "gaussian" implies a continuous response and "binomial" implies a binary response. Default is "gaussian".
`nlambda`	Length of automatically generated 'lambda' sequence. If lambda' is non NULL, 'nlambda' is ignored. Default is 100
`lambda.min.ratio`	Ratio that determines the minimum value of 'lambda' when automatically generating a 'lambda' sequence. If 'lambda' is not NULL, 'lambda.min.ratio' is ignored. Default is 1e-4
`lambda`	Optional numeric vector of lambdas to fit. If NULL, `galasso` will automatically generate a lambda sequence based off of `nlambda` and codelambda.min.ratio. Default is NULL
`nfolds`	Number of foldid to use for cross validation. Default is 5, minimum is 3
`foldid`	an optional length `n` vector of values between 1 and `cv.galasso` will automatically generate folds
`maxit`	Maximum number of iterations to run. Default is 10000
`eps`	Tolerance for convergence. Default is 1e-5

cv.galasso works by adding a group penalty to the aggregated objective function to ensure selection consistency across imputations. Simulations suggest that the "stacked" objective function approaches (i.e., saenet) tend to be more computationally efficient and have better estimation and selection properties.

An object of type "cv.galasso" with 7 elements:

call: The call that generated the output.
lambda: The sequence of lambdas fit.
cvm: Average cross validation error for each 'lambda'. For family = "gaussian", 'cvm' corresponds to mean squared error, and for binomial 'cvm' corresponds to deviance.
cvse: Standard error of 'cvm'.
galasso.fit: A 'galasso' object fit to the full data.
lambda.min: The lambda value for the model with the minimum cross validation error.
lambda.1se: The lambda value for the sparsest model within one standard error of the minimum cross validation error.
df: The number of nonzero coefficients for each value of lambda.

Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods. Jiacong Du, Jonathan Boss, Peisong Han, Lauren J Beesley, Stephen A Goutman, Stuart Batterman, Eva L Feldman, and Bhramar Mukherjee. 2020. arXiv:2003.07398

library(miselect)
library(mice)

set.seed(48109)

# Using the mice defaults for sake of example only.
mids <- mice(miselect.df, m = 5, printFlag = FALSE)
dfs <- lapply(1:5, function(i) complete(mids, action = i))

# Generate list of imputed design matrices and imputed responses
x <- list()
y <- list()
for (i in 1:5) {
    x[[i]] <- as.matrix(dfs[[i]][, paste0("X", 1:20)])
    y[[i]] <- dfs[[i]]$Y
}

pf       <- rep(1, 20)
adWeight <- rep(1, 20)

fit <- cv.galasso(x, y, pf, adWeight)

# By default 'coef' returns the betas for lambda.min.
coef(fit)