TrioSGL (version 1.1.0)

TrioSGL: Trio Model with a Combination of Lasso and Group Lasso Regularization

Description

Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.

Usage

TrioSGL(X, index = NULL, maxit = 10000, thresh = 0.0001, min.frac = 0.01, 
    nlam = 25, lambdas = NULL, alpha = 0.95, gamma = 0.8, step = 1, reset = 20, 
		standardize = FALSE, verbose = FALSE)

Arguments

X

$X$ should be an input matrix of dimension n-obs by p-vars. The number of rows must be a multiple of 4 (case followed by 3 pseudo-controls).

index

A p-vector indicating group membership of each covariate

maxit

Maximum number of iterations to convergence

thresh

Convergence threshold for change in beta

min.frac

The minimum value of the penalty parameter, as a fraction of the maximum value

nlam

Number of lambda to use in the regularization path

lambdas

A user specified sequence of lambda values for fitting. We recommend leaving this NULL and letting TrioSGL self-select values

alpha

The mixing parameter. alpha = 1 is the lasso penalty. alpha = 0 is the group lasso penalty.

gamma

Fitting parameter used for tuning backtracking (between 0 and 1)

step

Fitting parameter used for inital backtracking step size (between 0 and 1)

reset

Fitting parameter used for taking advantage of local strong convexity in nesterov momentum (number of iterations before momentum term is reset)

standardize

Logical flag for variable standardization prior to fitting the model.

verbose

Logical flag for whether or not step number will be output

Value

An object with S3 class "TrioSGL"

beta

A p by nlam matrix, giving the penalized MLEs for the nlam different models, where the index corresponds to the penalty parameter lambda

lambdas

The actual sequence of lambda values used (penalty parameter)

X.transform

A list used in predict which gives the empirical mean and variance of the x matrix used to build the model

Details

The sequence of models along the regularization path is fit by accelerated generalized gradient descent.

References

Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011). A Sparse-Group Lasso. Journal of Computational and Graphical Statistics, 22, 231-245.

Examples

# NOT RUN {
trios <- 4 * 10; snps <- 20; size.groups <- 4
index <- ceiling(1:snps / size.groups)
x <- floor(matrix(runif(trios * snps, min = 0, max = 3), ncol = snps, nrow = trios))
fit <- TrioSGL(x, index)
# }