smog

Structural Modeling by using Overlapped Group Penalty

Installation

# Install smog from CRAN
install.packages("smog")

# or install the source type package from GitHub:
# install.packages("devtools")
devtools::install_github("chongma8903/smog")

Features

• fits a linear non-penalized phenotype (demographic) variables and penalized groups of prognostic effect and predictive effect.
• satisfies such hierarchy structures that if a predictive effect exists, its prognostic effect must also exist.
• can deal with continuous, binomial or multinomial, and survival response variables.
• incorporates the iterative shrinkage-thresholding algorithm (ISTA) and the alternating direction method of multipliers algorithms (ADMM).

Usage

Create a new S3 class of smog, and the kernal function smog.default (or smog.formula) returns an object of the S3 class smog. The kernel functions include:

• smog.default: input the data and parameters to yield a model of the class smog.
• smog.formula: can accept formula to fit the model for the data.
• predict.smog: produces the predicted response values for new data, provided a fitted model.
• cv.smog: provides cross-validation analysis based on the data.
  
• cv.cglasso: cross-validation for conditional group lasso approach

Examples

sim = sim_rct_biomarker(n = 100, p = 20, p_prog = 2, p_pred = 2, p_both = 2)
y = sim$Y
x = sim$M
d = 20
g = c(d+1, rep(1:d,2))
v = c(rep(0,1), rep(1,2*d))
label = c("trt", rep(c("prog","pred"), c(d,d)))

sfit1 = cv.smog(x,y,g,v,label,family = "gaussian", type = "AIC")
plot(sfit1)

sfit2 = cv.cglasso(x,y,g,v,label,family = "gaussian", nlambda.max = 20)
plot(sfit2)