vennLasso (version 0.1.1)

plot.cv.vennLasso: Plot method for cv.vennLasso fitted objects

Description

Plot method for cv.vennLasso fitted objects

Prediction method for vennLasso fitted objects

Usage

# S3 method for cv.vennLasso
plot(x, sign.lambda = 1, ...)
# S3 method for vennLasso
plot(x, which.subpop = 1, xvar = c("norm", "lambda",
  "loglambda", "dev"), xlab = iname, ylab = "Coefficients", ...)

Arguments

x

fitted vennLasso or cv.vennLasso model object

sign.lambda

Either plot against log(lambda) (default) or its negative if sign.lambda = -1.

...

other graphical parameters for the plot

which.subpop

which row in the coefficient matrix should be plotting? Each row corresponds to a particular combination of the specified stratifying variables

xvar

What is on the X-axis. "norm" plots against the L1-norm of the coefficients, "lambda" against the log-lambda sequence, and "dev" against the percent deviance explained.

xlab

character value supplied for x-axis label

ylab

character value supplied for y-axis label

Examples

# NOT RUN {
set.seed(123)

dat.sim <- genHierSparseData(ncats = 3, nvars = 25,
                             nobs = 100, 
                             hier.sparsity.param = 0.5,
                             prop.zero.vars = 0.5,
                             effect.size.max = 0.25,
                             family = "gaussian")

x        <- dat.sim$x
x.test   <- dat.sim$x.test
y        <- dat.sim$y
y.test   <- dat.sim$y.test
grp      <- dat.sim$group.ind
grp.test <- dat.sim$group.ind.test

fit.adapt <- cv.vennLasso(x, y,
                          grp,
                          adaptive.lasso = TRUE,
                          nlambda        = 25,
                          nfolds         = 4)
                                     
plot(fit.adapt) 

library(Matrix)

set.seed(123)
n.obs <- 200
n.vars <- 50

true.beta.mat <- array(NA, dim = c(3, n.vars))
true.beta.mat[1,] <- c(-0.5, -1, 0, 0, 2, rep(0, n.vars - 5))
true.beta.mat[2,] <- c(0.5, 0.5, -0.5, -0.5, 1, -1, rep(0, n.vars - 6))
true.beta.mat[3,] <- c(0, 0, 1, 1, -1, rep(0, n.vars - 5))
rownames(true.beta.mat) <- c("1,0", "1,1", "0,1")
true.beta <- as.vector(t(true.beta.mat))

x.sub1 <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)
x.sub2 <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)
x.sub3 <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)

x <- as.matrix(rbind(x.sub1, x.sub2, x.sub3))

conditions <- as.matrix(cbind(c(rep(1, 2 * n.obs), rep(0, n.obs)),
                              c(rep(0, n.obs), rep(1, 2 * n.obs))))

y <- rnorm(n.obs * 3, sd = 3) + drop(as.matrix(bdiag(x.sub1, x.sub2, x.sub3)) %*% true.beta)

fit <- vennLasso(x = x, y = y, groups = conditions)

layout(matrix(1:3, ncol = 3))
plot(fit, which.subpop = 1)
plot(fit, which.subpop = 2)
plot(fit, which.subpop = 3)

# }

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