oem (version 2.0.12)

plot.oem: Plot method for Orthogonalizing EM fitted objects

Description

Plot method for Orthogonalizing EM fitted objects

Plot method for Orthogonalizing EM fitted objects

Usage

# S3 method for oem
plot(
  x,
  which.model = 1,
  xvar = c("norm", "lambda", "loglambda", "dev"),
  labsize = 0.6,
  xlab = iname,
  ylab = NULL,
  main = x$penalty[which.model],
  ...
)
# S3 method for cv.oem
plot(x, which.model = 1, sign.lambda = 1, ...)
# S3 method for xval.oem
plot(
  x,
  which.model = 1,
  type = c("cv", "coefficients"),
  xvar = c("norm", "lambda", "loglambda", "dev"),
  labsize = 0.6,
  xlab = iname,
  ylab = NULL,
  main = x$penalty[which.model],
  sign.lambda = 1,
  ...
)

Arguments

x

fitted "oem" model object

which.model

If multiple penalties are fit and returned in the same oem object, the which.model argument is used to specify which model to plot. For example, if the oem object "oemobj" was fit with argument penalty = c("lasso", "grp.lasso"), then which.model = 2 provides a plot for the group lasso model.

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.

labsize

size of labels for variable names. If labsize = 0, then no variable names will be plotted

xlab

label for x-axis

ylab

label for y-axis

main

main title for plot

...

other graphical parameters for the plot

sign.lambda

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

type

one of "cv" or "coefficients". type = "cv" will produce a plot of cross validation results like plot.cv.oem. type = "coefficients" will produce a coefficient path plot like plot.oem()

Examples

set.seed(123)
n.obs <- 1e4
n.vars <- 100
n.obs.test <- 1e3

true.beta <- c(runif(15, -0.5, 0.5), rep(0, n.vars - 15))

x <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)
y <- rnorm(n.obs, sd = 3) + x %*% true.beta

fit <- oem(x = x, y = y, penalty = c("lasso", "grp.lasso"), groups = rep(1:10, each = 10))

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

set.seed(123)
n.obs <- 1e4
n.vars <- 100
n.obs.test <- 1e3

true.beta <- c(runif(15, -0.5, 0.5), rep(0, n.vars - 15))

x <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)
y <- rnorm(n.obs, sd = 3) + x %*% true.beta

fit <- cv.oem(x = x, y = y, penalty = c("lasso", "grp.lasso"), groups = rep(1:10, each = 10))

layout(matrix(1:2, ncol = 2))
plot(fit, which.model = 1)
plot(fit, which.model = "grp.lasso")

set.seed(123)
n.obs <- 1e4
n.vars <- 100
n.obs.test <- 1e3

true.beta <- c(runif(15, -0.5, 0.5), rep(0, n.vars - 15))

x <- matrix(rnorm(n.obs * n.vars), n.obs, n.vars)
y <- rnorm(n.obs, sd = 3) + x %*% true.beta

fit <- xval.oem(x = x, y = y, penalty = c("lasso", "grp.lasso"), groups = rep(1:10, each = 10))

layout(matrix(1:4, ncol = 2))
plot(fit, which.model = 1)
plot(fit, which.model = 2)

plot(fit, which.model = 1, type = "coef")
plot(fit, which.model = 2, type = "coef")