Plot method for Orthogonalizing EM fitted objects

# 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)
#> Warning: collapsing to unique 'x' values

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")
#> Warning: collapsing to unique 'x' values