Model stability and variable inclusion plots

Source: R/vis.R

Calculates and provides the plot methods for standard and bootstrap enhanced model stability plots (lvk and boot) as well as variable inclusion plots (vip).

vis(
  mf,
  nvmax,
  B = 100,
  lambda.max,
  nbest = "all",
  use.glmulti = FALSE,
  cores,
  force.in = NULL,
  screen = FALSE,
  redundant = TRUE,
  seed = NULL,
  ...
)

Arguments

mf

a fitted 'full' model, the result of a call to lm or glm (and in the future lme or lmer)
nvmax

size of the largest model that can still be considered as a viable candidate
B

number of bootstrap replications
lambda.max

maximum penalty value for the vip plot, defaults to 2*log(n)
nbest

maximum number of models at each model size that will be considered for the lvk plot. Can also take a value of "all" which displays all models.
use.glmulti

logical. Whether to use the glmulti package instead of bestglm. Default use.glmulti=FALSE.
cores

number of cores to be used when parallel processing the bootstrap
force.in

the names of variables that should be forced into all estimated models. (Not yet implemented.)
screen

logical, whether or not to perform an initial screen for outliers. Highly experimental, use at own risk. Default = FALSE.
redundant

logical, whether or not to add a redundant variable. Default = TRUE.
seed

random seed for reproducible results
...

further arguments (currently unused)

Details

The result of this function is essentially just a list. The supplied plot method provides a way to visualise the results.

See ?plot.vis or help("plot.vis") for details of the plot method associated with the result.

References

Mueller, S. and Welsh, A. H. (2010), On model selection curves. International Statistical Review, 78:240-256. doi: 10.1111/j.1751-5823.2010.00108.x

Murray, K., Heritier, S. and Mueller, S. (2013), Graphical tools for model selection in generalized linear models. Statistics in Medicine, 32:4438-4451. doi: 10.1002/sim.5855

Tarr G, Mueller S and Welsh AH (2018). mplot: An R Package for Graphical Model Stability and Variable Selection Procedures. Journal of Statistical Software, 83(9), pp. 1-28. doi: 10.18637/jss.v083.i09

See also

plot.vis

Examples

n = 100
set.seed(11)
e = rnorm(n)
x1 = rnorm(n)
x2 = rnorm(n)
x3 = x1^2
x4 = x2^2
x5 = x1*x2
y = 1 + x1 + x2 + e
dat = data.frame(y, x1, x2, x3, x4, x5)
lm1 = lm(y ~ ., data = dat)
# \dontshow{
v1 = vis(lm1, B = 5, cores = 1, seed = 1)
plot(v1, highlight = "x1", which = "lvk")

plot(v1, which = "boot")

plot(v1, which = "vip")

# }
if (FALSE) {
v1 = vis(lm1, seed = 1)
plot(v1, highlight = "x1", which = "lvk")
plot(v1, which = "boot")
plot(v1, which = "vip")
}