plot_MAC: Visualize model confidence sets

In maclogp: Measures of Uncertainty for Model Selection

Description

This funcion generates a heat map for a given model confidence set. Each row represents a model in the confidence set, and colored cell represents the variables in that model.

Usage

plot_MAC(models, alpha, con_sets, p, xnames = NULL, color = "lightblue")

Arguments

models	A list with one entry for each model. Each entry is an integer vector that specifies the columns of matrix X without intercept to be used as a regressor in that model. Intercept will be fitted automatically for every model. such as 1:p.
alpha	Significance levels. The confidence levels for confidence sets are 1-alpha.
con_sets	a list with with one entry for a 1-alpha model confidence set. Each entry is an integer vector that specifies the models selected in this set. The model indexes used in con_sets are their orders in models.
p	the number of candidate variables.
xnames	variable names of all candidate variables. Default is 1:p.
color	the color that indicates a variable is selected. Default is "lightblue".

Value

Returns a logical matrix per confidence set with one row per model and one column per variable indicating whether that variable is in the model.

Generates a corresponding heat map per confidence set with one row per model and one column per variable indicating whether that variable is in the model. A cell in white means the variable is not in that model; a cell in user-specified color means the variable is in that model.

See Also

MAC

Examples

n= 50
B= 100
p= 5
x = matrix(rnorm(n*p, mean=0, sd=1), n, p)
true_b = c(1:3, rep(0,p-3))
y = x%*% true_b+rnorm(n)
alpha=c(0.1,0.05,0.01)
data=list(x=x,y=y)
models=Models_gen(1:p)
result=MAC(models, data, B, alpha)
plot_MAC(models, alpha, result$con_sets, p)
result2=bms(data, alpha)
plot_MAC(result2$models, alpha, result2$con_sets, p)

maclogp documentation built on April 22, 2021, 9:06 a.m.