plot.post_jointVIP {jointVIP}

plot the post_jointVIP object this plot uses the same custom options as the jointVIP object

Description

plot the post_jointVIP object this plot uses the same custom options as the jointVIP object

Usage

## S3 method for class 'post_jointVIP'
plot(
  x,
  ...,
  smd = "cross-sample",
  use_abs = TRUE,
  plot_title = "Joint Variable Importance Plot",
  add_post_labs = TRUE,
  post_label_cut_bias = 0.005
)

Arguments

x

a post_jointVIP object

...

custom options: bias_curve_cutoffs, text_size, max.overlaps, label_cut_std_md, label_cut_outcome_cor, label_cut_bias, bias_curves, add_var_labs, expanded_y_curvelab

smd

specify the standardized mean difference is cross-sample or pooled

use_abs

TRUE (default) for absolute measures

plot_title

optional string for plot title

add_post_labs

TRUE (default) show post-measure labels

post_label_cut_bias

0.005 (default) show cutoff above this number; suppressed if show_post_labs is FALSE

Value

a post-analysis joint variable importance plot of class ggplot

Examples

data <- data.frame(year = rnorm(50, 200, 5),
                   pop = rnorm(50, 1000, 500),
                   gdpPercap = runif(50, 100, 1000),
                   trt = rbinom(50, 1, 0.5),
                   out = rnorm(50, 1, 0.2))
# random 20 percent of control as pilot data                  
pilot_sample_num = sample(which(data$trt == 0),
                          length(which(data$trt == 0)) *
                          0.2)
pilot_df = data[pilot_sample_num, ]
analysis_df = data[-pilot_sample_num, ]
treatment = "trt"
outcome = "out"
covariates = names(analysis_df)[!names(analysis_df)
                                %in% c(treatment, outcome)]
new_jointVIP = create_jointVIP(treatment = treatment,
                               outcome = outcome,
                               covariates = covariates,
                               pilot_df = pilot_df,
                               analysis_df = analysis_df)
                               
## at this step typically you may wish to do matching or weighting
## the results after can be stored as a post_data
## the post_data here is not matched or weighted, only for illustrative purposes
post_data <- data.frame(year = rnorm(50, 200, 5),
                        pop = rnorm(50, 1000, 500),
                        gdpPercap = runif(50, 100, 1000),
                        trt = rbinom(50, 1, 0.5),
                        out = rnorm(50, 1, 0.2))
post_dat_jointVIP = create_post_jointVIP(new_jointVIP, post_data)
plot(post_dat_jointVIP)