prep_for_plots {OVtool} | R Documentation |
prep_for_plots
Description
Data preparation for producing the graphics and summary results.
Usage
prep_for_plots(r1, p_contours)
Arguments
r1 |
An object returned from ov_sim |
p_contours |
P-value countours to plot. The default plots: 0.01, 0.05, and 0.1. We only recommend changing this if the raw effect p-value is very close to one of these values. Do not specify more than four p-value contours. |
Value
prep_for_plots returns a list containing the following components:
r1 |
a list with the components returned from ov_simgrid |
r1_df |
a data frame with components used to create the contour graphic |
obs_cors |
a data frame with components used to plot the observed covariates on plot_graphic = "2" and plot_graphic = "3" |
text_high |
a character noting the covariates whose absolute correlation with the outcome is greater than the grid allows |
text_high_es |
a character noting the covariates with effect sizes greater than the maximum the plot will allow |
pvals |
a vector of p-value thresholds to be plotted on the graphics |
pval_lines |
a vector of line types to represent pvals |
raw |
a character with the raw effect and pvalue from the outcome model |
Examples
data(sud)
sud = data.frame(sud[sample(1:nrow(sud),100),])
sud$treat = ifelse(sud$treat == "A", 1, 0)
sud$wts = sample(seq(1, 10, by=.01), size=nrow(sud), replace = TRUE)
outcome_mod = outcome_model(data = sud,
weights = "wts",
treatment = "treat",
outcome = "eps7p_6",
model_covariates = c("sfs8p_0", "eps7p_0",
"ada_0"),
estimand = "ATE")
ovtool_results = ov_sim(model_results=outcome_mod,
plot_covariates=c("sfs8p_0", "ada_0"),
es_grid = 0,
rho_grid = 0,
n_reps = 2,
progress=FALSE)
prep = prep_for_plots(ovtool_results, p_contours=.05)