plot_gdfa function

Plot Log-OR vs. X for Gamma Discriminant Function Approach

When p_gdfa is fit with constant_or = FALSE, the log-OR for X depends on the value of X (and covariates, if any). This function plots the log-OR vs. X for one or several sets of covariate values.

plot_gdfa(estimates, varcov = NULL, xrange, xname = "X",
  cvals = NULL, set_labels = NULL, set_panels = TRUE, ncol = 1)

Arguments

• estimates: Numeric vector of point estimates for (gamma_0, gamma_y, gamma_c^T, b1, b0).
• varcov: Numeric matrix with variance-covariance matrix for estimates. If NULL, 95% confidence bands are omitted.
• xrange: Numeric vector specifying range of X values to plot.
• xname: Character vector specifying name of X variable, for plot title and x-axis label.
• cvals: Numeric vector or list of numeric vectors specifying covariate values to use in log-odds ratio calculations.
• set_labels: Character vector of labels for the sets of covariate values. Only used if cvals is a list.
• set_panels: Logical value for whether to use separate panels for each set of covariate values, as opposed to using different colors on a single plot.
• ncol: Integer value specifying number of columns for multi-panel figure. Only used if there are multiple sets of covariate values (i.e. cvals is a list).

Returns

Plot of log-OR vs. X generated by ggplot.

Examples

# Fit Gamma discriminant function model for poolwise X vs. (Y, C), without
# assuming a constant log-OR. Note that data were generated with a constant
# log-OR of 0.5.
data(dat_p_gdfa)
dat <- dat_p_gdfa$dat
c.list <- dat_p_gdfa$c.list
fit <- p_gdfa(
  g = dat$g,
  y = dat$y,
  xtilde = dat$x,
  c = c.list,
  errors = "neither",
  constant_or = FALSE
)

# Plot estimated log-OR vs. X, holding C fixed at the sample mean.
p <- plot_gdfa(
  estimates = fit$estimates,
  varcov = fit$theta.var,
  xrange = range(dat$x[dat$g == 1]),
  cvals = mean(unlist(c.list))
)
p