plot_dfa2 function

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

Archived on 7/23/2018. Please use plot_gdfa instead.

plot_dfa2(estimates, varcov = NULL, xrange, xname = "X",
  cvals = NULL, set_labels = NULL, set_panels = TRUE)

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.

Returns

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

Examples

# Fit Gamma discriminant function model for poolwise Xtilde vs. (Y, C),
# without assuming a constant log-OR. Ignoring processing errors for simplicity.
data(pdat2)
dat <- pdat2$dat
c.list <- pdat2$c.list
fit <- p_dfa_xerrors2(
  g = dat$g,
  y = dat$y,
  xtilde = dat$xtilde,
  c = c.list,
  errors = "neither",
  constant_or = FALSE
)

# Plot estimated log-OR vs. X at mean value for C
p <- plot_dfa2(
  estimates = fit$estimates,
  varcov = fit$theta.var,
  xrange = range(dat$xtilde / dat$g),
  cvals = mean(unlist(c.list))
)
p