pooling (version 1.1.2)

plot_dfa: Plot Log-OR vs. X for Normal Discriminant Function Approach

Description

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

Usage

plot_dfa(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, sigsq).

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.

Value

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

Examples

# NOT RUN {
# Fit discriminant function model for poolwise Xtilde vs. (Y, C), without
# assuming a constant log-OR. Ignoring processing errors for simplicity.
data(pdat1)
fit <- p_dfa_xerrors(g = pdat1$g, y = pdat1$numcases, xtilde = pdat1$xtilde,
                     c = pdat1$c, errors = "neither", constant_or = FALSE)

# Plot estimated log-OR vs. X at mean value for C
p <- plot_dfa(estimates = fit$estimates, varcov = fit$theta.var,
              xrange = range(pdat1$xtilde / pdat1$g),
              cvals = mean(pdat1$c / pdat1$g))
p


# }