plot_dfa: Plot Log-OR vs. X for Normal Discriminant Function Approach
In pooling: Fit Poolwise Regression Models

Description Usage Arguments Value Examples

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

1 2	plot_dfa(estimates, varcov = NULL, xrange, xname = "X", cvals = NULL, set_labels = NULL, set_panels = TRUE)

`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.

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

# 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