condPlot function

Conditional effects plot

Creates a plot of the relationships between two variables at different levels of the moderator. Only works for relationships that include an interaction.

condPlot(
  out,
  to,
  from,
  swap = FALSE,
  avg = FALSE,
  compare = NULL,
  hist = FALSE,
  xlab = NULL,
  mods = NULL,
  nsims = 500,
  xn = NULL,
  getCIs = FALSE,
  discrete = FALSE,
  ylab = NULL,
  main = NULL,
  midline = TRUE
)

Arguments

• out: Output from fitNetwork or resample. Can also provide the fixedNets or betweenNet element of the mlGVAR output.
• to: Outcome variable, specified with character string or numeric value.
• from: Predictor variable, specified with character string or numeric value.
• swap: Logical. Serves to switch the arguments for to and from.
• avg: Logical. If TRUE then the average relationship between the two variables is displayed. Only works for GGMs.
• compare: Two values can be supplied to indicate levels of the moderator to be compared.
• hist: Logical. Determines whether to show a histogram of the data distribution at the bottom of the plot.
• xlab: Character string for labeling the x-axis.
• mods: This argument will be removed. Model output is automatically detected based on fit argument.
• nsims: Number of iterations to simulate the posterior distribution.
• xn: Numeric value to indicate how many values of the moderator should be evaluated.
• getCIs: Logical. Only applies when avg = TRUE. If getCIs = TRUE, then the confidence intervals for the average difference between the maximum and minimum of the moderator will be returned.
• discrete: Logical. Determines whether to treat the moderator as a discrete or continuous variable.
• ylab: Character string for labeling the y-axis.
• main: Character string for labeling the title of the plot.
• midline: Logical. Only applies when discrete = TRUE. Shows a line at the average level of the outcome.

Returns

A plot of the conditional effects of one variable on another given different levels of the moderator.

Examples

fit <- fitNetwork(ggmDat, 'M')
condPlot(fit, to = 'V5', from = 'V4')
condPlot(fit, to = 2, from = 3, avg = TRUE)

See Also

fitNetwork, resample