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Plots of Estimated Marginal Effects in Explanatory IRT Models

Source: R/marginalplot.R
marginalplot.Rd

This function uses ggpredict to calculate marginal effects for explanatory variables in an explanatory IRT model estimated with the eirm function. It returns a plot of estimated probabilities generated by the explanatory IRT model while holding some predictors constant.

Usage

marginalplot(x, predictors, conf.int = 0.95, plot.title = NULL)

Arguments

x

An eirm object returned from the eirm function.

predictors

Character vector with the names of up to three categorical predictors from the eirm model. The first predictor is plotted on the x-axis; the second predictor is used as a group variable; the third predictor is used as a facet in the plot.

conf.int

Confidence interval to be used in the plot (default = 0.95 for 95% confidence intervals).

plot.title

A title to be used in the plot.

Value

A ggplot2 object.

Examples

data("VerbAgg")
mod <- eirm(formula = "r2 ~ -1 + situ + btype + mode + (1|id)", data = VerbAgg)

# Only one predictor
p1 <- marginalplot(mod, predictors = c("situ"))

# Two predictors
p2 <- marginalplot(mod, predictors = c("situ", "btype"))

# All three predictors
p3 <- marginalplot(mod, predictors = c("situ", "btype", "mode"))