marginalplot: Plots of Estimated Marginal Effects in Explanatory IRT Models

In okanbulut/eirm: Explanatory Item Response Modeling for Dichotomous and Polytomous Items

Plots of Estimated Marginal Effects in Explanatory IRT Models

Description

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"))

okanbulut/eirm documentation built on March 5, 2023, 6:22 a.m.