plot.amce: Plot AMCE Estimates

Description

plot method for "amce" objects

Usage

## S3 method for class 'amce':
plot(x, main = "", xlab = "Change in E[Y]", ci = 0.95,
                    colors = NULL, xlim = NULL, breaks = NULL,
                    labels = NULL, attribute_names = NULL, level_names = NULL,
                    label.baseline = TRUE, text.size = 11, text.color = "black",
                    point.size = 0.6, plot.theme = NULL, facet.name = NULL, 
                    facet.levels = NULL, show.all =	FALSE, 
                    show.interaction.base = FALSE, ...)

Arguments

An object of class "amce", a result of a call to amce

main

Title of the plot.

xlab

Label of the x-axis of the plot (AMCE or ACIE). Default is "Change in E[Y]"

Levels for confidence intervals to plot around point estimates. Must be between 0 and 1. Default is .95

colors

Vector of color names to be used in the plot. The plot function will alternate between the colors in the vector for each attribute being plotted. If NULL, plot will use a default ggplot2 color scheme.

xlim

Numeric vector denoting the upper and lower bounds of the x-axis in the plot. If NULL the plot function will automatically set a range that includes all effect estimates.

breaks

Numeric vector denoting where x-axis tick marks should be placed. If NULL plot will use ggplot2 defaults.

labels

Vector denoting how x-axis tick marks should be labeled. If NULL plot will use ggplot2 defaults.

attribute_names

Character vector of attribute names to be plotted as labels. By default plot.amce will use the attribute names in the "amce" object passed to it.

level_names

A list containing character vector elements with names in attribute_names. Each character vector in the list contains the level names to be plotted as labels beneath the corresponding attribute. By default plot.amce will use the

label.baseline

If TRUE, the baseline levels for each attribute will be labeled as such. Defaults to TRUE.

text.size

Size of text. Defaults to 11.

text.color

Color of text in plot. Defaults to "black".

point.size

Size of points in the plot. Defaults to 0.6.

plot.theme

A ggplot2 'theme' object to be added to the plot. If NULL, defaults to black-and-white theme. Note that passing a theme object will override text and point color/size options.

facet.name

To facet plots (i.e., make separate plots for each value of a variable) give "facet.name" the name of the variable (either profile attribute or respondent-varying) to facet by. Unless given specific levels in "facet.levels", the plotted levels will consis

facet.levels

To manually set facet levels, provide a list to "facet.levels". Names of list entries should correspond with variable names. The content of each entry should be a vector giving the desired levels, whether factors or continuous, with the rows named with th

show.all

A logical asking if variables that do not vary by facet level should also have their estimates reported in all of the facetted plots.

show.interaction.base

A logical asking if the base term of an interaction sbould be included in facetted plots.

...

Other graphical parameters passed to ggplot.

Value

A ggplot object containing a dotplot of estimated AMCEs.

References

Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30

Examples

Run this code

# Immigration Choice Conjoint Experiment Data from Hainmueller et. al. (2014).
data("immigrationconjoint")
data("immigrationdesign")

# Run AMCE estimator using all attributes in the design
results <- amce(Chosen_Immigrant ~  Gender + Education + `Language Skills` +
                `Country of Origin` + Job + `Job Experience` + `Job Plans` +
                `Reason for Application` + `Prior Entry`, data=immigrationconjoint,
                cluster=TRUE, respondent.id="CaseID", design=immigrationdesign)

# Plot results
plot(results, xlab="Change in Pr(Immigrant Preferred for Admission to U.S.)",
    ylim=c(-.3,.3), breaks=c(-.2, 0, .2), labels=c("-.2","0",".2"), text.size=13)

# Run AMCE estimator with an interaction with a respondent-varying characteristic
interaction_results <- amce(Chosen_Immigrant ~ Gender + Education
		    + Job + ethnocentrism*Job,
		    data = subset(immigrationconjoint, !is.na(immigrationconjoint$ethnocentrism)),
        cluster=TRUE, respondent.id="CaseID",
		    design = immigrationdesign,
		    respondent.varying = c("ethnocentrism"))

# Plot results with different plots for quantiles of the respondent-varying characteristic
# Do not show output for variables that do not vary with facetted levels
plot(interaction_results,facet.name = c("ethnocentrism"))

# RUN AMCE estimator with an interaction between two factor variables
interaction_results <- amce(Chosen_Immigrant ~ Gender + Education + Job
		    + Education*Job, data = immigrationconjoint,
		    cluster = FALSE, design = immigrationdesign)

# Plot results with different plots for all levels of ACIE
plot(interaction_results,facet.name = c("Education"))

# Plot results with different plots for only two levels of one interacted variable
facet.levels1 <- list()
facet.levels1[["Education"]] <- data.frame(c("college degree","graduate degree"))
rownames(facet.levels1[["Education"]]) <- c("college degree","graduate degree")

plot(interaction_results,facet.name = c("Education"),facet.levels = facet.levels1)