plot.amce: Plot AMCE Estimates

Description

plot method for "amce" objects

Usage

# S3 method for 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 = 0.5, dodge.size = 0.9, plot.theme = NULL,
                    plot.display = "all",
					facet.names = NULL, facet.levels = NULL, 
					group.order = NULL,font.family = NULL, ...)

Value

A ggplot object containing a dotplot of estimated AMCEs.

Arguments

x: 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]"
ci: 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 for points and confidence intervals. 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 level names in the "amce" object passed to it.
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 0.5.
dodge.size: Width to dodge overlaps to the side. Defaults to 0.9.
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.
plot.display: Character string, one of "all", "unconditional", or "interaction". Option "all" will display both unconditional and interaction estimates. The "unconditional" option will display only 1 plot for unconditional estimates (both AMCE and ACIE) ignoring any facets provided to "facet.names" or respondent-varying characteristics. Option "interaction" will drop the unconditional plot and instead display only (unconditional) ACIE's or estimates conditional on respondent-varying characteristics as specified in the user-supplied option "facet.names". Defaults to "all".
facet.names: To facet plots (i.e., make separate plots for each value of a variable) give "facet.names" a vector of character strings containing the names of the variable(s) (either profile attribute or respondent-varying) to facet by. Unless given specific levels in "facet.levels", the plotted levels will consist of all levels of a factor variable or the quantiles of a continuous variable. Multiple facet variables cannot currently be varied at the same time within the same plot. Instead conditional effects will be calculated for one facet at a time while others are held at their first value in facet.levels, by default the bottom quantile for continuous variables and the baseline for factors.
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. To change the displayed names of the levels, assign names to each vector entry.
group.order: To manually set the order of the attributes, provide a vector to "group.order". Names of the vector entries should correspond with name of the attribute.
...: Other graphical parameters passed to ggplot.
font.family: Will be passed to the ggplot function as the argument for font.family. If NULL, defaults will be used.

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

if (FALSE) {
# 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)
	
# Plot results with user-specified order of attributes
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,
     group.order=c("Gender","Education","Job",
                     "Language Skills","Job Experience",
                     "Job Plans","Reason for Application",
                     "Prior Entry","Country of Origin"))


# Run AMCE estimator with an interaction with a respondent-varying characteristic
interaction_results <- amce(Chosen_Immigrant ~ Gender + Education
		    + Job + ethnocentrism:Job,
		    data = immigrationconjoint,na.ignore=TRUE,
		    design = immigrationdesign, cluster = FALSE,
		    respondent.varying = "ethnocentrism")

# Plot results with additional plots for quantiles of the respondent-varying characteristic
plot(interaction_results)

# Plot results with user-specified order of attributes
plot(interaction_results, group.order=c("Gender","Education","Job"))

# Do not show output for variables that do not vary with facetted levels
plot(interaction_results,plot.display="unconditional")

# 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.names = "Education")


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

# Display only interaction panes
plot(interaction_results,facet.names = "Education",plot.display="interaction")

#Display only non-interaction panes
plot(interaction_results,facet.names = "Education",plot.display="unconditional")

#Change displayed attribute and level names
results <- amce(Chosen_Immigrant ~ Gender + Education + Job, data = immigrationconjoint,
		    cluster = FALSE, design = immigrationdesign)
levels.test<-list()
levels.test[["Gender"]]<-c("level1","level2")
levels.test[["Education"]]<-c("level1","b","c","d","e","f","g")
levels.test[["Job"]]<-c("a","b","c","d","e","f","g","h","i","j","k")
plot(results, level.names = levels.test, main="test", xlab="test", 
ci=0.95, breaks=c(-0.2,-0.1,0,0.1,0.2), attribute.names = c("attribute1","attribute2","attribute3"))

}