plot.UPG.MNL {UPG} | R Documentation |
plot
generates plots from UPG.MNL
objects using ggplot2
. Coefficient plots show point estimates for all coefficients in all groups except the baseline as well as their credible intervals.
## S3 method for class 'UPG.MNL' plot( x = NULL, ..., sort = FALSE, names = NULL, groups = NULL, xlab = NULL, ylab = NULL, q = c(0.025, 0.975), include = NULL )
x |
an object of class |
... |
other plot parameters. |
sort |
a logical variable indicating whether the plotted coefficients should be sorted according to average effect sizes across groups. Default is FALSE. |
names |
a character vector indicating names for the variables used in the plots. |
groups |
a character vector indicating names for the groups excluding the baseline. The group names must correspond to the ordering in the dependent variable used for estimation. |
xlab |
a character vector of length 1 indicating a title for the x-axis. |
ylab |
a character vector of length 1 indicating a title for the y-axis. |
q |
a numerical vector of length two holding the posterior quantiles to be extracted. Default are 0.025 and 0.975 quantiles. |
include |
can be used to plot only a subset of variables. Specificy the columns of X that should be kept in the plot. See examples for further information. |
Returns a ggplot2 object.
Gregor Zens
summary.UPG.MNL
to summarize the estimates of a discrete choice model from an UPG.MNL
object and create tables.
predict.UPG.MNL
to predict probabilities from a discrete choice model from an UPG.MNL
object.
coef.UPG.MNL
to extract coefficients from an UPG.MNL
object.
# estimate a multinomial logit model using example data library(UPG) data(program) y = program[,1] X = program[,-1] results.mnl = UPG(y = y, X = X, type = "mnl") # plot the results and sort coefficients by average effect size plot(results.mnl, sort = TRUE) # plot only variables 1 and 3 with custom group and variable names # also, customize credible intervals and axis labels plot(results.mnl, include = c(1,3), names = c("Custom 1", "Custom 2"), groups = c("Alpha", "Beta"), q = c(0.1, 0.9), xlab = c("Custom X"), ylab = c("Custom Y"))