Plot Legislator/Person Over-time Variances

Description

This function can be used on a fitted idealstan object to plot the over-time variances (average rates of change in ideal points) for all the persons/legislators in the model.

Usage

id_plot_legis_var(
  object,
  return_data = FALSE,
  include = NULL,
  high_limit = 0.95,
  low_limit = 0.05,
  text_size_label = 2,
  text_size_group = 2.5,
  point_size = 1,
  hjust_length = -0.7,
  person_labels = TRUE,
  group_labels = F,
  person_ci_alpha = 0.1,
  group_color = TRUE,
  ...
)

Arguments

object	A fitted idealstan object
return_data	If true, the calculated legislator/bill data is returned along with the plot in a list
include	Specify a list of person/legislator IDs to include in the plot (all others excluded)
high_limit	The quantile (number between 0 and 1) for the high end of posterior uncertainty to show in plot
low_limit	The quantile (number between 0 and 1) for the low end of posterior uncertainty to show in plot
text_size_label	ggplot2 text size for legislator labels
text_size_group	ggplot2 text size for group text used for points
point_size	If person_labels and group_labels are set to FALSE, controls the size of the points plotted.
hjust_length	horizontal adjustment of the legislator labels
person_labels	if TRUE, use the person_id column to plot labels for the person (legislator) ideal points
group_labels	if TRUE, use the group column to plot text markers for the group (parties) from the person/legislator data
person_ci_alpha	The transparency level of the dot plot and confidence bars for the person ideal points
group_color	If TRUE, give each group/bloc a different color
…	Other options passed on to plotting function, currently ignored

Details

This function will plot the person/legislator over-time variances as a vertical dot plot with associated high-density posterior interval (can be changed with high_limit and low_limit options).

Examples

library(idealstan)


# To demonstrate, we load the 114th Senate data and fit a time-varying model

data('senate114_fit')

senate_data <- id_make(senate114,outcome = 'cast_code',
person_id = 'bioname',
item_id = 'rollnumber',
group_id= 'party_code',
time_id='date',
miss_val='Absent')

 senate114_time_fit <- id_estimate(senate_data,
 model_type = 2,
 use_vb = T,
 fixtype='vb_partial',
 vary_ideal_pts='random_walk',
 restrict_ind_high = "WARREN, Elizabeth",
 restrict_ind_low="BARRASSO, John A.",
 seed=84520)
# We plot the variances for all the Senators

id_plot_legis_var(senate114_fit)