id_plot_legis_dyn: Function to plot dynamic ideal point models

In saudiwin/idealstan: Generalized IRT Ideal Point Models with 'Stan'

Function to plot dynamic ideal point models

Description

This function can be used on a fitted idealstan object to plot the relative positions and uncertainties of legislator/persons and bills/items when the legislator/person ideal points are allowed to vary over time.

Usage

id_plot_legis_dyn(
  object,
  return_data = FALSE,
  include = NULL,
  item_plot = NULL,
  text_size_label = 2,
  text_size_group = 2.5,
  high_limit = 0.95,
  low_limit = 0.05,
  line_size = 1,
  highlight = NULL,
  plot_text = TRUE,
  use_ci = TRUE,
  person_line_alpha = 0.3,
  person_ci_alpha = 0.8,
  item_plot_type = "non-inflated",
  show_true = FALSE,
  group_color = TRUE,
  hpd_limit = 10,
  sample_persons = NULL,
  plot_sim = FALSE,
  ...
)

Arguments

object	A fitted idealstan object or a named list of idealstan objects if the plot is supposed to show a comparison of different fitted idealstan models (see Time Series vignette)
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)
item_plot	The value of the item/bill for which to plot its midpoint (character value)
text_size_label	ggplot2 text size for legislator labels
text_size_group	ggplot2 text size for group text used for points
high_limit	A number between 0 and 1 showing the upper limit to compute the posterior uncertainty interval (defaults to 0.95).
low_limit	A number between 0 and 1 showing the lower limit to compute the posterior uncertainty interval (defaults to 0.05).
line_size	Sets the size of the line of the time-varying ideal points.
highlight	A character referring to one of the persons in person_labels that the plot can highlight relative to other persons
plot_text	If TRUE, will plot person_labels over the lines.
use_ci	Whether or not high-posterior density intervals (credible intervals) should be plotted over the estimates (turn off if the plot is too busy)
person_line_alpha	The transparency level of the time-varying ideal point line
person_ci_alpha	The transparency level of ribbon confidence interval around the time-varying ideal points
item_plot_type	Whether to show the 'non-inflated' item/bill midpoints, the 'inflated' item/bill midpoints, or produce plots for 'both' kinds of models. Defaults to 'non-inflated' and will only display an item/bill midpoint if one has been specified in item_plot.
show_true	Whether to show the true values of the legislators (if model has been simulated)
group_color	If TRUE, use the groups instead of individuals to plot colours
hpd_limit	The greatest absolute difference in high-posterior density interval shown for any point. Useful for excluding imprecisely estimated persons/legislators from the plot. Leave NULL if you don't want to exclude any.
sample_persons	If you don't want to use the full number of persons/legislators from the model, enter a proportion (between 0 and 1) to select only a fraction of the persons/legislators.
plot_sim	Whether to plot the true values of parameters if a simulation was used to generate data (see id_sim_gen)
...	Other options passed on to plotting function, currently ignored

Details

This plot shows the distribution of ideal points for the legislators/persons in the model, and also traces the path of these ideal points over time. It will plot them as a vertical line with associated high-density posterior interval (10% to 90%). In addition, if the column index for a bill/item from the response matrix is passed to the item_plot option, then an item/bill midpoint will be overlain on the ideal point plot, showing the point at which legislators/persons are indifferent to voting/answering on the bill/item. Note that because this is an ideal point model, it is not possible to tell from the midpoint itself which side will be voting which way. For that reason, the legislators/persons are colored by their votes/scores to make it clear.

Examples

## Not run: 

# First create data and run a model

to_idealstan <-   id_make(score_data = senate114,
outcome = 'cast_code',
person_id = 'bioname',
item_id = 'rollnumber',
group_id= 'party_code',
time_id='date',
high_val='Yes',
low_val='No',
miss_val='Absent')

sen_est <- id_estimate(senate_data,
model_type = 2,
use_vb = TRUE,
vary_ideal_pts='random_walk',
fixtype='vb_partial',
restrict_ind_high = "BARRASSO, John A.",
restrict_ind_low = "WARREN, Elizabeth")

# After running the model, we can plot 
# the results of the person/legislator ideal points

id_plot_legis_dyn(sen_est)

## End(Not run)

saudiwin/idealstan documentation built on Sept. 2, 2023, 1:29 a.m.