library(idealstan)
# 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,
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(sen_est)Plot Legislator/Person and Bill/Item Ideal Points
Description
This function can be used on a fitted idealstan object to plot the relative positions and uncertainties of legislator/persons and bills/items.
Usage
id_plot_legis(
object,
return_data = FALSE,
include = NULL,
high_limit = 0.95,
low_limit = 0.05,
item_plot = NULL,
item_plot_type = "non-inflated",
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 = 0.2,
show_true = FALSE,
group_color = TRUE,
hpd_limit = 10,
sample_persons = NULL,
...
)
Arguments
object
|
A fitted idealstan object or a named list of idealstan objects to compare across models
|
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 |
item_plot
|
The IDs (character vector) of the bill/item midpoints to overlay on the plot |
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.
|
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 |
show_true
|
Whether to show the true values of the legislators (if model has been simulated) |
group_color
|
If TRUE, give each group/bloc a different color
|
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. |
…
|
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. It will plot them as a vertical dot plot with associated high-density posterior interval (can be changed with high_limit and low_limit options). In addition, if item/bill IDs as a character vector 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.
To compare across multiple idealstan models, pass a named list list(model1=model1,model2=model2,etc) to the object option. Note that these comparisons will done by individual persons/groups, so if there are a lot of persons/groups, consider using the include option to only compare a specific set of persons/groups.