id_plot_legis_dyn | R Documentation |
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.
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,
plot_lines = 0,
draw_line_alpha = 0.5,
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,
use_chain = NULL,
add_cov = TRUE,
...
)
object |
A fitted |
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 |
plot_text |
If |
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) |
plot_lines |
The number of lines of actual draws of time-varying ideal points to draw on the plot. Note that these are grouped by persons. Specific draws selected at random from total number of draws of the estimation. Default is 0. |
draw_line_alpha |
The opacity of lines plotted over the distribution (should be between 0 and 1, default is 0.5). |
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 |
show_true |
Whether to show the true values of the legislators (if model has been simulated) |
group_color |
If |
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 |
use_chain |
ID of MCMC chain to use rather than combining all chains. Default is NULL which will use all chains and is recommended. |
add_cov |
Whether to add values of hierarchical person-level covariates to the time trends (defaults to TRUE). |
... |
Other options passed on to plotting function, currently ignored |
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\
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.
## 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)
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