get_effects: extract stm effect estimates

In methodds/stminsights: A 'Shiny' Application for Inspecting Structural Topic Models

extract stm effect estimates

Description

get_effects() is a helper function to store effect estimates from stm in a data frame.

Usage

get_effects(
  estimates,
  variable,
  type,
  ci = 0.95,
  moderator = NULL,
  modval = NULL,
  cov_val1 = NULL,
  cov_val2 = NULL
)

Arguments

estimates	The object containing estimates calculated with estimateEffect.
variable	The variable for which estimates should be extracted.
type	The estimate type. Must be either 'pointestimate', 'continuous', or 'difference'.
ci	The confidence interval for uncertainty estimates. Defaults to 0.95.
moderator	The moderator variable in case you want to include an interaction effect.
modval	The value of the moderator variable for an interaction effect. See examples for combining data for multiple values.
cov_val1	The first value of a covariate for type 'difference'.
cov_val2	The second value of a covariate for type 'difference'. The topic proportion of 'cov_val2' will be subtracted from the proportion of 'cov_val1'.

Value

Returns effect estimates in a tidy data frame.

Examples

library(stm)
library(dplyr)
library(ggplot2)

# store effects
prep <- estimateEffect(1:3 ~ treatment + pid_rep, gadarianFit, gadarian)

effects <- get_effects(estimates = prep,
                      variable = 'treatment',
                      type = 'pointestimate')


# plot effects
effects |> filter(topic == 3) |>
ggplot(aes(x = value, y = proportion)) +
 geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.1, size = 1) +
 geom_point(size = 3) +
 coord_flip() + theme_light() + labs(x = 'Treatment', y = 'Topic Proportion')


# combine estimates for interaction effects
prep_int <- estimateEffect(1:3 ~ treatment * s(pid_rep),
 gadarianFit, gadarian)

effects_int <- get_effects(estimates = prep_int,
                          variable = 'pid_rep',
                          type = 'continuous',
                          moderator = 'treatment',
                          modval = 1) |>
 bind_rows(
   get_effects(estimates = prep_int,
               variable = 'pid_rep',
               type = 'continuous',
               moderator = 'treatment',
               modval = 0)
 )

# plot interaction effects
effects_int  |>  filter(topic == 2) |>
 mutate(moderator = as.factor(moderator)) |>
 ggplot(aes(x = value, y = proportion, color = moderator,
 group = moderator, fill = moderator)) +
 geom_line() +
 geom_ribbon(aes(ymin = lower, ymax = upper), alpha = 0.2)  +
 theme_light() + labs(x = 'PID Rep.', y = 'Topic Proportion',
 color = 'Treatment', group = 'Treatment', fill = 'Treatment')

methodds/stminsights documentation built on April 19, 2023, 11:58 a.m.