stminsights (version 0.4.3)

get_effects: 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
)

Value

Returns effect estimates in a tidy data frame.

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'.

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')