plte_builder {pltesim}

Create simulations for long-term effects in models with temporal dependence

Description

Create simulations for long-term effects in models with temporal dependence

Usage

plte_builder(obj, obj_tvar, cf, cf_duration = "permanent", t_points,
  FUN = logistic_prob_FUN, ci = 0.95, nsim = 1000)

Arguments

obj

a fitted model object.

obj_tvar

character string specifying the name of the base time variable in obj.

cf

a data frame with the first row containing the counterfactual. An optional second row could be supplied with values for the baseline scenario. If not supplied then all values are set to zero for the baseline. Columns should have names that match variables in obj and contain fitted values to find quantities of interest for. Note, this should not include your time variable as this is handled using t_points.

cf_duration

a character string or numeric specifying the counterfactual's duration. If 'permanent' then the counterfacutal lasts for the full time span in t_points. If 'one-time' then the counterfactual only lasts for one period. If cf_duration is numeric then the number specifies the number of time increments at which the counterfactual resets.

t_points

a numeric vector with a minimum length of 2 and a maximum lentgh of 3. The first and last values should be the time starting and ending points for the simulatinos. The (optional) middle value can specify a point between the first and last time points where a subsequent event occurs.

FUN

a function for finding a quantity of interest from the linear systematic component. See qi_builder. The default is a function for finding the predicted probability from a logistic regression model.

ci

the proportion of the central interval of the simulations to return. Must be in (0, 1] or equivalently (0, 100].

nsim

number of simulations to draw.

Value

A data frame with the medians and central intervals of the simulated scenarios. Note that the column scenario_name encodes scenarios where y = 0 as baseline and y = 1 as counterfactual.

Source

Williams, Laron K. 2016. "Long-Term Effects in Models with Temporal Dependence". Political Analysis: 24(2): 243-262.

Examples

data('negative')

# BTSCS set the data
neg_set <- btscs(df = negative, event = 'y', t_var = 'tim',
                cs_unit = 'group', pad_ts = FALSE)

# Create temporal dependence variable
neg_set$t <- neg_set$spell + 1

m1 <- glm(y ~ x + t + I(t^2) + I(t^3),
          family = binomial(link = 'logit'), data = neg_set)

# Create fitted counterfactual
counterfactual <- data.frame(x = 0.5)

# Permanent counterfactual, one event
sim1 <- plte_builder(obj = m1, obj_tvar = 't',
                     cf = counterfactual, t_points = c(13, 25))

# Multiple events
sim2 <- plte_builder(obj = m1, obj_tvar = 't',
                     cf = counterfactual, t_points = c(13, 18, 25))

# One-time counterfactual
sim3 <- plte_builder(obj = m1, obj_tvar = 't',
                     cf = counterfactual, t_points = c(13, 25),
                     cf_duration = 'one-time')

# Temporary (4 period counterfactual)
sim4 <- plte_builder(obj = m1, obj_tvar = 't',
                     cf = counterfactual, t_points = c(13, 25),
                     cf_duration = 4)

# Custom baseline scenario
# Note: the second row is the custom baseline
counterfactual_baseline <- data.frame(x = c(1, 0.5))

sim5 <- plte_builder(obj = m1, obj_tvar = 't', cf_duration = 4,
                     cf = counterfactual_baseline, t_points = c(13, 25))

# Time splines
library(splines)
m2 <- glm(y ~ x + bs(t, degree = 3), family = binomial(link = 'logit'),
          data = neg_set)

sim6 <- plte_builder(obj = m2, obj_tvar = 't', cf_duration = 4,
                     cf = counterfactual, t_points = c(13, 25))