Description Usage Arguments Value Source Examples
Create simulations for long-term effects in models with temporal dependence
1 2 | plte_builder(obj, obj_tvar, cf, cf_duration = "permanent", t_points,
FUN = logistic_prob_FUN, ci = 0.95, nsim = 1000)
|
obj |
a fitted model object. |
obj_tvar |
character string specifying the name of the base time variable
in |
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 |
cf_duration |
a character string or numeric specifying the
counterfactual's duration. If |
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 |
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. |
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
.
Williams, Laron K. 2016. "Long-Term Effects in Models with Temporal Dependence". Political Analysis: 24(2): 243-262.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | 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))
|
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