R package to simulate Probabilistic Long-Term Effects in models with temporal dependence
Christopher Gandrud and Laron K. Williams
pltesim implements Williams’s (2016) method for simulating probabilistic long-term effects in models with temporal dependence.
It is built on the coreSim package.
To find and show probabilistic long-term effects in models with temporal dependence with pltesim:
Estimate the coefficients. Currently pltesim works with binary outcome models, e.g. logit, so use
glm from the default R installation.
Create a data frame with your counterfactual. This should have a row with the fitted counterfactual values and columns with names matching those in your fitted model. All variables without values will be treated as 0 in the counterfactual.
Simulate the long-term effects with
Plot the results with
These examples replicate Figure 1 in Williams (2016). First estimate your model. You may need to use
btscs to generate spells for the binary dependent variable.
library(pltesim) library(ggplot2) data('negative_year') # BTSCS set the data neg_set <- btscs(df = negative_year, event = 'y', t_var = 'year', cs_unit = 'group', pad_ts = FALSE) # Estimate the model m1 <- glm(y ~ x + spell_time + I(spell_time^2) + I(spell_time^3), family = binomial(link = 'logit'), data = neg_set)
Then fit the counterfactual:
counterfactual <- data.frame(x = 0.5)
Now simulate and plot long-term effects for a variety of scenarios using
plte_builder takes as its input the fitted model object with the estimated coefficients (
obj), an identification of the basic time period variable (
obj_tvar), the counterfactual (
cf), how long the counterfactual persists (
cf_duration, it is
permanent by default), and the time period points over which to simulate the effects.
Note that by default the predicted probabilities from logistic regression models are found. You can specify a custom quantity of interest function with the
In this first example the counterfactual is persistent throughout the entire time span:
# Permanent sim1 <- plte_builder(obj = m1, obj_tvar = 'spell_time', cf = counterfactual, t_points = c(13, 25)) plte_plot(sim1) + ggtitle('Permanent')
Note that the numbers next to each simulation point indicate the time since the last event. You can choose to not show these numbers by setting
t_labels = FALSE in the
In the next example, the effect only lasts for one time period:
# One-time sim2 <- plte_builder(obj = m1, obj_tvar = 'spell_time', cf_duration = 'one-time', cf = counterfactual, t_points = c(13, 25)) plte_plot(sim2) + ggtitle('One-time')
We can also have the counterfactual effect last for short periods of time and simulate the effect if another event occurs:
# Temporary sim3 <- plte_builder(obj = m1, obj_tvar = 'spell_time', cf_duration = 4, cf = counterfactual, t_points = c(13, 25)) plte_plot(sim3) + ggtitle('Temporary')
# Multiple events, permanent counter factual sim4 <- plte_builder(obj = m1, obj_tvar = 'spell_time', cf = counterfactual, t_points = c(13, 20, 25)) plte_plot(sim4) + ggtitle('Permanent, Multiple Events')
By default the baseline scenario has all covariate values fitted at 0. You can supply a custom baseline scenario in the second row of the counterfactual (
cf) data frame. For example:
# Custom baseline scenario counterfactual_baseline <- data.frame(x = c(1, 0.5)) sim5 <- plte_builder(obj = m1, obj_tvar = 'spell_time', cf_duration = 4, cf = counterfactual_baseline, t_points = c(13, 25)) plte_plot(sim5) + ggtitle('Temporary, Custom Baseline')