plot_trends
Plot the observed and synthetic trends for the treated units.
plot_trends(data, time_window = NULL)
data |
nested data of type |
time_window |
time window of the trend plot. |
Synthetic control is a visual-based method, like Regression Discontinuity, so inspection of the pre-intervention period fits is key assessing the sythetic control's fit. A poor fit in the pre-period reduces confidence in the post-period trend capturing the counterfactual.
See ?generate_control() for information on how to generate a synthetic
control unit.
ggplot object of the observed and synthetic trends.
# Smoking example data
data(smoking)
smoking_out <-
smoking %>%
# initial the synthetic control object
synthetic_control(outcome = cigsale,
unit = state,
time = year,
i_unit = "California",
i_time = 1988,
generate_placebos=TRUE) %>%
# Generate the aggregate predictors used to generate the weights
generate_predictor(time_window=1980:1988,
lnincome = mean(lnincome, na.rm = TRUE),
retprice = mean(retprice, na.rm = TRUE),
age15to24 = mean(age15to24, na.rm = TRUE)) %>%
generate_predictor(time_window=1984:1988,
beer = mean(beer, na.rm = TRUE)) %>%
generate_predictor(time_window=1975,
cigsale_1975 = cigsale) %>%
generate_predictor(time_window=1980,
cigsale_1980 = cigsale) %>%
generate_predictor(time_window=1988,
cigsale_1988 = cigsale) %>%
# Generate the fitted weights for the synthetic control
generate_weights(optimization_window =1970:1988,
Margin.ipop=.02,Sigf.ipop=7,Bound.ipop=6) %>%
# Generate the synthetic control
generate_control()
# Plot the observed and synthetic trend
smoking_out %>% plot_trends(time_window = 1970:2000)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.