Gaussian Process Interrupted Time Series
Usage
gp_its(
y,
dates,
date_treat,
covariates = NULL,
kernel_type = "gaussian",
b = NULL,
s2 = 0.3,
period = NULL,
auto_period = FALSE,
time_col = NULL,
scale = TRUE,
optimize = TRUE,
interval_type = c("prediction", "confidence"),
alpha = 0.05,
mixed_data = FALSE,
cat_columns = NULL,
placebo_check = FALSE,
placebo_periods = NULL,
verbose = FALSE
)Arguments
- y
numeric vector or ts object of outcome values
- dates
Date vector of observation dates
- date_treat
Date of treatment/intervention
- covariates
optional matrix/data.frame of covariates
- kernel_type
kernel type for GP
- b
bandwidth (NULL for automatic)
- s2
noise variance (default 0.3)
- period
period for periodic kernels
- auto_period
logical; auto-detect period from pre-treatment data
- time_col
character or numeric; which column is the time variable
- scale
logical; scale covariates
- optimize
logical; optimize s2 via MLE
- interval_type
"prediction" or "confidence"
- alpha
significance level
- mixed_data
logical; whether covariates contain categorical variables
- cat_columns
character vector of categorical column names
- placebo_check
logical; run placebo checks
- placebo_periods
number of placebo periods (NULL for automatic)
- verbose
logical; print progress messages
Value
An object of class "gp_its", a list containing:
- y
the outcome vector
- dates
the date vector
- date_treat
the treatment date
- y0_hat
counterfactual predictions for post-treatment periods
- estimates
data frame with columns
tau_t,tau_cum,tau_avg- se
data frame of standard errors
- ci
data frame of confidence interval bounds
- gp_model
the fitted GP model from
gp_train- placebo_estimates
placebo check results (if requested)
Examples
# \donttest{
# Simulated interrupted time series
set.seed(42)
n <- 50
dates <- seq(as.Date("2020-01-01"), by = "month", length.out = n)
y <- rnorm(n) + c(rep(0, 30), rep(2, 20))
res <- gp_its(y, dates, date_treat = as.Date("2022-07-01"))
print(res)
#> GP-ITS Model
#> ----------------------------------------
#> Observations: 50 (pre: 30, post: 20)
#> Treatment date: 2022-07-01
#> Kernel: gaussian
#> Interval type: prediction (95%)
#>
#> Average treatment effect (final period):
#> tau_avg = 1.918 (SE = 0.921)
#> 95% CI: [0.113, 3.723]
# }