Prints a detailed summary of the IPD method/model combination.
Usage
# S3 method for class 'summary.ipd'
print(x, ...)
Examples
#-- Generate Example Data
set.seed(2023)
dat <- simdat(n = c(300, 300, 300), effect = 1, sigma_Y = 1)
head(dat)
#> X1 X2 X3 X4 Y f set
#> 1 -0.08378436 1.42014307 -0.1615291 -0.2004525 -0.2871755 NA training
#> 2 -0.98294375 -1.30333434 -1.0273652 -1.6867582 0.3242501 NA training
#> 3 -1.87506732 0.05942012 -1.0964411 -0.0696362 -2.2601440 NA training
#> 4 -0.18614466 -1.30651333 -0.2979100 1.4198123 1.1182872 NA training
#> 5 -0.63348570 0.32404466 -1.0075244 -0.4614362 -1.5729777 NA training
#> 6 1.09079746 0.11532808 -0.6296308 -0.4045431 0.1953583 NA training
formula <- Y - f ~ X1
#-- Fit IPD
fit <- ipd(formula, method = "postpi_analytic", model = "ols",
data = dat, label = "set")
#-- Summarize Output
summ_fit <- summary(fit)
print(summ_fit)
#>
#> Call:
#> Y - f ~ X1
#>
#> Method: postpi_analytic
#> Model: ols
#> Intercept: Yes
#>
#> Coefficients:
#> Estimate Std.Error Lower.CI Upper.CI
#> (Intercept) 0.79053 0.10833 0.57821 1.0029
#> X1 0.98330 0.10148 0.78441 1.1822