simdat {ipd} | R Documentation |
Data generation function for various underlying models
Description
Data generation function for various underlying models
Usage
simdat(
n = c(300, 300, 300),
effect = 1,
sigma_Y = 1,
model = "ols",
shift = 0,
scale = 1
)
Arguments
n |
Integer vector of size 3 indicating the sample sizes in the training, labeled, and unlabeled data sets, respectively |
effect |
Regression coefficient for the first variable of interest for inference. Defaults is 1. |
sigma_Y |
Residual variance for the generated outcome. Defaults is 1. |
model |
The type of model to be generated. Must be one of
|
shift |
Scalar shift of the predictions for continuous outcomes (i.e., "mean", "quantile", and "ols"). Defaults to 0. |
scale |
Scaling factor for the predictions for continuous outcomes (i.e., "mean", "quantile", and "ols"). Defaults to 1. |
Value
A data.frame containing n rows and columns corresponding to the labeled outcome (Y), the predicted outcome (f), a character variable (set) indicating which data set the observation belongs to (training, labeled, or unlabeled), and four independent, normally distributed predictors (X1, X2, X3, and X4), where applicable.
Examples
#-- Mean
dat_mean <- simdat(c(100, 100, 100), effect = 1, sigma_Y = 1,
model = "mean")
head(dat_mean)
#-- Linear Regression
dat_ols <- simdat(c(100, 100, 100), effect = 1, sigma_Y = 1,
model = "ols")
head(dat_ols)