datagen.norm.intercept

Function to generate data according to the linear model of the form Y = X*beta + epsilon where the noise epsilon follows a standard normal distribution and the first column of X consists of 1's such that an intercept is included in the model.

models

regression

datagen

Conduct post-selection inference for regression coefficients in linear models after they have been selected by adjusted R squared. The p-values and confidence intervals are valid after model selection with the same data. This allows the user to use all data for both model selection and inference without losing control over the type I error rate. The provided tests are more powerful than data splitting, which bases inference on less data since it discards all information used for selection.

Sarah Pirenne

PoSIAdjRSquared

Post-Selection Inference for Adjusted R Squared

Gerda Claeskens

datagen.norm.intercept function

<dl> <dt>seed</dt>
<dd>Integer for seed</dd> <dt>n</dt>
<dd>Integer for sample size</dd> <dt>p</dt>
<dd>Integer for number of variables in the design matrix</dd> <dt>rho</dt>
<dd>Integer for correlation between variables in the design matrix</dd> <dt>beta_vec</dt>
<dd>True regression coefficient vector of length p</dd></dl>

Arguments

Data generation normal with intercept — datagen.norm.intercept

<dl>

 <dt>seed</dt>
<dd>Integer for seed</dd>

 <dt>n</dt>
<dd>Integer for sample size</dd>

 <dt>p</dt>
<dd>Integer for number of variables in the design matrix</dd>

 <dt>rho</dt>
<dd>Integer for correlation between variables in the design matrix</dd>

 <dt>beta_vec</dt>
<dd>True regression coefficient vector of length p</dd>

</dl>

datagen.norm.intercept: Data generation normal with intercept

Description

Usage

Value

Arguments

Examples