Description Usage Arguments Value Examples
This function optimizes a model using information critera as a decision rule. This solves many of the problems related to model selection based on hypothesis tests, see also opt.h
.
In finite samples, the AIC is known to favor large models. The corrected AIC is a slightly stricter measure.
1 2 |
model |
The model to be optimized. Supports "lm" for the linear probability model, "logit" for the logistic probability model, and "probit" for the probit model. |
Y |
The binary response variable. |
X |
A dataframe with collumns of exogenous regressors. |
KLIC |
information criterion to be used, "AIC" or "AICc", See also |
returntype |
"model", "data", or "colnames" |
tracelevel |
level of printing. |
memorymanagement |
logical, indicating whether memory should be more actively managed. |
"model", "data", or "colnames", to be specified in returnype.
1 2 3 4 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.