reconstruct_formulas

Data frame with results from <code><a rd-options="" href="/link/cross_validate?package=cvms&version=0.2.0" data-mini-rdoc="cvms::cross_validate">cross_validate</a>()</code> or <code><a rd-options="" href="/link/validate?package=cvms&version=0.2.0" data-mini-rdoc="cvms::validate">validate</a>()</code>. (tbl)
Must contain at least the columns "<code>Dependent</code>" and "<code>Fixed</code>". For random effects
 the "<code>Random</code>" column should be included.

results

Number of top rows to return. Simply applies head() to the results tibble.

topn

In the results tibble from cross_validate and validate,
 the model formulas have been split into the columns Dependent, Fixed and Random.
 Quickly reconstruct the model formulas from these columns.

Cross-validate one or multiple regression models and get relevant evaluation
metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation.
Alternatively, evaluate predictions from an external model.
Currently supports linear regression, logistic regression and
(some functions only) multiclass classification.
Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

Ludvig Renbo Olsen

cvms

Cross-Validation for Model Selection

Benjamin Hugh Zachariae

reconstruct_formulas function

Data frame with results from <code><a rd-options='' href='cross_validate'>cross_validate</a>()</code> or <code><a rd-options='' href='validate'>validate</a>()</code>. (tbl)
Must contain at least the columns "<code>Dependent</code>" and "<code>Fixed</code>". For random effects
 the "<code>Random</code>" column should be included.

reconstruct_formulas: Reconstruct model formulas from results tibbles

Description

Usage

Arguments

Value