NSFG_data_everypreg

<p>Same as `NSFG_data` but using *every* birth, not just the *first* birth,
out of the initial subset there
(live births with gestational age &lt; 45 weeks
 for mothers aged 20 to 40 at time of conception).</p>

datasets

Functions to generate K-fold cross validation (CV) folds
and CV test error estimates that take into account
how a survey dataset's sampling design was constructed
(SRS, clustering, stratification, and/or unequal sampling weights).
You can input linear and logistic regression models, along with data and a
type of survey design in order to get an output that can help you determine
which model best fits the data using K-fold cross validation.
Our paper on "K-Fold Cross-Validation for Complex Sample Surveys"
by Wieczorek, Guerin, and McMahon (2022)
<doi:10.1002/sta4.454>
explains why differing how we take folds based on survey design is useful.

Jerzy Wieczorek

surveyCV

Cross Validation Based on Survey Design

Cole Guerin

Thomas McMahon

Hunter Ratliff

NSFG_data_everypreg function

<p>A data frame with 5089 rows and 17 variables</p>

Format

Subset of the 2015-2017 National Survey of Family Growth (NSFG): all live births per respondent. — NSFG_data_everypreg

Subset of the 2015-2017 National Survey of Family Growth (NSFG): all live births per respondent.

NSFG_data_everypreg: Subset of the 2015-2017 National Survey of Family Growth (NSFG): all live births per respondent.

Description

Usage

Arguments

Format