optim_est

<code>optim_est</code> function for One-step update for obtaining estimator

Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2024) <doi:10.48550/arXiv.2410.09665>.

Stephen Salerno

Inference on Predicted Data

Jiacheng Miao

Awan Afiaz

Kentaro Hoffman

Anna Neufeld

Qiongshi Lu

Tyler H McCormick

Jeffrey T Leek

optim_est function

<dl><dt>X_lab</dt>
<dd>Array or data.frame containing observed covariates in
labeled data.</dd>
<dt>X_unlab</dt>
<dd>Array or data.frame containing observed or
predicted covariates in unlabeled data.</dd>
<dt>Y_lab</dt>
<dd>Array or data.frame of observed outcomes in labeled data.</dd>
<dt>Yhat_lab</dt>
<dd>Array or data.frame of predicted outcomes in labeled data.</dd>
<dt>Yhat_unlab</dt>
<dd>Array or data.frame of predicted outcomes in
unlabeled data.</dd>
<dt>w</dt>
<dd>weights vector PSPA linear regression (d-dimensional, where
d equals the number of covariates).</dd>
<dt>theta</dt>
<dd>parameter theta</dd>
<dt>quant</dt>
<dd>quantile for quantile estimation</dd>
<dt>method</dt>
<dd>indicates the method to be used for M-estimation.
Options include "mean", "quantile", "ols", "logistic", and "poisson".</dd></dl>

Arguments

One-step update for obtaining estimator — optim_est

<dl>

<dt>X_lab</dt>
<dd>Array or data.frame containing observed covariates in
labeled data.</dd>


<dt>X_unlab</dt>
<dd>Array or data.frame containing observed or
predicted covariates in unlabeled data.</dd>


<dt>Y_lab</dt>
<dd>Array or data.frame of observed outcomes in labeled data.</dd>


<dt>Yhat_lab</dt>
<dd>Array or data.frame of predicted outcomes in labeled data.</dd>


<dt>Yhat_unlab</dt>
<dd>Array or data.frame of predicted outcomes in
unlabeled data.</dd>


<dt>w</dt>
<dd>weights vector PSPA linear regression (d-dimensional, where
d equals the number of covariates).</dd>


<dt>theta</dt>
<dd>parameter theta</dd>


<dt>quant</dt>
<dd>quantile for quantile estimation</dd>


<dt>method</dt>
<dd>indicates the method to be used for M-estimation.
Options include "mean", "quantile", "ols", "logistic", and "poisson".</dd>

</dl>

optim_est: One-step update for obtaining estimator

Description

Usage

Value

Arguments