predict.eiv_mlr

Generates fitted values or confidence intervals for new observations
using plug-in estimates. Measurement error is not corrected for new data.

Estimation and inference for coefficients of linear EIV models with symmetric measurement errors.
The measurement errors can be homoscedastic or heteroscedastic, for the latter, replication for at least some observations needs to be available. The estimation method and asymptotic inference are
based on a generalised method of moments framework, where the estimating equations are formed from (1) minimising the distance between the empirical phase function (normalised characteristic function) of the response and that of the linear combination of all the covariates at the estimates,
and (2) minimising a corrected least-square discrepancy function. Specifically, for a linear EIV model with p error-prone and q error-free covariates, if replicates are available, the GMM approach is based on a 2(p+q) estimating equations if some replicates are available and based on p+2q estimating equations if no replicate is available.
The details of the method are described in Nghiem and Potgieter (2020) <doi:10.1093/biomet/asaa025> and Nghiem and Potgieter (2025) <doi:10.5705/ss.202022.0331>.

Chang Liu

PhaseGMM

Phase-Function Based Estimation and Inference for Linear
Errors-in-Variables (EIV) Models

Linh Nghiem

predict.eiv_mlr function

<dl><dt>object</dt>
<dd>An object of class <code>"eiv_mlr"</code>.</dd>
<dt>newdata</dt>
<dd>Optional data frame containing covariates. If omitted,
predictions are returned for the training data.</dd>
<dt>interval</dt>
<dd>Type of interval to compute: <code>"none"</code> or
<code>"confidence"</code>.</dd>
<dt>level</dt>
<dd>Confidence level for intervals.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Predictions from an Errors-in-Variables Linear Model — predict.eiv_mlr

<dl>

<dt>object</dt>
<dd>An object of class <code>"eiv_mlr"</code>.</dd>


<dt>newdata</dt>
<dd>Optional data frame containing covariates. If omitted,
predictions are returned for the training data.</dd>


<dt>interval</dt>
<dd>Type of interval to compute: <code>"none"</code> or
<code>"confidence"</code>.</dd>


<dt>level</dt>
<dd>Confidence level for intervals.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

predict.eiv_mlr: Predictions from an Errors-in-Variables Linear Model

Description

Usage

Value

Arguments