pcoriaccel_estimate_pmf

Directly estimate the probability mass function of Y.

Sensitivity analysis for trials with irregular and informative
assessment times, based on a new influence function-based, augmented
inverse intensity-weighted estimator.

Andrew Redd

SensIAT

Sensitivity Analysis for Irregular Assessment Times

Yujing Gao

Shu Yang

Bonnie Smith

Agatha Mallett

Daniel Scharfstein

University of Utah 

pcoriaccel_estimate_pmf function

<dl><dt>Xb</dt>
<dd>Numeric vector of individual linear predictors from the data</dd>
<dt>Y</dt>
<dd>Numeric vector of individual responses from the data</dd>
<dt>xi</dt>
<dd>value of the individuals linear predictor at the point of estimation</dd>
<dt>y_seq</dt>
<dd>Numeric vector of unique values of <code>Y</code>.</dd>
<dt>h</dt>
<dd>bandwidth of the kernel</dd>
<dt>kernel</dt>
<dd>character string specifying the kernel to use, either <code>"dnorm"</code>, <code>"K2_Biweight"</code>, or <code>"K4_Biweight"</code></dd></dl>

Arguments

Directly estimate the probability mass function of Y. — pcoriaccel_estimate_pmf

<dl>

<dt>Xb</dt>
<dd>Numeric vector of individual linear predictors from the data</dd>


<dt>Y</dt>
<dd>Numeric vector of individual responses from the data</dd>


<dt>xi</dt>
<dd>value of the individuals linear predictor at the point of estimation</dd>


<dt>y_seq</dt>
<dd>Numeric vector of unique values of <code>Y</code>.</dd>


<dt>h</dt>
<dd>bandwidth of the kernel</dd>


<dt>kernel</dt>
<dd>character string specifying the kernel to use, either <code>"dnorm"</code>, <code>"K2_Biweight"</code>, or <code>"K4_Biweight"</code></dd>

</dl>

pcoriaccel_estimate_pmf: Directly estimate the probability mass function of Y.

Description

Usage

Arguments