proportion

Evaluate the proportion of test above the statistical significance level

utilities

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
taught by the section of Biostatistics of the University of Copenhagen.
It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...).
Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom.
Normalized residuals are provided to assess model misspecification.
Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients.
Predictions can be computed conditional to covariates only or also to outcome values.

Brice Ozenne

LMMstar

Repeated Measurement Models for Discrete Times

Julie Forman

proportion function

<dl><dt>object</dt>
<dd><code>Wald_lmm</code> object</dd>
<dt>n.sample</dt>
<dd>[numeric,&gt;=0] number of bootstrap sample used to assess the uncertainty.
If 0, then only the point estimate is computed.</dd>
<dt>trace</dt>
<dd>[logical] shoudl the execution of the boostrap be trace.</dd>
<dt>...</dt>
<dd>additional arguments passed to <code>confint.Wald_lmm</code></dd></dl>

Arguments

Proportion of Significant Findings — proportion

<dl>

<dt>object</dt>
<dd><code>Wald_lmm</code> object</dd>


<dt>n.sample</dt>
<dd>[numeric,&gt;=0] number of bootstrap sample used to assess the uncertainty.
If 0, then only the point estimate is computed.</dd>


<dt>trace</dt>
<dd>[logical] shoudl the execution of the boostrap be trace.</dd>


<dt>...</dt>
<dd>additional arguments passed to <code>confint.Wald_lmm</code></dd>

</dl>

proportion: Proportion of Significant Findings

Description

Usage

Value

Arguments