Compute population marginal means (PMM) from a model fit, for
 a chosen population and statistic.

yates

Contains the core survival analysis routines, including
definition of Surv objects,
Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
and parametric accelerated failure time models.

Terry Therneau

survival

Survival Analysis

Thomas Lumley

Atkinson Elizabeth

Crowson Cynthia

yates function

Population prediction — yates

<dl>

 <dt>fit</dt>
<dd>a model fit. Examples using lm, glm, and coxph objects
 are given in the vignette.</dd>

<dt>term</dt>
<dd>the term from the model whic is to be evaluated.
 This can be written as a character string or as a formula.</dd>

 <dt>population</dt>
<dd>the population to be used for the adjusting
 variables. User can supply their own data frame or select one
 of the built in choices.
 The argument also allows "empirical" and "yates" as aliases for
 data and factorial, respectively, and ignores case.</dd>

 <dt>levels</dt>
<dd>optional, what values for <code>term</code> should be used.</dd>

 <dt>test</dt>
<dd>the test for comparing the population predictions.</dd>

<dt>predict</dt>
<dd>what to predict. For a glm model this might be the
 'link' or 'response'. For a coxph model it can be linear, risk, or
 survival. User written functions are allowed.</dd>

 <dt>options</dt>
<dd>optional arguments for the prediction method.</dd>

<dt>nsim</dt>
<dd>number of simulations used to compute a variance for the
 predictions. This is not needed for the linear predictor.</dd>

<dt>method</dt>
<dd>the computational approach for testing equality of the
 population predictions. Either the direct approach or the algorithm
 used by the SAS glim procedure for "type 3" tests.</dd>

</dl>

Arguments

Author

Population prediction

yates: Population prediction

Description

Usage

Arguments

Value

Details

Examples