pros

data

When the values of the outcome variable Y are either 0 or 1,
the function lsm() calculates the estimation of the log likelihood
in the saturated model.
This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3
through the assumptions 1 and 2.
The function LogLik() works (almost perfectly) when the number of independent
variables K is high, but for small K it calculates wrong values in some cases.
For this reason, when Y is dichotomous and the data are grouped in J populations,
it is recommended to use the function lsm() because it works very well for all K.

Jorge Villalba

Estimation of the log Likelihood of the Saturated Model

Humberto Llinas

Omar Fabregas

pros function

A data frame with 380 observations on the following 9 variables.<dl>
 <dt><code>ID</code></dt>
<dd>a numeric vector</dd> <dt><code>CAPSULE</code></dt>
<dd>a numeric vector</dd> <dt><code>AGE</code></dt>
<dd>a numeric vector</dd> <dt><code>RACE</code></dt>
<dd>a numeric vector</dd> <dt><code>DPROS</code></dt>
<dd>a numeric vector</dd> <dt><code>DCAPS</code></dt>
<dd>a numeric vector</dd> <dt><code>PSA</code></dt>
<dd>a numeric vector</dd> <dt><code>VOL</code></dt>
<dd>a numeric vector</dd> <dt><code>GLEASON</code></dt>
<dd>a numeric vector</dd>
</dl>

Format

pros — pros

A data frame with 380 observations on the following 9 variables.<dl>
 <dt><code>ID</code></dt>
<dd>a numeric vector</dd>

 <dt><code>CAPSULE</code></dt>
<dd>a numeric vector</dd>

 <dt><code>AGE</code></dt>
<dd>a numeric vector</dd>

 <dt><code>RACE</code></dt>
<dd>a numeric vector</dd>

 <dt><code>DPROS</code></dt>
<dd>a numeric vector</dd>

 <dt><code>DCAPS</code></dt>
<dd>a numeric vector</dd>

 <dt><code>PSA</code></dt>
<dd>a numeric vector</dd>

 <dt><code>VOL</code></dt>
<dd>a numeric vector</dd>

 <dt><code>GLEASON</code></dt>
<dd>a numeric vector</dd>


</dl>

pros: pros

Description

Usage

Arguments

Format

References

Examples