arjasplot

Examine the potential role of treatment in treatment in 
a model already including sex.
Straight lines that are not 45 degrees
indicate the appropriateness of new variable as a linear
effect.

Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.  Supported by grant NSF DMS 1712839.

John E Kolassa

PHInfiniteEstimates

Tools for Inference in the Presence of a Monotone Likelihood

arjasplot function

<dl><dt>formulastring</dt>
<dd>A formula for a coxph fit.</dd>
<dt>time</dt>
<dd>The name of the time variable</dd>
<dt>stratifier</dt>
<dd>The name of the stratifier variable</dd>
<dt>status</dt>
<dd>The name of the status variable</dd>
<dt>mydata</dt>
<dd>The data frame.</dd></dl>

Arguments

Examine the potential role of treatment in treatment in 
a model already including sex.
Straight lines that are not 45 degrees
indicate the appropriateness of new variable as a linear
effect. — arjasplot

<dl>

<dt>formulastring</dt>
<dd>A formula for a coxph fit.</dd>


<dt>time</dt>
<dd>The name of the time variable</dd>


<dt>stratifier</dt>
<dd>The name of the stratifier variable</dd>


<dt>status</dt>
<dd>The name of the status variable</dd>


<dt>mydata</dt>
<dd>The data frame.</dd>

</dl>

Examine the potential role of treatment in treatment in 
a model already including sex.
Straight lines that are not 45 degrees
indicate the appropriateness of new variable as a linear
effect.

arjasplot: Examine the potential role of treatment in treatment in a model already including sex. Straight lines that are not 45 degrees indicate the appropriateness of new variable as a linear effect.

Description

Usage

Arguments