predict.survival_ln_mixture_em

Predict from a lognormal_em Mixture Model fitted using EM algorithm.

Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) <doi:10.1007/s11336-009-9121-4>, and data augmentation techniques Tanner and Wong (1987) <doi:10.1080/01621459.1987.10478458>.

Victor Hugo Soares Ney 

lnmixsurv

Bayesian Mixture Log-Normal Survival Model

Viviana das Graças Ribeiro Lobo 

Thaís Cristina Oliveira da Fonseca 

Mariane Branco Alves 

Vitor Capdeville 

predict.survival_ln_mixture_em function

<dl><dt>object</dt>
<dd>A <code>survival_ln_mixture_em</code> object.</dd>
<dt>new_data</dt>
<dd>A data frame or matrix of new predictors.</dd>
<dt>type</dt>
<dd>A single character. The type of predictions to generate.
Valid options are:<ul>
<li><code>"survival"</code> for the survival probability.</li>
<li><code>"hazard"</code> for the hazard theoretical hazard.</li>
</ul></dd>
<dt>eval_time</dt>
<dd>For type = "hazard" or type = "survival", the times for the distribution.</dd>
<dt>...</dt>
<dd>Not used, but required for extensibility.</dd></dl>

Arguments

Predict from a lognormal_em Mixture Model fitted using EM algorithm. — predict.survival_ln_mixture_em

<dl>

<dt>object</dt>
<dd>A <code>survival_ln_mixture_em</code> object.</dd>


<dt>new_data</dt>
<dd>A data frame or matrix of new predictors.</dd>


<dt>type</dt>
<dd>A single character. The type of predictions to generate.
Valid options are:<ul>
<li><code>"survival"</code> for the survival probability.</li>
<li><code>"hazard"</code> for the hazard theoretical hazard.</li>
</ul></dd>


<dt>eval_time</dt>
<dd>For type = "hazard" or type = "survival", the times for the distribution.</dd>


<dt>...</dt>
<dd>Not used, but required for extensibility.</dd>

</dl>

predict.survival_ln_mixture_em: Predict from a lognormal_em Mixture Model fitted using EM algorithm.

Description

Usage

Value

Arguments