fun.opt

This function computes the negative log-likelihood for a given set of parameters.

Single-index mixture cure models allow estimating the probability of cure and the latency depending on a vector (or functional) covariate, avoiding the curse of dimensionality. The vector of parameters that defines the model can be estimated by maximum likelihood. A nonparametric estimator for the conditional density of the susceptible population is provided. For more details, see Piñeiro-Lamas (2024) (<https://ruc.udc.es/dspace/handle/2183/37035>). Funding: This work, integrated into the framework of PERTE for Vanguard Health, has been co-financed by the Spanish Ministry of Science, Innovation and Universities with funds from the European Union NextGenerationEU, from the Recovery, Transformation and Resilience Plan (PRTR-C17.I1) and from the Autonomous Community of Galicia within the framework of the Biotechnology Plan Applied to Health.

Beatriz Piñeiro-Lamas

sicure

Single-Index Mixture Cure Models

Ana López-Cheda

Ricardo Cao

fun.opt function

<dl><dt>theta_h</dt>
<dd>A numeric vector containing the initial iterant for the vector of parameters and the initial bandwidths.</dd>
<dt>x_cov</dt>
<dd>A matrix or data frame giving the covariate values. Each row represents an individual and each column corresponds to a variable.</dd>
<dt>time</dt>
<dd>A numeric vector giving the observed times.</dd>
<dt>delta</dt>
<dd>A numeric vector giving the values of the uncensoring indicator, where 1 indicates that the event of interest has been observed and 0 indicates that the observation is censored.</dd></dl>

Arguments

Objective function — fun.opt

<dl>

<dt>theta_h</dt>
<dd>A numeric vector containing the initial iterant for the vector of parameters and the initial bandwidths.</dd>


<dt>x_cov</dt>
<dd>A matrix or data frame giving the covariate values. Each row represents an individual and each column corresponds to a variable.</dd>


<dt>time</dt>
<dd>A numeric vector giving the observed times.</dd>


<dt>delta</dt>
<dd>A numeric vector giving the values of the uncensoring indicator, where 1 indicates that the event of interest has been observed and 0 indicates that the observation is censored.</dd>

</dl>

fun.opt: Objective function

Description

Usage

Value

Arguments