log_likelihood_copula_model: Computes loglikelihood for a given copula model

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Computes loglikelihood for a given copula model

Description

log_likelihood_copula_model() computes the loglikelihood for a given bivariate copula model and data set while allowin for right-censoring of both outcome variables.

Usage

log_likelihood_copula_model(
  theta,
  X,
  Y,
  d1,
  d2,
  copula_family,
  cdf_X,
  cdf_Y,
  pdf_X,
  pdf_Y
)

Arguments

theta	Copula parameter
X	Numeric vector corresponding to first outcome variable.
Y	Numeric vector corresponding to second outcome variable.
d1	An integer vector. Indicates whether first variable is observed or right-censored, d1[i] = 1 if u[i] corresponds to non-censored value d1[i] = 0 if u[i] corresponds to right-censored value d1[i] = -1 if u[i] corresponds to left-censored value
d2	An integer vector. Indicates whether first variable is observed or right-censored, d2[i] = 1 if v[i] corresponds to non-censored value d2[i] = 0 if v[i] corresponds to right-censored value d2[i] = -1 if v[i] corresponds to left-censored value
copula_family	Copula family, one of the following: "clayton" "frank" "gumbel" "gaussian"
cdf_X	Distribution function for the first outcome variable.
cdf_Y	Distribution function for the second outcome variable.
pdf_X	Density function for the first outcome variable.
pdf_Y	Density function for the second outcome variable.

Value

Loglikelihood of the bivariate copula model evaluated in the observed data.

Surrogate documentation built on June 22, 2024, 9:16 a.m.