vcov.gformula {gfoRmula}

Variance-covariance method for objects of class "gformula"

Description

This function extracts the variance-covariance matrices of the parameters of the fitted models for the time-varying covariates, outcome, and competing event (if applicable).

Usage

## S3 method for class 'gformula'
vcov(object, ...)

Arguments

object

Object of class "gformula".

...

Other arguments.

Value

If bootdiag was set to FALSE in gformula, this function returns a list of the variance-covariance matrices of the parameters of the fitted models to the observed data set. If bootstrapping was used and bootdiag was set to TRUE in gformula, this function returns a list described as follows. The first element (named 'Original sample') is a list of the variance-covariance matrices of the parameters of the fitted models to the observed data set. The kth element (named 'Bootstrap sample k-1') is a list of the variance-covariance matrices of the parameters of the fitted models corresponding to the k-1th bootstrap sample.

See Also

gformula

Examples

## Estimating the effect of static treatment strategies on risk of a
## failure event

id <- 'id'
time_points <- 7
time_name <- 't0'
covnames <- c('L1', 'L2', 'A')
outcome_name <- 'Y'
outcome_type <- 'survival'
covtypes <- c('binary', 'bounded normal', 'binary')
histories <- c(lagged, lagavg)
histvars <- list(c('A', 'L1', 'L2'), c('L1', 'L2'))
covparams <- list(covmodels = c(L1 ~ lag1_A + lag_cumavg1_L1 + lag_cumavg1_L2 +
                                  L3 + t0,
                                L2 ~ lag1_A + L1 + lag_cumavg1_L1 +
                                  lag_cumavg1_L2 + L3 + t0,
                                A ~ lag1_A + L1 + L2 + lag_cumavg1_L1 +
                                  lag_cumavg1_L2 + L3 + t0))
ymodel <- Y ~ A + L1 + L2 + L3 + lag1_A + lag1_L1 + lag1_L2 + t0
intvars <- list('A', 'A')
interventions <- list(list(c(static, rep(0, time_points))),
                      list(c(static, rep(1, time_points))))
int_descript <- c('Never treat', 'Always treat')
nsimul <- 10000

gform_basic <- gformula(obs_data = basicdata_nocomp, id = id,
                        time_points = time_points,
                        time_name = time_name, covnames = covnames,
                        outcome_name = outcome_name,
                        outcome_type = outcome_type, covtypes = covtypes,
                        covparams = covparams, ymodel = ymodel,
                        intvars = intvars,
                        interventions = interventions,
                        int_descript = int_descript,
                        histories = histories, histvars = histvars,
                        basecovs = c('L3'), nsimul = nsimul,
                        seed = 1234)
vcov(gform_basic)