Summarise a random effects joint model fit

Generic function used to produce summary information from a fitted random effects joint model as represented by object of class joint.

Usage

# S3 method for class 'joint'
summary(object, variance = TRUE, ...)

Arguments

object: an object of class joint.
variance: should the variance components be output as variances or standard deviations? Defaults to variance = TRUE.
...: further arguments for the summary.

Value

An object inheriting from class summary.joint with all components included in object (see joint for a full description of the components) plus the following components:

nobs: the total number of (typically longitudinal) observations (i.e. rows in an unbalanced data set).
ngrps: the number of groups in the analyzed dataset, often individual subjects.

Author

Pete Philipson

Examples

data(heart.valve)
heart.surv <- UniqueVariables(heart.valve,
                              var.col = c("fuyrs","status"),
                              id.col = "num")
heart.long <- heart.valve[, c("num", "time", "log.lvmi")]
heart.cov <- UniqueVariables(heart.valve,
                             c("age", "hs", "sex"),
                             id.col = "num")
heart.valve.jd <- jointdata(longitudinal = heart.long,
                            baseline = heart.cov,
                            survival = heart.surv,
                            id.col = "num",
                            time.col = "time")
fit <- joint(data = heart.valve.jd,
             long.formula = log.lvmi ~ 1 + time + hs,
             surv.formula = Surv(fuyrs,status) ~ hs,
             model = "intslope")
summary(fit)
#> 
#> Call:
#> joint(data = heart.valve.jd, long.formula = log.lvmi ~ 1 + time + 
#>     hs, surv.formula = Surv(fuyrs, status) ~ hs, model = "intslope")
#> 
#> Random effects joint model
#>  Data: heart.valve.jd 
#>  Log-likelihood: -423.7073 
#> 
#> Longitudinal sub-model fixed effects: log.lvmi ~ 1 + time + hs                              
#> (Intercept)        4.992709176
#> time              -0.006977116
#> hsStentless valve  0.055921729
#> 
#> Survival sub-model fixed effects: Surv(fuyrs, status) ~ hs                           
#> hsStentless valve 0.8106134
#> 
#> Latent association:                 
#> gamma_0 0.8476275
#> 
#> Variance components:
#>         U_0         U_1    Residual 
#> 0.113583056 0.001765284 0.037074269 
#> 
#> Convergence at iteration: 9 
#> 
#> Number of observations: 988 
#> Number of groups: 256