Skip to contents

Tidy a joint model fit

Source: R/tidy.joint.R
tidy.joint.Rd

Summarises parameter estimates from a fitted joint model into a tidy data.frame, one row per term. Follows the conventions of the broom package.

Usage

# S3 method for class 'joint'
tidy(x, se = NULL, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

an object of class joint.

se

a data.frame returned by jointSE, optionally supplied to include standard errors, Wald p-values, and confidence intervals in the output. If NULL (the default), these columns are included but set to NA.

conf.int

logical; if TRUE and se is provided, include conf.low and conf.high columns. Defaults to FALSE.

conf.level

the confidence level for the intervals, matching the level used in se. Defaults to 0.95.

...

additional arguments (currently unused).

Value

A data.frame with columns:

component

sub-model: "longitudinal", "survival", "association", or "variance".

term

parameter name.

estimate

point estimate.

std.error

bootstrap standard error (NA if se not supplied).

statistic

Wald z-statistic (NA if se not supplied or for variance components).

p.value

two-sided Wald p-value (NA if se not supplied or for variance components).

conf.low, conf.high

bootstrap percentile confidence bounds, included only when conf.int = TRUE and se is provided.

See also

glance.joint, joint, jointSE

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(heart.valve.jd,
             long.formula = log.lvmi ~ 1 + time + hs,
             surv.formula = Surv(fuyrs, status) ~ hs,
             model = "intslope")
tidy(fit)
#>      component              term     estimate std.error statistic p.value
#> 1 longitudinal       (Intercept)  4.992709176        NA        NA      NA
#> 2 longitudinal              time -0.006977116        NA        NA      NA
#> 3 longitudinal hsStentless valve  0.055921729        NA        NA      NA
#> 4     survival hsStentless valve  0.810613443        NA        NA      NA
#> 5  association           gamma_0  0.847627525        NA        NA      NA
#> 6     variance               U_0  0.113583056        NA        NA      NA
#> 7     variance               U_1  0.001765284        NA        NA      NA
#> 8     variance          Residual  0.037074269        NA        NA      NA