methods function

Various Methods for Standard Generics

Methods for object of class "jm" for standard generic functions.

coef(object, ...)

## S3 method for class 'jm'
coef(object, ...)

fixef(object, ...)

## S3 method for class 'jm'
fixef(object, outcome = Inf, ...)

ranef(object, ...)

## S3 method for class 'jm'
ranef(object, outcome = Inf, post_vars = FALSE, ...)

terms(x, ...)

## S3 method for class 'jm'
terms(x, process = c("longitudinal", "event"),
                      type = c("fixed", "random"), ...)

model.frame(formula, ...)

## S3 method for class 'jm'
model.frame(formula, process = c("longitudinal", "event"),
                            type = c("fixed", "random"), ...)

model.matrix(object, ...)

## S3 method for class 'jm'
model.matrix(object, ...)

family(object, ...)

## S3 method for class 'jm'
family(object, ...)

compare_jm(..., type = c("marginal", "conditional"),
  order = c("WAIC", "DIC", "LPML", "none"))

Arguments

• object, x, formula: object inheriting from class "jm".

• outcome: the index of the linear mixed submodel to extract the estimated fixed effects. If greater than the total number of submodels, extracts from all of them.

• post_vars: logical; if TRUE, returns the variance of the posterior distribution.

• process: which submodel(s) to extract the terms:

  • if "longitudinal", the linear mixed model(s), or
  • if "event", the survival model.

• type: in terms() and model.frame(), which effects to select in the longitudinal process:

  • if "fixed", the fixed-effects, or
  • if "random", the random-efects.

  in compare_jm(), which log-likelihood function use to calculate the criteria:

  • if "marginal", the marginal log-likelihood, or
  • if "conditional", the conditional log-likelihood.

• ...: further arguments; currently, none is used.

  in compare_jm(), a series of jm objects.

• order: which criteria use to sort the models in the output.

Details

• coef(): Extracts estimated fixed effects for the event process from a fitted joint model.
• fixef(): Extracts estimated fixed effects for the longitudinal processes from a fitted joint model.
• ranef(): Extracts estimated random effects from a fitted joint model.
• terms(): Extracts the terms object(s) from a fitted joint model.
• model.frame(): Creates the model frame from a fitted joint model.
• model.matrix(): Creates the design matrices for linear mixed submodels from a fitted joint model.
• family(): Extracts the error distribution and link function used in the linear mixed submodel(s) from a fitted joint model.
• compare_jm(): Compares two or more fitted joint models using the criteria WAIC, DIC, and LPML.

Returns

• coef(): a list with the elements:

    * `gammas`: estimated baseline fixed effects, and
    * `association`: estimated association parameters.
  

• fixef(): a numeric vector of the estimated fixed effects for the outcome selected. If the outcome is greater than the number of linear mixed submodels, it returns a list of numeric vectors for all outcomes.

• ranef(): a numeric matrix with rows denoting the individuals and columns the random effects. If postVar = TRUE, the numeric matrix has the extra attribute "postVar".

• terms(): if process = "longitudinal", a list of the terms object(s) for the linear mixed model(s).

   if `process = "event"`, the terms object for the survival model.
  

• model.frame(): if process = "longitudinal", a list of the model frames used in the linear mixed model(s).

   if `process = "event"`, the model frame used in the survival model.
  

• model.matrix(): a list of the design matrix(ces) for the linear mixed submodel(s).

• family(): a list of family objects.

• compare_jm(): a list with the elements:

    * `table`: a table with the criteria calculated for each joint model, and
    * `type`: the log-likelihood function used to calculate the criteria.
  

Author(s)

Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

See Also

jm

Examples

# linear mixed model fits
fit_lme1 <- lme(log(serBilir) ~ year:sex + age,
                random = ~ year | id, data = pbc2)

fit_lme2 <- lme(prothrombin ~ sex,
                random = ~ year | id, data = pbc2)

# cox model fit
fit_cox <- coxph(Surv(years, status2) ~ age, data = pbc2.id)

# joint model fit
fit_jm <- jm(fit_cox, list(fit_lme1, fit_lme2), time_var = "year",
    n_chains = 1L, n_iter = 11000L, n_burnin = 1000L)

# coef(): fixed effects for the event process
coef(fit_jm)

# fixef(): fixed effects for the first linear mixed submodel
fixef(fit_jm, outcome = 1)

# ranef(): random effects from all linear mixed submodels
head(ranef(fit_jm))

# terms(): random effects terms for the first linear mixed submodel
terms(fit_jm, process = "longitudinal", type = "random")[[1]]

# mode.frame(): model frame for the fixed effects in the second
# linear mixed submodel
head(model.frame(fit_jm, process = "longitudinal", type = "fixed")[[2]])

# model.matrix(): fixed effects design matrix for the first linear
# mixed submodel
head(model.matrix(fit_jm)[[1]])

# family(): family objects from both linear mixed submodels
family(fit_jm)

# compare_jm(): compare two fitted joint models
fit_lme1b <- lme(log(serBilir) ~ 1,
                  random = ~ year | id, data = pbc2)

fit_jm2 <- jm(fit_cox, list(fit_lme1b, fit_lme2), time_var = "year",
    n_chains = 1L, n_iter = 11000L, n_burnin = 1000L)

compare_jm(fit_jm, fit_jm2)