JMbayes2 (version 0.4-5)

jm Methods: Various Methods for Standard Generics

Description

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

Usage

coef(object, ...)
# S3 method for jm
coef(object, ...)
fixef(object, ...)
# S3 method for jm
fixef(object, outcome = Inf, ...)
ranef(object, ...)
# S3 method for jm
ranef(object, outcome = Inf, post_vars = FALSE, ...)
terms(x, ...)
# S3 method for jm
terms(x, process = c("longitudinal", "event"),
                      type = c("fixed", "random"), ...)
model.frame(formula, ...)
# S3 method for jm
model.frame(formula, process = c("longitudinal", "event"),
                            type = c("fixed", "random"), ...)
model.matrix(object, ...)
# S3 method for jm
model.matrix(object, ...)
family(object, ...)
# S3 method for jm
family(object, ...)
compare_jm(..., type = c("marginal", "conditional"),
  order = c("WAIC", "DIC", "LPML", "none"))

Value

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.

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.

Author

Dimitris Rizopoulos

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.

See Also

jm

Examples

# \donttest{
# 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)
# }