methods.RdMethods for object of class "jm" for standard generic functions.
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"))object inheriting from class "jm".
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.
logical; if TRUE, returns the variance of the posterior distribution.
which submodel(s) to extract the terms:
if "longitudinal", the linear mixed model(s), or
if "event", the survival model.
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.
which criteria use to sort the models in the output.
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.
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.
# \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)
#> $gammas
#> Mean
#> 0.06070451
#>
#> $association
#> value(log(serBilir)) value(prothrombin)
#> 1.3604613 0.1063424
#>
# fixef(): fixed effects for the first linear mixed submodel
fixef(fit_jm, outcome = 1)
#> (Intercept) age year:sexmale year:sexfemale
#> 0.66594783 -0.00445036 0.23584373 0.16253524
# ranef(): random effects from all linear mixed submodels
head(ranef(fit_jm))
#> [,1] [,2] [,3] [,4]
#> [1,] 2.23319313 0.20276852 1.05194350 0.126098411
#> [2,] -0.36290322 0.00382308 -0.03130737 0.089480915
#> [3,] -0.19671871 0.06974686 0.51040201 0.210441867
#> [4,] 0.03344513 0.10239115 0.85942217 0.574548016
#> [5,] 0.32900876 0.22520488 -0.04018424 0.442201332
#> [6,] -0.62705384 -0.16161656 -0.11058573 -0.003544499
# terms(): random effects terms for the first linear mixed submodel
terms(fit_jm, process = "longitudinal", type = "random")[[1]]
#> ~year
#> attr(,"variables")
#> list(year)
#> attr(,"factors")
#> year
#> year 1
#> attr(,"term.labels")
#> [1] "year"
#> attr(,"order")
#> [1] 1
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 0
#> attr(,".Environment")
#> <environment: R_GlobalEnv>
#> attr(,"predvars")
#> list(year)
#> attr(,"dataClasses")
#> year
#> "numeric"
# mode.frame(): model frame for the fixed effects in the second
# linear mixed submodel
head(model.frame(fit_jm, process = "longitudinal", type = "fixed")[[2]])
#> prothrombin sex
#> 1 12.2 female
#> 2 11.2 female
#> 3 10.6 female
#> 4 11.0 female
#> 5 11.6 female
#> 6 10.6 female
# model.matrix(): fixed effects design matrix for the first linear
# mixed submodel
head(model.matrix(fit_jm)[[1]])
#> (Intercept) age year:sexmale year:sexfemale
#> 1 1 58.76684 0 0.0000000
#> 2 1 58.76684 0 0.5256817
#> 3 1 56.44782 0 0.0000000
#> 4 1 56.44782 0 0.4983025
#> 5 1 56.44782 0 0.9993429
#> 6 1 56.44782 0 2.1027270
# family(): family objects from both linear mixed submodels
family(fit_jm)
#> [[1]]
#>
#> Family: gaussian
#> Link function: identity
#>
#>
#> [[2]]
#>
#> Family: gaussian
#> Link function: identity
#>
#>
# 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)
#>
#> DIC WAIC LPML
#> fit_jm2 10512.30 10540.46 -5268.228
#> fit_jm 10665.69 11146.24 -6097.345
#>
#> The criteria are calculated based on the marginal log-likelihood.
# }