Description Usage Arguments Value See Also
View source: R/EMalgRandprop.R
Function to run EM algorithm during one stage model fit. Used when the jointmeta1 function is called.
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data 
the original 
longdat 
the longitudinal data with factors and interaction terms expanded, ordered by increasing survival time 
survdat 
the survival data with factors and interaction terms expanded, ordered by increasing survival time 
long.rand.ind 
a vector of character strings to indicate what variables
to assign individual level random effects to. A maximum of three
individual level random effects can be assigned. To assign a random
intercept include 'int' in the vector. To not include an individual level
random intercept include 'noint' in the vector. For example to fit a model
with individual level random intercept and random slope set

long.rand.stud 
a vector of character strings to indicate what
variables to assign study level random effects to. If no study level
random effects then this either not specified in function call or set to

id.name 
character string specifying the id variable in the dataset 
study.name 
a character string denoting the name of the variable in the
baseline dataset in 
gpt 
the number of quadrature points across which the integration with
respect to the random effects will be performed. If random effects are
specified at both the individual and the study level, the same number of
quadrature points is used in both cases. Defaults to 
max.it 
the maximum number of iterations of the EM algorithm that the
function will perform. Defaults to 
tol 
the tolerance level used to determine convergence in the EM
algorithm. Defaults to 
time.long 
the name of the variable holding the longitudinal time covariate 
surv.formula 
a formula object with the survival time, censoring
indicator and the covariates to include in the survival submodel. The
response must be a survival object as returned by the

long.formula 
a formula object with the response varaible, and the covariates to include in the longitudinal submodel 
long.formula.orig 
the original longitudinal formula as supplied to the function call 
paraests 
a list of the estimates present from the separate
longitudinal and survival fits. Same structure as 
studies 
the names of the studies present in the supplied data 
p1 
the number of fixed effects included in the longitudinal submodel 
p2 
the number of fixed effects included in the survival submodel 
strat 
logical value: if 
print.detail 
logical value: if 
bootrun 
logical value: if 
q 
the number of individual level random effects 
r 
the number of study level random effects, set to 
This function returns a list of the estimates of parameters and other information from the run of the EM algorithm. The list has the following components:
beta1
a data frame containing the estimates of the fixed effect parameters from the longitudinal submodel.
beta2
a data frame containing the estimates of the fixed effect parameters from the survival submodel.
sigma.e
the estimate of the variance of the measurement errors.
haz
the estimated baseline hazard. If strat = TRUE
in
the function call to jointmeta1
then this is a list of length equal
to the number of studies in the supplied dataset, each element of the list
being the baseline hazard for the corresponding study. Otherwise there is a
common baseline across all studies in the dataset and this is one vector.
D
the estimated covariance matrix for the individual level random effects
A
the estimated covariance matrix for the study level random
effects. This is only present in the output if study level random effects
were specified in the function call to jointmeta1
.
random2
a list of matrices containing the conditional modes of the individual level random effects given the supplied data and the estimated parameters of the joint model. The list is of length equal to the number of studies in the dataset, and each element of the list has number of rows equal to the number of individuals in the study, and number of columns equal to the number of specified individual level random effects.
random3
a matrix containing the conditional modes of the study level random effects given the supplied data and the estimated parameters of the joint model. The matrix has number of rows equal to the number of studies, and number of columns equal to the number of specified study level random effects.
long.rand.ind.form
a character string giving the formulation of the individual level random effects.
long.rand.stud.form
a character string giving the formulation of the study level random effects if included in the model.
conv
a logical value indicating whether convergence of the EM algorithm was achieved or not.
iters
the number of iterations completed by the EM algorithm
n.bystudy
the number of individuals present in each study in the data supplied to the function.
jointmeta1
, tojointdata
,
jointdata
,lmer
,
coxph
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