This function fits joint latent class models for multivariate longitudinal markers and competing causes of event. It is a multivariate extension of the Jointlcmm function. It defines each longitudinal dimension as a latent process (mp in mpjlcmm is for multivariate processes), possibly measured by sereval continuous markers (Gaussian or curvilinear). For the moment, theses processes are assumed independent given the latent classes. The (optional) survival part handles competing risks, right censoring and left truncation. The specification of the function is similar to other estimating functions of the package.
Usage
mpjlcmm(
longitudinal,
subject,
classmb,
ng,
survival,
hazard = "Weibull",
hazardtype = "Specific",
hazardnodes = NULL,
TimeDepVar = NULL,
data,
B,
convB = 1e-04,
convL = 1e-04,
convG = 1e-04,
maxiter = 100,
nsim = 100,
prior,
logscale = FALSE,
subset = NULL,
na.action = 1,
posfix = NULL,
partialH = FALSE,
verbose = FALSE,
nproc = 1,
clustertype = NULL
)Arguments
- longitudinal
list of longitudinal models of type hlme, lcmm or multlcmm. Each model defines the structure of one latent process.
- subject
name of the covariate representing the grouping structure (called subject identifier)
- classmb
optional one-sided formula describing the covariates in the class-membership multinomial logistic model
- ng
number of latent classes considered
- survival
two-sided formula object specifying the survival part of the model
- hazard
optional family of hazard function assumed for the survival model (Weibull, piecewise or splines)
- hazardtype
optional indicator for the type of baseline risk function (Specific, PH or Common)
- hazardnodes
optional vector containing interior nodes if
splinesorpiecewiseis specified for the baseline hazard function inhazard- TimeDepVar
optional vector specifying the name of the time-depending covariate in the survival model
- data
data frame containing all the variables used in the model
- B
optional specification for the initial values of the parameters. Three options are allowed: (1) a vector of initial values is entered (the order in which the parameters are included is detailed in
detailssection). (2) nothing is specified. Initial values are extracted from the models specified inlongitudinal, and default initial values are chosen for the survival part (3) when ng>1, a mpjlcmm object is entered. It should correspond to the exact same structure of model but with ng=1. The program will automatically generate initial values from this model. Note that due to possible local maxima, theBvector should be specified and several different starting points should be tried. This can be done automatically using gridsearch function.- convB
optional threshold for the convergence criterion based on the parameter stability
- convL
optional threshold for the convergence criterion based on the log-likelihood stability
- convG
optional threshold for the convergence criterion based on the derivatives
- maxiter
optional maximum number of iterations for the Marquardt iterative algorithm
- nsim
optional number of points for the predicted survival curves and predicted baseline risk curves
- prior
optional name of a covariate containing a prior information about the latent class membership
- logscale
optional boolean indicating whether an exponential (logscale=TRUE) or a square (logscale=FALSE -by default) transformation is used to ensure positivity of parameters in the baseline risk functions
- subset
a specification of the rows to be used: defaults to all rows. This can be any valid indexing vector for the rows of data or if that is not supplied, a data frame made up of the variable used in formula.
- na.action
Integer indicating how NAs are managed. The default is 1 for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as 'na.pass' or 'na.exclude' are not implemented in the current version.
- posfix
Optional vector specifying the indices in vector B of the parameters that should not be estimated. Default to NULL, all parameters are estimated
- partialH
optional logical for Piecewise and Splines baseline risk functions and Splines link functions only. Indicates whether the parameters of the baseline risk or link functions can be dropped from the Hessian matrix to define convergence criteria (can solve non convergence due to estimates at the boundary of the parameter space - usually 0).
- verbose
logical indicating whether information about computation should be reported. Default to FALSE.
- nproc
the number cores for parallel computation. Default to 1 (sequential mode).
- clustertype
optional character indicating the type of cluster for parallel computation.
Examples
# \dontrun{
paquid$age65 <- (paquid$age-65)/10
##############################################################################
### EXAMPLE 1 : ###
###two outcomes measuring the same latent process along with dementia onset###
##############################################################################
## multlcmm model for MMSE and BVRT for 1 class
mult1 <- multlcmm(MMSE+BVRT~age65+I(age65^2)+CEP+male,random=~age65+I(age65^2),
subject="ID",link=c("5-quant-splines","4-quant-splines"),data=paquid)
summary(mult1)
#> General latent class mixed model
#> fitted by maximum likelihood method
#>
#> multlcmm(fixed = MMSE + BVRT ~ age65 + I(age65^2) + CEP + male,
#> random = ~age65 + I(age65^2), subject = "ID", link = c("5-quant-splines",
#> "4-quant-splines"), data = paquid)
#>
#> Statistical Model:
#> Dataset: paquid
#> Number of subjects: 500
#> Number of observations: 4164
#> Number of latent classes: 1
#> Number of parameters: 24
#> Link functions: Quadratic I-splines with nodes 0 25 27 29 30 for MMSE
#> Quadratic I-splines with nodes 0 10 12 15 for BVRT
#>
#> Iteration process:
#> Maximum number of iteration reached without convergence
#> Number of iterations: 100
#> Convergence criteria: parameters= 0.00033
#> : likelihood= 0.0026
#> : second derivatives= 0.0012
#>
#> Goodness-of-fit statistics:
#> maximum log-likelihood: -8591.58
#> AIC: 17231.16
#> BIC: 17332.31
#>
#> Maximum Likelihood Estimates:
#>
#> Fixed effects in the longitudinal model:
#>
#> coef Se Wald p-value
#> intercept (not estimated) 0.00000
#> age65 0.25690
#> I(age65^2) -0.42035
#> CEP 1.14461
#> male -0.01949
#>
#>
#> Variance-covariance matrix of the random-effects:
#> (the variance of the first random effect is not estimated)
#> intercept age65 I(age65^2)
#> intercept 1.00000
#> age65 -0.84014 1.87736
#> I(age65^2) 0.17690 -0.54491 0.2217
#>
#> MMSE BVRT
#> Residual standard error: 0.86255 1.20479
#>
#> Parameters of the link functions:
#>
#> coef Se Wald p-value
#> MMSE-I-splines1 -4.78275
#> MMSE-I-splines2 1.04927
#> MMSE-I-splines3 0.00046
#> MMSE-I-splines4 1.89931
#> MMSE-I-splines5 1.15827
#> MMSE-I-splines6 0.90200
#> MMSE-I-splines7 0.82392
#> BVRT-I-splines1 -5.32401
#> BVRT-I-splines2 1.19831
#> BVRT-I-splines3 1.33843
#> BVRT-I-splines4 1.86317
#> BVRT-I-splines5 0.97656
#> BVRT-I-splines6 1.28464
#>
## joint model for 1 class
m1S <- mpjlcmm(longitudinal=list(mult1),subject="ID",ng=1,data=paquid,
survival=Surv(age_init,agedem,dem)~1)
summary(m1S)
#> Multivariate joint latent class model for quantitative outcome and competing risks
#> fitted by maximum likelihood method
#>
#> mpjlcmm(longitudinal = list(mult1), subject = "ID", ng = 1, survival = Surv(age_init,
#> agedem, dem) ~ 1, data = paquid)
#>
#> Statistical Model:
#> Dataset: paquid
#> Number of subjects: 500
#> Number of longitudinal models: 1
#> Number of observations: 4164
#> Number of latent classes: 1
#> Number of parameters: 26
#> Event 1 :
#> Number of events: 128
#> Weibull baseline risk function
#> Link functions: Quadratic I-splines with nodes 0 25 27 29 30 for MMSE
#> Quadratic I-splines with nodes 0 10 12 15 for BVRT
#>
#> Iteration process:
#> Maximum number of iteration reached without convergence
#> Number of iterations: 100
#> Convergence criteria: parameters= 1e-07
#> : likelihood= 1.9e-05
#> : second derivatives= 0.00021
#>
#> Goodness-of-fit statistics:
#> maximum log-likelihood: -9125.69
#> AIC: 18303.39
#> BIC: 18412.97
#>
#>
#>
#> Maximum Likelihood Estimates:
#>
#>
#> Parameters in the proportional hazard model:
#>
#>
#> coef Se Wald p-value
#> event1 +/-sqrt(Weibull1) 0.10303
#> event1 +/-sqrt(Weibull2) 3.67379
#>
#>
#> Longitudinal model for MMSE/BVRT :
#>
#> Fixed effects in the longitudinal model:
#> coef Se Wald p-value
#> intercept (not estimated) 0.00000
#> age65 0.25850
#> I(age65^2) -0.42581
#> CEP 1.16029
#> male -0.02090
#>
#> Variance-covariance matrix of the random-effects:
#> (the variance of the first random effect is not estimated)
#> intercept age65 I(age65^2)
#> intercept 1.00000
#> age65 -0.82588 1.88169
#> I(age65^2) 0.17041 -0.54597 0.22386
#>
#> MMSE BVRT
#> Residual standard error: -0.87483 -1.22205
#>
#> Parameters of the link functions:
#>
#> coef Se Wald p-value
#> MMSE-I-splines1 -4.85308
#> MMSE-I-splines2 1.05674
#> MMSE-I-splines3 0.00004
#> MMSE-I-splines4 1.91268
#> MMSE-I-splines5 1.16632
#> MMSE-I-splines6 0.90827
#> MMSE-I-splines7 0.82967
#> BVRT-I-splines1 -5.41148
#> BVRT-I-splines2 1.21194
#> BVRT-I-splines3 1.34634
#> BVRT-I-splines4 1.87665
#> BVRT-I-splines5 0.98320
#> BVRT-I-splines6 1.29371
#>
##### joint model for 2 classes #####
## specify longitudinal model for 2 classes, without estimation
mult2 <- multlcmm(MMSE+BVRT~age65+I(age65^2)+CEP+male,random=~age65+I(age65^2),
subject="ID",link=c("5-quant-splines","4-quant-splines"),ng=2,
mixture=~age65+I(age65^2),data=paquid,B=random(mult1),maxiter=0)
#> Error in eval(cl$B[[2]], parent.env(environment())): object 'mult1' not found
## estimation of the associated joint model
m2S <- mpjlcmm(longitudinal=list(mult2),subject="ID",ng=2,data=paquid,
survival=Surv(age_init,agedem,dem)~1)
#> Error in mpjlcmm(longitudinal = list(mult2), subject = "ID", ng = 2, data = paquid, survival = Surv(age_init, agedem, dem) ~ 1): object 'mult2' not found
## estimation by a grid search with 50 replicates, initial values
## randomly generated from m1S
m2S_b <- gridsearch(mpjlcmm(longitudinal=list(mult2),subject="ID",ng=2,
data=paquid,survival=Surv(age_init,agedem,dem)~1), minit=m1S, rep=50, maxiter=30)
#> Error in gridsearch(mpjlcmm(longitudinal = list(mult2), subject = "ID", ng = 2, data = paquid, survival = Surv(age_init, agedem, dem) ~ 1), minit = m1S, rep = 50, maxiter = 30): The model minit did not converge
##### joint model for 3 classes #####
mult3 <- multlcmm(MMSE+BVRT~age65+I(age65^2)+CEP+male,random=~age65+I(age65^2),
subject="ID",link=c("5-quant-splines","4-quant-splines"),ng=3,
mixture=~age65+I(age65^2),data=paquid,B=random(mult1),maxiter=0)
#> Error in eval(cl$B[[2]], parent.env(environment())): object 'mult1' not found
m3S <- mpjlcmm(longitudinal=list(mult3),subject="ID",ng=3,data=paquid,
survival=Surv(age_init,agedem,dem)~1)
#> Error in mpjlcmm(longitudinal = list(mult3), subject = "ID", ng = 3, data = paquid, survival = Surv(age_init, agedem, dem) ~ 1): object 'mult3' not found
m3S_b <- gridsearch(mpjlcmm(longitudinal=list(mult3),subject="ID",ng=3,
data=paquid,survival=Surv(age_init,agedem,dem)~1), minit=m1S, rep=50, maxiter=30)
#> Error in gridsearch(mpjlcmm(longitudinal = list(mult3), subject = "ID", ng = 3, data = paquid, survival = Surv(age_init, agedem, dem) ~ 1), minit = m1S, rep = 50, maxiter = 30): The model minit did not converge
##### summary of the models #####
summarytable(m1S,m2S,m2S_b,m3S,m3S_b)
#> Error in summarytable(m1S, m2S, m2S_b, m3S, m3S_b): object 'm2S' not found
##### post-fit #####
## update longitudinal models :
mod2 <- update(m2S)
#> Error in update(m2S): object 'm2S' not found
mult2_post <- mod2[[1]]
#> Error in eval(expr, envir, enclos): object 'mod2' not found
## -> use the available functions for multlcmm on the mult2_post object
## fit of the longitudinal trajectories
par(mfrow=c(2,2))
plot(mult2_post,"fit","age65",marg=TRUE,shades=TRUE,outcome=1)
#> Error in plot(mult2_post, "fit", "age65", marg = TRUE, shades = TRUE, outcome = 1): object 'mult2_post' not found
plot(mult2_post,"fit","age65",marg=TRUE,shades=TRUE,outcome=2)
#> Error in plot(mult2_post, "fit", "age65", marg = TRUE, shades = TRUE, outcome = 2): object 'mult2_post' not found
plot(mult2_post,"fit","age65",marg=FALSE,shades=TRUE,outcome=1)
#> Error in plot(mult2_post, "fit", "age65", marg = FALSE, shades = TRUE, outcome = 1): object 'mult2_post' not found
plot(mult2_post,"fit","age65",marg=FALSE,shades=TRUE,outcome=2)
#> Error in plot(mult2_post, "fit", "age65", marg = FALSE, shades = TRUE, outcome = 2): object 'mult2_post' not found
## predicted trajectories
dpred <- data.frame(age65=seq(0,3,0.1),male=0,CEP=0)
predL <- predictL(mult2_post,newdata=dpred,var.time="age65",confint=TRUE)
#> Error in predictL(mult2_post, newdata = dpred, var.time = "age65", confint = TRUE): object 'mult2_post' not found
plot(predL,shades=TRUE) # in the latent process scale
#> Error in plot(predL, shades = TRUE): object 'predL' not found
predY <- predictY(mult2_post,newdata=dpred,var.time="age65",draws=TRUE)
#> Error in predictY(mult2_post, newdata = dpred, var.time = "age65", draws = TRUE): object 'mult2_post' not found
plot(predY,shades=TRUE,ylim=c(0,30),main="MMSE") #in the 0-30 scale for MMSE
#> Error in plot(predY, shades = TRUE, ylim = c(0, 30), main = "MMSE"): object 'predY' not found
plot(predY,shades=TRUE,ylim=c(0,15),outcome=2,main="BVRT") #in 0-15 for BVRT
#> Error in plot(predY, shades = TRUE, ylim = c(0, 15), outcome = 2, main = "BVRT"): object 'predY' not found
## baseline hazard and survival curves :
plot(m2S,"hazard")
#> Error in plot(m2S, "hazard"): object 'm2S' not found
plot(m2S,"survival")
#> Error in plot(m2S, "survival"): object 'm2S' not found
## posteriori probabilities and classification :
postprob(m2S)
#> Error in postprob(m2S): object 'm2S' not found
####################################################################################
### EXAMPLE 2 : ###
### two latent processes measured each by one outcome along with dementia onset ###
####################################################################################
## define the two longitudinal models
mMMSE1 <- lcmm(MMSE~age65+I(age65^2)+CEP,random=~age65+I(age65^2),subject="ID",
link="5-quant-splines",data=paquid)
mCESD1 <- lcmm(CESD~age65+I(age65^2)+male,random=~age65+I(age65^2),subject="ID",
link="5-quant-splines",data=paquid)
## joint estimation
mm1S <- mpjlcmm(longitudinal=list(mMMSE1,mCESD1),subject="ID",ng=1,data=paquid,
survival=Surv(age_init,agedem,dem)~CEP+male)
## with 2 latent classes
mMMSE2 <- lcmm(MMSE~age65+I(age65^2)+CEP,random=~age65+I(age65^2),subject="ID",
link="5-quant-splines",data=paquid,ng=2,mixture=~age65+I(age65^2),
B=random(mMMSE1),maxiter=0)
#> Error in eval(mm$B[[2]], parent.env(environment())): object 'mMMSE1' not found
mCESD2 <- lcmm(CESD~age65+I(age65^2)+male,random=~age65+I(age65^2),subject="ID",
link="5-quant-splines",data=paquid,ng=2,mixture=~age65+I(age65^2),
B=random(mCESD1),maxiter=0)
#> Error in eval(mm$B[[2]], parent.env(environment())): object 'mCESD1' not found
mm2S <- mpjlcmm(longitudinal=list(mMMSE2,mCESD2),subject="ID",ng=2,data=paquid,
survival=Surv(age_init,agedem,dem)~CEP+mixture(male),classmb=~CEP+male)
#> Error in mpjlcmm(longitudinal = list(mMMSE2, mCESD2), subject = "ID", ng = 2, data = paquid, survival = Surv(age_init, agedem, dem) ~ CEP + mixture(male), classmb = ~CEP + male): object 'mMMSE2' not found
mm2S_b <- gridsearch(mpjlcmm(longitudinal=list(mMMSE2,mCESD2),subject="ID",ng=2,
data=paquid,survival=Surv(age_init,agedem,dem)~CEP+mixture(male),
classmb=~CEP+male),minit=mm1S,rep=50,maxiter=50)
#> Error in mpjlcmm(longitudinal = list(mMMSE2, mCESD2), subject = "ID", ng = 2, data = paquid, survival = Surv(age_init, agedem, dem) ~ CEP + mixture(male), classmb = ~CEP + male, maxiter = 50, B = random(structure(list(K = 2L, ny = c(1, 1), nbevt = 1L, ng = 1, ns = 500L, idprob = c(1, 0, 0), idcom = c(0, 1, 1), idspecif = c(0, 1, 1), idtdv = c(0, 0, 0), idg = c(1, 1, 1, 1, 1, 1, 1, 1), idcontr = c(0, 0, 0, 0, 0, 0, 0, 0), idea = c(1, 1, 1, 0, 1, 1, 1, 0), idcor = c(0, 0, 0, 0, 0, 0, 0, 0), nv = c(4, 4), nv2 = 3L, loglik = -11436.3479117203, best = c(`event1 +/-sqrt(Weibull1)` = 0.103895847728593, `event1 +/-sqrt(Weibull2)` = 3.6275360602097, CEP = -0.248907167053964, male = -0.201136331569669, age65 = 0.513643167969247, `I(age65^2)` = -0.587377858675825, CEP = 1.35635307568778, `varcov 1` = 2.04764065844757, `varcov 2` = -2.00105389203602, `varcov 3` = 4.22090478094071, `varcov 4` = 0.490968760875473, `varcov 5` = -1.31874913764694, `varcov 6` = 0.533171645568319, `I-splines1` = -5.96507093350106, `I-splines2` = 1.20706536022429, `I-splines3` = -0.00124144371945553, `I-splines4` = 2.12392622422541, `I-splines5` = 1.27660944663899, `I-splines6` = 1.00078771918176, `I-splines7` = 0.915233677852589, age65 = 0.293220034217904, `I(age65^2)` = 0.0652978922298892, male = -0.445561355393589, `varcov 1` = 2.21804537225626, `varcov 2` = -1.24517060437695, `varcov 3` = 1.98012472693113, `varcov 4` = 0.186795677393488, `varcov 5` = -0.463472103320252, `varcov 6` = 0.120644210520773, `I-splines1` = -2.06937297687338, `I-splines2` = 1.05309986181564, `I-splines3` = 0.747526422069736, `I-splines4` = 0.99284461514591, `I-splines5` = 1.5728394955278, `I-splines6` = 0.930373316427684, `I-splines7` = 1.41383740578681), V = c(3.55144497259884e-07, -5.72506594043286e-06, 0.0303628517522822, -8.15177446673215e-05, 0.00204912417931367, 0.0353532703639194, -2.66122822328525e-05, 0.00497457166243351, -0.0069147034099128, 0.0416254482424998, 5.41163498843072e-08, 1.87055081926076e-06, -3.69883504050165e-05, 3.64934497535018e-05, 0.0527504944796153, -1.69097579818076e-08, -7.2161769442613e-07, 1.21109874926965e-05, -1.27150064602361e-05, -0.0173843996804856, 0.0064473597581961, 5.88782937401036e-09, -4.64295428561245e-07, -2.51181175700329e-06, 3.32062333414128e-07, 0.00284894037556004, -0.00103134587399442, 0.0187655724143715, -9.20489917146401e-09, 2.96511572594067e-06, -4.4018950833116e-06, 1.91958978112617e-05, 0.00431845797638341, -0.00174624046923831, 0.00267645507797171, 0.0477386581905442, 2.97592746087665e-08, -8.69694622486705e-06, 1.05878375850742e-05, -5.35275996330088e-05, -0.00348564621786641, 0.0016592988099724, -0.00496327233599859, -0.0846067004611298, 0.200659662249556, -8.70550313766885e-09, 1.45098076402723e-06, -1.42117852596501e-07, 8.60763740804865e-06, -0.00181336021110374, 0.000276903694756714, 0.00101958043819965, 0.00787990917289725, -0.0279130974249294, 0.0190168283371081, -1.07010794605849e-08, 3.50941985710252e-06, -4.87202494021798e-06, 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1.9285275517721e-07, -1.04576279187777e-08, 1.27141820565332e-06, 1.26891603921375e-07, -4.20504305148724e-05, -6.28973571782377e-08, -5.32090233938784e-08, -7.89169752824734e-08, -6.01444712955771e-08, 0.000787858204448413, -0.000215534062703304, 2.42576900609198e-05, -0.000463075954535429, 0.000695296597216938, 0.000765624670049843, -0.000401455224264308, -6.25022434770895e-05, -0.000284631969319968, 0.000310463376677337, -5.07118967385382e-05, 0.000571594967647711, -0.000986689992060712, 0.00349171931362058, -0.0221098492299542, 0.0319421379055397), gconv = c(1.60321695499292e-07, 2.78769930446288e-05, 6.40190905292335e-07), conv = 1, call = mpjlcmm(longitudinal = list(mMMSE1, mCESD1), subject = "ID", ng = 1, survival = Surv(age_init, agedem, dem) ~ CEP + male, data = paquid), niter = 19, N = c(0, 2, 2, 6, 0, 12, 0, 0, 0, 0, 14, 2214, 2104, 128), Nprm = c(0, 2, 2, 3, 3, 0, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7), npmK = c(16, 16), idiag = c(0L, 0L), pred = structure(list( ID = c(1L, 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2.22659773314887, 2.33175180440635, 2.4414781906965, 2.55595870708377), .Dim = c(100L, 3L ), .Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96", "97", "98", "99", "100"), c("time", "event1.RiskFct1", "event1.CumRiskFct1" ))), typrisq = 2, hazardtype = "Specific", hazardnodes = structure(c(65.25, 99.489), .Dim = 2:1), nz = 2, scoretest = c(NA, NA), na.action = integer(0), contrainte = c(1, 1), levels = list( levelsdata = list(age65 = NULL, CEP = NULL, age65 = NULL, male = NULL), levelsclassmb = list(age65 = NULL, CEP = NULL, age65 = NULL, male = NULL), levelssurv = list( age65 = NULL, CEP = NULL, age65 = NULL, male = NULL), levelsFRM = list(list(levelsdata = list(intercept = NULL, age65 = NULL, CEP = NULL), levelsfixed = list( intercept = NULL, age65 = NULL, CEP = NULL), levelsrandom = list(intercept = NULL, age65 = NULL, CEP = NULL), levelsmixture = list(intercept = NULL, age65 = NULL, CEP = NULL), levelsclassmb = list( intercept = NULL, age65 = NULL, CEP = NULL)), list(levelsdata = list(intercept = NULL, age65 = NULL, male = NULL), levelsfixed = list(intercept = NULL, age65 = NULL, male = NULL), levelsrandom = list( intercept = NULL, age65 = NULL, male = NULL), levelsmixture = list(intercept = NULL, age65 = NULL, male = NULL), levelsclassmb = list(intercept = NULL, age65 = NULL, male = NULL)))), longicall = list( lcmm(fixed = MMSE ~ age65 + I(age65^2) + CEP, random = ~age65 + I(age65^2), subject = "ID", link = "5-manual-splines", intnodes = c(25, 27, 29), data = paquid, B = c(age65 = 0.513630556490176, `I(age65^2)` = -0.587373382782186, CEP = 1.35635540609323, `varcov 1` = 2.04775898507646, `varcov 2` = -2.00125566628283, `varcov 3` = 4.22124186031777, `varcov 4` = 0.491035977032957, `varcov 5` = -1.31886095992224, `varcov 6` = 0.53320891364287, `I-splines1` = -5.96507319790654, `I-splines2` = 1.2070635991733, `I-splines3` = 0.00105843085248852, `I-splines4` = 2.12392683629216, `I-splines5` = 1.27661021983948, `I-splines6` = 1.00078808202605, `I-splines7` = 0.915234377593288), maxiter = 0, range = c(0, 30), verbose = FALSE), lcmm(fixed = CESD ~ age65 + I(age65^2) + male, random = ~age65 + I(age65^2), subject = "ID", link = "5-manual-splines", intnodes = c(2, 6, 12), data = paquid, B = c(age65 = 0.293194931935816, `I(age65^2)` = 0.0653053751057962, male = -0.445561473424377, `varcov 1` = 2.21805506731407, `varcov 2` = -1.24518293980452, `varcov 3` = 1.98014533071386, `varcov 4` = 0.186797027981636, `varcov 5` = -0.463475796685172, `varcov 6` = 0.120644772975202, `I-splines1` = -2.06938941276063, `I-splines2` = 1.05309977754248, `I-splines3` = 0.747526320306885, `I-splines4` = 0.99284491061188, `I-splines5` = 1.57283927681055, `I-splines6` = 0.930375045938781, `I-splines7` = 1.41383564929924), maxiter = 0, range = c(0, 52), verbose = FALSE)), AIC = 22944.6958234406, BIC = 23096.4217149838, runtime = c(elapsed = 37.918), var.time = NULL), class = "mpjlcmm"))): object 'mMMSE2' not found
summarytable(mm1S,mm2S,mm2S_b)
#> Error in summarytable(mm1S, mm2S, mm2S_b): object 'mm2S' not found
mod1_biv <- update(mm1S)
#> Error in eval(object$call$longitudinal): object 'mMMSE1' not found
mod2_biv <- update(mm2S)
#> Error in update(mm2S): object 'mm2S' not found
## -> use post-fit functions as in exemple 1
# }