Specify GMM method using lcmm
Growth mixture modeling through latent-class linear mixed modeling.
lcMethodLcmmGMM(
fixed,
mixture = ~1,
random = ~1,
classmb = ~1,
time = getOption("latrend.time"),
id = getOption("latrend.id"),
nClusters = 2,
...
)fixed |
The fixed effects formula. |
mixture |
The mixture-specific effects formula. See lcmm::hlme for details. |
random |
The random effects formula. See lcmm::hlme for details. |
classmb |
The cluster membership formula for the multinomial logistic model. See lcmm::hlme for details. |
time |
The name of the time variable. |
id |
The name of the trajectory identifier variable. This replaces the |
nClusters |
The number of clusters to fit. This replaces the |
... |
Arguments passed to lcmm::hlme. The following arguments are ignored: data, fixed, random, mixture, subject, classmb, returndata, ng, verbose, subset. |
Proust-Lima C, Philipps V, Liquet B (2017). “Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm.” Journal of Statistical Software, 78, 1–56. doi: 10.18637/jss.v078.i02.
Proust-Lima C, Philipps V, Diakite A, Liquet B (2019). lcmm: Extended Mixed Models Using Latent Classes and Latent Processes. R package version: 1.8.1, https://cran.r-project.org/package=lcmm.
Other lcMethod implementations:
lcMethod-class,
lcMethodAkmedoids,
lcMethodCrimCV,
lcMethodCustom,
lcMethodDtwclust,
lcMethodFeature,
lcMethodFunFEM,
lcMethodGCKM,
lcMethodKML,
lcMethodLMKM,
lcMethodLcmmGBTM,
lcMethodLongclust,
lcMethodMclustLLPA,
lcMethodMixAK_GLMM,
lcMethodMixtoolsGMM,
lcMethodMixtoolsNPRM,
lcMethodRandom,
lcMethodStratify
data(latrendData)
method <- lcMethodLcmmGMM(fixed = Y ~ Time,
mixture = ~ Time, random = ~ 1,
id = "Id", time = "Time", , nClusters = 3)
gmm <- latrend(method, data = latrendData)
summary(gmm)
method <- lcMethodLcmmGMM(fixed = Y ~ Time,
mixture = ~ Time, random = ~ Time,
id = "Id", time = "Time", nClusters = 3)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.