The goal of longmixr is to provide consensus clustering for longitudinal clustering
flexmix. It uses the approach from
replaces the clustering of the longitudinal data with a
You can install longmixr from CRAN with:
You can install the latest version from github with:
If you want to render the vignette, use:
remotes::install_github("cellmapslab/longmixr", build_vignettes = TRUE, dependencies = TRUE)
Please note that for the vignette a lot more dependencies are installed.
You need a dataset with a column that identifies the subject, a column that denotes the time point of the measurement and variables that you want to model.
set.seed(5) test_data <- data.frame( patient_id = rep(1:10, each = 4), visit = rep(1:4, 10), var_1 = c(rnorm(20, -1), rnorm(20, 3)) + rep(seq(from = 0, to = 1.5, length.out = 4), 10), var_2 = c(rnorm(20, 0.5, 1.5), rnorm(20, -2, 0.3)) + rep(seq(from = 1.5, to = 0, length.out = 4), 10) )
In the following approach, the variables
var_2 each are modeled as
dependent on a smooth function of time, taking the multiple measurements for each
subject into account. The assumption is that
var_2 represent a
multivariate outcome. The modeling is specified in the
model_list <- list(flexmix::FLXMRmgcv(as.formula("var_1 ~ .")), flexmix::FLXMRmgcv(as.formula("var_2 ~ ."))) clustering <- longitudinal_consensus_cluster( data = test_data, id_column = "patient_id", max_k = 2, reps = 3, model_list = model_list, flexmix_formula = as.formula("~s(visit, k = 4) | patient_id"))
The results of the clustering can be assessed via several plots. For every specified number of clusters, the consensus matrix and the resulting hierarchical clustering on this matrix is shown. Additionally, the consensus CDF and the delta Area plots give a measure which number of cluster is optimal. The tracking plot gives an overview how the observations are distributed across the different clusters for different numbers of specified clusters. The item (subject) consensus plot shows the average consensus of each subject with all other subjects that belong to one cluster. The cluster consensus plot depicts the average consensus between all members of each cluster.
The above mentioned plots are generated when calling the
For a detailed explanation how you can use
longmixr to analyze your
longitudinal data, check out the
Example clustering analysis vignette.
Additionally, this package provides a wrapper function around the
ConsensusClusterPlus function to work with mixed continuous and categorical
data (by using the Gower distance):
dc <- mtcars # scale continuous variables dc <- sapply(mtcars[, 1:7], scale) # code factor variables dc <- cbind(as.data.frame(dc), vs = as.factor(mtcars$vs), am = as.factor(mtcars$am), gear = as.factor(mtcars$gear), carb = as.factor(mtcars$carb)) cc <- crosssectional_consensus_cluster( data = dc, reps = 10, seed = 1 )
The package is based on the code of
(version 1.52.0). For this code the copyright holders are Matt Wilkerson and
Peter Waltman. For all subsequent changes the copyright holder is the Max Planck
Institute of Psychiatry. The code is licensed under GPL v2.