rarcat: Robustness Assessment of Regressions using Cluster Analysis...
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rarcat

R Documentation

Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT)

Description

rarcat is a wrapper for the functions regressboot and rarcat that performs the entire RARCAT procedure on all possible associations between a typology and covariates of interest. See Roth et al. (2024) or the R tutorial as WeightedCluster vignette for all details on the corresponding methods and their utility.

Usage

rarcat(diss, covar, df, 
        clustering=NULL, robust=TRUE, B=500, count=FALSE, 
        algo="pam", method="ward.D", 
        fixed=FALSE, ncluster=10, eval="CH",
        parallel="no", ncpus=1, cl=NULL,
        transformation=FALSE, conflevel=0.05, digits=3)

Arguments

`diss`	The numerical dissimilarity matrix used for clustering. Only a pre-computed matrix (i.e., where pairwise dissimilarities do not depend on the resample) is currently supported.
`covar`	A character vector containing the names of the covariates whose association with the clustering is studied. The formula object is then created inside the function based on this.
`df`	The original dataset (data frame) containing the covariates of interest. Row number should be equal to the length of the `clustering` argument and column names should match the information in `covar`.
`clustering`	Optional. An integer vector containing the clustering solution (one entry for each individual) from the original analysis. If not given (default), it is computed based on the other information inside the function.
`robust`	Logical. TRUE (the default) indicates that RARCAT should be performed. FALSE implies a much faster function run but only output the original analysis, which is a standard regression analysis for all combinations of reference clusters and covariates.
`B`	The integer number of bootstrap. Set to 500 by default to attain a satisfactory precision around the estimates as the procedure involves multiple steps.
`count`	Logical. Whether the bootstrap runs are counted on the screen or not.
`algo`	The clustering algorithm as a character string. Currently only "pam" (calling the function `wcKMedRange`) and "hierarchical" (calling the function `fastcluster::hclust`) are supported. By default "pam".
`method`	A character string with the method argument of `hclust`, "ward.D" by default.
`fixed`	Logical. TRUE implies that the number of clusters is the same in every bootstrap. FALSE (default) implies that an optimal number of clusters is evaluated each time.
`ncluster`	Integer. Either the number of clusters in every bootstrap if `fixed` is TRUE or the maximum number of clusters (starting from 2) to be evaluated in each bootstrap if `fixed` is FALSE.
`eval`	A character string with the cluster quality index to be evaluated for each new partition. Any column of `as.clustrange` is supported, "CH" (the Calinski-Harabasz index) by default. Also works with `algo`= "pam".
`parallel`	A character string with the type of parallel operation to be used (if any) by the function `boot:boot`. Options are "no" (default), "multicore" and "snow" (for Windows).
`ncpus`	Integer. Number of processes to be used in case of parallel operation. Typically, one would chose this to be the number of available CPUs.
`cl`	A parallel cluster for use if `parallel` = "snow". If not supplied, a cluster on the local machine is created for the duration of the `boot` call.
`transformation`	Logical. TRUE means that a Fisher transformation is applied in the `rarcat` function. This can be recommended in case of extreme associations (close to the -1 or 1 boundaries). FALSE by default.
`conflevel`	Confidence level for the confidence intervals from the original analysis and the prediction intervals from the robustness assessment. 0.05 by default.
`digits`	Controls the number of significant digits to print. 3 by default.

Details

The rarcat function runs a standard typology-based association study and evaluates the impact of sampling uncertainty on the results, thus assessing the reproducibility of the analysis.

Value

The output of rarcattables contains the following tables:

`original.analysis`	Average Marginal Effects (AMEs) estimated with multivariable logistic regressions and representing the expected change in the probability of belonging to a trajectory group (a reference cluster) for a change in the level of a variable (a covariate of interest), together with their confidence intervals.
`robust.analysis`	Pooled AMEs from the bootstrap procedure and their prediction intervals, representing the range of expected values if the clustering and associated regressions were performed on a new sample from the same underlying distribution. This table provide robust estimates for a typology-based association study.

Author(s)

Leonard Roth

References

Roth, L., Studer, M., Zuercher, E., & Peytremann-Bridevaux, I. (2024). Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis. BMC medical research methodology, 24(1), 303. https://doi.org/10.1186/s12874-024-02435-8.

Examples


## Set the seed for reproducible results
set.seed(1)

## Loading the data (TraMineR package)
data(mvad)

## Creating the state sequence object
mvad.seq <- seqdef(mvad, 17:86)

## Distance computation
diss <- seqdist(mvad.seq, method="LCS")

## Hierarchical clustering
hc <- fastcluster::hclust(as.dist(diss), method="ward.D")

## Computing cluster quality measures
clustqual <- as.clustrange(hc, diss=diss, ncluster=6)

# A character vector with the names of the covariates of interest (to be related to the typology)
covar <- c("funemp", "gcse5eq")

## As in the original analysis, hierarchical clustering with Ward method is implemented
## The number of clusters is fixed to 6 here
## For illustration purposes, the number of bootstrap is smaller than what it ought to be
rarcatout <- rarcat(diss, covar, mvad, B = 50, 
                    algo = "hierarchical", method = "ward.D", 
                    fixed = TRUE, ncluster = 6)

## Assess the robustness of the original analysis
rarcatout$original.analysis
rarcatout$robust.analysis

WeightedCluster documentation built on April 24, 2025, 3:01 a.m.