MEDseq_meantime: Compute the mean time spent in each sequence category
In MEDseq: Mixtures of Exponential-Distance Models with Covariates

MEDseq_meantime

R Documentation

Compute the mean time spent in each sequence category

Description

Computes the mean time (per cluster) spent in each sequence category (i.e. state value) for a fitted MEDseq model.

Usage

MEDseq_meantime(x,
                MAP = FALSE,
                weighted = TRUE, 
                norm = TRUE,
                prop = FALSE, 
                map.size = FALSE,
                wt.size = FALSE,
                SPS = FALSE)

## S3 method for class 'MEDseqMeanTime'
print(x,
      digits = 3L,
      ...)

Arguments

`x`	An object of class `"MEDseq"` generated by `MEDseq_fit` or an object of class `"MEDseqCompare"` generated by `MEDseq_compare`.
`MAP`	A logical indicating whether to use the MAP classification in the computation of the averages, or the 'soft' clustering assignment probabilities given by `x$z`. Defaults to `FALSE`, but is always `TRUE` for models fitted by the CEM algorithm (see `MEDseq_control`). See `weighted` for incorporating the sampling weights (regardless of the value of `MAP`). See `map.size` below.
`weighted`	A logical indicating whether the sampling weights (if used during model fitting) are used to compute the weighted averages. These can be used alone (when `MAP` is `TRUE`) or in conjunction with the 'soft' clustering assignment probabilities (when `MAP` is `FALSE`). Defaults to `TRUE`. Note that, by default, the first column of the output is not affected by the value of `weighted` (see `wt.size`).
`norm`	A logical indicating whether the mean times (outputted values after the first column) are normalised to sum to the sequence length within each cluster (defaults to `TRUE`). Otherwise, when `FALSE`, entries beyond the first column give the total (weighted) number of times a given sequence category was observed in a given cluster.
`prop`	A logical (defaulting to `FALSE` and only invoked when `norm` is also `TRUE`) which further normalises the output to give the proportions of time spent in each state on average instead of the absolute values.
`map.size`	A logical (defaulting to `FALSE`, unless the model was fitted by the CEM algorithm (see `MEDseq_control`)) which overrides `MAP` in the `Size` column (or `Weighted.Size` column, see `wt.size`) of the output, e.g. if `MAP=FALSE` and `map.size=TRUE`, the MAP classification is used to determine the cluster sizes but the soft cluster-membership probabilities are used to calculate quantities in remaining columns. Only relevant when `MAP=FALSE` or `wt.size=TRUE`.
`wt.size`	A logical (defaults to `FALSE` and only invoked when when `weighted` is also `TRUE`) which toggles whether the weights are also used in the computation of the cluster sizes in the first column of the output (regardless of the values of `MAP` or `map.size`).
`SPS`	A logical indicating whether the output should be labelled according to the state-permanence-sequence representation of the central sequences. Defaults to `FALSE`. See `MEDseq_clustnames` and `seqformat`.
`digits`	Minimum number of significant digits to be printed in values.
`...`	Catches unused arguments.

Details

Models with weights, covariates, &/or a noise component are also accounted for.

Value

A matrix with sequence category and cluster-specific mean times, giving clusters on the rows, corresponding cluster sizes (or weighted cluster sizes) in the first column, and sequence categories in the remaining columns.

Note

The function plot.MEDseq with the option type="mt" can be used to visualise the mean times (by cluster). However, the results displayed therein (at present) always assume norm=TRUE, prop=FALSE, and wt.size=TRUE, while the MAP argument is renamed to soft, where MAP=!soft.

Author(s)

Keefe Murphy - <keefe.murphy@mu.ie>

References

Murphy, K., Murphy, T. B., Piccarreta, R., and Gormley, I. C. (2021). Clustering longitudinal life-course sequences using mixtures of exponential-distance models. Journal of the Royal Statistical Society: Series A (Statistics in Society), 184(4): 1414-1451. <doi:10.1111/rssa.12712>.

Examples

data(biofam)
seqs <- seqdef(biofam[10:25] + 1L,
               states = c("P", "L", "M", "L+M", "C", 
                          "L+C", "L+M+C", "D"))
mod <- MEDseq_fit(seqs, G=10, modtype="UUN")

MEDseq_meantime(mod)
MEDseq_meantime(mod, prop=TRUE)
MEDseq_meantime(mod, map.size=TRUE)
MEDseq_meantime(mod, MAP=TRUE, norm=FALSE, SPS=TRUE)

MEDseq documentation built on Dec. 28, 2022, 2:35 a.m.