MEDseq (version 1.4.1)

MEDseq_stderr: MEDseq gating network standard errors

Description

Computes standard errors of the gating network coefficients in a fitted MEDseq model using either the Weighted Likelihood Bootstrap or Jackknife methods.

Usage

MEDseq_stderr(mod,
              method = c("WLBS", "Jackknife"),
              N = 1000L,
              symmetric = TRUE,
              SPS = FALSE)

Value

A list with the following two elements:

Coefficients

The original matrix of estimated coefficients (coef(mod$gating)).

Std. Errors

The matrix of corresponding standard error estimates.

Arguments

mod

An object of class "MEDseq" generated by MEDseq_fit or an object of class "MEDseqCompare" generated by MEDseq_compare.

method

The method used to compute the standard errors (defaults to "WLBS", the Weighted Likelihood Bootstrap).

N

The (integer) number of samples to use when the "WLBS" method is employed. Defaults to 1000L. Not relevant when method="Jackknife", in which case N is always the number of observations. Must be > 1, though N being greater than or equal to the sample size is recommended under method="WLBS".

symmetric

A logical indicating whether symmetric draws from the uniform Dirichlet distribution are used for the WLBS method in the presence of existing sampling weights. Defaults to TRUE; when FALSE, the concentration parameters of the Dirichlet distribution are given by the sampling weights. Only relevant when method="WLBS" for models with existing sampling weights.

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.

Author

Keefe Murphy - <>

Details

A progress bar is displayed as the function iterates over the N samples. The function may take a long time to run for large N. The function terminates immediately if mod$G == 1.

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. <tools:::Rd_expr_doi("10.1111/rssa.12712")>.

O'Hagan, A., Murphy, T. B., Scrucca, L., and Gormley, I. C. (2019). Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap. Computational Statistics, 34(4): 1779-1813.

See Also

MEDseq_fit, MEDseq_clustnames, seqformat

Examples

# Load the MVAD data
data(mvad)
mvad$Location <- factor(apply(mvad[,5:9], 1L, function(x) 
                 which(x == "yes")), labels = colnames(mvad[,5:9]))
mvad          <- list(covariates = mvad[c(3:4,10:14,87)],
                      sequences = mvad[,15:86], 
                      weights = mvad[,2])
mvad.cov      <- mvad$covariates

# Create a state sequence object with the first two (summer) time points removed
states        <- c("EM", "FE", "HE", "JL", "SC", "TR")
labels        <- c("Employment", "Further Education", "Higher Education", 
                   "Joblessness", "School", "Training")
mvad.seq      <- seqdef(mvad$sequences[-c(1,2)], states=states, labels=labels)
# \donttest{
# Fit a model with weights and a gating covariate
# Have the probability of noise-component membership be constant
# mod         <- MEDseq_fit(mvad.seq, G=11, modtype="UUN", weights=mvad$weights, 
#                           gating=~ gcse5eq, covars=mvad.cov, noise.gate=FALSE)
                            
# Estimate standard errors using 100 WLBS samples
# (std        <- MEDseq_stderr(mod, N=100))# }