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Calculate variability of breakpoints

Source: R/calcBPvar.R
calcBPvar.Rd

calcBPvar() computes an estimate of the variability of the breakpoints for a given number of regions. This involves computing the weighted mean and standard deviation of each breakpoint using Akaike weights.

Usage

calcBPvar(regions_results, noregions, pct = 0.05, criterion = "aic")

Arguments

regions_results

a regions_results object; the output of a call to calcregions() or addregions().

noregions

the number of regions for which the weighted mean and standard deviation are to be computed.

pct

the proportion of best model to keep from the original total number of possible models

criterion

string; the criterion used to compute the weights. Allowable options include "aic" and "bic". Abbreviations allowed.

Value

A regions_BPvar object, which has two components:

  • WeightedBP is a matrix containing the weighted mean and standard deviation of each breakpoint

  • BestModels is a data frame containing the models used to compute the weighted breakpoint statistics and the weights each one is given.

See also

calcregions() for fitting segmented regression models to all combinations of breakpoints.

Examples

data("alligator")

alligator_data <- process_measurements(alligator,
                                       pos = "Vertebra")

# Compute PCOs
alligator_PCO <- svdPCO(alligator_data)

# Fit segmented regression models for 1 to 7 regions
# using PCOs 1 to 4 and a continuous model with a
# exhaustive search
regionresults <- calcregions(alligator_PCO,
                             scores = 1:4,
                             noregions = 7,
                             minvert = 3,
                             cont = TRUE,
                             exhaus = TRUE,
                             verbose = FALSE)

# Compute Akaike-weighted location and SD of optimal
# breakpoints using top 10% of models with 4 regions
calcBPvar(regionresults, noregions = 4,
          pct = .1, criterion = "aic")
#>        BP 1   BP 2   BP 3
#> wMean 8.904 12.808 19.506
#> wSD   0.441  0.596  0.976
#> - Computed using top 10.14% of models