RRPP (version 2.0.3)

interSubVar: Reveal the inter-subject variability from a measurement error analysis

Description

Function produces both a list of inter-subject Euclidean distance matrices, based on replicate measurements of the same subjects, and one matrix that summarizes the variability among the inter-subject distances, across subjects. This function can be considered a tool for the evaluation of subject estimate precision. The function, plot.interSubVar can produce a heat map of the inter-subject variability.

Usage

interSubVar(ME, type = c("range", "sd", "var", "cv"))

Value

An object of class interSubVar is a list containing the following

var.map

A distance matrix object with values that map the variability statistic used for inter-subject Euclidean distances.

distance.mats

The inter-subject distance matrices for every replicate.

subject.order

A vector of subject levels in the order that was used to guarantee consistent sorting across distance matrices.

var.map

The variability type (statistic) that was used.

Arguments

ME

A measurement error object

type

A value to indicate the type of variability (statistic) to measure, which can be one of range (the maximum value minus the minimum value, not the two values), standard deviation (sd), variance (var), or coefficient of variation (cv). No attempt is made to assure the distribution of values is appropriate for the statistics. For example, if only two replicates are available, using sd, var, or cv might not be wise. Or if the replicated values are exact, cv will be NA (and other stats will be 0). Choice of statistic should consider the distribution of values.

Author

Michael Collyer

Examples

if (FALSE) {
# Measurement error analysis on simulated data of fish shapes

data(fishy)

# Analysis unconcerned with groups 

ME1 <- measurement.error(
  Y = "coords",
  subjects = "subj",
  replicates = "reps",
  data = fishy)

anova(ME1)
ICCstats(ME1, subjects = "Subjects", with_in = "Systematic ME")
plot(ME1)

# Analysis concerned with groups 

ME2 <- measurement.error(
  Y = "coords",
  subjects = "subj",
  replicates = "reps",
  groups = "groups",
  data = fishy)

anova(ME2)
ICCstats(ME2, subjects = "Subjects", 
  with_in = "Systematic ME", groups = "groups")
P <- plot(ME2)
focusMEonSubjects(P, subjects = 18:20, shadow = TRUE)
 }