Nothing
RunTaxometrics <-
function(x, seed = 0, n.pop = 100000, n.samples = 100,
reps = 1, MAMBAC = TRUE, assign.MAMBAC = 1, n.cuts = 50, n.end = 25,
MAXEIG = TRUE, assign.MAXEIG = 1, windows = 50, overlap = .90, LMode = TRUE,
mode.l = -.001, mode.r = .001, MAXSLOPE = FALSE, graph = 1) {
#
# Performs a series of taxometric analyses for a sample of data.
#
# Args:
# x: Data (matrix).
# seed: Random number seed (scalar).
# n.pop: Size of populations of comparison data (scalar).
# n.samples: Number of samples of comparison data (scalar).
# reps: Number of times to resort tied scores and redo
# calculations, averaging to obtain final results (scalar).
# MAMBAC: Whether MAMBAC is performed (T/F).
# assign.MAMBAC: Whether variables are used in all input-output pairings
# (1) or variables are summed to form input (2).
# n.cuts: Number of cuts in MAMBAC (scalar).
# n.end: Number of cases beyond final cuts in MAMBAC (scalar).
# MAXEIG: Whether MAXEIG is performed (T/F).
# assign.MAXEIG: Whether variables are used in all input-output
# triplets (1), each variable serves as input once (2),
# or variables are summed to form input (3).
# windows: Number of overlapping windows (scalar).
# overlap: Proportion of overlap between windows (scalar).
# LMode: Whether L-Mode is performed (T/F).
# mode.l: Position beyond which to search for left mode (scalar).
# mode.r: Position beyond which to search for right mode (scalar).
# MAXSLOPE: Whether MAXSLOPE is performed (T/F).
# graph: Whether to display graphs on screen (1), save as a
# compressed .jpeg file (2), or save as a high-resolution
# .tiff file (3)
#
# Returns a list object containing CCFI values, base rate estimates, and
# analytic specifications.
#
if (seed != 0) {
set.seed(seed)
}
cat("\nSTATUS OF PROGRAM EXECUTION\n\n")
cat("Checking for missing data\n")
x <- RemoveMissingData(x)
n <- dim(x)[1]
k <- dim(x)[2] - 1
group <- x[, k + 1]
x <- x[, 1:k]
cat("Checking classification variable\n")
CheckClassification(group, n)
p <- sum(group == 2) / n
parameters <- list(n = n, k = k, p = p, n.pop = n.pop,
n.samples = n.samples, reps = reps, MAMBAC = MAMBAC,
assign.MAMBAC = assign.MAMBAC, n.cuts = n.cuts,
n.end = n.end, MAXEIG = MAXEIG,
assign.MAXEIG = assign.MAXEIG, windows = windows,
overlap = overlap, LMode = LMode, mode.l = mode.l,
mode.r = mode.r, MAXSLOPE = MAXSLOPE, graph = graph,
profile = FALSE)
cat("Checking for variance\n")
x <- AddVariance(x, k, parameters)
x <- apply(x, 2, scale)
cat("Checking program parameters\n")
parameters <- CheckParameters(x, parameters)
cat("Generating population of dimensional comparison data\n")
x.dim <- GenerateData(x, n = parameters$n.pop)
cat("Generating population of categorical comparison data\n")
cat(" Generating taxon\n")
x.cat.taxon <- GenerateData(x[(group == 2), ],
n = round(parameters$n.pop * p))
cat(" Generating complement\n")
x.cat.complement <- GenerateData(x[(group == 1), ],
n = parameters$n.pop -
round(parameters$n.pop * p))
x.cat <- rbind(x.cat.taxon, x.cat.complement)
cat("Analyzing empirical data\n")
x.results <- RunProcedures(x, parameters)
cat("Analyzing samples of dimensional comparison data\n")
x.dim.results <- RunProceduresComp(x.dim, parameters)
cat("Analyzing samples of categorical comparison data\n")
x.cat.results <- RunProceduresComp(x.cat, parameters)
cat("Plotting graphs\n")
DisplayPanels(x.results, x.dim.results, x.cat.results, parameters)
cat("Returning results\n\n")
output.1 <- CalculateCCFIs(x.results, x.dim.results, x.cat.results, parameters)
output.2 <- CalculateBaseRates(x.results, parameters)
output.3 <- GetSpecifications(parameters)
output <- c(output.1, output.2, output.3)
class(output) <- "taxometrics"
return(output)
}
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