AlphaSimR (version 2.0.0)

asCategorical: Convert a normal (Gaussian) trait to an ordered categorical (threshold) trait

Description

Convert a normal (Gaussian) trait to an ordered categorical (threshold) trait

Usage

asCategorical(
  x,
  p = NULL,
  mean = 0,
  var = 1,
  threshold = c(-Inf, 0, Inf),
  include.lowest = TRUE,
  right = FALSE
)

Value

matrix of values with some traits recorded as ordered categories in the form of 1:nC with nC being the number of categories.

Arguments

x

matrix, values for one or more traits (if not a matrix, we cast to a matrix)

p

NULL, numeric or list, when NULL the threshold argument takes precedence; when numeric, provide a vector of probabilities of categories to convert continuous values into categories for a single trait (if probabilities do not sum to 1, another category is added and a warning is raised); when list, provide a list of numeric probabilities - list node with NULL will skip conversion for a specific trait (see examples); internally p is converted to threshold hence input threshold is overwritten

mean

numeric, assumed mean(s) of the normal (Gaussian) trait(s); used only when p is given

var

numeric, assumed variance(s) of the normal (Gaussian) trait(s); used only when p is given

threshold

NULL, numeric or list, when numeric, provide a vector of threshold values to convert continuous values into categories for a single trait (the thresholds specify left-closed and right-opened intervals [t1, t2), which can be changed with include.lowest and right; ensure you add -Inf and Inf or min and max to cover the whole range of values; otherwise you will get NA values); when list, provide a list of numeric thresholds - list node with NULL will skip conversion for a specific trait (see examples)

include.lowest

logical, see cut

right

logical, see cut

Details

If input trait is normal (Gaussian) then this function generates a categorical trait according to the ordered probit model.

Examples

founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)
SP = SimParam$new(founderPop)
SP$nThreads = 1L
trtMean = c(0, 0)
trtVarG = c(1, 2)
SP$addTraitA(nQtlPerChr = 10, mean = trtMean, var = trtVarG,
             corA = matrix(data = c(1.0, 0.6,
                                    0.6, 1.0), ncol = 2))
pop = newPop(founderPop)
trtVarE = c(1, 1)
trtVarP = trtVarG + trtVarE
pop = setPheno(pop, varE = trtVarE)
pheno(pop)

#Convert a single input trait
asCategorical(x = pheno(pop)[, 1])

#Demonstrate threshold argument (in units of pheno SD)
asCategorical(x = pheno(pop)[, 1], threshold = c(-1, 0, 1) * sqrt(trtVarP[1]))
asCategorical(x = pheno(pop)[, 1], threshold = c(-Inf, -1, 0, 1, Inf) * sqrt(trtVarP[1]))
asCategorical(x = pheno(pop)[, 1], threshold = c(-Inf, 0, Inf))

#Demonstrate p argument
asCategorical(x = pheno(pop)[, 1], p = 0.5, var = trtVarP[1])
asCategorical(x = pheno(pop)[, 1], p = c(0.5, 0.5), var = trtVarP[1])
asCategorical(x = pheno(pop)[, 1], p = c(0.25, 0.5, 0.25), var = trtVarP[1])

#Convert multiple input traits (via threshold or p argument)
try(asCategorical(x = pheno(pop)))
asCategorical(x = pheno(pop),
              threshold = list(c(-Inf, 0, Inf),
                               NULL))
try(asCategorical(x = pheno(pop), p = c(0.5, 0.5)))
asCategorical(x = pheno(pop),
              p = list(c(0.5, 0.5),
                       NULL),
              mean = trtMean, var = trtVarP)

asCategorical(x = pheno(pop),
              threshold = list(c(-Inf, 0, Inf),
                               c(-Inf, -2, -1, 0, 1, 2, Inf) * sqrt(trtVarP[2])))
q = c(-2, -1, 0, 1, 2)
p = pnorm(q)
p = c(p[1], p[2]-p[1], p[3]-p[2], p[4]-p[3], p[5]-p[4], 1-p[5])
asCategorical(x = pheno(pop),
              p = list(c(0.5, 0.5),
                       p),
              mean = trtMean, var = trtVarP)