Transform p-values to continuous scores with the quantile function of the normal distribution

Quantile function, also known as the inverse of cumulative distribution function of the normal distribution, is used to map p-values to continuous scores raging on \(R\). The signs of the resulting scores are positive by default and are determined by the parameter sign.

Usage

pQnormScore(p, sign = 1, replaceZero = TRUE)

Arguments

p

p-value(s) between \((0,1]\)

sign

Signs of the scores, either positive (in case of positive numbers), negative (in case of negative numbers), or zero. In case of a logical vector, TRUE is interpreted as positive and FALSE is interpreted as negative.

replaceZero

Logical, whether to replace zero p-values with the minimal double value specified by the machine. Default is TRUE. If set to FALSE, results will contain infinite values.

Value

A numeric vector of transformed p-values using signed quantile normal transformation.

See also

pAbsLog10Score, pScore, double

Examples

testPvals <- c(0.001, 0.01, 0.05, 0.1, 0.5, 1)
pQnormScore(testPvals)
#> [1] 3.2905267 2.5758293 1.9599640 1.6448536 0.6744898 0.0000000
testPvalSign <- rep(c(-1,1), 3)
pQnormScore(testPvals, sign=testPvalSign)
#> [1] -3.2905267  2.5758293 -1.9599640  1.6448536 -0.6744898  0.0000000
testLog <- rep(c(TRUE, FALSE),3)
pQnormScore(testPvals, testLog)
#> [1]  3.2905267 -2.5758293  1.9599640 -1.6448536  0.6744898  0.0000000