Learn R Programming

QQreflimits (version 1.0.3)

BCr_Pval: P value testing for normality of (transformed) data

Description

Computes the P value of the quantile-quantile correlation coefficient (QQCC).

Usage

BCr_Pval(correl, n, censor=0, winsor=0, isBC=FALSE, is2pBC=FALSE)

Value

Pval

the P value

Arguments

correl

the QQ correlation coefficient

n

the sample size

censor

optional (default of 0) - the number of readings censored on the left

winsor

optional (default of 0) - the number of readings winsorized in each tail

isBC

optional (default of FALSE) - if TRUE, the QQCC is after Box-Cox transformation

is2pBC

optional (default of FALSE) - if TRUE, the QQCC is after a shifted Box-Cox transformation.

Author

Douglas M. Hawkins, Jessica J. Kraker krakerjj@uwec.edu

Details

Lower-level function, called by other functions in package. It takes information from a quantile-quantile regression, along with the circumstances leading up to it, to produce a P value testing for normality.

References

Hawkins DM, Esquivel RN (2024). A Quantile-Quantile Toolbox for Reference Intervals. The Journal of Applied Laboratory Medicine, 9:2, 357-370.

Examples

Run this code
# compute the Pvalue for two QQCC's
BCr_Pval(c(0.993, 0.99), 120)
# if censored
BCr_Pval(c(0.993, 0.99), 120, censor=3)
# if winsorized
BCr_Pval(c(0.993, 0.99), 120, winsor=3)
# on Box-Cox transformed data
BCr_Pval(c(0.993, 0.99), 120, isBC=TRUE)
# on Box-Cox transformed data, and winsorized
BCr_Pval(c(0.993, 0.99), 120, isBC=TRUE, winsor=3)

Run the code above in your browser using DataLab