Produces three quantile-quantile (Q-Q) plots, also called probability plots, based on three distributions (normal, lognormal and gamma distributions).

cenCompareQQ(x.var, cens.var, Yname = yname, printrslt = TRUE, ...)

Arguments

x.var The column of x (response variable) values plus detection limits The column of indicators, where 1 (or TRUE) indicates a detection limit in the y.var column, and 0 (or FALSE) indicates a detected value in y.var. Optional – input text in quotes to be used as the variable name on all plots. The default Yname is the name of the y.var input variable. Logical TRUE/FALSE option of whether to print the best distribution in the console window, or not. Default is TRUE. further graphical parameters (from par), such as srt, family and xpd.

Value

Plots three Q-Q plots based on normal, lognormal and gamma distributions and prints the best-fit distribution.

Details

Produces three Q-Q plots and reports which one has the highest Shapiro-Francia test statistic (W). The distribution with the highest W is the best fit of the three.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Millard, S.P., 2013. EnvStats: An R Package for Environmental Statistics. Springer-Verlag, New York.

Shapiro, S.S., Francia, R.S., 1972. An approximate analysis of variance test for normality. Journal of the American Statistical Association 67, 215–216.

Examples


data(Brumbaugh)
cenCompareQQ(Brumbaugh$Hg,Brumbaugh$HgCen)
#> Lognormal is a good fit