Dear list, I am calculating the 95th percentile of a set of values with R and with SPSS
In R: > normal200<-rnorm(200,0,1) > qnorm(0.95,mean=mean(normal200),sd=sd(normal200),lower.tail =TRUE) [1] 1.84191 In SPSS, if I use the same 200 values and select Analyze -> Descriptive Statistics -> Frequencies and under "Statistics", I type in '95' under Percentiles, then the output is Percentile 95 1.9720 I think the main difference is that SPSS only calculates critical values within the range of values in the data, while R fits a normal and calculates the critical value using the fitted distribution. This is more obvious if the size of the data is much lower: > normal20 [1] 0.27549020 0.87994304 -0.23737370 0.04565484 -1.10207183 -0.68035949 0.01698773 -2.15812038 0.26296513 0.21873981 0.03266598 -0.01318572 [13] 0.83492830 0.54652613 0.73993948 -0.31937556 -0.03060194 -0.96028421 0.27745331 -1.01292410 > max(normal20) [1] 0.879943 > qnorm(0.95,mean=mean(normal20),sd=sd(normal20),lower.tail =TRUE) [1] 1.118065 And in SPSS Percentile 95 0.8777 Can anyone comment on my statement? and thus, is R more exact? The differences are quite large and this is important for setting thresholds. Cheers, Dave [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.