
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
 		 	   		  
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