On 12-11-08 7:17 AM, David A. wrote:
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.
The part of your statement where you say "R fits a normal and calculates
the critical value using the fitted distribution" is false. *You* did
that (in your call to qnorm, rather than using the quantile function),
it's not something R would normally do.
Is R "more exact"? It's possible, but I doubt it. I imagine SPSS could
do what R did, and R could do what SPSS did.
Duncan Murdoch
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