difference percentile R vs SPSS
Hi, David, I think you're confusing the q-th percentile of your data, i. e., the empirical q-th percentile, which is -- roughly -- the value x_q for which q * 100 % of the data are less than or equal to x_q, with the q-th percentile of a distribution (here the normal distribution) that has as population mean the arithmetic mean of the data and as population standard deviation the standard deviation of the data. Those are different things. Your SPSS code seems to compute the empirical quantile, but you R code produces the other quantile. To get empirical quantiles of your data in R see ?quantile Hth -- Gerrit
On Thu, 8 Nov 2012, 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. Cheers, Dave [[alternative HTML version deleted]]
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