I tried to use shapiro.test or ks.test to check the normality of some data, the problem is, the distribution function is a mixture of a Gaussian and some other distributions at the tails. The hypothesis is that if the tails are excluded, the distribution is perfect Gaussian, and I want to test that.
But I cannot simply cut the tails off and do a normality test on the truncated data, as shown in the following example, this will fail. So that question is: how can I test whether the middle chunk of the distribution is Gaussian? Thanks! Yong > r<-rnorm(1000) > r.trunc<-r[which(abs(r)<1.5)] > shapiro.test(r.trunc) Shapiro-Wilk normality test data: r.trunc W = 0.9855, p-value = 1.237e-07 > ks.test(r.trunc, "pnorm") One-sample Kolmogorov-Smirnov test data: r.trunc D = 0.0873, p-value = 3.116e-06 alternative hypothesis: two.sided > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html