[R] Is there any design based two proportions z test?
Hello Everyone, I was analysing big survey data using survey packages on RStudio. Survey package allows survey data analysis with the design effect.The survey package included functions for all other statistical analysis except two-proportion z tests. I was trying to calculate the difference in prevalence of Diabetes and Prediabetes between the year 2011 and 2017 (with 95%CI). I was able to calculate the weighted prevalence of diabetes and prediabetes in the Year 2011 and 2017 and just subtracted the prevalence of 2011 from the prevalence of 2017 to get the difference in prevalence. But I could not calculate the 95%CI of the difference in prevalence considering the weight of the survey data. I was also trying to see if this difference in prevalence is statistically significant. I could do it using the simple two-proportion z test without considering the weight of the sample. But I want to do it considering the weight of the sample. Example: Prevalence of Diabetes: 2011: 11.0 (95%CI 10.1-11.9) 2017: 10.1 (95%CI 9.4-10.9) Diff: 0.9% (95%CI: ??) Proportion Z test P Value: ?? Your cooperation will be highly appreciated. Thanks in advance. With Regards ** *Md Kamruzzaman* *PhD **Research Fellow (**Medicine**)* Discipline of Medicine and Centre of Research Excellence in Translating Nutritional Science to Good Health Adelaide Medical School | Faculty of Health and Medical Sciences The University of Adelaide Adelaide SA 5005 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
Re: [R] different results between cor and ccf
G'day Patrick, On Tue, 16 Jan 2024 09:19:40 +0100 Patrick Giraudoux wrote: [...] > So far so good, but when I lag one of the series, I cannot find the > same correlation as with ccf > > > cor(x[1:(length(x)-1)],y[2:length(y)]) [1] -0.7903428 > > ... where I expect -0.668 based on ccf > > Can anyone explain why ? The difference is explained by cff() seeing the complete data on x and y and calculating the sample means only once, which are then used in the calculations for each lag. cor() sees only the data you pass down, so calculates different estimates for the means of the two sequences. To illustrate: [...first execute your code...] R> xx <- x-mean(x) R> yy <- y-mean(y) R> n <- length(x) R> vx <- sum(xx^2)/n R> vy <- sum(yy^2)/n R> (c0 <- sum(xx*yy)/n/sqrt(vx*vy)) [1] -0.5948694 R> xx <- x[1:(length(x)-1)] - mean(x) R> yy <- y[2:length(y)] - mean(y) R> (c1 <- sum(xx*yy)/n/sqrt(vx*vy)) [1] -0.6676418 The help page of cff() points to MASS, 4ed, the more specific reference is p 389ff. :) Cheers, Berwin __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
[R] different results between cor and ccf
Dear listers, I am working on a time series but find that for a given non-zero time lag correlations obtained by ccf and cor are different. x <- c(0.85472102802704641, 1.6008990694641689, 2.5019632258894835, 2.514654801253164, 3.3359198688206368, 3.5401357138398208, 2.6304117871193538, 3.6694074965420009, 3.9125153101706776, 4.4006592535478566, 3.0208991912866829, 2.959090589344433, 3.8434635568566056, 2.1683644330520457, 2.3060571563512973, 1.4680350663043942, 2.0346918622459054, 2.3674524446877538) y <- c(2.3085729270534765, 2.0809088217491416, 1.6249456563631131, 1.513338933177, 0.66754156827555422, 0.3080839731181978, 0.5265304299394, 0.89070463020837132, 0.71600791432232669, 0.82152341002975027, 0.22200290782700527, 0.6608410635137173, 0.90715232876618945, 0.45624062770725898, 0.35074487486980244, 1.1681750562971052, 1.6976462236079737, 0.88950230250556417) cc<-ccf(x,y) > cc Autocorrelations of series ‘X’, by lag -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 0.098 0.139 0.127 -0.043 -0.049 0.069 -0.237 -0.471 -0.668 -0.595 -0.269 -0.076 3 4 5 6 7 8 9 -0.004 0.123 0.272 0.283 0.401 0.435 0.454 cor(x,y) [1] -0.5948694 So far so good, but when I lag one of the series, I cannot find the same correlation as with ccf > cor(x[1:(length(x)-1)],y[2:length(y)]) [1] -0.7903428 ... where I expect -0.668 based on ccf Can anyone explain why ? Best, Patrick [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
