Rick's question is a good one. It is unlikely that the results will be informative, but from a technical standpoint, you can estimate the p value using the simulate.p.value=TRUE argument to chisq.test().
> chisq.test(TT, simulate.p.value=TRUE) Pearson's Chi-squared test with simulated p-value (based on 2000 replicates) data: TT X-squared = 7919.632, df = NA, p-value = 0.0004998 ------------------------------------- David L Carlson Department of Anthropology Texas A&M University College Station, TX 77840-4352 -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Rick Bilonick Sent: Monday, September 15, 2014 10:18 AM To: r-help@r-project.org Subject: Re: [R] chi-square test On 09/15/2014 10:57 AM, eliza botto wrote: > Dear useRs of R, > I have two datasets (TT and SS) and i wanted to to see if my data is > uniformly distributed or not?I tested it through chi-square test and results > are given at the end of it.Now apparently P-value has a significant > importance but I cant interpret the results and why it says that "In > chisq.test(TT) : Chi-squared approximation may be incorrect" > ############################################################### >> dput(TT) > structure(list(clc5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.26, 0.14, 0, 0.44, > 0.26, 0, 0, 0, 0, 0, 0, 0.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.17, 0.16, > 0.56, 0, 1.49, 0, 0.64, 0.79, 0.66, 0, 0, 0.17, 0, 0, 0, 0, 0.56, 0, 0, 0, 0, > 0, 0, 0, 0, 0, 0, 0, 0.43, 0.41, 0, 0.5, 0.44, 0, 0, 0, 0, 0.09, 0.46, 0, > 0.27, 0.45, 0.15, 0.31, 0.16, 0.44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.07, 0, 0, > 0, 0, 0, 0.06, 0, 0.09, 0.07, 0, 0, 7.89, 0, 0.22, 0.29, 0.33, 0.27, 0, 0.36, > 0.41, 0, 0, 0, 0, 0.55, 0.81, 0, 0.09, 0.13, 0.28, 0, 0, 0), quota_massima = > c(1167L, 1167L, 4572L, 3179L, 3141L, 585L, 585L, 876L, 876L, 1678L, 2667L, > 1369L, 1369L, 1369L, 1381L, 1381L, 1381L, 1381L, 2284L, 410L, 2109L, 2507L, > 2579L, 2507L, 1436L, 3234L, 3234L, 3234L, 3234L, 2792L, 2569L, 2569L, 2569L, > 1669L, 4743L, 4743L, 4743L, 3403L, 3197L, 3267L, 3583L, 3583L, 3583L, 2584L, > 2584L, 2579L, 1241L, 1241L, 4174L, 3006L, 3197L, 2366L, 2618L, 2670L, 4487L, > 3196L, 3196L, 2107L, 2107L, 2427L, 1814L, 2622L, 1268L, 1268L, 1268! L,! > 3885L, 3885L, 3092L, 3234L, 2625L, 2625L, 3760L, 4743L, 3707L, 3760L, > 4743L, 3760L, 3885L, 3760L, 4743L, 2951L, 782L, 2957L, 3343L, 2697L, 2697L, > 3915L, 2277L, 1678L, 1678L, 3197L, 2957L, 2957L, 2957L, 4530L, 4530L, 4530L, > 2131L, 3618L, 3618L, 3335L, 2512L, 2390L, 1616L, 3526L, 3197L, 3197L, 2625L, > 2622L, 3197L, 3197L, 2622L, 2622L, 2622L, 368L, 4572L, 3953L, 863L, 3716L, > 3716L, 3716L, 2697L, 2697L, 1358L)), .Names = c("clc5", "quota_massima"), > class = "data.frame", row.names = c(NA, -124L)) > >> chisq.test(TT) > Pearson's Chi-squared test > data: TT > X-squared = 411.5517, df = 123, p-value < 2.2e-16 > Warning message: > In chisq.test(TT) : Chi-squared approximation may be incorrect > ####################################################################### >> dput(SS) > structure(list(NDVIanno = c(0.57, 0.536, 0.082, 0.262, 0.209, 0.539, 0.536, > 0.543, 0.588, 0.599, 0.397, 0.63, 0.616, 0.644, 0.579, 0.597, 0.617, 0.622, > 0.548, 0.528, 0.541, 0.436, 0.509, 0.467, 0.534, 0.412, 0.324, 0.299, 0.41, > 0.462, 0.427, 0.456, 0.508, 0.581, 0.242, 0.291, 0.324, 0.28, 0.291, 0.305, > 0.365, 0.338, 0.399, 0.516, 0.357, 0.558, 0.605, 0.638, 0.191, 0.377, 0.325, > 0.574, 0.458, 0.426, 0.188, 0.412, 0.464, 0.568, 0.582, 0.494, 0.598, 0.451, > 0.577, 0.572, 0.602, 0.321, 0.38, 0.413, 0.427, 0.55, 0.437, 0.481, 0.425, > 0.234, 0.466, 0.464, 0.491, 0.463, 0.489, 0.435, 0.267, 0.564, 0.256, 0.156, > 0.476, 0.498, 0.122, 0.508, 0.582, 0.615, 0.409, 0.356, 0.284, 0.285, 0.444, > 0.303, 0.478, 0.557, 0.345, 0.408, 0.347, 0.498, 0.534, 0.576, 0.361, 0.495, > 0.502, 0.553, 0.519, 0.504, 0.53, 0.547, 0.559, 0.505, 0.557, 0.377, 0.36, > 0.613, 0.452, 0.397, 0.277, 0.42, 0.443, 0.62), delta_z = c(211L, 171L, 925L, > 534L, 498L, 50L, 53L, 331L, 135L, 456L, 850L, 288L, 286L, 233L, 342L, ! 27! > 4L, 184L, 198L, 312L, 67L, 476L, 676L, 349L, 873L, 65L, 963L, 553L, 474L, > 948L, 1082L, 616L, 704L, 814L, 450L, 865L, 987L, 1265L, 720L, 565L, 652L, > 941L, 822L, 1239L, 929L, 477L, 361L, 199L, 203L, 642L, 788L, 818L, 450L, > 703L, 760L, 711L, 1015L, 1351L, 195L, 511L, 617L, 296L, 604L, 381L, 389L, > 287L, 1043L, 1465L, 963L, 1125L, 582L, 662L, 1424L, 1762L, 575L, 1477L, > 1364L, 1236L, 1483L, 1201L, 1644L, 498L, 142L, 510L, 482L, 811L, 788L, 466L, > 626L, 461L, 350L, 1177L, 826L, 575L, 568L, 916L, 767L, 1017L, 532L, 1047L, > 1370L, 902L, 686L, 703L, 440L, 1016L, 1148L, 1089L, 753L, 650L, 1065L, 568L, > 712L, 762L, 636L, 79L, 1092L, 955L, 158L, 1524L, 1145L, 673L, 513L, 596L, > 239L)), .Names = c("NDVIanno", "delta_z"), class = "data.frame", row.names = > c(NA, -124L)) >> chisq.test(SS) > Pearson's Chi-squared test > data: SS > X-squared = 72.8115, df = 123, p-value = 0.9999 > Warning message: > In chisq.test(SS) : Chi-squared approximation may be incorrect > ##################################################################################### > Kindly guide me through like you always did :) > thanks in advance, > > > Eliza > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > 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. You are using a Chi-squared test on a 124x2 matrix of values (not all integers) and many are zeros. The expected frequencies for many cells are very small (near zero, less than 1) hence the warning message. More importantly, does this application of the Chi-squared test make sense? What am I missing? Rick -- Richard A. Bilonick, PhD Assistant Professor Dept. of Ophthalmology, School of Medicine Dept. of Biostatistics, Graduate School of Public Health Dept. of Orthodontics, School of Dental Medicine University of Pittsburgh Principal Investigator for the Pittsburgh Aerosol Research and Inhalation Epidemiology Study (PARIES) 412 647 5756 ______________________________________________ R-help@r-project.org mailing list 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-help@r-project.org mailing list 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.