An embedded and charset-unspecified text was scrubbed... Name: not available URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20120918/8670b0e7/attachment.pl>
Cochran-Mantel-Haenszel test
3 messages · McPhie, Romney, Bert Gunter, Peter Langfelder
This looks more like a statistics than an R issue. Try posting on stats.stackexchange.com, a statistics list, instead. ALternatively, talk to your local statistician (if there is one). -- Bert On Tue, Sep 18, 2012 at 3:02 PM, McPhie, Romney
<Romney.McPhie at dfo-mpo.gc.ca> wrote:
Hello, I have some satellite tag time-at-depth (TAD) frequency data that I would like some help with. The data was transmitted via satellite as percent time spent in each of 7 depth bins (0m, 0-1m, 1-10m, 10-50m etc.), binned over 6-hour intervals. I categorized each row of data corresponding to a date and time into summer vs. winter, and day vs. night, and then summed and averaged the given % for each depth bin. My data looks like this (for one individual, HG03): HG03.dat Season Time Depth Sum Avrg 1 summ day 0 17.2 0.1702970 2 summ day 1 23.9 0.2366337 3 summ day 10 868.5 8.5990099 4 summ day 50 2698.2 26.7148515 5 summ day 100 419.7 4.1554455 6 summ day 200 266.1 2.6346535 7 summ day 300 1668.6 16.5207921 8 summ day 500 4138.2 40.9722772 9 summ night 0 283.6 5.7877551 10 summ night 1 229.1 4.6755102 11 summ night 10 479.3 9.7816327 12 summ night 50 761.9 15.5489796 13 summ night 100 235.8 4.8122449 14 summ night 200 40.9 0.8346939 15 summ night 300 763.1 15.5734694 16 summ night 500 2106.1 42.9816327 17 wint day 0 0.0 0.0000000 18 wint day 1 0.0 0.0000000 19 wint day 10 0.0 0.0000000 20 wint day 50 0.0 0.0000000 21 wint day 100 7.9 1.1285714 22 wint day 200 92.1 13.1571429 23 wint day 300 0.0 0.0000000 24 wint day 500 600.0 85.7142857 25 wint night 0 43.9 1.7560000 26 wint night 1 0.3 0.0120000 27 wint night 10 0.3 0.0120000 28 wint night 50 0.8 0.0320000 29 wint night 100 10.5 0.4200000 30 wint night 200 51.6 2.0640000 31 wint night 300 411.4 16.4560000 32 wint night 500 1981.2 79.2480000 I wanted to test whether significant differences existed between depth in summer vs. winter, and day vs. night, controlling first for season and then for time of day. I carried out a Cochran-Mantel-Haenszel test, using Average Frequency (Avrg) as the dependent variable (2x2x8 contingency table).
ct<-xtabs(Avrg~Time+Depth+Season,data=HG03.dat) mantelhaen.test(ct)
Cochran-Mantel-Haenszel test
data: ct
Cochran-Mantel-Haenszel M^2 = 28.4548, df = 7, p-value = 0.0001818
ct<-xtabs(Avrg~Season+Depth+Time,data=HG03.dat) mantelhaen.test(ct)
Cochran-Mantel-Haenszel test
data: ct
Cochran-Mantel-Haenszel M^2 = 111.5986, df = 7, p-value < 2.2e-16
However, I'm not sure if these results are valid, since my raw data is
already in frequencies, not in counts. When I used Sum as the dependent
variable, I obtained different results.
I am at a loss on how to proceed. If anyone has any ideas, they would
be greatly appreciated.
Thanks!
Romney
[[alternative HTML version deleted]]
______________________________________________ R-help at 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.
Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
Bert is correct that this is a statistics questions, but I'll throw in my 2 cents anyway. The CMH test is formulated for count data and makes certain assumptions on the distribution of the observed values. Since you don't have count data (your data are not integer), chances are that the assumptions of the CMH test are not satisfied and you will get incorrect p-values. Without knowing a bit more how the satellite behaves (or, in statistical terms, what is the distribution of the depths and whether it can be approximated by one of the standard distributions used in statistics) it is, to the best of my knowledge, impossible to do a meaningful statistical test of the differences you want to study. At the very least, instead of producing a summary table of the overall time the satellite spends at each depth, you should start with the raw data. That way you can obtain some measure of how variable the percentage of time spent at each depth is and whether there is some dependence (if the satellite is at depth 0 at time t, it may be more likely to be at depth 0 or near 0 at time t+1, right?). Peter
On Tue, Sep 18, 2012 at 3:46 PM, Bert Gunter <gunter.berton at gene.com> wrote:
This looks more like a statistics than an R issue. Try posting on stats.stackexchange.com, a statistics list, instead. ALternatively, talk to your local statistician (if there is one). -- Bert On Tue, Sep 18, 2012 at 3:02 PM, McPhie, Romney <Romney.McPhie at dfo-mpo.gc.ca> wrote:
Hello, I have some satellite tag time-at-depth (TAD) frequency data that I would like some help with. The data was transmitted via satellite as percent time spent in each of 7 depth bins (0m, 0-1m, 1-10m, 10-50m etc.), binned over 6-hour intervals. I categorized each row of data corresponding to a date and time into summer vs. winter, and day vs. night, and then summed and averaged the given % for each depth bin. My data looks like this (for one individual, HG03): HG03.dat Season Time Depth Sum Avrg 1 summ day 0 17.2 0.1702970 2 summ day 1 23.9 0.2366337 3 summ day 10 868.5 8.5990099 4 summ day 50 2698.2 26.7148515 5 summ day 100 419.7 4.1554455 6 summ day 200 266.1 2.6346535 7 summ day 300 1668.6 16.5207921 8 summ day 500 4138.2 40.9722772 9 summ night 0 283.6 5.7877551 10 summ night 1 229.1 4.6755102 11 summ night 10 479.3 9.7816327 12 summ night 50 761.9 15.5489796 13 summ night 100 235.8 4.8122449 14 summ night 200 40.9 0.8346939 15 summ night 300 763.1 15.5734694 16 summ night 500 2106.1 42.9816327 17 wint day 0 0.0 0.0000000 18 wint day 1 0.0 0.0000000 19 wint day 10 0.0 0.0000000 20 wint day 50 0.0 0.0000000 21 wint day 100 7.9 1.1285714 22 wint day 200 92.1 13.1571429 23 wint day 300 0.0 0.0000000 24 wint day 500 600.0 85.7142857 25 wint night 0 43.9 1.7560000 26 wint night 1 0.3 0.0120000 27 wint night 10 0.3 0.0120000 28 wint night 50 0.8 0.0320000 29 wint night 100 10.5 0.4200000 30 wint night 200 51.6 2.0640000 31 wint night 300 411.4 16.4560000 32 wint night 500 1981.2 79.2480000 I wanted to test whether significant differences existed between depth in summer vs. winter, and day vs. night, controlling first for season and then for time of day. I carried out a Cochran-Mantel-Haenszel test, using Average Frequency (Avrg) as the dependent variable (2x2x8 contingency table).
ct<-xtabs(Avrg~Time+Depth+Season,data=HG03.dat) mantelhaen.test(ct)
Cochran-Mantel-Haenszel test
data: ct
Cochran-Mantel-Haenszel M^2 = 28.4548, df = 7, p-value = 0.0001818
ct<-xtabs(Avrg~Season+Depth+Time,data=HG03.dat) mantelhaen.test(ct)
Cochran-Mantel-Haenszel test
data: ct
Cochran-Mantel-Haenszel M^2 = 111.5986, df = 7, p-value < 2.2e-16
However, I'm not sure if these results are valid, since my raw data is
already in frequencies, not in counts. When I used Sum as the dependent
variable, I obtained different results.
I am at a loss on how to proceed. If anyone has any ideas, they would
be greatly appreciated.
Thanks!
Romney
[[alternative HTML version deleted]]
______________________________________________ R-help at 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.
-- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
______________________________________________ R-help at 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.