[R] Multiple comparisons: was Re: langage R
See library(multcomp) and ?glht ?contrMat for several procedures for multiple comparisons. The Newman Keuls test is not on the list. The related Tukey method is on the list. __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] Multiple comparisons when interacction]
Hello, I send the message again with the data file as txt because it seems not to be accepted as csv in the R-help list. Data comes from a multiyear field experiment in which 4 levels of a treatment (2, 3, 4, 6) are compared to see the effect on yield. It is a randomized complete block design. The SAS code follows: options ls=95; data uno; infile 'data.txt' delimiter=';' firstobs=2; input year plot block treat yield; run; proc mixed data=uno; class treat year block; model yield=block year treat treat*year; lsmeans year treat /pdiff; lsmeans treat*year /slice=year pdiff; ods output diffs=dos; run; data tres; set dos; if year=_year; proc print data=tres; var year _year treat _treat estimate stderr df tvalue probt; run; Data are attached as a file: data.csv. In fact, I do not know if this is the best approach to analyze the data: - Should block be considered as random? We use the same file and randomization every year. We are interested in the long term effect of the treatment. - Data should be considered as repeated measurements over time (years)? In multcomp package: - What is the equivalence between the tests proposed (Sequen, AVE, Changepoint, Williams, Marcus, McDermott) and the tests agronomist are used to do: LSD (least significant difference), Duncan multiple range test, Scheffe, S-N-K (Student-Newman-Keuls)? Thanks a lot for your interest. Jorge Lampurlanés Agronomist Is it possible to do this analysis in R? Yes, it is possible. The syntax isn't in place yet. If you send me the complete SAS code and data for an example using slice, I will duplicate it for you in the multcomp package in R. I will send that to the R-help list and to Torsten and it will bring us one step closer to the syntax. The example I showed before was designed to get the same answer as S-Plus multicomp using the adjust= argument. Rich YEAR;PLOT;BLOC;LEVEL;YIELD 15;1;1;3;3896.00 15;2;1;2;3881.33 15;3;1;4;3394.11 15;4;1;6;3261.11 15;5;2;6;3273.66 15;6;2;4;3568.89 15;7;2;2;3535.33 15;8;2;3;3218.66 15;9;3;3;3311.00 15;10;3;4;3458.77 15;11;3;2;3543.44 15;12;3;6;2684.00 15;13;4;4;3591.77 15;14;4;3;3555.55 15;15;4;2;3511.11 15;16;4;6;2755.55 16;1;1;3;2736.33 16;2;1;2;2769.66 16;3;1;4;2353.11 16;4;1;6;2964.44 16;5;2;6;2631.11 16;6;2;4;2882.77 16;7;2;2;2203.66 16;8;2;3;2636.89 16;9;3;3;2461.55 16;10;3;4;2285.00 16;11;3;2;2361.66 16;12;3;6;2636.89 16;13;4;4;2639.78 16;14;4;3;2596.66 16;15;4;2;2928.44 16;16;4;6;2745.44 17;1;1;3;2751.63 17;2;1;2;2855.81 17;3;1;4;2089.71 17;4;1;6;4096.04 17;5;2;6;4254.52 17;6;2;4;2483.10 17;7;2;2;2920.86 17;8;2;3;3399.48 17;9;3;3;3443.28 17;10;3;4;3447.03 17;11;3;2;3839.87 17;12;3;6;4135.01 17;13;4;4;3549.82 17;14;4;3;3542.12 17;15;4;2;3597.47 17;16;4;6;4017.49 18;1;1;3;2106.00 18;2;1;2;2622.78 18;3;1;4;3023.33 18;4;1;6;3811.44 18;5;2;6;3204.11 18;6;2;4;2739.00 18;7;2;2;2700.33 18;8;2;3;2273.89 18;9;3;3;2131.78 18;10;3;4;3075.00 18;11;3;2;2997.44 18;12;3;6;3010.33 18;13;4;4;2868.22 18;14;4;3;2519.44 18;15;4;2;2855.33 18;16;4;6;3462.55 19;1;1;3;3802.27 19;2;1;2;3987.03 19;3;1;4;3879.28 19;4;1;6;2992.50 19;5;2;6;2221.98 19;6;2;4;3739.80 19;7;2;2;4523.07 19;8;2;3;4460.97 19;9;3;3;4587.39 19;10;3;4;4104.34 19;11;3;2;4018.42 19;12;3;6;2504.81 19;13;4;4;3819.31 19;14;4;3;4163.23 19;15;4;2;3718.99 19;16;4;6;2399.38 20;1;1;3;2320.64 20;2;1;2;1540.83 20;3;1;4;1473.81 20;4;1;6;1271.28 20;5;2;6;1025.46 20;6;2;4;1488.39 20;7;2;2;1728.99 20;8;2;3;2196.10 20;9;3;3;2037.82 20;10;3;4;1745.93 20;11;3;2;1876.27 20;12;3;6;1045.36 20;13;4;4;1376.80 20;14;4;3;2176.18 20;15;4;2;1985.51 20;16;4;6;647.27 21;1;1;3;5183.94 21;2;1;2;3583.61 21;3;1;4;3041.82 21;4;1;6;3645.95 21;5;2;6;3525.90 21;6;2;4;2989.62 21;7;2;2;3174.04 21;8;2;3;3021.54 21;9;3;3;2985.57 21;10;3;4;3099.75 21;11;3;2;3214.95 21;12;3;6;3454.56 21;13;4;4;3168.78 21;14;4;3;3244.26 21;15;4;2;3747.98 21;16;4;6;3060.55 22;1;1;3;2920.53 22;2;1;2;1987.68 22;3;1;4;2235.97 22;4;1;6;2693.58 22;5;2;6;2461.07 22;6;2;4;2255.39 22;7;2;2;2815.35 22;8;2;3;3192.49 22;9;3;3;2909.77 22;10;3;4;2832.01 22;11;3;2;2663.89 22;12;3;6;2010.96 22;13;4;4;2471.86 22;14;4;3;2615.70 22;15;4;2;2981.77 22;16;4;6;1719.13 23;1;1;3;4134.62 23;2;1;2;3276.45 23;3;1;4;3136.52 23;4;1;6;3784.49 23;5;2;6;3817.49 23;6;2;4;3506.78 23;7;2;2;4109.16 23;8;2;3;4009.86 23;9;3;3;4099.24 23;10;3;4;4111.49 23;11;3;2;4388.40 23;12;3;6;4090.12 23;13;4;4;4001.93 23;14;4;3;4195.77 23;15;4;2;4247.79 23;16;4;6;4119.71 24;1;1;3;1157.99 24;2;1;2;1008.99 24;3;1;4;822.09 24;4;1;6;565.01 24;5;2;6;518.99 24;6;2;4;889.10 24;7;2;2;1048.63 24;8;2;3;1396.62 24;9;3;3;1238.06 24;10;3;4;1061.40 24;11;3;2;1198.28 24;12;3;6;761.29 24;13;4;4;805.48 24;14;4;3;1172.09 24;15;4;2;1055.87 24;16;4;6;527.04__ 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 and provide commented, minimal, self-contained,
[R] multiple comparisons
Dear R users, I need a professional help: i am a relatively new R user and I am just writing my diploma tesis where I have to conduct some multiple comparison. I am searching a method which include interaction between fixed factors. The following is my model: lmer(Leaf~water*region+(1|pop)+(1|pop:sib)+(1|block)+(1|block:pool),data=datx,method=ML) I want to compare: leaf dependent from water * region. I would prefer to make a comparision according to tukey or student newman keuls if this is possible. I am looking forward to your reply, sincerely Petra:-) __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] Multiple comparisons when interacction
Hello, Data comes from a multiyear field experiment in which 4 levels of a treatment (2, 3, 4, 6) are compared to see the effect on yield. It is a randomized complete block design. The SAS code follows: options ls=95; data uno; infile 'data.csv' delimiter=';' firstobs=2; input year plot block treat yield; run; proc mixed data=uno; class treat year block; model yield=block year treat treat*year; lsmeans year treat /pdiff; lsmeans treat*year /slice=year pdiff; ods output diffs=dos; run; data tres; set dos; if year=_year; proc print data=tres; var year _year treat _treat estimate stderr df tvalue probt; run; Data are attached as a file: data.csv. In fact, I do not know if this is the best approach to analyze the data: - Should block be considered as random? We use the same file and randomization every year. We are interested in the long term effect of the treatment. - Data should be considered as repeated measurements over time (years)? In multcomp package: - What is the equivalence between the tests proposed (Sequen, AVE, Changepoint, Williams, Marcus, McDermott) and the tests agronomist are used to do: LSD (least significant difference), Duncan multiple range test, Scheffe, S-N-K (Student-Newman-Keuls)? Thanks a lot for your interest. Jorge Lampurlanés Agronomist Is it possible to do this analysis in R? Yes, it is possible. The syntax isn't in place yet. If you send me the complete SAS code and data for an example using slice, I will duplicate it for you in the multcomp package in R. I will send that to the R-help list and to Torsten and it will bring us one step closer to the syntax. The example I showed before was designed to get the same answer as S-Plus multicomp using the adjust= argument. Rich __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] Multiple comparisons when interacction
Data is here. I'm sorry. Is it possible to do this analysis in R? Yes, it is possible. The syntax isn't in place yet. If you send me the complete SAS code and data for an example using slice, I will duplicate it for you in the multcomp package in R. I will send that to the R-help list and to Torsten and it will bring us one step closer to the syntax. The example I showed before was designed to get the same answer as S-Plus multicomp using the adjust= argument. Rich -- ** Jorge Lampurlanés Castel Departament d'Enginyeria Agroforestal Escola Tècnica Superior d'Enginyeria Agrària Universitat de Lleida Avinguda Rovira Roure, 191 25198-LLEIDA SPAIN Tl.: +34 973 70 25 37 Fax.:+34 073 70 26 73 e-mail: [EMAIL PROTECTED] ** __ 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 and provide commented, minimal, self-contained, reproducible code.
[R] Multiple comparisons
Dear list, I have to do an ANOVA analysis with one fixed effect A and one random effect SUBJECT. TO do this I used aov in the form aov.m1 - aov(depvar ~ A + Error(SUBJECT/(A))); My question is if I obtain significant differences within the strata, does it make any sense to make multiple comparisons in order to know what levels of the factor are significant?. I asked this because I get an error when I try TukeyHSD(aov.m1), and I am not sure if this is correct. thanks Pablo Hoffmann __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] Multiple comparisons when interacction
As I understand from the WoodEnergy example in package HH, You are proposing to compute a separate lm for each level of YEAR factor to compare TIL means. This is the way I used to do this kind of analysis. But now, it is also possible, with PROC GLM, to adjust only the general model (variable ~ BLOC + TIL * YEAR ) and then to compare TIL means inside every YEAR with the SLICE option. I am not statistician but I think the difference is that, in the first approach, we use a different error term for every YEAR, whereas in the second approach, we use the same error term for all comparisons. Perhaps the differences between both approaches are negligible. If not, it is possible to do this analysis in R? Thanks a lot. -- ** Jorge Lampurlanés Castel Departament d'Enginyeria Agroforestal Escola Tècnica Superior d'Enginyeria Agrària Universitat de Lleida Avinguda Rovira Roure, 191 25198-LLEIDA SPAIN Tl.: +34 973 70 25 37 Fax.:+34 073 70 26 73 e-mail: [EMAIL PROTECTED] __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] Multiple comparisons when interacction
Yes, it can be done. It is not currently easy because multcomp doesn't have the syntax yet. Making this easy is on Torsten's to-do list for the multcomp package. See the MMC.WoodEnergy example in the HH package. The current version on CRAN is HH_1.17. Please see the discussion of this example in R-help: https://stat.ethz.ch/pipermail/r-help/2007-January/123451.html Rich __ 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 and provide commented, minimal, self-contained, reproducible code.
[R] Multiple comparisons when interacction
In the model: lm.1 - lm(variable ~ BLOC + TIL * YEAR , data=selvanera) I found TIL*YEAR interaction significant. Then I am trying to compare means of the different levels of TIL inside every YEAR using: mc.2 - glht(lm.1, linfct = mcp(TIL*YEAR=Tukey)) summary(mc.2, test = univariate()) but it does not work. There is any way of doing this, like the SLICE option in PROC GLM (SAS)? Thanks a lot, Jorge -- ** Jorge Lampurlanés Castel Departament d'Enginyeria Agroforestal Escola Tècnica Superior d'Enginyeria Agrària Universitat de Lleida Avinguda Rovira Roure, 191 25198-LLEIDA SPAIN Tl.: +34 973 70 25 37 Fax.:+34 073 70 26 73 e-mail: [EMAIL PROTECTED] __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] multiple comparisons of time series data (Stephan Moratti)
To account for the strong serial correlation you could try the lme() function of the nlme package. There you can apply different covariance structures in your linear model such as a first-order autoregressive covariance structure (AR1). example: model.fit - lme(response ~ condition * time, data = time.series.data, random=~1|case, correlation = corCAR1()); This model uses an autoregressive process for continous data. The random expression defines the intercept for each case (or observation, subject) as a random factor. Condition and time would be fixed factors in this case. See also help(lme) and help(corClasses). Hopes that helps, Stephan Stephan Moratti, PhD Centro de Magnetoencefalografía Dr. Perez Modrego Faculdad de Medicina Universidad Complutense de Madrid Pabellón 8 Avda. Complutense, s/n 28040 Madrid Spain email: [EMAIL PROTECTED] Tel.: +34 91 394 2292 Fax.: +34 91 394 2294 [[alternative HTML version deleted]] __ 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
Re: [R] multiple comparisons of time series data
Kyle, You might try the Wilcoxon Rank Sum test (and there is also the paired rank sum test) that may be useful. Both are found in R. There is an application of the test in the textbook by Loucks, D.P., Stedinger J.R., and Haith, D., 1981. Water Resources Systems Planning and Analysis, Prentice-Hall, Eaglewood Cliffs, New Jersey. I hope this helps… Go HOKIES! Tom Kyle Hall wrote: I am interested in a statistical comparison of multiple (5) time series' generated from modeling software (Hydrologic Simulation Program Fortran). The model output simulates daily bacteria concentration in a stream. The multiple time series' are a result of varying our representation of the stream within the model. Our main question is: Do the different methods used to represent a stream produce different results at a statistically significant level? We want to compare each otput time series to determine if there is a difference before looking into the cause within the model. In a previous study, the Kolmogorov-Smirnov k-sample test was used to compare multiple time series'. I am unsure about the strength of the Kolmogorov-Smirnov test and I have set out to determine if there are any other tests to compare multiple time series'. I know htat R has the ks.test but I am unsure how this test handles multiple comparisons. Is there something similar to a pairwise.t.test with a bonferroni corection, only with time series data? Does R currently (v 2.3.0) have a comparison test that takes into account the strong serial correlation of time series data? Kyle Hall Graduate Research Assistant Biological Systems Engineering Virginia Tech __ 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 -- Thomas E Adams National Weather Service Ohio River Forecast Center 1901 South State Route 134 Wilmington, OH 45177 EMAIL: [EMAIL PROTECTED] VOICE: 937-383-0528 FAX:937-383-0033 __ 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
Re: [R] multiple comparisons of time series data
PAIRWISE KOLMOGOROV-SMIRNOV: I don't know, but it looks like you could just type pairwise.t.test at a command prompt, copy the code into an R script file, and create a function pairwise.ks.test just by changing the call to t.test with one to ks.test. Try it. If you have trouble making it work, submit a post on that. I would NOT do this, however, because the ks.test assumes samples of INDEPENDENT observations. If you've got time series, I would expect the assumption of independence to be violated, and I would not believe the results of a KS test. If you what to try what I just suggested, please also try it with multiple time series WITHOUT varying our representation of the stream within the model, preferably several times. COMPARING MULTIPLE TIME SERIES If I had k different time series to compare, I might proceed as follows: 1. Make normal probability plots using, e.g., qqnorm. If the observations did NOT look normal, I'd consider some transformation. If the numbers were all positive, I might consider using the boxcox function in library(MASS) to help select one. However, I wouldn't completely believe the results, because this also assumes the observations are independent, and I know they're not. 2. Try to fit some traditional time series model as describe, e.g., in the chapter on time series on Venables and Ripley (2002) Modern Applied Statistics with S (Springer). There are better books on time series, but this is probably the first book I would recommend to anyone using R, and this chapter would be a reasonable start. I'd play with this until I seemed to get sensible fits for nearly all series with the same model and with residuals that looked fairly though not totally (a) white by the Box-Ljung criteria, and (b) normal in normal probability plots. If I saw consistent non-normal behavior in the residuals, it would indicate a problem bigger than I can handle in a brief email like this. 3. With k different time series, most of the results of 2 could be summarized in k sets of estimated regression coefficients, all for the same model, with estimated standard errors plus whitened residuals. If you had m parameters, each pair of time series could then be summarized into m z-scores = (b.i-b.j)/(var.b.i+var.b.j), which could then be further converted into m p.values. You would then add the p.values from ks.test, making (m+1) p.values for each of the k*(k-1)/2 = 10 pairs of series with k = 5 series. I'd then feed these k*(m+1) p.values into p.adjust to get an answer. (Note: pairwise.t.test calls pairwise.table, which further calls p.adjust. I didn't know any of this before I read your post.) I might experiment with the different methods for p.adjust, and I got different answers from the different methods, I might worry about which to believe. The Bonferroni is the simplest, most widely known and understood, but also perhaps the most conservative. I might tend to believe some of the others more, but if I got different answers, I'd suspect that the case was marginal, and I might want to generate other sets of simulations and try those. 4. There are other facilities in R for multiple comparisons, e.g., in the multcomp and pgirmess packages. Before I actually undertook steps 1, 2, and 3, above, I might review these packages to familiarize myself more with their contents. 5. Virginia Tech has an excellent Statistics department with a consulting center. You might try them. hope this helps, Spencer Graves Kyle Hall wrote: I am interested in a statistical comparison of multiple (5) time series' generated from modeling software (Hydrologic Simulation Program Fortran). The model output simulates daily bacteria concentration in a stream. The multiple time series' are a result of varying our representation of the stream within the model. Our main question is: Do the different methods used to represent a stream produce different results at a statistically significant level? We want to compare each otput time series to determine if there is a difference before looking into the cause within the model. In a previous study, the Kolmogorov-Smirnov k-sample test was used to compare multiple time series'. I am unsure about the strength of the Kolmogorov-Smirnov test and I have set out to determine if there are any other tests to compare multiple time series'. I know htat R has the ks.test but I am unsure how this test handles multiple comparisons. Is there something similar to a pairwise.t.test with a bonferroni corection, only with time series data? Does R currently (v 2.3.0) have a comparison test that takes into account the strong serial correlation of time series data? Kyle Hall Graduate Research Assistant Biological Systems Engineering Virginia Tech
[R] multiple comparisons of time series data
I am interested in a statistical comparison of multiple (5) time series' generated from modeling software (Hydrologic Simulation Program Fortran). The model output simulates daily bacteria concentration in a stream. The multiple time series' are a result of varying our representation of the stream within the model. Our main question is: Do the different methods used to represent a stream produce different results at a statistically significant level? We want to compare each otput time series to determine if there is a difference before looking into the cause within the model. In a previous study, the Kolmogorov-Smirnov k-sample test was used to compare multiple time series'. I am unsure about the strength of the Kolmogorov-Smirnov test and I have set out to determine if there are any other tests to compare multiple time series'. I know htat R has the ks.test but I am unsure how this test handles multiple comparisons. Is there something similar to a pairwise.t.test with a bonferroni corection, only with time series data? Does R currently (v 2.3.0) have a comparison test that takes into account the strong serial correlation of time series data? Kyle Hall Graduate Research Assistant Biological Systems Engineering Virginia Tech __ 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
[R] multiple comparisons for proportions
Dear all I am still fishing for help on this theme. In Zar 1999 page 563-565 he describes a Tukey-type multiple comparison for testing among proportions. It involves comparisons of ranked proportions transformed to degrees. In the following pages there are a couple of similar comparisons. I cannot find an example of this in R. Is there such a thing? Cheers Chris Buddenhagen, Botany Department, Charles Darwin Research Station, Santa Cruz,Galapagos. Mail: Charles Darwin Foundation, Casilla 17-01-3891 Avenida 6 de Diciembre N36-109 y Pasaje California Quito, ECUADOR __ EL CONTENIDO DE ESTE MENSAJE ES DE ABSOLUTA RESPONSABILIDAD DEL AUTOR. FUNDACION CHARLES DARWIN WWW.DARWINFOUNDATION.ORG [[alternative HTML version deleted]] __ 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
[R] Multiple comparisons like a Chi2 or Fisher's exact test
Dear all I have an experiment where plots either have or have not regrown (in 40 plots) after receiving 12 different herbicide treatments and a control (no treatment). The data are significant with a Chi2, but to later distinguish if the differences are significant between each of the 12 treatments apart I need to do multiple Chi2 s between each. Is there a way to run those multiple Chi2 or something like them so that each comparison is done in an automated fashion between the treatments? Have I made myself clear? Chris Buddenhagen, Botany Department, Charles Darwin Research Station, Santa Cruz,Galapagos. Mail: Charles Darwin Foundation, Casilla 17-01-3891 Avenida 6 de Diciembre N36-109 y Pasaje California Quito, ECUADOR __ EL CONTENIDO DE ESTE MENSAJE ES DE ABSOLUTA RESPONSABILIDAD DEL AUTOR. FUNDACION CHARLES DARWIN WWW.DARWINFOUNDATION.ORG [[alternative HTML version deleted]] __ 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
[R] multiple comparisons for lme using multcomp
Dear R-help list, I would like to perform multiple comparisons for lme. Can you report to me if my way to is correct or not? Please, note that I am not nor a statistician nor a mathematician, so, some understandings are sometimes quite hard for me. According to the previous helps on the topic in R-help list May 2003 (please, see Torsten Hothorn advices) and books such as Venables Ripley or Pinheiro Bates. I need multcomp library. I followed the different examples and get these results : In this example, response is the mass of earthworms after one month of exposure to different concentrations of pollutants (treatment). The experimental design was three containers per treatment with five animals in each container (or less if mortality occurred). Containers were referred as box and considered as the random variable. tab-read.delim(example1.txt) tab treatment box response 1control a 1.8 2control a 2.3 3control a 1.3 4control a 0.8 5control a 2.0 6control b 1.1 7control b 2.2 8control b 1.3 9control b 2.0 10 control b 1.3 11 control c 1.5 12 control c 1.4 13 control c 2.1 14 control c 1.7 15 control c 1.3 16 Al100 d 1.6 17 Al100 d 2.1 18 Al100 d 0.7 19 Al100 d 1.8 20 Al100 d 1.2 21 Al100 e 1.5 22 Al100 e 1.5 23 Al100 e 2.0 24 Al100 e 1.0 25 Al100 e 1.6 26 Al100 f 0.9 27 Al100 f 2.0 28 Al100 f 1.9 29 Al100 f 1.7 30 Al100 f 1.7 68 Al800 q 1.0 69 Al800 r 0.8 70 Al800 r 0.9 71 Al800 r 0.9 72 Al800 r 0.6 73 Al800 s 0.9 74 Al800 s 1.0 75 Al800 s 0.8 76 Al800 s 0.8 77 Al800 s 0.7 attach(tab) library(nlme) lm1-lme(response~treatment,random=~1|box) library(multcomp) Loading required package: mvtnorm # first way to do (seem uncorrect) summary(csimtest(coef(lm1),vcov(lm1),cmatrix=contrMat(table(treatment), type=Tukey),df=59)) Error in csimtest(coef(lm1), vcov(lm1), cmatrix = contrMat(table(treatment), : estpar not a vector #indeed coef(lm1) (Intercept) treatmentAl200 treatmentAl400 treatmentAl600 treatmentAl800 a1.546679 -0.1648540 -0.4895219 -0.6383375 -0.7066632 b1.546657 -0.1648540 -0.4895219 -0.6383375 -0.7066632 c1.546664 -0.1648540 -0.4895219 -0.6383375 -0.7066632 d1.546643 -0.1648540 -0.4895219 -0.6383375 -0.7066632 s1.546667 -0.1648540 -0.4895219 -0.6383375 -0.7066632 treatmentcontrol a 0.06 b 0.06 c 0.06 d 0.06 s 0.06 # second way to do could be to get a vector for lm1 coefficient removing intercept(is it correct?) vect-as.numeric(coef(lm1)[1,]) vect [1] 1.5466787 -0.1648540 -0.4895219 -0.6383375 -0.7066632 0.060 summary(csimtest(vect,vcov(lm1),cmatrix=contrMat(table(treatment),type=Tukey),df=59)) Simultaneous tests: user-defined contrasts user-defined contrasts for factor Contrast matrix: Al100 Al200 Al400 Al600 Al800 control Al200-Al100 -1 10 00 0 Al400-Al100 -1 0 1 0 0 0 Al600-Al100 -1 0 01 0 0 Al800-Al100 -1 0 00 1 0 control-Al100-1 0 00 0 1 Al400-Al200 0-1 10 0 0 Al600-Al200 0-1 0 1 0 0 Al800-Al200 0-1 0 01 0 control-Al200 0-1 0 00 1 Al600-Al400 0 0-1 10 0 Al800-Al400 0 0-1 0 1 0 control-Al400 0 0-1 0 0 1 Al800-Al600 0 00-1 1 0 control-Al600 0 00-1 0 1 control-Al800 0 00 0-1 1 Absolute Error Tolerance: 0.001 Coefficients: Estimate t value Std.Err. p raw p Bonf p adj Al800-Al100 -2.253 -10.4670.213 0.000 0.000 0.000 Al600-Al100 -2.185 -10.3890.207 0.000 0.000 0.000 Al400-Al100 -2.036 -9.8500.210 0.000 0.000 0.000 Al200-Al100 -1.712 -8.0510.215 0.000 0.000 0.000 control-Al100 -1.487 -7.2430.205 0.000 0.000 0.000 control-Al8000.767 -5.2820.143 0.000 0.000 0.000 control-Al6000.698 -5.0720.148 0.000 0.000 0.000 control-Al4000.550 -4.1600.155 0.000 0.001 0.001 Al800-Al200 -0.542 -3.4880.141 0.001 0.006 0.006 Al600-Al200 -0.473 -3.1910.140 0.002 0.014 0.012 Al400-Al200 -0.325 -2.2670.147 0.027 0.135 0.110 control-Al2000.225 -1.5930.132 0.116 0.466 0.341 Al800-Al400 -0.217 -1.4750.152
Re: [R] multiple comparisons for lme using multcomp
Michaël Coeurdassier [EMAIL PROTECTED] writes: summary(csimtest(vect,vcov(lm1),cmatrix=contrMat(table(treatment),type=Tukey),df=59)) Coefficients: Estimate t value Std.Err. p raw p Bonf p adj Al800-Al100 -2.253 -10.4670.213 0.000 0.000 0.000 Al600-Al100 -2.185 -10.3890.207 0.000 0.000 0.000 Al400-Al100 -2.036 -9.8500.210 0.000 0.000 0.000 Al200-Al100 -1.712 -8.0510.215 0.000 0.000 0.000 control-Al100 -1.487 -7.2430.205 0.000 0.000 0.000 control-Al8000.767 -5.2820.143 0.000 0.000 0.000 control-Al6000.698 -5.0720.148 0.000 0.000 0.000 control-Al4000.550 -4.1600.155 0.000 0.001 0.001 Al800-Al200 -0.542 -3.4880.141 0.001 0.006 0.006 Al600-Al200 -0.473 -3.1910.140 0.002 0.014 0.012 Al400-Al200 -0.325 -2.2670.147 0.027 0.135 0.110 control-Al2000.225 -1.5930.132 0.116 0.466 0.341 Al800-Al400 -0.217 -1.4750.152 0.145 0.466 0.341 Al600-Al400 -0.149 -1.0640.138 0.292 0.583 0.466 Al800-Al600 -0.068 -0.4490.145 0.655 0.655 0.655 #a friend told me that it is possible to do multiple comparisons for lme in a simplest way, i.e. : anova(lm1,L=c(treatmentcontrol=1,treatmentAl200=-1)) F-test for linear combination(s) treatmentAl200 treatmentcontrol -11 numDF denDF F-value p-value 1 112 2.538813 0.1371 anova(lm1,L=c(treatmentcontrol=1,treatmentAl400=-1)) F-test for linear combination(s) treatmentAl400 treatmentcontrol -11 numDF denDF F-value p-value 1 112 17.30181 0.0013 anova(lm1,L=c(treatmentcontrol=1,treatmentAl600=-1)) F-test for linear combination(s) treatmentAl600 treatmentcontrol -11 numDF denDF F-value p-value 1 112 25.72466 3e-04 anova(lm1,L=c(treatmentcontrol=1,treatmentAl800=-1)) F-test for linear combination(s) treatmentAl800 treatmentcontrol -11 numDF denDF F-value p-value 1 112 27.9043 2e-04 # however, p values are different that those obtained above. Is this way OK or not? Notice that in all cases, the F-value is exactly the square of the t-value from Csimtest. The main difference is that you have claimed that the vcov matrix has 59 DF, whereas the lme analysis says 12. I'd suspect the latter to be (more) correct. Apart from that, notice that the L approach at best gives you the p raw value, i.e., it is uncorrected for multiple testing. -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 __ 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
[R] multiple comparisons for lme using multcomp
Dear R-help list, I would like to perform multiple comparisons for lme. Can you report to me if my way to is correct or not? Please, note that I am not nor a statistician nor a mathematician, so, some understandings are sometimes quite hard for me. According to the previous helps on the topic in R-help list May 2003 (please, see Torsten Hothorn advices) and books such as Venables Ripley or Pinheiro Bates. I need multcomp library. I followed the different examples and get these results : In this example, response is the mass of earthworms after one month of exposure to different concentrations of pollutants (treatment). The experimental design was three containers per treatment with five animals in each container (or less if mortality occurred). Containers were referred as box and considered as the random variable. tab-read.delim(example1.txt) tab treatment box response 1control a 1.8 2control a 2.3 3control a 1.3 4control a 0.8 5control a 2.0 6control b 1.1 7control b 2.2 8control b 1.3 9control b 2.0 10 control b 1.3 11 control c 1.5 12 control c 1.4 13 control c 2.1 14 control c 1.7 15 control c 1.3 16 Al100 d 1.6 17 Al100 d 2.1 18 Al100 d 0.7 19 Al100 d 1.8 20 Al100 d 1.2 21 Al100 e 1.5 22 Al100 e 1.5 23 Al100 e 2.0 24 Al100 e 1.0 25 Al100 e 1.6 26 Al100 f 0.9 27 Al100 f 2.0 28 Al100 f 1.9 29 Al100 f 1.7 30 Al100 f 1.7 68 Al800 q 1.0 69 Al800 r 0.8 70 Al800 r 0.9 71 Al800 r 0.9 72 Al800 r 0.6 73 Al800 s 0.9 74 Al800 s 1.0 75 Al800 s 0.8 76 Al800 s 0.8 77 Al800 s 0.7 attach(tab) library(nlme) lm1-lme(response~treatment,random=~1|box) library(multcomp) Loading required package: mvtnorm # first way to do (seem uncorrect) summary(csimtest(coef(lm1),vcov(lm1),cmatrix=contrMat(table(treatment), type=Tukey),df=59)) Error in csimtest(coef(lm1), vcov(lm1), cmatrix = contrMat(table(treatment), : estpar not a vector #indeed coef(lm1) (Intercept) treatmentAl200 treatmentAl400 treatmentAl600 treatmentAl800 a1.546679 -0.1648540 -0.4895219 -0.6383375 -0.7066632 b1.546657 -0.1648540 -0.4895219 -0.6383375 -0.7066632 c1.546664 -0.1648540 -0.4895219 -0.6383375 -0.7066632 d1.546643 -0.1648540 -0.4895219 -0.6383375 -0.7066632 s1.546667 -0.1648540 -0.4895219 -0.6383375 -0.7066632 treatmentcontrol a 0.06 b 0.06 c 0.06 d 0.06 s 0.06 # second way to do could be to get a vector for lm1 coefficient removing intercept(is it correct?) vect-as.numeric(coef(lm1)[1,]) vect [1] 1.5466787 -0.1648540 -0.4895219 -0.6383375 -0.7066632 0.060 summary(csimtest(vect,vcov(lm1),cmatrix=contrMat(table(treatment),type=Tukey),df=59)) Simultaneous tests: user-defined contrasts user-defined contrasts for factor Contrast matrix: Al100 Al200 Al400 Al600 Al800 control Al200-Al100 -1 1 0 0 0 0 Al400-Al100 -1 0 1 0 0 0 Al600-Al100 -1 0 0 1 0 0 Al800-Al100 -1 0 0 0 1 0 control-Al100-1 0 0 0 0 1 Al400-Al200 0-1 1 0 0 0 Al600-Al200 0-1 0 1 0 0 Al800-Al200 0-1 0 0 1 0 control-Al200 0-1 0 0 0 1 Al600-Al400 0 0-1 1 0 0 Al800-Al400 0 0-1 0 1 0 control-Al400 0 0-1 0 0 1 Al800-Al600 0 0 0-1 1 0 control-Al600 0 0 0-1 0 1 control-Al800 0 0 0 0-1 1 Absolute Error Tolerance: 0.001 Coefficients: Estimate t value Std.Err. p raw p Bonf p adj Al800-Al100 -2.253 -10.4670.213 0.000 0.000 0.000 Al600-Al100 -2.185 -10.3890.207 0.000 0.000 0.000 Al400-Al100 -2.036 -9.8500.210 0.000 0.000 0.000 Al200-Al100 -1.712 -8.0510.215 0.000 0.000 0.000 control-Al100 -1.487 -7.2430.205 0.000 0.000 0.000 control-Al8000.767 -5.2820.143 0.000 0.000 0.000 control-Al6000.698 -5.0720.148 0.000 0.000 0.000 control-Al4000.550 -4.1600.155 0.000 0.001 0.001 Al800-Al200 -0.542 -3.4880.141 0.001 0.006 0.006 Al600-Al200 -0.473 -3.1910.140 0.002 0.014 0.012 Al400-Al200 -0.325 -2.2670.147 0.027 0.135 0.110 control-Al2000.225 -1.5930.132 0.116 0.466 0.341 Al800-Al400 -0.217
RE: [R] Multiple comparisons following nlme
I need to do multiple comparisons following nlme analysis (Compare the effects of different treatments on a response measured repeatedly over time; fixed = response ~ treat*time). If you have an interaction it does not really make sense to conduct a multiple comparison because the difference in treatment depends on time, i.e. this presumed post-hoc test could only give you a correct result for one of your points in time. Why not conduct this analysis and then interpret the pattern based on the estimates of your parameters and/or on a graphical display of your data? If your interaction is non-significant and you drop it, you still have a mulitvariate problem and I have never come across a multiple comparison test for such multivariate problems (but perhaps someone else has a pointer). In your case it might be enough to carefully decide on how the set contrasts. Then, an additional issue would be what kind of multiple comparisons to conduct (for univarite anova's there are at least a dozen methods). You can always conduct several to see which of the comparisons are highly significant and which ones might not be so strong. But usually you do not learn more than what you get when you interpret your parameters and/or graphs of your data. ... and by the way, I guess your model is using lme (linear mixed effects model) in package nlme and not actually an nlme (non-linear mixed effects model) itself. Regards, Lorenz - Lorenz Gygax, Dr. sc. nat. Centre for proper housing of ruminants and pigs Swiss Federal Veterinary Office, agroscope FAT Tänikon __ 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
[R] Multiple comparisons following nlme
Dear Madam or Sir, I need to do multiple comparisons following nlme analysis (Compare the effects of different treatments on a response measured repeatedly over time; fixed = response ~ treat*time). On the web I found the notion that one might use the L argument from ANOVA. Do you have an example to show how this works together with nlme? Are there other ways to do a post-hoc analysis in nlme? Thank you very much. Stefan Uhlig Prof. Dr.Stefan Uhlig Head Division Pulmonary Pharmacology Research Center Borstel Leibniz Center for Medicine and Biosciences 23845 Borstel Germany Tel. 04537-188 478 FAX 04537- 188 778 [EMAIL PROTECTED] [[alternative HTML version deleted]] __ 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
Re: [R] Multiple comparisons in a non parametric case
Thanks Rolf and Thomas, It looks to me like what you are doing is trying to judge significance of differences by non-overlap of single-sample confidence intervals. While this is appealing, it's not quite right. Yes, this is what I am trying to do. Apparently, when the replicates are the same for each experimental unit and the experiment is balanced the CI should be the same for all sample-pairs, therefore it is somehow like having single sample CI. I just looked into my copy of Applied Nonparametric Statistics (second ed.) by Wayne W. Daniel (Duxbury, 1990) but that only deals with the situation where there is a single replicate per block-treatment combination (whereas you have 10 reps) and block-treatment interaction is assumed to be non-existent. The problems (or instances of problems) are my blocking factor. But this factor has significant interaction in the ANOVA model. The method that Daniel prescribes in this simple setting seems to be no more than applying the Bonferroni method of multiple comparisons. (Daniel does not say; his book is very much a cook-book.) So you might simply try Bonferroni --- i.e. do all k-choose-2 pairwise comparisons between treatments (using the appropriate 2 sample method for each comparison) doing each comparison at the alpha/k-choose-2 significance level. Where k = the number of treatments = 4 in your case. This method is not going to be super-powerful but it is sometimes surprizing how well Bonferroni stacks up against more ``sophisticated'' methods. I knew about Bonferroni. But I am confused. I have actually two references: Conover Practical Nonparametric statistics (page 371) and Sheskin Handbook and Nonparmetric statistical procedures (page 675). Both these books deal with multiple comparison when the Friedman test would be appropriate. But the formula given are different and the CI I obtain are also different. Sheskin, citing various sources (among them Daniel 1990), uses a formula with the normal distribution z and adjust the alfa value according to Bonferroni (strangely no sample statistic appears in the formula). Conover (which is also a good reference) uses a formula with Student't distribution but does not adjust alfa either in the example he provides where 4 treatments are pairwise compared. The CI I obtain are much smaller if I use the Conover procedure than the Sheskin's. And this happens in spite of the p-adjustment in Sheskin. Smaller CI are for me nicer because I can distinguish better differences But the a factor of 3 between them let me doubt I can really use Conover. Which is your opinion? Thansk again for the help, Ragards, Marco -- Marco Chiarandini, Fachgebiet Intellektik, Fachbereich Informatik, Technische Universität Darmstadt, Hochschulstraße 10, D-64289 Darmstadt - Germany, Office: S2/02 Raum E317 Tel: +49 (0)6151 16-6802 Fax: +49 (0)6151 16-5326 email: [EMAIL PROTECTED] web page: http://www.intellektik.informatik.tu-darmstadt.de/~machud __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Multiple comparisons in a non parametric case
Dear all, I am conducting a full factorial analysis. I have one factor consisting in algorithms, which I consider my treatments, and another factor made of the problems I want to solve. For each problem I obtain a response variable which is stochastic. I replicate the measure of this response value 10 times. When I apply ANOVA the assumptions do not hold, hence I must rely on non parametric tests. By transforming the response data in ranks, the Friedman test tells me that there is statistical significance in the difference of the sum of ranks of at least one of the treatments. I would like now to produce a plot for the multiple comparisons similar to the Least Significant Difference or the Tukey's Honest Significant Difference used in ANOVA. Since I am in the non parametric case I can not use these methods. Instead, I compare graphically individual treatments by plotting the sum of ranks of each treatment togehter with the 95% confidence interval. To compute the interval I use the Friedman test as suggested by Conover in Practical Nonparametric statistics. I obtain something like this: Treat. A|-+-| Treat. B |-+-| Treat. C |-+-| Treat. D |-+-| The intervals have all the same spread because the number of replications was the same for all experimental units. I would like to know if someone in the list had a similar experience and if what I am doing is correct. In alternative also a reference to another list which could better fit my request is welcome. Thank you for the help, Marco -- Marco Chiarandini, Fachgebiet Intellektik, Fachbereich Informatik, Technische Universität Darmstadt, Hochschulstraße 10, D-64289 Darmstadt - Germany, Office: S2/02 Raum E317 Tel: +49 (0)6151 16-6802 Fax: +49 (0)6151 16-5326 email: [EMAIL PROTECTED] web page: http://www.intellektik.informatik.tu-darmstadt.de/~machud __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Multiple comparisons in a non parametric case
It looks to me like what you are doing is trying to judge significance of differences by non-overlap of single-sample confidence intervals. While this is appealing, it's not quite right. I just looked into my copy of Applied Nonparametric Statistics (second ed.) by Wayne W. Daniel (Duxbury, 1990) but that only deals with the situation where there is a single replicate per block-treatment combination (whereas you have 10 reps) and block-treatment interaction is assumed to be non-existent. The method that Daniel prescribes in this simple setting seems to be no more than applying the Bonferroni method of multiple comparisons. (Daniel does not say; his book is very much a cook-book.) So you might simply try Bonferroni --- i.e. do all k-choose-2 pairwise comparisons between treatments (using the appropriate 2 sample method for each comparison) doing each comparison at the alpha/k-choose-2 significance level. Where k = the number of treatments = 4 in your case. This method is not going to be super-powerful but it is sometimes surprizing how well Bonferroni stacks up against more ``sophisticated'' methods. Daniel gives a reference to ``Nonparametric Statistical Methods'' by Myles Hollander and Douglas A. Wolfe, New York, Wiley, 1973, for ``an alternative multiple comparisons formula''. I don't have this book, and don't know what direction Hollander and Wolfe ride off in, but it ***might*** be worth trying to get your hands on it and see. Finally --- in what way are the assumptions of Anova violated? The conventional wisdom is that Anova is actually quite robust to non-normality. Particularly when the sample size is large --- and 10 reps per treatment combination is pretty good. Heteroskedasticity is more of a worry, but it's not so much of a worry when the design is nicely balanced. As yours is. And finally-finally --- have you tried transforming your data to make them a bit more normal and/or homoskedastic? I hope this is some help. cheers, Rolf Turner [EMAIL PROTECTED] Marco Chiarandini wrote: I am conducting a full factorial analysis. I have one factor consisting in algorithms, which I consider my treatments, and another factor made of the problems I want to solve. For each problem I obtain a response variable which is stochastic. I replicate the measure of this response value 10 times. When I apply ANOVA the assumptions do not hold, hence I must rely on non parametric tests. By transforming the response data in ranks, the Friedman test tells me that there is statistical significance in the difference of the sum of ranks of at least one of the treatments. I would like now to produce a plot for the multiple comparisons similar to the Least Significant Difference or the Tukey's Honest Significant Difference used in ANOVA. Since I am in the non parametric case I can not use these methods. Instead, I compare graphically individual treatments by plotting the sum of ranks of each treatment togehter with the 95% confidence interval. To compute the interval I use the Friedman test as suggested by Conover in Practical Nonparametric statistics. I obtain something like this: Treat. A|-+-| Treat. B |-+-| Treat. C |-+-| Treat. D |-+-| The intervals have all the same spread because the number of replications was the same for all experimental units. I would like to know if someone in the list had a similar experience and if what I am doing is correct. In alternative also a reference to another list which could better fit my request is welcome. __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Multiple comparisons in a non parametric case
Great summary, Rolf. Just one minor issue that recently bit me: In a data mining application with hundred of p-values, people want to make subtle distinctions based on extremely small p-values. In such applications, even a modest amount of skewness (to say nothing of outliers) might have a surprising (and not necessarily monotonic) impact on p-values. Best Wishes, Spencer Graves Rolf Turner wrote: It looks to me like what you are doing is trying to judge significance of differences by non-overlap of single-sample confidence intervals. While this is appealing, it's not quite right. I just looked into my copy of Applied Nonparametric Statistics (second ed.) by Wayne W. Daniel (Duxbury, 1990) but that only deals with the situation where there is a single replicate per block-treatment combination (whereas you have 10 reps) and block-treatment interaction is assumed to be non-existent. The method that Daniel prescribes in this simple setting seems to be no more than applying the Bonferroni method of multiple comparisons. (Daniel does not say; his book is very much a cook-book.) So you might simply try Bonferroni --- i.e. do all k-choose-2 pairwise comparisons between treatments (using the appropriate 2 sample method for each comparison) doing each comparison at the alpha/k-choose-2 significance level. Where k = the number of treatments = 4 in your case. This method is not going to be super-powerful but it is sometimes surprizing how well Bonferroni stacks up against more ``sophisticated'' methods. Daniel gives a reference to ``Nonparametric Statistical Methods'' by Myles Hollander and Douglas A. Wolfe, New York, Wiley, 1973, for ``an alternative multiple comparisons formula''. I don't have this book, and don't know what direction Hollander and Wolfe ride off in, but it ***might*** be worth trying to get your hands on it and see. Finally --- in what way are the assumptions of Anova violated? The conventional wisdom is that Anova is actually quite robust to non-normality. Particularly when the sample size is large --- and 10 reps per treatment combination is pretty good. Heteroskedasticity is more of a worry, but it's not so much of a worry when the design is nicely balanced. As yours is. And finally-finally --- have you tried transforming your data to make them a bit more normal and/or homoskedastic? I hope this is some help. cheers, Rolf Turner [EMAIL PROTECTED] Marco Chiarandini wrote: I am conducting a full factorial analysis. I have one factor consisting in algorithms, which I consider my treatments, and another factor made of the problems I want to solve. For each problem I obtain a response variable which is stochastic. I replicate the measure of this response value 10 times. When I apply ANOVA the assumptions do not hold, hence I must rely on non parametric tests. By transforming the response data in ranks, the Friedman test tells me that there is statistical significance in the difference of the sum of ranks of at least one of the treatments. I would like now to produce a plot for the multiple comparisons similar to the Least Significant Difference or the Tukey's Honest Significant Difference used in ANOVA. Since I am in the non parametric case I can not use these methods. Instead, I compare graphically individual treatments by plotting the sum of ranks of each treatment togehter with the 95% confidence interval. To compute the interval I use the Friedman test as suggested by Conover in Practical Nonparametric statistics. I obtain something like this: Treat. A|-+-| Treat. B |-+-| Treat. C |-+-| Treat. D |-+-| The intervals have all the same spread because the number of replications was the same for all experimental units. I would like to know if someone in the list had a similar experience and if what I am doing is correct. In alternative also a reference to another list which could better fit my request is welcome. __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Spencer Graves, PhD, Senior Development Engineer O: (408)938-4420; mobile: (408)655-4567 __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Multiple comparisons: its a trap!
Barry Rowlingson [EMAIL PROTECTED] writes: Liaw, Andy wrote: Stupid me: fell into this trap: 0 == 0 == 0 [1] FALSE Ouch! Python's comparison operators don't have this trap, since they unravel each comparison pair in a chain so that: (A op1 B op2 C) becomes: (A op1 B) and (B op2 C) [chop] Of course old hand Fortran programmers understand all this since the second thing they learnt (after learning how to tap the space bar six times) was the order of precedence of operators... SAS does likewise, at least in recent versions. Whether this kind of syntactical exceptions is actually helpful is debatable. The problem is that you get to teach people that comparisons are binary operators except when they are not... I wonder how Python actually manages this; doesn't look like something that is easy to implement in a yacc-style parser. -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Multiple comparisons: its a trap!
Peter Dalgaard [EMAIL PROTECTED] writes: Barry Rowlingson [EMAIL PROTECTED] writes: Liaw, Andy wrote: Stupid me: fell into this trap: 0 == 0 == 0 [1] FALSE Ouch! Python's comparison operators don't have this trap, since they unravel each comparison pair in a chain so that: (A op1 B op2 C) becomes: (A op1 B) and (B op2 C) [chop] Of course old hand Fortran programmers understand all this since the second thing they learnt (after learning how to tap the space bar six times) was the order of precedence of operators... SAS does likewise, at least in recent versions. Whether this kind of syntactical exceptions is actually helpful is debatable. The problem is that you get to teach people that comparisons are binary operators except when they are not... I wonder how Python actually manages this; doesn't look like something that is easy to implement in a yacc-style parser. Don't know how Python does it but its not the only one and I believe its often done like this. Rather than have a Boolean type, NULL is defined to be false and anything else is true. If the comparison is TRUE then the right argument is returned; otherwise NULL is returned. Thus 3 5 6 == (3 5) 6 == 5 6 == 6 which is interpreted as TRUE in if statements, etc. Note that the 5 is only evaluated once in the above whereas in (3 5) and (5 6) it would evaluated twice -- not important here but if 5 is replaced by a function with side effects then it matters. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Multiple comparisons: its a trap!
On 25-Jul-04 Gabor Grothendieck wrote: Don't know how Python does it but its not the only one and I believe its often done like this. Rather than have a Boolean type, NULL is defined to be false and anything else is true. If the comparison is TRUE then the right argument is returned; otherwise NULL is returned. Thus 3 5 6 == (3 5) 6 == 5 6 == 6 which is interpreted as TRUE in if statements, etc. Note that the 5 is only evaluated once in the above whereas in (3 5) and (5 6) it would evaluated twice -- not important here but if 5 is replaced by a function with side effects then it matters. This is weird, and I'm not sure what is being discussed here. I had been hanging fire on this, to see what others say. Not having seen anyone else say what I'd thought originally, here it is. If you write, in R, 3 5 6 you get TRUE. I understand this to be parsed as (3 5) 6, not as (3 5) and (5 6). Am I right? If so, then what happens depends on what (3 5) evaluates to. In R, this is TRUE, and in R it is the case that TRUE 6: 356 [1] TRUE 35 [1] TRUE TRUE6 [1] TRUE However, in R it is also the case that 354 [1] TRUE since TRUE4 [1] TRUE the point being, as I understand it, that in a numerical context TRUE = 1 e.g. TRUE + 3 [1] 4 and, since 1 4, TRUE 4 is TRUE and so 3 5 4 is TRUE. However, 0.3 0.5 0.6 [1] FALSE for precisely the same reason. So there should be no problem so long as you remember to bear in mind what values binary comparisons have, following evaluation. Best wishes to all, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 167 1972 Date: 26-Jul-04 Time: 00:09:26 -- XFMail -- __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Multiple comparisons: its a trap!
Ted.Harding at nessie.mcc.ac.uk writes: : : On 25-Jul-04 Gabor Grothendieck wrote: : Don't know how Python does it but its not the only one and : I believe its often done like this. Rather than have a Boolean : type, NULL is defined to be false and anything else is true. : If the comparison is TRUE then the right argument is returned; : otherwise NULL is returned. : : Thus : :3 5 6 :== (3 5) 6 :== 5 6 :== 6 : : which is interpreted as TRUE in if statements, etc. : : Note that the 5 is only evaluated once in the above whereas in : : (3 5) and (5 6) : : it would evaluated twice -- not important here but if 5 is replaced : by a function with side effects then it matters. : : This is weird, and I'm not sure what is being discussed here. We were discussing how some other languages string together comparison operators without an intermediate and to connect them. This discussion has nothing to do with R other than possibly to understand whether it could fit within the R framework. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Multiple comparisons: its a trap!
Gabor Grothendieck [EMAIL PROTECTED] writes: Ted.Harding at nessie.mcc.ac.uk writes: : : On 25-Jul-04 Gabor Grothendieck wrote: : Don't know how Python does it but its not the only one and : I believe its often done like this. Rather than have a Boolean : type, NULL is defined to be false and anything else is true. : If the comparison is TRUE then the right argument is returned; : otherwise NULL is returned. : This is weird, and I'm not sure what is being discussed here. We were discussing how some other languages string together comparison operators without an intermediate and to connect them. This discussion has nothing to do with R other than possibly to understand whether it could fit within the R framework. Yes. The other side of the coin is that we do actually use the TRUE/FALSE == 1/0 convention in places. E.g. (x0)-(x0) for the sign of x, or x*(x0) for x left-censored at 0. So changing the current semantics is not really in the cards. Turning xyz into a syntax error is on the other hand quite simple (at least according to 5 seconds worth of googling for yacc nonassoc) and we should probably consider doing so. -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Multiple comparisons: its a trap!
Liaw, Andy wrote: Stupid me: fell into this trap: 0 == 0 == 0 [1] FALSE Ouch! Python's comparison operators don't have this trap, since they unravel each comparison pair in a chain so that: (A op1 B op2 C) becomes: (A op1 B) and (B op2 C) If you want: (A op1 B) op2 C you have to put the parens in, and that makes you remember there's some Boolean arithmetic going on in there. This is a nice feature, since we all are used to reading expressions like 2 X 10, and you can write them like that in Python, and they mean what they look like. You can write like that in R, but beware, its not just 0 == 0 == 0 that opens the trap: X = 5 10 X 0 [1] FALSE 0 X 10 [1] TRUE Of course old hand Fortran programmers understand all this since the second thing they learnt (after learning how to tap the space bar six times) was the order of precedence of operators... Baz PS oh, and in Perl (0 == 0 == 0) is a syntax error! __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Multiple comparisons in R; multicomp
Patrick I'm not familiar with the multcomp package from s-plus, but there is a package available through bioconductor (www.bioconductor.org) called multtest that has some functions for multiple testing procedures and adjusting p-values and computing false discovery rates. Sean -- Sean Davis, M.D., Ph.D. Clinical Fellow National Institutes of Health National Cancer Institute National Human Genome Research Institute Clinical Fellow, Johns Hopkins Department of Pediatric Oncology -- On 1/15/04 5:03 PM, Patrick Giraudoux [EMAIL PROTECTED] wrote: Is there a fonction for multiple comparison tests (similar to multicomp in Splus) in a package of R? Thanks in advance for any hint... Cheers, Patrick Giraudoux University of Franche-Comte Department of Environmental Biology EA3184 af. INRA F-25030 Besancon Cedex tel.: +33 381 665 745 fax.: +33 381 665 797 http://lbe.univ-fcomte.fr [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Multiple comparisons in R; multicomp
Is there a fonction for multiple comparison tests (similar to multicomp in Splus) in a package of R? Thanks in advance for any hint... Cheers, Patrick Giraudoux University of Franche-Comté Department of Environmental Biology EA3184 af. INRA F-25030 Besançon Cedex tel.: +33 381 665 745 fax.: +33 381 665 797 http://lbe.univ-fcomte.fr [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] Multiple comparisons in R; multicomp
There is now a package in R called multcomp for general multiple comparisons that does things similar to the Splus library you mentioned. BTW, a search of the help archives for multicomp or multiple comparisons brings this up. HTH, Andy __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Multiple comparisons with a glm
I've never seen anything written about multiple comparisons, as in the multcomp package or with TukeyHSD, but using a glm. Do such procedures exist? Are they sensible? Are there any packages in R that implement such comparisons? Thank you. -- Ken Knoblauch Inserm U371 Cerveau et Vision 18 avenue du Doyen Lepine 69675 Bron cedex France Tel: +33 (0)4 72 91 34 77 Fax: +33 (0)4 72 91 34 61 Portable: 06 84 10 64 10 email: [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] Multiple comparisons with a glm
On Wednesday 05 November 2003 17:28, Ken Knoblauch wrote: I've never seen anything written about multiple comparisons, as in the multcomp package or with TukeyHSD, but using a glm. Do such procedures exist? Are they sensible? Are there any packages in R that implement such comparisons? simint() and simtest() both have methods for glm objects. hth, Z Thank you. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] Multiple comparisons with a glm
I've never seen anything written about multiple comparisons, as in the multcomp package or with TukeyHSD, but using a glm. Do such procedures exist? Are they sensible? Are there any packages in R that implement such comparisons? since version 0.4-0 in `multcomp': 0.4-0 (13.08.2003) `simint' and `simtest' now have methods for `lm' and `glm' But you are right that there is not much theory about it (at least to my knowledge). The procedures in `multcomp' allow for inference on parameter estimates which are, asymptotically, multivariate normal with known correlation structure. Best, Torsten Thank you. -- Ken Knoblauch Inserm U371 Cerveau et Vision 18 avenue du Doyen Lepine 69675 Bron cedex France Tel: +33 (0)4 72 91 34 77 Fax: +33 (0)4 72 91 34 61 Portable: 06 84 10 64 10 email: [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
[R] multiple comparisons
I'm having trouble finding an R equivalent to the S-Plus multicomp function, which does post-hoc comparisons of treatments means in ANOVAs. Am I missing something obvious? Thanks, Peter Peter Adler, PhD Dept. Ecology, Evolution and Marine Biology University of California Santa Barbara, CA 93106 tel: (805) 893-7416 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] multiple comparisons
On 10/07/03 09:05, Peter Adler wrote: I'm having trouble finding an R equivalent to the S-Plus multicomp function, which does post-hoc comparisons of treatments means in ANOVAs. Am I missing something obvious? The package called multcomp? I don't know if it is the same. -- Jonathan Baron, Professor of Psychology, University of Pennsylvania Home page:http://www.sas.upenn.edu/~baron R page: http://finzi.psych.upenn.edu/ __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] multiple comparisons
On Tue, 7 Oct 2003, Jonathan Baron wrote: On 10/07/03 09:05, Peter Adler wrote: I'm having trouble finding an R equivalent to the S-Plus multicomp function, which does post-hoc comparisons of treatments means in ANOVAs. Am I missing something obvious? The package called multcomp? I don't know if it is the same. It is not, but can often be used in a different way to get similar results. There is also function TukeyHSD() in base R (which is often the simplest competitive method). THe MASS scripts show some of the ways to use these in R to emulate S-PLUS's multicomp. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
