Have you been asking statistics related questiongs on StackExchange? I must say I had the luxury when at school that we had a very strong (free) stats consulting service. I was the envy of several friends at other universities and I suspect we (many depts of the university) turned out better work.
John Kane Kingston ON Canada > -----Original Message----- > From: angelo.arc...@virgilio.it > Sent: Tue, 21 Jul 2015 12:12:58 +0200 (CEST) > To: li...@dewey.myzen.co.uk, bgunter.4...@gmail.com > Subject: [R] R: Re: R: Re: R: Re: Differences in output of lme() when > introducing interactions > > Dear Michael, > thanks a lot. I am studying the marginality and I came across to this > post: > > http://www.ats.ucla.edu/stat/r/faq/type3.htm > > Do you think that the procedure there described is the right one to solve > my problem? > > Would you have any other online resources to suggest especially dealing > with R? > > My department does not have a statician, so I have to find a solution > with my own capacities. > > Thanks in advance > > Angelo > > > > > ----Messaggio originale---- > Da: li...@dewey.myzen.co.uk > Data: 21-lug-2015 11.58 > A: "angelo.arc...@virgilio.it"<angelo.arc...@virgilio.it>, > <bgunter.4...@gmail.com> > Cc: <r-help@r-project.org> > Ogg: Re: R: Re: [R] R: Re: Differences in output of lme() when > introducing interactions > > Dear Angelo > > I suggest you do an online search for marginality which may help to > explain the relationship between main effects and interactions. As I > said in my original email this is a complicated subject which we are not > going to retype for you. > > If you are doing this as a student I suggest you sue your university for > failing to train you appropriately and if it is part of your employment > I suggest you find a better employer. > > On 21/07/2015 10:04, angelo.arc...@virgilio.it wrote: >> Dear Bert, >> thank you for your feedback. Can you please provide some references >> online so I can improve "my ignorance"? >> Anyways, please notice that it is not true that I do not know statistics >> and regressions at all, and I am strongly >> convinced that my question can be of interest for some one else in the >> future. >> >> This is what forums serve for, isn't it? This is why people help each >> other, isn't it? >> >> Moreover, don't you think that I would not have asked to this R forum if >> I had the possibility to ask or pay a statician? >> Don't you think I have done already my best to study and learn before >> posting this message? Trust me, I have read different >> online tutorials on lme and lmer, and I am confident that I have got the >> basic concepts. Still I have not found the answer >> to solve my problem, so if you know the answer can you please give me >> some suggestions that can help me? >> >> I do not have a book where to learn and unfortunately I have to analyze >> the results soon. Any help? Any online reference to-the-point >> that can help me in solving this problem? >> >> Thank you in advance >> >> Best regards >> >> Angelo >> >> >> ----Messaggio originale---- >> Da: bgunter.4...@gmail.com >> Data: 21-lug-2015 3.45 >> A: "angelo.arc...@virgilio.it"<angelo.arc...@virgilio.it> >> Cc: <li...@dewey.myzen.co.uk>, <r-help@r-project.org> >> Ogg: Re: [R] R: Re: Differences in output of lme() when introducing >> interactions >> >> I believe Michael's point is that you need to STOP asking such >> questions and START either learning some statistics or work with >> someone who already knows some. You should not be doing such >> analyses >> on your own given your present state of statistical ignorance. >> >> Cheers, >> Bert >> >> >> Bert Gunter >> >> "Data is not information. Information is not knowledge. And >> knowledge >> is certainly not wisdom." >> -- Clifford Stoll >> >> >> On Mon, Jul 20, 2015 at 5:45 PM, angelo.arc...@virgilio.it >> <angelo.arc...@virgilio.it> wrote: >> > Dear Michael, >> > thanks for your answer. Despite it answers to my initial >> question, it does not help me in finding the solution to my problem >> unfortunately. >> > >> > Could you please tell me which analysis of the two models should >> I trust then? >> > My goal is to know whether participants’ choices >> > of the dependent variable are linearly related to their own >> weight, height, shoe size and >> > the combination of those effects. >> > Would the analysis of model 2 be more >> > correct than that of model 1? Which of the two analysis should I >> trust according to my goal? >> > What is your recommendation? >> > >> > >> > Thanks in advance >> > >> > Angelo >> > >> > >> > >> > >> > >> > ----Messaggio originale---- >> > Da: li...@dewey.myzen.co.uk >> > Data: 20-lug-2015 17.56 >> > A: "angelo.arc...@virgilio.it"<angelo.arc...@virgilio.it>, >> <r-help@r-project.org> >> > Ogg: Re: [R] Differences in output of lme() when introducing >> interactions >> > >> > In-line >> > >> > On 20/07/2015 15:10, angelo.arc...@virgilio.it wrote: >> >> Dear List Members, >> >> >> >> >> >> >> >> I am searching for correlations between a dependent variable and >> a >> >> factor or a combination of factors in a repeated measure design. >> So I >> >> use lme() function in R. However, I am getting very different >> results >> >> depending on whether I add on the lme formula various factors >> compared >> >> to when only one is present. If a factor is found to be >> significant, >> >> shouldn't remain significant also when more factors are >> introduced in >> >> the model? >> >> >> > >> > The short answer is 'No'. >> > >> > The long answer is contained in any good book on statistics which >> you >> > really need to have by your side as the long answer is too long >> to >> > include in an email. >> > >> >> >> >> I give an example of the outputs I get using the two models. In >> the first model I use one single factor: >> >> >> >> library(nlme) >> >> summary(lme(Mode ~ Weight, data = Gravel_ds, random = ~1 | >> Subject)) >> >> Linear mixed-effects model fit by REML >> >> Data: Gravel_ds >> >> AIC BIC logLik >> >> 2119.28 2130.154 -1055.64 >> >> >> >> Random effects: >> >> Formula: ~1 | Subject >> >> (Intercept) Residual >> >> StdDev: 1952.495 2496.424 >> >> >> >> Fixed effects: Mode ~ Weight >> >> Value Std.Error DF t-value p-value >> >> (Intercept) 10308.966 2319.0711 95 4.445299 0.000 >> >> Weight -99.036 32.3094 17 -3.065233 0.007 >> >> Correlation: >> >> (Intr) >> >> Weight -0.976 >> >> >> >> Standardized Within-Group Residuals: >> >> Min Q1 Med Q3 Max >> >> -1.74326719 -0.41379593 -0.06508451 0.39578734 2.27406649 >> >> >> >> Number of Observations: 114 >> >> Number of Groups: 19 >> >> >> >> >> >> As you can see the p-value for factor Weight is significant. >> >> This is the second model, in which I add various factors for >> searching their correlations: >> >> >> >> library(nlme) >> >> summary(lme(Mode ~ Weight*Height*Shoe_Size*BMI, data = >> Gravel_ds, random = ~1 | Subject)) >> >> Linear mixed-effects model fit by REML >> >> Data: Gravel_ds >> >> AIC BIC logLik >> >> 1975.165 2021.694 -969.5825 >> >> >> >> Random effects: >> >> Formula: ~1 | Subject >> >> (Intercept) Residual >> >> StdDev: 1.127993 2494.826 >> >> >> >> Fixed effects: Mode ~ Weight * Height * Shoe_Size * BMI >> >> Value Std.Error DF t-value >> p-value >> >> (Intercept) 5115955 10546313 95 0.4850941 >> 0.6287 >> >> Weight -13651237 6939242 3 -1.9672518 >> 0.1438 >> >> Height -18678 53202 3 -0.3510740 >> 0.7487 >> >> Shoe_Size 93427 213737 3 0.4371115 >> 0.6916 >> >> BMI -13011088 7148969 3 -1.8199949 >> 0.1663 >> >> Weight:Height 28128 14191 3 1.9820883 >> 0.1418 >> >> Weight:Shoe_Size 351453 186304 3 1.8864467 >> 0.1557 >> >> Height:Shoe_Size -783 1073 3 -0.7298797 >> 0.5183 >> >> Weight:BMI 19475 11425 3 1.7045450 >> 0.1868 >> >> Height:BMI 226512 118364 3 1.9136867 >> 0.1516 >> >> Shoe_Size:BMI 329377 190294 3 1.7308827 >> 0.1819 >> >> Weight:Height:Shoe_Size -706 371 3 -1.9014817 >> 0.1534 >> >> Weight:Height:BMI -109 63 3 -1.7258742 >> 0.1828 >> >> Weight:Shoe_Size:BMI -273 201 3 -1.3596421 >> 0.2671 >> >> Height:Shoe_Size:BMI -5858 3200 3 -1.8306771 >> 0.1646 >> >> Weight:Height:Shoe_Size:BMI 2 1 3 1.3891782 >> 0.2589 >> >> Correlation: >> >> (Intr) Weight Height Sho_Sz BMI >> Wght:H Wg:S_S Hg:S_S Wg:BMI Hg:BMI S_S:BM Wg:H:S_S W:H:BM W:S_S: >> H:S_S: >> >> Weight -0.895 >> >> Height -0.996 0.869 >> >> Shoe_Size -0.930 0.694 0.933 >> >> BMI -0.911 0.998 0.887 0.720 >> >> Weight:Height 0.894 -1.000 -0.867 -0.692 -0.997 >> >> Weight:Shoe_Size 0.898 -0.997 -0.873 -0.700 -0.999 >> 0.995 >> >> Height:Shoe_Size 0.890 -0.612 -0.904 -0.991 -0.641 >> 0.609 0.619 >> >> Weight:BMI 0.911 -0.976 -0.887 -0.715 -0.972 >> 0.980 0.965 0.637 >> >> Height:BMI 0.900 -1.000 -0.875 -0.703 -0.999 >> 0.999 0.999 0.622 0.973 >> >> Shoe_Size:BMI 0.912 -0.992 -0.889 -0.726 -0.997 >> 0.988 0.998 0.649 0.958 0.995 >> >> Weight:Height:Shoe_Size -0.901 0.999 0.876 0.704 1.000 >> -0.997 -1.000 -0.623 -0.971 -1.000 -0.997 >> >> Weight:Height:BMI -0.908 0.978 0.886 0.704 0.974 >> -0.982 -0.968 -0.627 -0.999 -0.975 -0.961 0.973 >> >> Weight:Shoe_Size:BMI -0.949 0.941 0.928 0.818 0.940 >> -0.946 -0.927 -0.751 -0.980 -0.938 -0.924 0.935 0.974 >> >> Height:Shoe_Size:BMI -0.901 0.995 0.878 0.707 0.998 >> -0.992 -1.000 -0.627 -0.960 -0.997 -0.999 0.999 0.964 0.923 >> >> Weight:Height:Shoe_Size:BMI 0.952 -0.948 -0.933 -0.812 -0.947 >> 0.953 0.935 0.747 0.985 0.946 0.932 -0.943 -0.980 -0.999 >> -0.931 >> >> >> >> Standardized Within-Group Residuals: >> >> Min Q1 Med Q3 Max >> >> -2.03523736 -0.47889716 -0.02149143 0.41118126 2.20012158 >> >> >> >> Number of Observations: 114 >> >> Number of Groups: 19 >> >> >> >> >> >> This time the p-value associated to Weight is not significant >> anymore. Why? Which analysis should I trust? >> >> >> >> >> >> In addition, while in the first output the field "value" (which >> >> should give me the slope) is -99.036 in the second output it is >> >> -13651237. Why they are so different? The one in the first >> output is the >> >> one that seems definitively more reasonable to me. >> >> I would very grateful if someone could give me an answer >> >> >> >> >> >> Thanks in advance >> >> >> >> >> >> Angelo >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> [[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. >> >> >> > >> > -- >> > Michael >> > http://www.dewey.myzen.co.uk/home.html >> > >> > >> > >> > >> > [[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. >> >> > > -- > Michael > http://www.dewey.myzen.co.uk/home.html > > > > > [[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. ____________________________________________________________ FREE 3D MARINE AQUARIUM SCREENSAVER - Watch dolphins, sharks & orcas on your desktop! ______________________________________________ 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.
