[R] R: Re: R: Re: Differences in output of lme() when introducing interactions
Michael Dewey
lists at dewey.myzen.co.uk
Tue Jul 21 11:58:50 CEST 2015
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.arcadi at 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.4567 at gmail.com
> Data: 21-lug-2015 3.45
> A: "angelo.arcadi at virgilio.it"<angelo.arcadi at virgilio.it>
> Cc: <lists at dewey.myzen.co.uk>, <r-help at 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.arcadi at virgilio.it
> <angelo.arcadi at 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: lists at dewey.myzen.co.uk
> > Data: 20-lug-2015 17.56
> > A: "angelo.arcadi at virgilio.it"<angelo.arcadi at virgilio.it>,
> <r-help at r-project.org>
> > Ogg: Re: [R] Differences in output of lme() when introducing
> interactions
> >
> > In-line
> >
> > On 20/07/2015 15:10, angelo.arcadi at 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 at 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 at 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
More information about the R-help
mailing list