[R] R: Re: R: Re: R: Re: Differences in output of lme() when introducing interactions
angelo.arcadi at virgilio.it
angelo.arcadi at virgilio.it
Tue Jul 21 12:12:58 CEST 2015
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: lists at dewey.myzen.co.uk
Data: 21-lug-2015 11.58
A: "angelo.arcadi at virgilio.it"<angelo.arcadi at virgilio.it>, <bgunter.4567 at gmail.com>
Cc: <r-help at 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.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
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