[R] R: Re: R: Re: R: Re: Differences in output of lme() when introducing interactions
John Kane
jrkrideau at inbox.com
Tue Jul 21 13:39:31 CEST 2015
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.arcadi at virgilio.it
> Sent: Tue, 21 Jul 2015 12:12:58 +0200 (CEST)
> To: lists at dewey.myzen.co.uk, bgunter.4567 at 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: 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
>
>
>
>
> [[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.
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