angelo.arcadi at virgilio.it
2015-Jul-21 00:45 UTC
[R] R: Re: Differences in output of lme() when introducing interactions
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]]
Bert Gunter
2015-Jul-21 01:45 UTC
[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.