[R] Differences in output of lme() when introducing interactions

Michael Dewey lists at dewey.myzen.co.uk
Mon Jul 20 17:56:39 CEST 2015


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



More information about the R-help mailing list