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angelo.arcadi at virgilio.it

2015-Jul-20 14:10 UTC

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

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?


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]]

Michael Dewey

2015-Jul-20 15:56 UTC

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[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

R help - Jul 2015 - Differences in output of lme() when introducing interactions

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

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