In-line
On 20/07/2015 15:10, angelo.arc...@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
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Michael
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