[R] decide between polynomial vs ordered factor model (lme)
Leo Gürtler
leog at anicca-vijja.de
Mon Jan 9 14:59:06 CET 2006
Dear alltogether,
two lme's, the data are available at:
http://www.anicca-vijja.de/lg/hlm3_nachw.Rdata
explanations of the data:
nachw = post hox knowledge tests over 6 measure time points (= equally
spaced)
zeitn = time points (n = 6)
subgr = small learning groups (n = 28)
gru = 4 different groups = treatment factor
levels: time (=zeitn) (n=6) within subject (n=4) within smallgroups
(=gru) (n = 28), i.e. n = 4 * 28 = 112 persons and 112 * 6 = 672 data points
library(nlme)
fitlme7 <- lme(nachw ~ I(zeitn-3.5) + I((zeitn-3.5)^2) +
I((zeitn-3.5)^3) + I((zeitn-3.5)^4)*gru, random = list(subgr = ~ 1,
subject = ~ zeitn), data = hlm3)
fit5 <- lme(nachw ~ ordered(I(zeitn-3.5))*gru, random = list(subgr =
~ 1, subject = ~ zeitn), data = hlm3)
anova( update(fit5, method="ML"), update(fitlme7, method="ML") )
> anova( update(fit5, method="ML"), update(fitlme7, method="ML") )
Model df AIC BIC logLik Test
update(fit5, method = "ML") 1 29 2535.821 2666.619 -1238.911
update(fitlme7, method = "ML") 2 16 2529.719 2601.883 -1248.860 1 vs 2
L.Ratio p-value
update(fit5, method = "ML")
update(fitlme7, method = "ML") 19.89766 0.0978
>
shows that both are ~ equal, although I know about the uncertainty of ML
tests with lme(). Both models show that the ^2 and the ^4 terms are
important parts of the model.
My question is:
- Is it legitim to choose a model based on these outputs according to
theoretical considerations instead of statistical tests that not really
show a superiority of one model over the other one?
- Is there another criterium I've overseen to decide which model can be
clearly prefered?
- The idea behind that is that in the one model (fit5) the second
contrast of the factor (gru) is statistically significant, although not
the whole factor in the anova output.
In the other model, this is not the case.
Theoretically interesting is of course the significance of the second
contrast of gru, as it shows a tendency of one treatment being slightly
superior. I want to choose this model but I am not sure whether this is
proper action. Both models shows this trend, but only one model clearly
indicates that this trend bears some empirical meaning.
Thanks for any suggestions,
leo
here are the outputs for each model:
> fitlme7 <- lme(nachw ~ I(zeitn-3.5) + I((zeitn-3.5)^2) +
I((zeitn-3.5)^3) + I((zeitn-3.5)^4)*gru, random = list(subgr = ~ 1,
subject = ~ zeitn), data = hlm3)
> plot(augPred(fitlme7), layout=c(14,8))
> summary(fitlme7); anova(fitlme7); intervals(fitlme7)
Linear mixed-effects model fit by REML
Data: hlm3
AIC BIC logLik
2582.934 2654.834 -1275.467
Random effects:
Formula: ~1 | subgr
(Intercept)
StdDev: 0.5833797
Formula: ~zeitn | subject %in% subgr
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 0.6881908 (Intr)
zeitn 0.1936087 -0.055
Residual 1.3495785
Fixed effects: nachw ~ I(zeitn - 3.5) + I((zeitn - 3.5)^2) + I((zeitn -
3.5)^3) + I((zeitn - 3.5)^4) * gru
Value Std.Error DF t-value p-value
(Intercept) 4.528757 0.17749012 553 25.515542 0.0000
I(zeitn - 3.5) 0.010602 0.08754449 553 0.121100 0.9037
I((zeitn - 3.5)^2) 0.815693 0.09765075 553 8.353171 0.0000
I((zeitn - 3.5)^3) 0.001336 0.01584169 553 0.084329 0.9328
I((zeitn - 3.5)^4) -0.089655 0.01405811 553 -6.377486 0.0000
gru1 0.187181 0.30805090 24 0.607630 0.5491
gru2 0.532665 0.30805090 24 1.729147 0.0966
gru3 -0.046305 0.30805090 24 -0.150317 0.8818
I((zeitn - 3.5)^4):gru1 -0.007860 0.00600928 553 -1.307993 0.1914
I((zeitn - 3.5)^4):gru2 -0.001259 0.00600928 553 -0.209516 0.8341
I((zeitn - 3.5)^4):gru3 -0.000224 0.00600928 553 -0.037225 0.9703
Correlation:
(Intr) I(-3.5 I((-3.5)^2 I((-3.5)^3 I((z-3.5)^4)
I(zeitn - 3.5) 0.071
I((zeitn - 3.5)^2) -0.465 0.000
I((zeitn - 3.5)^3) 0.000 -0.914 0.000
I((zeitn - 3.5)^4) 0.401 0.000 -0.977 0.000
gru1 0.000 0.000 0.000 0.000 0.000
gru2 0.000 0.000 0.000 0.000 0.000
gru3 0.000 0.000 0.000 0.000 0.000
I((zeitn - 3.5)^4):gru1 0.000 0.000 0.000 0.000 0.000
I((zeitn - 3.5)^4):gru2 0.000 0.000 0.000 0.000 0.000
I((zeitn - 3.5)^4):gru3 0.000 0.000 0.000 0.000 0.000
gru1 gru2 gru3 I((-3.5)^4):1 I((-3.5)^4):2
I(zeitn - 3.5)
I((zeitn - 3.5)^2)
I((zeitn - 3.5)^3)
I((zeitn - 3.5)^4)
gru1
gru2 0.000
gru3 0.000 0.000
I((zeitn - 3.5)^4):gru1 -0.287 0.000 0.000
I((zeitn - 3.5)^4):gru2 0.000 -0.287 0.000 0.000
I((zeitn - 3.5)^4):gru3 0.000 0.000 -0.287 0.000 0.000
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-3.1326192 -0.5888543 0.0239228 0.6519002 2.1238820
Number of Observations: 672
Number of Groups:
subgr subject %in% subgr
28 112
numDF denDF F-value p-value
(Intercept) 1 553 1426.5275 <.0001
I(zeitn - 3.5) 1 553 0.2381 0.6258
I((zeitn - 3.5)^2) 1 553 98.6712 <.0001
I((zeitn - 3.5)^3) 1 553 0.0071 0.9328
I((zeitn - 3.5)^4) 1 553 40.6723 <.0001
gru 3 24 1.0410 0.3924
I((zeitn - 3.5)^4):gru 3 553 0.5854 0.6248
Approximate 95% confidence intervals
Fixed effects:
lower est. upper
(Intercept) 4.18011938 4.5287566579 4.877393940
I(zeitn - 3.5) -0.16135875 0.0106016498 0.182562052
I((zeitn - 3.5)^2) 0.62388162 0.8156933820 1.007505144
I((zeitn - 3.5)^3) -0.02978133 0.0013359218 0.032453178
I((zeitn - 3.5)^4) -0.11726922 -0.0896553959 -0.062041570
gru1 -0.44860499 0.1871808283 0.822966643
gru2 -0.10312045 0.5326653686 1.168451183
gru3 -0.68209096 -0.0463051419 0.589480673
I((zeitn - 3.5)^4):gru1 -0.01966389 -0.0078600880 0.003943709
I((zeitn - 3.5)^4):gru2 -0.01306284 -0.0012590380 0.010544759
I((zeitn - 3.5)^4):gru3 -0.01202749 -0.0002236923 0.011580105
attr(,"label")
[1] "Fixed effects:"
Random Effects:
Level: subgr
lower est. upper
sd((Intercept)) 0.3459779 0.5833797 0.9836812
Level: subject
lower est. upper
sd((Intercept)) 0.4388885 0.68819079 1.0791046
sd(zeitn) 0.1320591 0.19360866 0.2838449
cor((Intercept),zeitn) -0.4835884 -0.05541043 0.3941661
Within-group standard error:
lower est. upper
1.267548 1.349579 1.436918
#########################################################
an the other model:
> summary(fit5); anova(fit5); intervals(fit5)
Linear mixed-effects model fit by REML
Data: hlm3
AIC BIC logLik
2564.135 2693.878 -1253.067
Random effects:
Formula: ~1 | subgr
(Intercept)
StdDev: 0.5833753
Formula: ~zeitn | subject %in% subgr
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 0.6453960 (Intr)
zeitn 0.1709843 0.13
Residual 1.3497627
Fixed effects: nachw ~ ordered(I(zeitn - 3.5)) + gru + ordered(I(zeitn -
3.5)):gru
Value Std.Error DF t-value p-value
(Intercept) 5.587313 0.1505852 540 37.10400 0.0000
ordered(I(zeitn - 3.5)).L 0.072572 0.1443422 540 0.50278 0.6153
ordered(I(zeitn - 3.5)).Q 1.266731 0.1275406 540 9.93198 0.0000
ordered(I(zeitn - 3.5)).C 0.010754 0.1275406 540 0.08432 0.9328
ordered(I(zeitn - 3.5))^4 -0.813277 0.1275406 540 -6.37662 0.0000
ordered(I(zeitn - 3.5))^5 0.070373 0.1275406 540 0.55177 0.5813
gru1 0.056700 0.3011704 24 0.18826 0.8523
gru2 0.679057 0.3011704 24 2.25473 0.0335
gru3 -0.141425 0.3011704 24 -0.46958 0.6429
ordered(I(zeitn - 3.5)).L:gru1 -0.070352 0.2886844 540 -0.24370 0.8076
ordered(I(zeitn - 3.5)).Q:gru1 -0.360380 0.2550812 540 -1.41281 0.1583
ordered(I(zeitn - 3.5)).C:gru1 -0.162411 0.2550812 540 -0.63670 0.5246
ordered(I(zeitn - 3.5))^4:gru1 0.086343 0.2550812 540 0.33849 0.7351
ordered(I(zeitn - 3.5))^5:gru1 -0.017207 0.2550812 540 -0.06746 0.9462
ordered(I(zeitn - 3.5)).L:gru2 0.788896 0.2886844 540 2.73273 0.0065
ordered(I(zeitn - 3.5)).Q:gru2 0.033386 0.2550812 540 0.13089 0.8959
ordered(I(zeitn - 3.5)).C:gru2 0.089757 0.2550812 540 0.35188 0.7251
ordered(I(zeitn - 3.5))^4:gru2 -0.402616 0.2550812 540 -1.57839 0.1151
ordered(I(zeitn - 3.5))^5:gru2 -0.507855 0.2550812 540 -1.99095 0.0470
ordered(I(zeitn - 3.5)).L:gru3 -0.439200 0.2886844 540 -1.52138 0.1287
ordered(I(zeitn - 3.5)).Q:gru3 0.026105 0.2550812 540 0.10234 0.9185
ordered(I(zeitn - 3.5)).C:gru3 -0.273643 0.2550812 540 -1.07277 0.2839
ordered(I(zeitn - 3.5))^4:gru3 -0.163738 0.2550812 540 -0.64191 0.5212
ordered(I(zeitn - 3.5))^5:gru3 0.204174 0.2550812 540 0.80043 0.4238
Correlation:
(Intr) or(I(-3.5)).L or(I(-3.5)).Q
or(I(-3.5)).C or(I(-3.5))^4 or(I(-3.5))^5 gru1 gru2 gru3 o(I(-3.5)).L:1
o(I(-3.5)).Q:1 o(I(-3.5)).C:1
ordered(I(zeitn - 3.5)).L
0.2
ordered(I(zeitn - 3.5)).Q 0.0
0.0
ordered(I(zeitn - 3.5)).C 0.0 0.0
0.0
ordered(I(zeitn - 3.5))^4 0.0 0.0 0.0
0.0
ordered(I(zeitn - 3.5))^5 0.0 0.0 0.0
0.0
0.0
gru1 0.0 0.0 0.0
0.0 0.0
0.0
gru2 0.0 0.0 0.0
0.0 0.0 0.0
0.0
gru3 0.0 0.0 0.0
0.0 0.0 0.0 0.0
0.0
ordered(I(zeitn - 3.5)).L:gru1 0.0 0.0 0.0
0.0 0.0 0.0 0.2 0.0
0.0
ordered(I(zeitn - 3.5)).Q:gru1 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0
0.0
ordered(I(zeitn - 3.5)).C:gru1 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0
ordered(I(zeitn - 3.5))^4:gru1 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5))^5:gru1 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5)).L:gru2 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.2 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5)).Q:gru2 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5)).C:gru2 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5))^4:gru2 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5))^5:gru2 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5)).L:gru3 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.2 0.0
0.0 0.0
ordered(I(zeitn - 3.5)).Q:gru3 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5)).C:gru3 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5))^4:gru3 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5))^5:gru3 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0
o(I(-3.5))^4:1 o(I(-3.5))^5:1
o(I(-3.5)).L:2 o(I(-3.5)).Q:2 o(I(-3.5)).C:2 o(I(-3.5))^4:2
o(I(-3.5))^5:2 o(I(-3.5)).L:3 o(I(-3.5)).Q:3
ordered(I(zeitn -
3.5)).L
ordered(I(zeitn -
3.5)).Q
ordered(I(zeitn -
3.5)).C
ordered(I(zeitn -
3.5))^4
ordered(I(zeitn -
3.5))^5
gru1
gru2
gru3
ordered(I(zeitn -
3.5)).L:gru1
ordered(I(zeitn -
3.5)).Q:gru1
ordered(I(zeitn -
3.5)).C:gru1
ordered(I(zeitn -
3.5))^4:gru1
ordered(I(zeitn - 3.5))^5:gru1
0.0
ordered(I(zeitn - 3.5)).L:gru2 0.0
0.0
ordered(I(zeitn - 3.5)).Q:gru2 0.0 0.0
0.0
ordered(I(zeitn - 3.5)).C:gru2 0.0 0.0
0.0
0.0
ordered(I(zeitn - 3.5))^4:gru2 0.0 0.0
0.0 0.0
0.0
ordered(I(zeitn - 3.5))^5:gru2 0.0 0.0
0.0 0.0 0.0
0.0
ordered(I(zeitn - 3.5)).L:gru3 0.0 0.0
0.0 0.0 0.0 0.0
0.0
ordered(I(zeitn - 3.5)).Q:gru3 0.0 0.0
0.0 0.0 0.0 0.0
0.0 0.0
ordered(I(zeitn - 3.5)).C:gru3 0.0 0.0
0.0 0.0 0.0 0.0
0.0 0.0 0.0
ordered(I(zeitn - 3.5))^4:gru3 0.0 0.0
0.0 0.0 0.0 0.0
0.0 0.0 0.0
ordered(I(zeitn - 3.5))^5:gru3 0.0 0.0
0.0 0.0 0.0 0.0
0.0 0.0 0.0
o(I(-3.5)).C:3 o(I(-3.5))^4:3
ordered(I(zeitn - 3.5)).L
ordered(I(zeitn - 3.5)).Q
ordered(I(zeitn - 3.5)).C
ordered(I(zeitn - 3.5))^4
ordered(I(zeitn - 3.5))^5
gru1
gru2
gru3
ordered(I(zeitn - 3.5)).L:gru1
ordered(I(zeitn - 3.5)).Q:gru1
ordered(I(zeitn - 3.5)).C:gru1
ordered(I(zeitn - 3.5))^4:gru1
ordered(I(zeitn - 3.5))^5:gru1
ordered(I(zeitn - 3.5)).L:gru2
ordered(I(zeitn - 3.5)).Q:gru2
ordered(I(zeitn - 3.5)).C:gru2
ordered(I(zeitn - 3.5))^4:gru2
ordered(I(zeitn - 3.5))^5:gru2
ordered(I(zeitn - 3.5)).L:gru3
ordered(I(zeitn - 3.5)).Q:gru3
ordered(I(zeitn - 3.5)).C:gru3
ordered(I(zeitn - 3.5))^4:gru3 0.0
ordered(I(zeitn - 3.5))^5:gru3 0.0 0.0
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-3.10206117 -0.62626454 0.02807962 0.64554138 2.13155536
Number of Observations: 672
Number of Groups:
subgr subject %in% subgr
28 112
numDF denDF F-value p-value
(Intercept) 1 540 1426.5315 <.0001
ordered(I(zeitn - 3.5)) 5 540 27.9740 <.0001
gru 3 24 1.0410 0.3924
ordered(I(zeitn - 3.5)):gru 15 540 1.4115 0.1363
Approximate 95% confidence intervals
Fixed effects:
lower est. upper
(Intercept) 5.2915086 5.58731309 5.883117621
ordered(I(zeitn - 3.5)).L -0.2109689 0.07257212 0.356113124
ordered(I(zeitn - 3.5)).Q 1.0161942 1.26673073 1.517267227
ordered(I(zeitn - 3.5)).C -0.2397825 0.01075396 0.261290456
ordered(I(zeitn - 3.5))^4 -1.0638138 -0.81327731 -0.562740815
ordered(I(zeitn - 3.5))^5 -0.1801634 0.07037312 0.320909612
gru1 -0.5648856 0.05669953 0.678284624
gru2 0.0574723 0.67905739 1.300642487
gru3 -0.7630097 -0.14142458 0.480160517
ordered(I(zeitn - 3.5)).L:gru1 -0.6374343 -0.07035232 0.496729683
ordered(I(zeitn - 3.5)).Q:gru1 -0.8614532 -0.36038020 0.140692783
ordered(I(zeitn - 3.5)).C:gru1 -0.6634839 -0.16241093 0.338662057
ordered(I(zeitn - 3.5))^4:gru1 -0.4147301 0.08634286 0.587415843
ordered(I(zeitn - 3.5))^5:gru1 -0.5182803 -0.01720729 0.483865692
ordered(I(zeitn - 3.5)).L:gru2 0.2218139 0.78889594 1.355977946
ordered(I(zeitn - 3.5)).Q:gru2 -0.4676866 0.03338637 0.534459352
ordered(I(zeitn - 3.5)).C:gru2 -0.4113159 0.08975711 0.590830099
ordered(I(zeitn - 3.5))^4:gru2 -0.9036894 -0.40261640 0.098456584
ordered(I(zeitn - 3.5))^5:gru2 -1.0089275 -0.50785453 -0.006781542
ordered(I(zeitn - 3.5)).L:gru3 -1.0062815 -0.43919953 0.127882479
ordered(I(zeitn - 3.5)).Q:gru3 -0.4749680 0.02610502 0.527178001
ordered(I(zeitn - 3.5)).C:gru3 -0.7747163 -0.27364336 0.227429629
ordered(I(zeitn - 3.5))^4:gru3 -0.6648114 -0.16373838 0.337334604
ordered(I(zeitn - 3.5))^5:gru3 -0.2968991 0.20417390 0.705246883
attr(,"label")
[1] "Fixed effects:"
Random Effects:
Level: subgr
lower est. upper
sd((Intercept)) 0.3464888 0.5833753 0.9822158
Level: subject
lower est. upper
sd((Intercept)) 0.3640439 0.6453960 1.1441916
sd(zeitn) 0.1000264 0.1709843 0.2922790
cor((Intercept),zeitn) -0.6712236 0.1295558 0.7907922
Within-group standard error:
lower est. upper
1.265702 1.349763 1.439406
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