[R-sig-ME] treatment and sum contrasts in ME models

Phillip Alday ph||||p@@|d@y @end|ng |rom mp|@n|
Tue Mar 5 13:55:34 CET 2019 

Following up on this, wouldn't the direct comparison be with

anova.lme(linM1,type="marginal")
anova.lme(linM2,type="marginal")

?

And potentially doing the same thing with car::Anova() /

Best,
Phillip

On 21/2/19 7:57 pm, Cristiano Alessandro wrote:
> Unfortunately, it is a bit hard for me to do so at this point. I would have
> done it otherwise, as I did in the past. Sorry.
> Can a guess be made also without the dataset?
> 
> On Thu, Feb 21, 2019 at 12:50 PM Ben Bolker <bbolker using gmail.com> wrote:
> 
>>   Not totally necessary, but would it be easy to post your data somewhere?
>>
>> On Thu, Feb 21, 2019 at 1:44 PM Cristiano Alessandro
>> <cri.alessandro using gmail.com> wrote:
>>>
>>> Hi all,
>>>
>>> I am fitting a linear mixed effect models with two factors (mPair with 6
>>> levels, and spd_des with 3 levels) and their interaction in R. I obtain
>>> inconsistent results depending on the contrasts that I choose, and I
>> would
>>> like to understand why and how to deal with it.
>>>
>>> If I use treatment contrasts, I obtain the following (I only copy the
>>> relevant info of the results)
>>>
>>>> options(contrasts = c("contr.treatment","contr.poly"))
>>>> linM1 <- lme(cc_marg ~ mPair*spd_des , random = ~mPair|ratID,
>> data=dat_trf, na.action=na.omit, method = "ML", control=lCtr )
>>>> summary(linM1)
>>>
>>> Fixed effects: cc_marg ~ mPair * spd_des
>>>                          Value  Std.Error DF   t-value p-value
>>> (Intercept)          1.4628761 0.09618167 94 15.209511  0.0000
>>> mPairRFVI           -0.8180718 0.10454920 94 -7.824754  0.0000
>>> mPairVLRF           -0.7990828 0.13193991 94 -6.056415  0.0000
>>> mPairVLVI           -0.6077804 0.13734253 94 -4.425289  0.0000
>>> mPairVMRF           -0.7444267 0.13294167 94 -5.599649  0.0000
>>> mPairVMVI           -0.4799995 0.12194383 94 -3.936234  0.0002
>>> spd_des15           -0.0830016 0.07990370 94 -1.038771  0.3016
>>> spd_des20           -0.0856339 0.08321984 94 -1.029008  0.3061
>>> mPairRFVI:spd_des15 -0.1576193 0.13500809 94 -1.167481  0.2460
>>> mPairVLRF:spd_des15  0.0866510 0.11385875 94  0.761039  0.4485
>>> mPairVLVI:spd_des15  0.0083311 0.13500809 94  0.061708  0.9509
>>> mPairVMRF:spd_des15  0.0184844 0.11385875 94  0.162345  0.8714
>>> mPairVMVI:spd_des15 -0.0672286 0.13500809 94 -0.497960  0.6197
>>> mPairRFVI:spd_des20 -0.1705514 0.14201095 94 -1.200973  0.2328
>>> mPairVLRF:spd_des20  0.0899629 0.11949193 94  0.752879  0.4534
>>> mPairVLVI:spd_des20 -0.0626845 0.14359174 94 -0.436547  0.6634
>>> mPairVMRF:spd_des20 -0.0106400 0.11969131 94 -0.088895  0.9294
>>> mPairVMVI:spd_des20 -0.0608750 0.14286017 94 -0.426116  0.6710
>>>
>>> I interpret this as follows: Since the interaction terms are all
>>> non-significant, then the factor spd_des (also non-significant) does not
>>> influence the data at any level of the factor mPair.
>>>
>>> On the other hand, using sum contrasts I obtain the following results.
>>>
>>>> options(contrasts = c("contr.sum","contr.poly"))
>>>> linM2 <- lme(cc_marg ~ mPair*spd_des , random = ~mPair|ratID,
>> data=dat_trf, na.action=na.omit, method = "ML", control=lCtr )
>>>> summary(linM2)
>>>
>>> Fixed effects: cc_marg ~ mPair * spd_des
>>>                      Value  Std.Error DF   t-value p-value
>>> (Intercept)      0.8137433 0.04791890 94 16.981678  0.0000
>>> mPair1           0.5929117 0.06609665 94  8.970373  0.0000
>>> mPair2          -0.3341386 0.04969616 94 -6.723629  0.0000
>>> mPair3          -0.1472874 0.07260892 94 -2.028503  0.0453
>>> mPair4          -0.0328631 0.08993236 94 -0.365421  0.7156
>>> mPair5          -0.1488959 0.06991733 94 -2.129600  0.0358
>>> spd_des1         0.0743293 0.02315254 94  3.210416  0.0018
>>> spd_des2        -0.0272358 0.02325774 94 -1.171043  0.2445
>>> mPair1:spd_des1 -0.0181081 0.04414399 94 -0.410206  0.6826
>>> mPair2:spd_des1  0.0912334 0.05726538 94  1.593168  0.1145
>>> mPair3:spd_des1 -0.0769866 0.04518813 94 -1.703691  0.0917
>>> mPair4:spd_des1  0.0000066 0.05743544 94  0.000114  0.9999
>>> mPair5:spd_des1 -0.0207337 0.04518548 94 -0.458859  0.6474
>>> mPair1:spd_des2  0.0004559 0.04558473 94  0.010002  0.9920
>>> mPair2:spd_des2 -0.0478225 0.05730295 94 -0.834555  0.4061
>>> mPair3:spd_des2  0.0282279 0.04525282 94  0.623781  0.5343
>>> mPair4:spd_des2  0.0269011 0.05747689 94  0.468034  0.6408
>>> mPair5:spd_des2  0.0163141 0.04525367 94  0.360503  0.7193
>>>
>>>> anova.lme(linM2,type="marginal")
>>>               numDF denDF   F-value p-value
>>> (Intercept)       1    94 288.37740  <.0001
>>> mPair             5    94  35.30799  <.0001
>>> spd_des           2    94   5.17279  0.0074
>>> mPair:spd_des    10    94   0.52159  0.8710
>>>
>>> The results are now telling me that the first level of the factor spd_des
>>> is significant; i.e. the mean of the data at that level of spd_des is
>>> significantly different from the grand mean (Intercept), and since the
>>> interactions are all non-significant, this is true at all levels of
>> mPair.
>>>
>>> So, with treatment contrasts spd_des does not influence the data at any
>>> level of mPair, and with sum contrast spd_des influence the data at all
>>> level of mPair. How do I deal with this? What result should I trust?
>>> Thanks in advance for your help
>>>
>>> Best
>>> Cristiano
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
> 
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