[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]]
>>>
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>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
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