[R-sig-ME] lme function to obtain pvalue for fixed effect
Ben Bolker
bbolker at gmail.com
Wed May 27 02:23:48 CEST 2015
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On 15-05-26 06:56 PM, li li wrote:
> You are right! Then I am not sure whether the test in ANOVA
> corresponding to a continuous variable makes sense. Hanna
>
I'm not sure what you mean.
summary() gives Wald (marginal) tests of individual parameters;
anova gives *sequential* F tests. (They agree for the interaction
term.) This is a large topic. You might find car::Anova() to be a
useful tool for this ...
> 2015-05-26 16:09 GMT-04:00, Thierry Onkelinx
> <thierry.onkelinx at inbo.be>:
>> Because they test different hypothesis.
>>
>> ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek /
>> Research Institute for Nature and Forest team Biometrie &
>> Kwaliteitszorg / team Biometrics & Quality Assurance
>> Kliniekstraat 25 1070 Anderlecht Belgium
>>
>> To call in the statistician after the experiment is done may be
>> no more than asking him to perform a post-mortem examination: he
>> may be able to say what the experiment died of. ~ Sir Ronald
>> Aylmer Fisher The plural of anecdote is not data. ~ Roger
>> Brinner The combination of some data and an aching desire for an
>> answer does not ensure that a reasonable answer can be extracted
>> from a given body of data. ~ John Tukey
>>
>> 2015-05-26 21:46 GMT+02:00 li li <hannah.hlx at gmail.com>:
>>
>>> Thanks so much for replying. Yes LimerTest package could be
>>> used to get pvalues when using lmer function. But still the
>>> summary and anova function give different pvalues. Hanna
>>>
>>> 2015-05-26 15:19 GMT-04:00, byron vinueza
>>> <byronvinu_8 at hotmail.com>:
>>>> You can use the lmerTest package .
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> Enviado desde mi iPhone
>>>>
>>>>> El 26/5/2015, a las 13:18, li li <hannah.hlx at gmail.com>
>>>>> escribió:
>>>>>
>>>>> Hi all, I am using the lme function to run a random
>>>>> coefficient model. Please see output (mod1) as below. I
>>>>> need to obtain the pvalue for the fixed effect. As you can
>>>>> see, the pvalues given using the summary function is
>>>>> different from the resutls given in anova function. Why
>>>>> should they be different and which one is the correct one
>>>>> to use? Thanks! Hanna
>>>>>
>>>>>
>>>>>> summary(mod1)
>>>>> Linear mixed-effects model fit by REML Data: minus20C1 AIC
>>>>> BIC logLik -82.60042 -70.15763 49.30021
>>>>>
>>>>> Random effects: Formula: ~1 + months | lot Structure:
>>>>> General positive-definite, Log-Cholesky parametrization
>>>>> StdDev Corr (Intercept) 8.907584e-03 (Intr) months
>>>>> 6.039781e-05 -0.096 Residual 4.471243e-02
>>>>>
>>>>> Fixed effects: ti ~ type * months Value Std.Error DF
>>>>> t-value p-value (Intercept) 0.25831245 0.016891587 31
>>>>> 15.292373 0.0000 type 0.13502089 0.026676101
>>>>> 4 5.061493 0.0072 months 0.00804790 0.001218941
>>>>> 31 6.602368 0.0000 type:months -0.00693679 0.002981859 31
>>>>> -2.326329 0.0267 Correlation: (Intr) typ months type
>>>>> -0.633 months -0.785 0.497 type:months 0.321
>>>>> -0.762 -0.409
>>>>>
>>>>> Standardized Within-Group Residuals: Min Q1
>>>>> Med Q3 Max -2.162856e+00 -1.962972e-01
>>>>> -2.771184e-05 3.749035e-01 2.088392e+00
>>>>>
>>>>> Number of Observations: 39 Number of Groups: 6
>>>>>> anova(mod1)
>>>>> numDF denDF F-value p-value (Intercept) 1 31
>>>>> 2084.0265 <.0001 type 1 4 10.8957
>>>>> 0.0299 months 1 31 38.3462 <.0001
>>>>> type:months 1 31 5.4118 0.0267
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-mixed-models at r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>
>>> _______________________________________________
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>>>
>>
>
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