[R-sig-ME] lme function to obtain pvalue for fixed effect

[li li]

You are right! Then I am not sure whether the test in ANOVA
corresponding to a continuous variable makes sense.
    Hanna

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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