lme function to obtain pvalue for fixed effect

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>
escribi?:

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