[R-sig-ME] Significance and lmer

[Adam D. I. Kramer]

Dear colleagues,

Please consider this series of commands:

a <- lmer(log(stddiff+.1539) ~ pred + m*v + option + (option|studyID),
data=r1, subset=option>1, REML=FALSE)

b <- update(a, . ~ . - pred)

anova(a,b)

...am I mistaken in thinking that the latter command will produce a test of
whether "pred" is a significant predictor of log(stddiff+.1539)? I am
concerned because of the results:

> coef(a)
                  Estimate   Std. Error    t value
(Intercept) -0.6608993664 0.1591862808 -4.1517357
pred         0.0879255592 0.1715599954  0.5125062
ml           0.0656916428 0.1173308419  0.5598838
vl          -0.0980204413 0.1276648229 -0.7677952
option       0.0003197903 0.0008134259  0.3931400
ml:vl       -0.1890574941 0.1710443092 -1.1053130

...note a t-value of 0.51 for this item...very small! ...but anova(a,b) produces this:

Models:
b: log(stddiff + 0.1539) ~ m + v + option + (option | studyID) +
b:     m:v
a: log(stddiff + 0.1539) ~ pred + m * v + option + (option | studyID)
   Df    AIC    BIC  logLik  Chisq Chi Df Pr(>Chisq)
b  9 3969.2 4019.1 -1975.6
a 10 3955.9 4011.2 -1967.9 15.345      1  8.954e-05 ***
---

...a significant result completely unrelated to the t-value. My
interpretation of this would be that we have no good evidence that the
estimate for 'pred' is nonzero, but including pred in the model improves
prediction.

I think I must be missing something here--I would appreciate anyone's input
on what that "something" is.

Cordially,
--
Adam D. I. Kramer
Ph.D. Candidate, Social Psychology
University of Oregon
adik-rhelp at ilovebacon.org