# lmer model doesn't return t-value [closed]

I'm currently running a time series analysis which requires me to fit lmer models to each of my data points. Here is my code :

# import data
dt$cond <- as.factor(dt$cond)
dt$sub <- as.factor(dt$sub)

# close file
H5close()

# apply model to each time point
fitted <- by(dt, dt$tp, function(x) lmer(size ~ cond + (1 + cond | sub), data = x))  In this model, cond is a 2 levels fixed effect factor and sub is a 14 levels random effect factor. For some reason, the model doesn't yield a t-value in its ouput : > fitted$0
Linear mixed model fit by REML ['lmerMod']
Formula: size ~ cond + (1 + cond | sub)
Data: x
REML criterion at convergence: -7694.797
Random effects:
Groups   Name        Std.Dev.  Corr
sub      (Intercept) 1.388e-02
cond1       1.169e-05 -1.00
Residual             7.922e-02
Number of obs: 3467, groups:  sub, 14
Fixed Effects:
(Intercept)        cond1
0.023743    -0.001154


Is it the output I should have expected? It seems that R doesn't take the difference between both levels of cond into account.

You can obtain t statistics for the fixed effects estimates by using the summary() function. For example:

require(lme4)
fm1 <- lmer ( Reaction ~ Days + ( Days | Subject ), sleepstudy )
summary(fm1)

Linear mixed model fit by REML ['lmerMod']
Formula: Reaction ~ Days + (Days | Subject)
Data: sleepstudy

REML criterion at convergence: 1743.6

Scaled residuals:
Min      1Q  Median      3Q     Max
-3.9536 -0.4634  0.0231  0.4634  5.1793

Random effects:
Groups   Name        Variance Std.Dev. Corr
Subject  (Intercept) 612.09   24.740
Days         35.07    5.922   0.07
Residual             654.94   25.592
Number of obs: 180, groups:  Subject, 18

Fixed effects:
Estimate Std. Error t value
(Intercept)  251.405      6.825   36.84
Days          10.467      1.546    6.77

Correlation of Fixed Effects:
(Intr)
Days -0.138

• The output above is already a summary. But for some reason, I have no t-value in mine. – Crolle May 18 '16 at 11:36
• @Crolle, no your code just outputs the model object itself. summary() is different. Use summary(fitted) instead – Robert Long May 18 '16 at 12:04
• actually you were almost there : summary(fitted[]) works just fine. Not used to these brackets yet! – Crolle May 18 '16 at 12:21