id:268levels
group:10levels

I used the gamm4 function of the package gamm4 to fit the following model.

gamm4_1 <- gamm4(y~z1+z2+z3+age+height+time+bmi,random=~(1|id)+(1|group),data=data,family=binomial)

In this case, the result is given as a list of mer and gam, but the standard error of mer is different from the standard error of gam.

summary(gamm4_1$mer)$coefficients
                Estimate  Std. Error     z value      Pr(>|z|)
X(Intercept) -5.59491005 0.001737789 -3219.55651  0.000000e+00
Xz1          -0.40110690 0.001739238  -230.62215  0.000000e+00
Xz2          -0.75774666 0.001737744  -436.05193  0.000000e+00
Xz3          -0.16008244 0.001737414   -92.13836  0.000000e+00
Xage         -0.05760610 0.001636062   -35.21022 1.394896e-271
Xheight       0.06065821 0.001120702    54.12520  0.000000e+00
Xtime         0.08710019 0.001737386    50.13290  0.000000e+00
Xbmi          0.04417464 0.001732751    25.49393 2.301696e-143

summary(gamm4_1$gam)

Family: binomial 
Link function: logit 

Formula:
y ~ z1 + z2 + z3 + age + height + time + bmi

Parametric coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -5.59491    2.69546  -2.076  0.03792 *  
z1          -0.40111    0.17302  -2.318  0.02044 *  
z2          -0.75775    0.37128  -2.041  0.04126 *  
z3          -0.16008    0.34723  -0.461  0.64478    
age         -0.05761    0.01850  -3.115  0.00184 ** 
height       0.06066    0.01329   4.563 5.03e-06 ***
time         0.08710    0.06609   1.318  0.18750    
bmi          0.04417    0.03350   1.319  0.18733    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Why are the coefficients the same, but the standard error values are completely different?