How to obtain the p-value of random variable in a lme4 mixed model?

I use lme4 in R to fit the mixed model

model<- glmer(responcevariable~ fixedvariable1 + fixedvariable2 +fixedvariable3 + fixedvariable+ (1|randomvariable1)+ (1|randomvariable2)+(1|randomvariable3), data=Dataset, family=binomial)

And I get

Data: Dataset

     AIC      BIC          logLik deviance df.resid 
  5005.8   5072.2  -2492.9   4985.8     5612 

Scaled residuals: 
    Min      1Q       Median      3Q     Max 
-3.5750 -0.4896 -0.2675  0.5618 11.6250 

Random effects:
                 Groups          Name        Variance Std.Dev.
 randomvariable1         (Intercept) 0.007826 0.08847 
 randomvariable2         (Intercept) 1.366346 1.16891 
 randomvariable3         (Intercept) 0.011879 0.10899 
Number of obs: 5622, groups:   randomvariable1, 49;  randomvariable2, 5;  randomvariable3, 4

Fixed effects:
                 Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -11.98557    0.66851 -17.929  < 2e-16 ***
fixedvariable1a      -0.31754    0.09732  -3.263  0.00110 ** 
fixedvariable1b      0.26805    0.08614   3.112  0.00186 ** 
fixedvariable2a  -0.61098    0.09521  -6.417 1.39e-10 ***
fixedvariable2b   -0.50402    0.10526  -4.788 1.68e-06 ***
fixedvariable3     7.57652    0.26308  28.799  < 2e-16 ***
fixedvariable4       -0.30746    0.07852  -3.915 9.03e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

How can I know that the effect of random variable is significant?