I have conducted a linear mixed effect model with the ```nlme``` package in R.

```
lmm.reg.slope <- lme(V1~ V2+V3+V4+V5+V6+V7+V8+V9+V10, data = data, random = V2+V3+V4+V5+V6+V7+V8+V9+V10|regions, method = 'ML', control = lmeControl(opt = "optim", msMaxIter=1000, maxIter = 1000, msMaxEval = 1000))
```

When I look at the random effect, I get the difference between the individual intercept and the global intercept. I remember it as the standard deviation of the random effect could also be retrieve if I somehow changed it to a data frame or similar. But it does not work.

```
ranef(lmm.reg.slope)

      (Intercept)
AK    9.815204e-09
NY   -6.132803e-09
MIN   2.393367e-08
WIS  -1.884604e-08
CA    1.469633e-08
WAS  -2.454771e-09
MAS  -1.397460e-09
CT    7.225472e-09
FL   -1.694695e-08
IL   -1.233468e-09
OH   -2.637688e-08
IO    7.647110e-09
TX   -2.296820e-09
AZ    1.448242e-08
NC   -2.484795e-09
SC    3.697730e-10
```

How can I retrieve the standard error related to the random effects for each state?

Edit:
I simply want to do this "*We tested for differences in effect sizes among ecoregions using Tukey–Kramer post hoc analysis for multiple comparisons in the package 'emmeans'*", as seen in [this][1] article. I thought by getting the random effect and the standard error that would be possible?

E.g
```emmeans(lmm.reg.slope, pairwise ~ states$Intercept

contrast                      estimate     SE     df     t-ratio     p-value
AK$Intercept - NY$Intercept   0.97831      2.22   1      0.288       0.0137
AK$Intercept - MIN$Intercept  0.01038      0.96   1      0.01        0.5101
...
```

  [1]: https://www.nature.com/articles/s41558-020-00920-8