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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))

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

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
...

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))

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

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
...

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 article. I thought by getting the random effect and the standard error that would be possible?

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 article. I thought by getting the random effect and the standard error that would be possible?

E.g

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
...

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?

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 article. I thought by getting the random effect and the standard error that would be possible?