Difference lme4 and nlme
Notice that the first model has 27 levels for J and the second model has 465 levels for PARTY %in% J. That's the difference. If you do indeed want to have PARTY nested within J then your call to lmer should use the formula REVENUES ~ INCUMBENCY + (1|PARTY) + (1|J:PARTY)
On Wed, Feb 23, 2011 at 6:27 AM, Daniel <dmsilv at gmail.com> wrote:
Hello list, I'm just try to find out how can I produce the results using both packages. Perhaps I'm using different equation. Trailer model are consistent to Stata output using (tmixed REVENUES INCUMBENCY || J: || PARTY:) lme2 <- lmer(REVENUES~INCUMBENCY+(1|J)+(1|PARTY),data=data,na.action = "na.omit", REML=TRUE) Linear mixed model fit by REML Formula: REVENUES ~ INCUMBENCY + (1 | J) + (1 | PARTY) ?Data: data ?AIC ? BIC logLik deviance REMLdev 78123 78153 -39057 ? ?78154 ? 78113 Random effects: Groups ? Name ? ? ? ?Variance ? Std.Dev. J ? ? ? ?(Intercept) 9.6263e+08 ?31026 PARTY ? ?(Intercept) 1.7502e+09 ?41836 Residual ? ? ? ? ? ? 3.0534e+10 174741 Number of obs: 2894, groups: J, 27; PARTY, 27 Fixed effects: ? ? ? ? ? Estimate Std. Error t value (Intercept) ? ?34244 ? ? ?11657 ? 2.938 INCUMBENCY ? ?211495 ? ? ? 9536 ?22.178 Correlation of Fixed Effects: ? ? ? ? ?(Intr) INCUMBENCY -0.097 lme3 <- lme(REVENUES~INCUMBENCY, random=~1 |J/PARTY,data=data,na.action = "na.omit", REML=TRUE) Linear mixed-effects model fit by REML ?Data: data ?Log-restricted-likelihood: -39078.07 ?Fixed: REVENUES ~ INCUMBENCY (Intercept) ?INCUMBENCY ? 52469.19 ? 220521.74 Random effects: ?Formula: ~1 | J ? ? ? ?(Intercept) StdDev: ? ?25424.31 ?Formula: ~1 | PARTY %in% J ? ? ? ?(Intercept) Residual StdDev: ? ? 45574.5 173465.7 Number of Observations: 2894 Number of Groups: ? ? ? ? ? J PARTY %in% J ? ? ? ? ?27 ? ? ? ? ?465 -- Daniel Marcelino Skype: dmsilv http://sites.google.com/ ? ? ? ?[[alternative HTML version deleted]]
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