[R] lme Random Effects and Covariates
patze003
patze003 at umn.edu
Thu Nov 18 18:22:11 CET 2010
1. I'm attempting to test for Random Effects. I've grouped the data on
subject (grid) but want to use lme to build the model without subject as a
RE then add it and do anova between the 2 models. This is the result I get
and it appears it's adding Random Effects.
tmp.dat4 <- groupedData(Trials ~ 1 | grid, data = tmp.dat4)
mod2a <- lme(Trials ~ factor(group_id) + reversal, data = tmp.dat4,
na.action = na.omit, method = "REML")
> summary(mod2a)
Linear mixed-effects model fit by REML
Data: tmp.dat4
AIC BIC logLik
4544.054 4587.718 -2262.027
Random effects:
Formula: ~factor(group_id) + reversal | grid
Structure: General positive-definite
StdDev Corr
(Intercept) 10.505303 (Intr) fc(_)2
factor(group_id)2 9.830679 -0.778
reversal2 7.106839 -0.275 0.023
Residual 9.995963
Fixed effects: Trials ~ factor(group_id) + reversal
Value Std.Error DF t-value p-value
(Intercept) 23.275874 1.876185 510 12.405960 0e+00
factor(group_id)2 -7.639842 2.151004 72 -3.551757 7e-04
reversal2 7.681495 1.206858 510 6.364869 0e+00
Correlation:
(Intr) fc(_)2
factor(group_id)2 -0.785
reversal2 -0.308 -0.015
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.6884393 -0.5059063 -0.1892908 0.4944976 2.8477377
Number of Observations: 585
Number of Groups: 74
2. Secondly is this the correct way to add covariates (such as age).
mod2i <- lme(Trials ~ factor(group_id)*factor(reversal) * age, data =
tmp.dat4, random = ~ 1 | grid, na.action = na.omit, method = "ML")
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