[R-sig-ME] mismatch between R lme and SPSS mixed model

[Mengni Zhang]

I'm using linear mixed model on an ecology dataset. I want to test whether location and year has an impact on bird egg which were sampled from different marshes and nests. I found a slight mismatch on the results between the R function "lme" and the SPSS option "Mixed Model". Does anyone know the potential reasons for the mismatch? The differences on p-values are not big. For example, the p-value of fixed factor Year is 0.031 from R and 0.029 from SPSS, and the p-value of fixed factor Location is 0.51 from R and 0.43 from SPSS.

My R code is: model1 <- lme(Results~Location+Year, data=data, random=~1|Marsh/NEST)

My SPSS syntax is set as below:
*Generalized Linear Mixed Models.
GENLINMIXED
  /DATA_STRUCTURE SUBJECTS=Marsh*NEST*EGG
  /FIELDS TARGET=Results TRIALS=NONE OFFSET=NONE
  /TARGET_OPTIONS DISTRIBUTION=NORMAL LINK=IDENTITY
  /FIXED  EFFECTS=Location Year USE_INTERCEPT=TRUE
  /RANDOM USE_INTERCEPT=TRUE SUBJECTS=Marsh COVARIANCE_TYPE=VARIANCE_COMPONENTS
  /RANDOM USE_INTERCEPT=TRUE SUBJECTS=Marsh*NEST COVARIANCE_TYPE=VARIANCE_COMPONENTS
  /BUILD_OPTIONS TARGET_CATEGORY_ORDER=ASCENDING INPUTS_CATEGORY_ORDER=ASCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=MODEL PCONVERGE=0.000001(ABSOLUTE) SCORING=0 SINGULAR=0.000000000001
  /EMMEANS TABLES=Location COMPARE=Location CONTRAST=PAIRWISE
   /EMMEANS TABLES=Year COMPARE=Year CONTRAST=PAIRWISE
  /EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=SEQSIDAK.

Thank you very much!




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