Hi, R-users: Last week I send a request for help to this list. I have receive until now two kindly responses from Spencer Graves and Renauld Lancelot. They both point interesting things to fit an adequate model to my data but unfortunately it persists without a satisfactory solution.
I propose again the same problem but using with a different dataset (Assay), taken from Pinheiro and Bates' book on page 163, that is relevant with crossed random effects. I have fitted the same model (p. 165) >fmAssay <- lme(logDens ~ sample * dilut, Assay, random=pdBlocked(list(, pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) ) and the results with "anova" function (p. 166) are numDF denDF F-value p-value (Intercept) 1 29 537.6294 <.0001 sample 5 29 11.2103 <.0001 dilut 4 29 420.5458 <.0001 sample:dilut 20 29 1.6072 0.1192 The problem is that with this approach one obtains correct F-values, but using a common residual term for DenDF that is a combination of (Block + Block:sample + Block:dilut). Then probability values are different to that obtained when we used the classical AOV funtion to fit the same model, because in this case each term is mapped with a error term (so "sample" uses "Block:sample", "dilut" uses "Block:dilut", and "sample:dilut" uses "Block:sample:dilut"): >mod<-aov(logDens ~ sample*dilut + Error(Block+Block/sample+Block/dilut), data=Assay) >summary(mod) Error: Block Df Sum Sq Mean Sq F value Pr(>F) Residuals 1 0.0083115 0.0083115 Error: Block:sample Df Sum Sq Mean Sq F value Pr(>F) sample 5 0.276153 0.055231 11.213 0.009522 Residuals 5 0.024627 0.004925 Error: Block:dilut Df Sum Sq Mean Sq F value Pr(>F) dilut 4 3.7491 0.9373 420.79 1.684e-05 Residuals 4 0.0089 0.0022 Error: Within Df Sum Sq Mean Sq F value Pr(>F) sample:dilut 20 0.055525 0.002776 1.6069 0.1486 Residuals 20 0.034555 0.001728 Obviously, the interest on linear mixed effects is open with the possibility of modeling with correlated and/or heterocedastic errors, and this end cannot be pursued with AOV function. Summarizing, the problem is that I have not found a way to obtain with NLME the same results (DF, F-ratios and probabilities) that I get with AOV and multistratum errors. Is this an inconvenience of program?, probably due to the impossibility to use multiple nested arguments as random(~1|Block/sample+dilut) or random(~1|Block/sample*dilut) SAS MIXED can also fit these data and obtain correct results by means of a combination of "random" and "repeated" arguments: model = sample dilut sample*dilut; random = Block sample*Block dilut*Block; repeated /type=cs Sub=Block; Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F sample 5 5 11.21 0.0095 dilut 4 4 420.79 <.0001 sample*dilut 20 20 1.61 0.1486 May be possible obtain the same results with NLME function? I would appreciate any kind of help. Best regards, Manuel Ato University of Murcia Spain e-mail: [EMAIL PROTECTED] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help