[R] df pseudoreplication in lme model

[Jeff Newmiller]

1) Wrong list... this belongs on R-sig-mixed, per the Posting Guide.

2) These lists are plain text only. HTML ends up getting corrupted. Please figure out your email client to avoid confusion.

3) It is difficult to troubleshoot with just the output... you are requested to make a minimal example that illustrates the type of data you are working with as well as your code.
---------------------------------------------------------------------------
Jeff Newmiller                        The     .....       .....  Go Live...
DCN:<jdnewmil at dcn.davis.ca.us>        Basics: ##.#.       ##.#.  Live Go...
                                      Live:   OO#.. Dead: OO#..  Playing
Research Engineer (Solar/Batteries            O.O#.       #.O#.  with
/Software/Embedded Controllers)               .OO#.       .OO#.  rocks...1k
--------------------------------------------------------------------------- 
Sent from my phone. Please excuse my brevity.

On January 29, 2015 3:09:24 PM PST, Lauren Meyer <lauren.meyer90 at gmail.com> wrote:
>Hello, I am trying to assess weather or not my df are pseudoreplicated
>in my
>lme model.
>
>my study was undertaken on five fish (labeled PC) each tested in two
>replicates(REP), across each combination of three treatments HOM, C18
>and
>CU, each of which had two levels; HOM(SON, BLD),C18 SML, BIG), CU (YES,
>NO).
>The variable we are assessing is the amount of toxin extracted (TOX1).
>Also,
>some data is missing, and has already been removed.
>
>Here is the model I am using and output:
>model<- lme(TOX1~HOM*C18*CU, random=~1|PC/REP, data=Data4, method="ML")
>Linear mixed-effects model fit by maximum likelihood
> Data: Data4
>      AIC      BIC    logLik
>  220.603 244.5213 -99.30151
>
>Random effects:
> Formula: ~1 | PC
>        (Intercept)
>StdDev:    1.574392
>
> Formula: ~1 | REP %in% PC
>         (Intercept)  Residual
>StdDev: 0.0001356862 0.9724221
>
>Fixed effects: TOX1 ~ HOM * C18 * CU
>                        Value Std.Error DF   t-value p-value
>(Intercept)          3.729044 0.8204586 48  4.545073  0.0000
>HOMSON               0.423330 0.5175211 48  0.817995  0.4174
>C18SML              -1.160060 0.5475120 48 -2.118784  0.0393
>CUYES                0.419067 0.4643966 48  0.902391  0.3714
>HOMSON:C18SML       -0.645514 1.0385203 48 -0.621571  0.5372
>HOMSON:CUYES        -0.436996 0.6953361 48 -0.628467  0.5327
>C18SML:CUYES        -0.137128 0.7179371 48 -0.191003  0.8493
>HOMSON:C18SML:CUYES  0.313720 1.2287607 48  0.255314  0.7996
> Correlation:
>                    (Intr) HOMSON C18SML CUYES  HOMSON:C18SML HOMSON:CU
>C18SML:
>HOMSON              -0.254
>C18SML              -0.240  0.361
>CUYES               -0.283  0.449  0.424
>HOMSON:C18SML        0.127 -0.472 -0.550 -0.224
>HOMSON:CUYES         0.189 -0.744 -0.268 -0.668  0.351
>C18SML:CUYES         0.183 -0.275 -0.763 -0.647  0.419         0.421
>HOMSON:C18SML:CUYES -0.107  0.399  0.464  0.378 -0.845        -0.549
>-0.599
>
>Standardized Within-Group Residuals:
>         Min           Q1          Med           Q3          Max
>-4.090875567 -0.433368736 -0.007582723  0.498944076  2.603341469
>
>Number of Observations: 65
>Number of Groups:
>         PC REP %in% PC
>          5          10
>
>As the three way interaction as well as all of the two way interactions
>were
>deemed non-significant, I simplified the model, removing first the
>three way
>interaction, then each two way interaction in turn, comparing each
>subsequent model with the previous one using an ANOVA as per the
>example in
>the R book on pg. 632. I have a final model of:
>
>> model5<- lme(TOX1~HOM+C18+CU, random=~1|PC/REP, data=Data4,
>method="ML")
>> summary(model5)
>Linear mixed-effects model fit by maximum likelihood
> Data: Data4
>       AIC      BIC   logLik
>  214.0699 229.2906 -100.035
>
>Random effects:
> Formula: ~1 | PC
>        (Intercept)
>StdDev:    1.567082
>
> Formula: ~1 | REP %in% PC
>         (Intercept)  Residual
>StdDev: 0.0005730032 0.9847228
>
>Fixed effects: TOX1 ~ HOM + C18 + CU
>                Value Std.Error DF   t-value p-value
>(Intercept)  3.927801 0.7623505 52  5.152225  0.0000
>HOMSON      -0.028203 0.2603204 52 -0.108341  0.9141
>C18SML      -1.437095 0.2605651 52 -5.515302  0.0000
>CUYES        0.214583 0.2675196 52  0.802122  0.4261
> Correlation:
>       (Intr) HOMSON C18SML
>HOMSON -0.125
>C18SML -0.114  0.047
>CUYES  -0.167 -0.152 -0.184
>
>Standardized Within-Group Residuals:
>         Min           Q1          Med           Q3          Max
>-4.212407492 -0.433128656  0.003244622  0.618291014  2.578288257
>
>Number of Observations: 65
>Number of Groups:
>         PC REP %in% PC
>          5          10
>
>However, I am unsure if these Df are pseudoreplicated and would like
>some
>help in how to determine if this is the case. Thank you
>
>	[[alternative HTML version deleted]]
>
>______________________________________________
>R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide
>http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.