[R] df pseudoreplication in lme model
Jeff Newmiller
jdnewmil at dcn.davis.CA.us
Fri Jan 30 08:29:38 CET 2015
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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]]
>
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