[R-sig-eco] ANCOVA with random effects for slope and intercept
peterhouk1 .
peterhouk at gmail.com
Tue Nov 4 14:11:28 CET 2014
Greetings Mollie -
Sure, the first general approach without explicitly telling R of my
grouping factor (not sure if that makes a difference, but second example
below does this). My best guess is that the full model has significance,
but the random effects model does not. However, simple partial
correlations show that within many grouping factors the relationship holds,
but not in all. If this were the case, why would our Corr = 0?
Mlme1<-lme(response ~ predictor,
random = ~1 + predictor | group_factor, data=mydata)
Linear mixed-effects model fit by REML
Data: mydata
AIC BIC logLik
74.80524 88.29622 -31.40262
Random effects:
Formula: ~1 + predictor | group_factor
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 1.106112e-05 (Intr)
predictor 1.577405e-10 0
Residual 3.492176e-01
Fixed effects: response ~ predictor
Value Std.Error DF t-value p-value
(Intercept) 0.29852308 0.09774997 60 3.053945 0.0034
predictor -0.03258404 0.00970759 60 -3.356554 0.0014
Correlation:
(Intr)
predictor -0.907
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.560560620 -0.688713759 -0.008759271 0.710084444 2.136060167
Number of Observations: 72
Number of Groups: 11
Second example where I explicitly tell R of the grouping factor:
mydata$fgroup_factor <- factor(mydata$group_factor)
Mlme1<-lme(response ~ predictor,
random = ~1 + predictor | fgroup_factor, data=mydata)
Linear mixed-effects model fit by REML
Data: mydata
AIC BIC logLik
74.80524 88.29622 -31.40262
Random effects:
Formula: ~1 + predictor | fgroup_factor
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 1.106112e-05 (Intr)
predictor 1.577405e-10 0
Residual 3.492176e-01
Fixed effects: response ~ predictor
Value Std.Error DF t-value p-value
(Intercept) 0.29852308 0.09774997 60 3.053945 0.0034
predictor -0.03258404 0.00970759 60 -3.356554 0.0014
Correlation:
(Intr)
predictor -0.907
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.560560620 -0.688713759 -0.008759271 0.710084444 2.136060167
Number of Observations: 72
Number of Groups: 11
On Tue, Nov 4, 2014 at 10:41 PM, Mollie Brooks <mbrooks at ufl.edu> wrote:
> Hi Dr. Houk,
>
> You say you get the same result from the lmer model as a linear model.
> Can you show us the summary of both models so that we might help you
> interpret it?
>
> Thanks,
> Mollie
> ------------------------
> Mollie Brooks, PhD
> Postdoctoral Researcher, Population Ecology Research Group
> Institute of Evolutionary Biology & Environmental Studies, University of
> Zürich
> http://www.popecol.org/team/mollie-brooks/
>
>
> On 4Nov 2014, at 13:30, peterhouk1 . <peterhouk at gmail.com> wrote:
>
> Greetings -
>
> Looking for advice and insight into ANCOVA models that allow for random
> slope and intercept effects. I have been using lme and lmer, but I can't
> seem to figure out where I've gone wrong. I have a grouping factor,
> predictor, and response. The response~predictor relationship should be
> nested within grouping factor, with a desire to allow for random effects of
> the slope and y-intercept. Data and my code are found below, greatly
> appreciate insight.
>
> I get the same response from both approaches:
>
> Mlme1<-lme(response~predictor,
> random = list(group_factor = ~1 | predictor), data=mydata)
>
> Second approach
>
> Mlmer1<-lmer(response~predictor + (1 + predictor|group_factor),
> data=mydata)
>
> Taking both approaches, I get the same results as a simple linear model
> that does not account for nesting within the group factor.
>
> mydata
>
> group_factor predictor response 1 13.75584744 -0.257259794 1 3.059971584
> 0.703113472 1 14.00447296 -0.260892287 1 6.705509001 -0.33269593 1
> 6.592728067 0.053814446 1 9.211047122 -0.002776485 2 12.50497696
> 0.22727311 2 7.059077939 0.18586719 2 6.617249805 -0.022951714 2
> 1.559557719 0.833397702 2 12.1962121 -0.57159955 2 11.29647955
> -0.963754818 2 15.54334219 0.489700014 3 8.93626518 -0.421471799 3
> 1.681675438 0.383260174 3 13.43826892 -0.330882653 3 10.8971089
> 0.47675377
> 3 7.600869443 -0.227033926 3 15.004137 0.104257183 4 12.61327214
> 0.131460302 4 3.788474342 0.079758467 4 14.37299098 0.294254076 4
> 3.564225024 -0.006581881 4 13.63301652 -0.498890965 5 4.36777119
> 0.90215334 5 5.150130473 0.098069832 5 7.875920526 0.468166528 5
> 13.91344257 -0.291551635 5 10.1061938 -0.35162982 5 13.32810817
> 0.133439251 5 15.64845612 -0.439418202 5 1.857959976 -0.468857357 5
> 11.31495202 -0.050371938 6 7.851162116 0.126588358 6 5.285251391
> 0.212699384 6 15.82353883 -0.202005195 6 11.90209318 0.34412633 6
> 5.547563146 -0.446233668 6 5.645270991 -0.303913602 6 8.668938138
> 0.268738394 7 15.66063395 -0.194882955 7 11.11830972 0.196309336 7
> 3.174056086 0.188199892 7 12.39006821 0.222261643 7 3.034014836
> 0.039201594 7 13.13551529 -0.451089511 8 9.990570964 -0.128411654 8
> 2.382739273 0.145897042 8 4.870002628 0.875223363 8 5.99541207
> 0.155610264
> 8 16.56247927 -0.461697164 8 14.58286908 -0.514244888 8 12.72137648
> -0.072376963 9 7.436676598 -0.306969441 9 5.196390457 -0.222063871 9
> 7.213670545 -0.411344562 9 9.243636562 0.095582355 9 7.029863529
> 0.31586562 9 6.074354561 0.459471079 9 8.282168564 0.632851023 9
> 13.85105359 -0.298326302 9 5.972744894 -0.180974348 9 14.09541111
> -0.084091553 10 5.304552826 0.265618935 10 4.089248332 0.270559629 10
> 7.482418571 0.049764287 10 16.44388769 0.008163962 10 13.78009474
> -0.594106812 11 10.34249094 -0.206619307 11 4.491492572 -0.207263365 11
> 7.350436798 0.083703414 11 8.38636562 0.330179258
>
> --
>
> Peter Houk, PhD
> Assistant Professor
> University of Guam Marine Laboratory
> http://www.guammarinelab.com/peterhouk.html
> www.pacmares.com
> www.micronesianfishing.com
>
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>
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>
--
Peter Houk, PhD
Assistant Professor
University of Guam Marine Laboratory
http://www.guammarinelab.com/peterhouk.html
www.pacmares.com
www.micronesianfishing.com
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