[R-sig-eco] ANCOVA with random effects for slope and intercept

[peterhouk1 .]

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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