[R-sig-ME] poly() and rcs()?
Vito Muggeo (UniPa)
vito.muggeo at unipa.it
Mon Nov 16 16:58:34 CET 2009
dear Antoine,
I don't know the rcs() function (from which package..?)
BTW, why do you want add a linear term? I presume it is "included" in
rcs().. I mean the linear term you want to include is linear combination
of two bases (colums) returned by rcs(). So if you want include it, you
should remove one column of rcs()...
Also the parameter estimates you get depend on parameterization of
poly() and rcs(). Using basis splines it is meaningless to test for some
spline coefficient to be different from zero.. I imagine, again I don't
know rcs(), the same holds for rcs().
If you want to simplify your model I suggest to use differences in log
likelihoods
Hope this helps you,
vito
Antoine Tremblay ha scritto:
> Dear all,
>
> Here is a question regarding the difference between using poly() and rcs().
>
> If you fit a model using poly(), as shown below, the summary returns
> statistics for the term "linear" term,
> poly(TrialContinuous,2,raw=TRUE)1, and statistics for the quadratic,
> poly(TrialContinuous,2,raw=TRUE)2. Here, the summary tells us that the
> "linear" and quadratic terms are significant, that the interaction
> between the linear term and the factor variable FreqGroup is
> significant, but that the interaction between the quadratic and
> FreqGroup is not significant.
>
>> m1=lmer(LogRT~poly(TrialContinuous,2,raw=TRUE)*FreqGroup+(1|Subject),data=dat.li)
>> print(m1,corr=F)
> Linear mixed model fit by REML
> Formula: LogRT ~ poly(TrialContinuous, 2, raw = TRUE) * FreqGroup + (1
> | Subject)
> Data: dat.li
> AIC BIC logLik deviance REMLdev
> 17199 17266 -8592 17056 17183
> Random effects:
> Groups Name Variance Std.Dev.
> Subject (Intercept) 0.014825 0.12176
> Residual 0.101560 0.31868
> Number of obs: 30742, groups: Subject, 25
>
> Fixed effects:
>
> Estimate Std. Error t value
> (Intercept)
> 6.307e+00 2.456e-02 256.85
> poly(TrialContinuous, 2, raw = TRUE)1 -1.570e-04
> 4.639e-06 -33.84
> poly(TrialContinuous, 2, raw = TRUE)2 9.779e-08
> 1.120e-08 8.73
> FreqGroupLow
> 3.299e-02 6.085e-03 5.42
> poly(TrialContinuous, 2, raw = TRUE)1:FreqGroupLow 1.769e-05
> 8.736e-06 2.03
> poly(TrialContinuous, 2, raw = TRUE)2:FreqGroupLow -1.168e-08
> 2.123e-08 -0.55
>
> Now, if you use rcs() instead, the summary return statistics for the
> first and second splines, as shown below. We see that the 2 splines
> are significant, but there is no interaction between any of the
> splines and FreqGroup. This reflects what we see in the model fitted
> with poly(), where the the linear and quadratic terms were significant
> but the quadratic by FreqGroup interaction was not significant. What's
> missing in the model fitted with rcs() is the linear term.
>
>> m2=lmer(LogRT~rcs(TrialContinuous,3)*FreqGroup+(1|Subject),data=dat.li)
>> print(m2,corr=F)
> Linear mixed model fit by REML
> Formula: LogRT ~ rcs(TrialContinuous, 3) * FreqGroup + (1 | Subject)
> Data: dat.li
> AIC BIC logLik deviance REMLdev
> 17180 17247 -8582 17065 17164
> Random effects:
> Groups Name Variance Std.Dev.
> Subject (Intercept) 0.014826 0.12176
> Residual 0.101592 0.31874
> Number of obs: 30742, groups: Subject, 25
>
> Fixed effects:
>
> Estimate Std. Error t value
> (Intercept)
> 6.286e+00 2.498e-02 251.62
> rcs(TrialContinuous, 3)TrialContinuous
> -2.538e-04 1.253e-05 -20.26
> rcs(TrialContinuous, 3)TrialContinuous'
> 1.285e-04 1.547e-05 8.31
> FreqGroupLow
> 3.599e-02 1.061e-02 3.39
> rcs(TrialContinuous, 3)TrialContinuous:FreqGroupLow 3.018e-05
> 2.379e-05 1.27
> rcs(TrialContinuous, 3)TrialContinuous':FreqGroupLow -1.660e-05
> 2.936e-05 -0.57
>
> I tried adding a linear term "TrialContinuous" to the model fitted
> with rcs(), as shown below, but that doesn't work, the model is not
> positive definite.
>
>> m3=lmer(LogRT ~ (TrialContinuous + rcs(TrialContinuous,3)) * FreqGroup + (1|Subject), data = dat.li)
>> Error in mer_finalize(ans) : Downdated X'X is not positive definite, 7.
>
> The question is where is the linear term (if there is one)?
> Is it hidden somewhere? Or is it simply not a question to ask when
> using rcs() to fit models?
> Should I use poly() rather than rcs()?
> Are there situations where you would want to use poly() over rcs() and
> vice versa?
>
> Additionally, how should we interpret the statistics for the splines?
> Does their significance mean that the slopes in the splines are
> significantly different than 0?
> In the case of the second spline, does it's significance mean that it
> is significantly different than the first spline?
>
> Thank you very much for your time and efforts. Your help is greatly appreciated.
> Sincerely
>
> --
> Antoine Tremblay
> Department of Neuroscience
> Georgetown University
> Washington DC
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
--
====================================
Vito M.R. Muggeo
Dip.to Sc Statist e Matem `Vianelli'
Università di Palermo
viale delle Scienze, edificio 13
90128 Palermo - ITALY
tel: 091 6626240
fax: 091 485726/485612
http://dssm.unipa.it/vmuggeo
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