[R-sig-ME] Mixed Model for Travel Distance
Dimitris Rizopoulos
d.rizopoulos at erasmusmc.nl
Mon Mar 30 20:26:38 CEST 2009
well, if you're only interested in the fixed effects, then you can also
use a Generalized Estimating Equations approach that does not make a
parametric assumption for the distribution of your error terms, e.g.,
have a look at the 'geepack' package. Furthermore and in case it is
relevant for your application, in GEE the estimated parameters will have
a population interpretation, contrary to the GLMMs approach in which
they will have a conditional on the random effects interpretation.
I hope it helps.
Best,
Dimitris
Chuck Cleland wrote:
> Hello:
> I am attempting to model the distance that clients travel to a
> treatment program. There are 14385 clients nested in 83 treatment
> programs (the grouping factor). The raw data are in miles driven from
> the client's residence to the treatment program. A natural logarithm
> transformation of miles driven works well to reduce the positive skew in
> miles driven. I fit a model with lme() that looks like this:
>
> Linear mixed-effects model fit by REML
> Data: dist.df
> AIC BIC logLik
> 38145.37 38319.54 -19049.68
>
> Random effects:
> Formula: ~1 | PROGRAM
> (Intercept) Residual
> StdDev: 0.4268969 0.8988483
>
> Fixed effects: log(DIST5DZ + 1) ~ QUAD + BEAL_TRI + log(RZIPAREA + 1) +
> log(PZIPAREA + 1) + AGE.TRI + P3GEND + P5RACEX + EMPLD + P13REASN +
> METHFST + URGE.DI + WDRAW.DI + RX_30 + P7HR30
>
> Value Std.Error DF t-value p-value
> (Intercept) 1.2235603 0.13153231 14288 9.30236 0.0000
> QUADSouthEast 0.2100666 0.13200891 76 1.59131 0.1157
> QUADMidWest 0.2760390 0.15709516 76 1.75715 0.0829
> QUADWest -0.1655914 0.15536003 76 -1.06586 0.2899
> BEAL_TRI250K-1M -0.0264939 0.11724713 76 -0.22597 0.8218
> BEAL_TRI<250K -0.0965256 0.16399464 76 -0.58859 0.5579
> log(RZIPAREA + 1) 0.2965304 0.00757138 14288 39.16463 0.0000
> log(PZIPAREA + 1) -0.0042061 0.04413826 76 -0.09529 0.9243
> AGE.TRI30-43 -0.0309444 0.01789442 14288 -1.72927 0.0838
> AGE.TRI43-83 -0.1281177 0.02168648 14288 -5.90772 0.0000
> P3GENDFemale -0.0195289 0.01632703 14288 -1.19611 0.2317
> P5RACEXLatino -0.3527584 0.02904416 14288 -12.14559 0.0000
> P5RACEXBlack -0.5485861 0.03306146 14288 -16.59292 0.0000
> P5RACEXOther -0.1580669 0.04811350 14288 -3.28529 0.0010
> EMPLDYes 0.0098856 0.01650635 14288 0.59890 0.5493
> P13REASNYes -0.0095057 0.01650128 14288 -0.57606 0.5646
> METHFSTYes 0.0073478 0.01672948 14288 0.43921 0.6605
> URGE.DI Strong-VeryStrong -0.0272769 0.02400068 14288 -1.13651 0.2558
> WDRAW.DISevere-VerySevere 0.0012054 0.01810067 14288 0.06659 0.9469
> RX_30Yes 0.0934411 0.02165389 14288 4.31521 0.0000
> P7HR30Yes -0.0408189 0.02256842 14288 -1.80867 0.0705
>
> Standardized Within-Group Residuals:
> Min Q1 Med Q3 Max
> -4.17921640 -0.49875991 0.08672984 0.59020542 4.54644432
>
> Number of Observations: 14385
> Number of Groups: 83
>
> I would like to summarize the fixed effects in terms of miles rather
> than log(miles + 1). How can that be done? Are there common
> generalized linear mixed models for miles driven that would avoid the
> transformation and allow effects to be presented in miles?
>
> thanks,
>
> Chuck
>
--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
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