[R-sig-ME] [R] nlme package: changing reference for varIdent parameter estimates in summary.gls

[ONKELINX, Thierry]

Dear John,

R-sig-mixed-models is more suited for this kind of questions. All follow-up mail should be posted only to that mailing list.

It seems like varIdent() by default relevels the grouping factor and that the user cannot control this.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op inbo.be
www.inbo.be

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~ Sir Ronald Aylmer Fisher

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-----Oorspronkelijk bericht-----
Van: R-help [mailto:r-help-bounces op r-project.org] Namens Kornak, John
Verzonden: dinsdag 23 december 2014 22:29
Aan: r-help op R-project.org
Onderwerp: [R] nlme package: changing reference for varIdent parameter estimates in summary.gls


Dear R experts,

I am running gls models with heterogeneous group variances using the glm function in the nlme package with varIdent weights. I am interested in controlling the baseline level for the group variance function standard deviation estimates by applying the relevel function to the group variable. When I use relevel, the baseline level in the coefficient table changes as expected, but the baseline level does not change for the variance function table; for example, see the fitted models gls1 vs. gls2 in the contrived example below. Does anyone have a suggestion as to how I can change the baseline level for the variance function output please? In addition to the example below, I have tried specifying the value argument as per the varIdent help page, e.g. variIdent(c(no=0.5), form = ~1|group) and have google searched / checked help pages for solutions without success.

I am running R version 3.1.0 on an iMac OSX v. 10.9.5

Thank you in advance

John Kornak

> library(nlme)
> group <- factor(c(rep("no",20),rep("yes",20)))
> set.seed(2)
> outcome <- c(rnorm(20,0,2),rnorm(20,5,4)) dataTest <-
> data.frame(outcome,group)

# Original model fit before releveling
> gls1 <- gls(outcome ~ group, weights=varIdent(form = ~1|group),
> data=dataTest)
> summary(gls1)

  snip

Variance function:
 Structure: Different standard deviations per stratum
 Formula: ~1 | group
 Parameter estimates:
     no     yes
1.00000 2.23034

Coefficients:
               Value Std.Error  t-value p-value
(Intercept) 0.390922 0.4734001 0.825775  0.4141
groupyes    4.607951 1.1571140 3.982279  0.0003

  snip

Residual standard error: 2.11711
Degrees of freedom: 40 total; 38 residual


# relevel the group so that  yes  is the reference
> dataTest$group <- relevel(dataTest$group,"yes")
> gls2 <- gls(outcome ~ group, weights=varIdent(form = ~1|group),
> data=dataTest)
> summary(gls2)

  snip

Variance function:
 Structure: Different standard deviations per stratum
 Formula: ~1 | group
 Parameter estimates:
     no     yes
1.00000 2.23034                 ###  no" is still the reference group here for the variance function

Coefficients:
                Value Std.Error   t-value p-value
(Intercept)  4.998873  1.055843  4.734484   0e+00
groupno     -4.607951  1.157114 -3.982279   3e-04  #  yes  has become the reference for the coefficients

   snip

Residual standard error: 2.11711
Degrees of freedom: 40 total; 38 residual
>

---------------------------------------------------
John Kornak, PhD
Associate Professor in Residence
Department of Epidemiology and Biostatistics University of California, San Francisco Mission Hall: Global Health & Clinical Sciences Building
550 16th St, 2nd floor, Box #0560
San Francisco, CA 94158-2549
Tel: 415-514-8028
Fax: 415-514-8150
Email: john.kornak op ucsf.edu<mailto:john.kornak op ucsf.edu>




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