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List:       r-sig-mixed-models
Subject:    [R-sig-ME] nlme package: changing reference for varIdent parameter estimates in summary.gls
From:       "Kornak, John" <john.kornak () ucsf ! edu>
Date:       2014-12-24 22:34:09
Message-ID: 5D011804-2D94-49DE-AC5A-48DB9578DE0A () ucsf ! edu
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Dear R mixed-effects modeling 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 suggested hack as to how I can change the \
baseline level for the variance function output please? One hack I have made work is \
to change the order of the rows in the data (see final part of example), but that \
seems rather ugly and will get painful for the real data I am working with for which \
I need to reset many different reference levels.

I have google searched / checked help pages for other solutions without success.

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

Thank you in advance and happy holidays!

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
> 


# Reorganize the data so that the group == “yes" entries come first
> dataTest2 <- dataTest[c(21:40,1:20), ]
> gls3 <- gls(outcome ~ group, weights=varIdent(value = c(yes = 0.5), form = \
> ~1|group), data=dataTest2) summary(gls3)

— snip —

Variance function:
 Structure: Different standard deviations per stratum
 Formula: ~1 | group
 Parameter estimates:
      yes        no
1.0000000 0.4483626           ## now I have the desired “yes” as reference level

Coefficients:
                Value Std.Error   t-value p-value
(Intercept)  4.998873  1.055843  4.734487   0e+00
groupno     -4.607951  1.157113 -3.982281   3e-04   # and “yes” also reference level \
here

— snip —

Residual standard error: 4.721872
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@ucsf.edu<mailto:john.kornak@ucsf.edu>



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