gls() for unequal variances? [🇷 for BE/BA]
Dear Helmut,
What about:
This gives (20 subjects bear data) :
But astonishing :
❝ ... I would go with Welch-tests, because ANOVA (or lm and lme) assume
❝ homoscedasticity.
What about:
require(nlme) # HS: added, because not loaded by default
# must use gls() because lme() does not allow for no random effect
glsModel <- gls(lnAUC0t ~ drug, weights=varIdent(form=~1|drug), data=TotalData)
summary(glsModel)
This gives (20 subjects bear data) :
Generalized least squares fit by REML
Model: lnAUC0t ~ drug
Data: TotalData
AIC BIC logLik
8.491722 12.05321 -0.245861
Variance function:
Structure: Different standard deviations per stratum
Formula: ~1 | drug
Parameter estimates:
1 2
1.0000000 0.4173818 (seems to give us different variances as portion of residual, hurrrrah)
Coefficients:
Value Std.Error t-value p-value
(Intercept) 7.10189 0.1056507 67.22045 0.0000
drug2 0.01594 0.1144841 0.13923 0.8908
But astonishing :
intervals(glsModel, level=0.9)
Approximate 90% confidence intervals
Coefficients:
lower est. upper
(Intercept) 6.9186849 7.10189 7.2850951
drug2 -0.1825826 0.01594 0.2144626
#back-transformed
T vs. R 83.31 101.61 123.92 (same as without weights or lm() )
—
Regards,
Detlew
Regards,
Detlew
Complete thread:
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- gls() for unequal variances?d_labes 2010-04-26 16:36
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- Sandwich - Simsalabim d_labes 2010-04-28 10:58
- gls() for unequal variances?d_labes 2010-04-26 16:36
- Equal variances yjlee168 2010-04-26 09:22
- Dataset yjlee168 2010-04-25 22:44
- Validating vs. WinNonlin... Helmut 2010-04-24 00:28
- Modelling Parallel bears yjlee168 2010-04-23 21:14
- Modelling Parallel bears d_labes 2010-04-23 09:12
- Parallel bears meeting at random in infinity ElMaestro 2010-04-22 12:53