| To: | s-news@lists.biostat.wustl.edu |
|---|---|
| Subject: | mulitcomp and covariates |
| From: | Karen_Byron@bd.com |
| Date: | Thu, 24 Jul 2008 18:41:14 -0400 |
|
I almost always resort to manually generating the lmat when I use the multicomp command with a covariate. Here is an example. From the help: # non-simultaneous intervals for adjusted mean fuel mileage, # adjusting to both Weight=2500 and Weight=3500 lmancova.fuel <- lm(Fuel~Type+Weight, data="" multicomp(lmancova.fuel, adjust=list(Weight=c(2500, 3500)), comparisons="none", method="lsd", error.type="cwe") This gives the following output: 95 % non-simultaneous confidence intervals for specified linear combinations, by the Fisher LSD method critical point: 2.0057 response variable: Fuel intervals excluding 0 are flagged by '****' Estimate Std.Error Lower Bound Upper Bound Compact 3.88 0.113 3.66 4.11 **** Large 4.81 0.213 4.38 5.24 **** Medium 3.99 0.171 3.64 4.33 **** Small 4.37 0.267 3.84 4.91 **** Sporty 3.69 0.135 3.42 3.96 **** Van 5.30 0.137 5.02 5.57 **** which doesn't indicate what value the Weight was set. I find the lmat surprising round(multicomp(lmancova.fuel, adjust = list(Weight = c(2500, 3500)), comparisons = "none", method = "lsd", error.type = "cwe")$lmat, 3) Compact Large Medium Small Sporty Van (Intercept) 1 1 1 1 1 1 TypeCompact 1 0 0 0 0 0 TypeLarge 0 1 0 0 0 0 TypeMedium 0 0 1 0 0 0 TypeSmall 0 0 0 1 0 0 TypeSporty 0 0 0 0 1 0 TypeVan 0 0 0 0 0 1 Weight 2500 3500 2500 3500 2500 3500 How could I get the bias's at both 2500 and 35000, i.e. all types at Weight = 2500 and Weight = 35000 within 1 call to multicomp (so can plot) and no manual lmat? I will summarize responses set just to me. Thanks,
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