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2004 May 27
1
Getting the same values of adjusted mean and standard errors as SAS
...ot;]**2 )) sxxM <- sum((dd$Age[d$Gender=="M"]-meanAgeM)**2) syM <- seRegM * sqrt(1/nM + (meanAge-meanAgeM)**2/sxxM); #0.1103335 cf 0.11032602 - matches to 5 decimal places nF <- summary(ddr$Gender)[["F"]] seRegF <- sqrt(mean( ddr$res[ddr$Gender=="F"]**2 )) sxxF <- sum((dd$Age[d$Gender=="F"]-meanAgeF)**2) syF <- seRegF * sqrt(1/nF + (meanAge-meanAgeF)**2/sxxF); # wrong: 0.07279221 cf 0.14256466 > dd Measurement Age Gender Group 1 3.8 94 M 3BCLP 2 2.7 88 F 3BCLP 3 3.0 155 M 3BCLP 4 2.7 33...