Any help would be most appreciated. (Don't make me get down on my
hands and knees and beg for help, cause I'll do it!!) My boss has me
learning R and doing nested regression with the report due Mon (Friday
night statistics...fun. ). Anyway, here's my problem:
In a regression equation not accounting for the fact that people are
nested in families, the result for Z variable is VERY strong (beta =
-4511), but this result is much weaker when I use lme and account for
people nested in families (beta = -2613). I'm struggling with a verbal
interpretation of this result. Is it because the effect of Z within
families is not very strong, but between family variation is high?
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If my R syntax would be helpful, it's below:
BIG EFFECT OF Z (using lm)
summary(lm(Y ~ X + Z + age ,data=pharma.data ,na.action='na.omit'))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 102.27 24.98 4.09 7e-05 ***
X -629.76 267.87 -2.35 0.020 *
Z -4511.96 2032.39 -2.22 0.028 *
age 1.88 1.42 1.32 0.188
SMALLER EFFECT OF Z (using lme)
summary(lme(Y ~ X + Z + age, random = ~1|Family.ID,
data=pharma.data, method="ML", na.action='na.omit'))
Value Std.Error DF t-value p-value
(Intercept) 103.9 20.0 85 5.200 0.0000
X. -417.3 179.3 85 -2.327 0.0223
Z -2613.0 1845.1 85 -1.416 0.1604
age 1.3 1.2 85 1.126 0.2632
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