Dear Karena,
x = 1:100
y = rnorm(100)
fit = lm(x~y)
# what properties does a fit have?
names(fit)
# [1] "coefficients" "residuals" "effects" "rank"
"fitted.values" "assign" "qr"
# [8] "df.residual" "xlevels" "call" "terms" "model"
x.resid = fit$residuals
plot(x~x.resid)
cor.test(y,x.resid) # -> r = 3.019388e-17
A gotcha is NAs in your data frame: Look at ?lm to see how to handle these
properly
best, t
On 20 May 2011, at 6:58 AM, karena wrote:
> Thank you so much for this reply, Peter. It helps.
>
> I know this is one way to adjust for covariates. However, if what I want is
> to get the 'remaining values' after adjustment. For example, say, 'gene
> expression' value is denoted as 'ge', and for each gene,
> ge=a*age+b*sex+c*per_se
>
> My question is: how to get the value of 'per_se' for each gene?
>
> thanks again.
>
> Karena
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/How-to-do-covariate-adjustment-in-R-tp3537463p3537711.html
> Sent from the R help mailing list archive at Nabble.com.
>
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