I want to adjust a function like this:
fit4 = lm(mut ~ ent + score + wt + I(ent^2) + I(score^2) +I(wt^2))
when I summary(fit4) I get:
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.779381 0.086256 -20.629 <2e-16
ent 2.724036 0.072543 37.550 <2e-16
score 0.473230 0.009450 50.077 <2e-16
wt -0.464216 0.031141 -14.907 <2e-16
I(ent^2) -0.473427 0.018814 -25.164 <2e-16
I(score^2) 0.030187 0.004851 6.222 5e-10
I(wt^2) 0.043386 0.004609 9.413 <2e-16
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Now I would like to obtain the same, but doing the root square error of the above function: sqrt(ent + score + wt + I(ent^2) + I(score^2) +I(wt^2)), but when I simply add "sqrt()", the summary returns something like:
Estimate
(Intercept) 1.066025
I(sqrt(ent + score + wt + I(ent^2) + I(score^2) + I(wt^2))) -0.24028
(and same for Std.Error, t-value, etc.)
How can I add "root squared" or "log" and still obtain the values for each element of the function?
You have to apply the function to all of them induvidually. So
fit4 = lm(mut ~ log(ent) + log(score) + log(wt) +
log(I(ent^2)) + log(I(score^2)) +log(I(wt^2)))
will do the desired
Reason:
log(ent + score + wt + I(ent^2) + I(score^2) +I(wt^2))
is interpreted as a single regressor.
So to r it is like lm(mut~x) where x=log(...) instead of
x=log(ent) + ... + log(I(wt^2))