On Dec 1, 2011, at 10:50 PM, Worik R wrote:
> I really would like to be able to read about this in a document but I
> cannot find my way around the documentation properly
>
> Given the code...
>
> M <- matrix(runif(5*20), nrow=20)
> colnames(M) <- c('a', 'b', 'c', 'd',
'e')
> ind <- c(1,2,3,4)
> dep <- 5
>
> I can then do...
> l2 <- lm(M[,dep]~M[,ind]) ## Clearly not useful!
> summary(l2)
>
> I am not sure what my regression formula is.
>
> The results are (edited for brevity)
>
>> summary(l2)$coefficients
>
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) 0.63842 0.16036 3.981 0.00120 **
> M[, ind]a -0.02912 0.17566 -0.166 0.87054
> M[, ind]b -0.21172 0.19665 -1.077 0.29865
> M[, ind]c 0.00752 0.18551 0.041 0.96820
> M[, ind]d 0.06357 0.18337 0.347 0.73366
>
> Is there some way I can do this so the coefficients have better
> names ('a'
> through 'd')?
>
Use `lm` the way it is designed to be used, with a data argument:
> l2 <- lm(e~. , data=as.data.frame(M))
> summary(l2)
Call:
lm(formula = e ~ ., data = as.data.frame(M))
Residuals:
Min 1Q Median 3Q Max
-0.5558 -0.2396 0.1257 0.2213 0.4586
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.41110 0.25484 1.613 0.128
a -0.03258 0.28375 -0.115 0.910
b 0.09088 0.25971 0.350 0.731
c 0.09382 0.29555 0.317 0.755
d 0.14725 0.33956 0.434 0.671
Residual standard error: 0.3317 on 15 degrees of freedom
Multiple R-squared: 0.04667, Adjusted R-squared: -0.2076
F-statistic: 0.1836 on 4 and 15 DF, p-value: 0.9433
--
David.
> As for what I am doing it is not...
>
> l3 <- lm(M[,1]+M[,2]+M[,3]+M[,4]~M[,5])
>
> or
>
> l4 <- lm(M[,1]*M[,2]*M[,3]*M[,4]~M[,5])
>
> cheers
> W
>
> [[alternative HTML version deleted]]
>
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David Winsemius, MD
West Hartford, CT