Hi Thomas,
Perhaps:
test = read.table("test.dat", header=T, dec=',')
x <- test[,1:21]
y <- test[, "Y"]
mymodel <- lm(y ~ x)
# Coefficients and more information
summary(mymodel)
# Plots for the residuals
plotspar(mfrow=c(2,2))
plot(mymodel)
See also ?lm
HTH,
Jorge
On Tue, May 13,
I just have read the guide and I can do some small steps with cran but
I still have no clue...
I have data like this:
X1 X2 X3 ... X21 Y
1 0 0 0 ... 18 -0,07254
2 1 0 0 ... 6 -0,14921
3 0
Abhijit Dasgupta wrote:
> Multiple linear regression is handled by the function lm() in the
> default installation of R. This takes inputs as lm(y~x1+x2+x3).
>
> If you're going to be using R regularly, there are several books which
> cover the basic statistical analyses available in R (and then so
Multiple linear regression is handled by the function lm() in the
default installation of R. This takes inputs as lm(y~x1+x2+x3).
If you're going to be using R regularly, there are several books which
cover the basic statistical analyses available in R (and then some),
including those by Peter
Hello
I have to solve a multiple linear regression. Most programs like Excel
or Mathlab only support 5-10 dimensions. Now I have installed CRAN and
I have no clue what to do next. At the moment I am entering my data
into an excelsheet (for quick copy- paste). The Y-array will be 20
columns (=dimen
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