[R-sig-teaching] Simulating Data with predefined reg-coefficients and R2
Greg Snow
Greg.Snow at imail.org
Wed Nov 19 17:29:39 CET 2008
Try this:
# generate x's
x1 <- sample(100, 100, TRUE)
x2 <- sample(100, 100, TRUE)
# generate yhat with b0=1, b1=2, b2=3
yhat <- 1 + 2*x1 + 3*x2
# compute ssr
ssr <- sum( (yhat-mean(yhat))^2 )
# generate errors
e <- rnorm(100)
e <- resid( lm( e ~ x1 + x2 ) )
# to get R^2 of 0.8, ssr/(ssr+sse)=0.8 so sse=0.2/0.8*ssr
e <- e* sqrt(0.2/0.8*ssr/(sum(e^2)))
# now for y
y <- yhat + e
# put into a data frame and test
mydata <- data.frame( y=y, x1=x1, x2=x2 )
fit <- lm(y ~ x1 + x2, data=mydata )
summary(fit)
Now just change the values that you want changed to match your situation. It does not matter how the x's are generated, so include more, include polynomials, include interactions, etc.
Hope this helps,
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111
> -----Original Message-----
> From: r-sig-teaching-bounces at r-project.org [mailto:r-sig-teaching-
> bounces at r-project.org] On Behalf Of markus
> Sent: Wednesday, November 19, 2008 1:19 AM
> To: r-sig-teaching at r-project.org
> Subject: [R-sig-teaching] Simulating Data with predefined reg-
> coefficients and R2
>
> Hi all at the R-teaching mailing list,
> I am currently preparing my first R-based regression course. Along
> this way I encountered the following problem:
>
> I want to simulate multivariate data that has some specific predefined
> attributes. For example I want to produce a Predictor-matrix (X)
> and a response-vector (y) that will yield a given vector of regression
> coefficients (b) and a given R2 when I perform a multivariate linear
> Regression
> on the dataset. This would be best described by the well known equation
> y=X*b+e.
> In some next step I also want to simulate polynomic relationships, but
> I
> think that should work not very different.
>
> I already searched the web and found some hints, but no clear answer.
> There is a pdf out there from John H. Walker (Teaching Regression with
> simulation)
> which does however not discuss this special topic. I also have a Paper
> from K.Baumann 'Chance Correlation in variable subset regression:
> Influence of the objective function, selection mechanism and Ensemble
> averaging' QCS, 2005. There an 'Autoregressive process' is used to
> simulate such data.
>
> Now my question is:
> Is it really that difficult to simulate such data? Is there perhaps a
> package in R facilitating at least parts of this work?
>
> Thanks in advance for the help,
> Markus
>
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