[R] summary[["r.squared"]] gives strange results

From: Felix Flory <felix_flory_at_yahoo.de>
Date: Wed 07 Dec 2005 - 12:41:27 EST


I am simulating an ANOVA model and get a strange behavior from the summary function. To be more specific: please run the following code and see for yourself: the summary()[["r.squared"]] values of two identical models are quite different!!

## 3 x 3 ANOVA of two factors x and z on outcome y
s.size <- 300 # the sample size
p.z <- c(0.25, 0.5, 0.25) # the probabilities of factor z
## probabilities of x given z

p.x.z <- matrix(c(40/60, 20/120, 6/60,
                  14/60, 80/120, 14/60,
                   6/60, 20/120, 40/60), 3, 3, byrow = TRUE)

## the regression coefficients according to the model.matrix X, that
## is computed later

beta <- c(140, -60, -50, -80, 120, 100, -40, 60, 50)/40
## the factor z and the factor x (in dependence of z)
z <- x <- vector(mode = "integer", length = s.size) for(j in 1:s.size) {
  z[j] <- sample(1:3, 1, prob = p.z)
  x[j] <- sample(1:3, 1, prob = p.x.z[, z[j]]) }
## constructing the model.matrix X

zf <- factor(z)
contrasts(zf) <- contr.treatment(nlevels(zf), base = 3) zm <- model.matrix(~ zf)
xf <- factor(x)
contrasts(xf) <- contr.treatment(nlevels(xf), base = 3) xm <- model.matrix(~ xf)
X <- cbind(zm, zm * xm[,2], zm * xm[,3])
## the outcome y

y <- X %*% beta + rnorm(s.size, 0, 4)
## the two linear models

lm.v1 <- lm(y ~ X -1)
lm.v2 <- lm(y ~ zf * xf)
## which are equivalent

anova(lm.v1, lm.v2)
print(sd(model.matrix(lm.v1) %*% coef(lm.v1))^2 / sd(y)^2) -   print(sd(model.matrix(lm.v2) %*% coef(lm.v2))^2 / sd(y)^2)
## so far everything is fine but why are the following r.squared
## values quite different?
print(summary(lm.v1)[["r.squared"]]) -
  print(summary(lm.v2)[["r.squared"]])

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Dec 07 12:48:35 2005

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