I'm afraid of your manual ANOVA. It may be correct, but it won't easily generalize. Instead, how about the following:

> df1 <- data.frame(x=1:3, y=1:3+rnorm(3))
> df2 <- data.frame(x=1:3, y=1:3+rnorm(3))
> fit1 <- lm(y~x, df1)
> s1 <- summary(fit1)$coefficients
> fit2 <- lm(y~x, df2)
> s2 <- summary(fit2)$coefficients
> db <- (s2[2,1]-s1[2,1])
> sd <- sqrt(s2[2,2]^2+s1[2,2]^2)
> df <- (fit1$df.residual+fit2$df.residual)
> td <- db/sd
> 2*pt(-abs(td), df)
[1] 0.9510506

The function "attributes" helped me figure this out.

hope this helps. spencer graves

Martin Biuw wrote:
I've written a simple (although probably overly roundabout) function to test whether two regression slope coefficients from two linear models on independent data sets are significantly different. I'm a bit concerned, because when I test it on simulated data with different sample sizes and variances, the function seems to be extremely sensitive both of these. I am wondering if I've missed something in my function? I'd be very grateful for any tips.



TwoSlope <-function(lm1, lm2) {

## lm1 and lm2 are two linear models on independent data sets

coef1 <-summary(lm1)$coef
coef2 <-summary(lm2)$coef

sigma <-(sum(lm1$residuals^2)+sum(lm2$residuals^2))/(lm1$df.residual + lm2$df.residual-4)
SSall <-sum(lm1$model[,2]^2) + sum(lm2$model[,2]^2)
SSprod <-sum(lm1$model[,2]^2) * sum(lm2$model[,2]^2)

F.val <-(as.numeric(coefficients(lm1)[2]) - as.numeric(coefficients(lm2) [2]))^2/((SSall/SSprod)*sigma)

p.val <-1-pf(F.val, 1, (lm1$df.residual + lm2$df.residual-4))

cat("\n\nTest for equality between two regression slopes\n\n")
cat("\nCoefficients model 1:\n\n")

cat("\nCoefficients model 2:\n\n")

cat("\nF-value on 1 and", lm1$df.residual + lm2$df.residual-4, "degrees of freedom:" ,F.val, "\n")
cat("p =", ifelse(p.val>=0.0001, p.val, "< 0.0001"), "\n")


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