Adaikalavan Ramasamy wrote:
I would try to construct the confidence intervals and compare them to the value that you want
x <- rnorm(20) y <- 2*x + rnorm(20) summary( m1 <- lm(y~x) )
<snip> Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1418 0.1294 1.095 0.288 x 2.2058 0.1289 17.108 1.4e-12 *** <snip>
That says that the slope estimate is 2.2058 with standard error of 0.1289. So the approximate 99% CI is 2.2058 +/- 3*0.1289 = (1.819, 2.593) which is clearly greater than 1.
summary(m1)[[4]][2,1] + 3* summary(m1)[[4]][2,2]
[1] 2.592629
summary(m1)[[4]][2,1] - 3* summary(m1)[[4]][2,2]
[1] 1.819026
I think the preferred way of doing this is with confint:
# 3 sigma limits confint(m1, parm = "x", level = 1 - pt(-3, m1$df.resid) * 2) # 95% confidence (default) confint(m1, parm = "x")
For your next question, you simply compare the CI of one slope to
another and see if they overlap.
There is probably a way to construct proper significance testing to get p-values and such. You can try reading MASS4 or hopefully someone in the list might provide with a neater answer.
On Tue, 2004-07-20 at 17:02, Avril Coghlan wrote:
Hello,
I'm a newcomer to R so please forgive me if this is a silly question.
It's that I have a linear regression: fm <- lm (x ~ y) and I want to test whether the slope of the regression is significantly less than 1. How can I do this in R?
I'm also interested in comparing the slopes of two regressions: fm1 <- lm (x ~ y) fm2 <- lm (a ~ b) and asking if the slope of fm1 is less than the slope of fm2. Is this easy to do in R?
I will be very grateful for any help.
regards, Avril Coghlan (University College Dublin, Ireland)
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