Hi Greg and others,
Thanks for your replies. Okay, I'm convinced that the offset is the best
approach and wonder if you might have a quick look at what I did.
Here's the original model containing the slope (0.56) that I'd like to test
if it's different from 1.0
model1 -
Yes, you can see that in the new model the slope is now 1- the old
slope, so it is measuring difference from 1. Since it is significant
that means the slope is significantly different from 1. To test the
null that slope = 0.5 just change the offset to
offset(0.5*log(data$SIZE,10))
To save
Doesn't the p-value from using offset work for you? if you really
need a p-value. The confint method is a quick and easy way to see if
it is significantly different from 1 (see Rolf's response), but does
not provide an exact p-value. I guess you could do confidence
intervals at different
You can also run two nls() models, one under h0 restriction, other under no
restriction or h1, and compare them (if they are nested) by likelihood
ratio test using anova() method, look
x1 - seq(0,10,l=15)
x2 - runif(x1)
set.seed(1)
y - x1+0.5*x2+rnorm(x1,0,0.01)
nls.h0 - nls(y~b0+x1+b2*x2,
Hi Greg. Thanks for your reply. Do you know if there is a way to use the
confint function to get a p-value on this test?
Thanks, Mark
On Mon, Apr 23, 2012 at 3:10 PM, Greg Snow 538...@gmail.com wrote:
One option is to subtract the continuous variable from y before doing
the regression (this
On 25/04/12 02:17, Mark Na wrote:
Hi Greg. Thanks for your reply. Do you know if there is a way to use the
confint function to get a p-value on this test?
In general it is at least *roughly* true that one rejects H_0: theta =
theta_0
vs. H_a: theta != theta_0 at significance level alpha if and
Dear R-helpers,
I would like to test if the slope corresponding to a continuous variable in
my model (summary below) is different than one.
I would appreciate any ideas for how I could do this in R, after having
specified and run this model?
Many thanks,
Mark Na
Call:
lm(formula =
One option is to subtract the continuous variable from y before doing
the regression (this works with any regression package/function). The
probably better way in R is to use the 'offset' function:
formula = I(log(data$AB.obs + 1, 10)-log(data$SIZE,10)) ~
log(data$SIZE, 10) + data$Y
formula =
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