Dear All:
> predict(m1, newdata = data.frame(x = x))
1 2 3 4 5
84.42477 84.26280 81.34729 60.29085 27.89632
> predict(m2, newdata = data.frame(x = x))
1 2 3 4 5
76.86037 76.69170 73.65554 51.72772 17.99261
>
for example: Are 84.42477 and 76.86037 statistically significant?
Are 84.26280 and 76.69170 statistically significant?
and so on.
with many thanks
abou
>>> "R. Michael Weylandt" <[email protected]> 3/31/2012 9:13 PM >>>
Again, you need to clarify what you mean by compare, but here's a
better way to do predict from a linear model -- my first guess was
simple interpolation (not very good):
m1 <- lm(y ~ x, data = data.frame(y = y1, x = x1))
m2 <- lm(y ~ x, data = data.frame(y = y2, x = x2))
predict(m1, newdata = data.frame(x = x))
predict(m2, newdata = data.frame(x = x))
Perhaps you want an ANOVA (?anova) test to compare the models: if you
want to compare the predicted values at x directly, you could use
interval="predict" for the different models and see if they overlap at
a given confidence level.
Michael
PS -- Please do cc the list: I'm not a statistics expert by any means
and I'd hate for what I say to go unreviewed by actual experts.
On Sat, Mar 31, 2012 at 8:19 PM, AbouEl-Makarim Aboueissa
<[email protected]> wrote:
> I need to compare the value of y1-hat versus y2-hat corresponding to these
> values of x<-c(0.10,0.20,2.0,15.0,35.0)
> e.g. for x=0.10 compare y1-hat(x=0.10) versus y2-hat(x=0.10) and so on.
>
> thanks
> abou
>
>
>>>> "R. Michael Weylandt" <[email protected]> 3/31/2012 8:10 PM >>>
> ? approxfun
>
> f1 <- approxfun(x1,y1)
> f2 <- approxfun(x2,y2)
>
> f1(x) - f2(x)
>
> But I'm not sure what you mean be compare...do you have a particular
> test in mind?
>
> Michael
>
> On Sat, Mar 31, 2012 at 8:04 PM, AbouEl-Makarim Aboueissa
> <[email protected]> wrote:
>> Dear All:
>>
>> I am trying to compare points on two regression lines given some specific
>> values of X. I am not sure how to do it in R and/or in SAS. Can someone help
>> me, and show me how to conduct such test in R.
>>
>>
>> You can use the following data for both lines.
>>
>> ### data for line 1:
>> ### =========
>>
>> x1<-c(0.0,0.25,2.5,25.0,50.0)
>> y1<-c(100.0,79.0,74.0,36.0,8.0)
>>
>>
>>
>> ### data for line 2:
>> ### =========
>>
>> x2<-c(0.0,0.25,2.5,25.0,50.0)
>> y2<-c(100.0,79.0,55.0,18.0,2.0)
>>
>>
>> ### these are the given values of X:
>> ### ===================
>>
>> x<-c(0.10,0.20,2.0,15.0,35.0)
>>
>>
>> thank you very much in advance
>> abou
>>
>>
>>
>>
>>
>>
>>
>>
>> ==========================
>> AbouEl-Makarim Aboueissa, Ph.D.
>> Associate Professor of Statistics
>> Graduate Program Coordinator
>> Department of Mathematics & Statistics
>> University of Southern Maine
>> 96 Falmouth Street
>> P.O. Box 9300
>> Portland, ME 04104-9300
>> USA
>>
>>
>> Tel: (207) 228-8389
>> Fax: (207) 780-5607
>> Email: [email protected]
>> [email protected]
>>
>> Office: 301C Payson Smith
>>
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> [email protected] mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-teaching
[[alternative HTML version deleted]]
_______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-teaching
