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...mial function that i would like to approximate with a polynomial. I already looked into poly, but was unable to understand what to do with it. So my problem is this. I can generate virtually any number of datapoints and would like to find the coeffs a1, a2, ... up to a given degree for a polynomial a1x^1 + a2x^2 + ... that approximates my simple function. How can i do this with R? Your help will be highly appreciated! -- View this message in context: http://r.789695.n4.nabble.com/Polynomial-fitting-tp3652816p3652816.html Sent from the R help mailing list archive at Nabble.com.

Hello , I want to compare two quadratic regression models with non-parametric bootstrap. However, I do not know which R package can serve the purpose, such as boot, rms, or bootstrap, DeltaR. Please kindly advise and thank you. Elaine The two quadratic regression models are y1=a1x^2+b1x+c1 y1= observed migration distance of butterflies() y2=a2x^2+b2x+c2 y2= predicted migration distance of butterflies (based on body mass) x= body mass of butterflies null hypothesis: a1=a2 and b1=b2 and c1=c2 bootstrap to test if the coeffients (a, b, c) of the y1 and the y2 model diffe...

...s is given below. If is is not possible to do this as an option within lm, is there another way rather than creating new data frames that are defined by a given cluster label? Thanks again for your help. library(stats4) #Define the Models #Two simple linear models: #Model A: respA = a0 + a1x + e(a), e(a) ~N(0, sigma^2(a)) #Model B: respB = b0 + b1x + e(b), e(b) ~N(0, sigma^2(b)) a0= 0; a1 = 1.5; sigmaA=4; b0=50; b1=-2; sigmaB=4; n=250; min=0; max=50; #Generate Data from the Models x1 = runif(n, min, max); #Explanatory variable eA = rnorm(n, 0, sigmaA); eB = rnorm(n, 0, sigmaB); r...

Hello, I am work with two quadratic regression models y=ax^2+bx+c with the function of lm. y1= observed migration distance of butterflies(y1=a1x^2+b1x+c1) y2= predicted migration distance of butterflies (based on body mass) (y2=a2x^2+b2x+c2) x= body mass of butterflies Now I would like to check the two regression model differ by testing if the coeffients (a, b, c) of the y1 and the y2 model differ (null hypothesis: a1=a2 and b1=b2 an...

Dear All, I may look ridiculous, but I am puzzled at the behavior of the nls with a fitting I am currently dealing with. My data are: x N 1 346.4102 145.428256 2 447.2136 169.530634 3 570.0877 144.081627 4 721.1103 106.363316 5 894.4272 130.390552 6 1264.9111 36.727069 7 1788.8544 52.848587 8 2449.4897 25.128742 9 3464.1016 7.531766 10 4472.1360 8.827367 11