How to test a model with two unkown constants
That's the linear model lm(y ~ I(1/f1) + f2), so yes, yes and fuller answers can be found in most of the books and guides mentioned in R's FAQ. Note that how `good' the fit is will have to be relative, unless you really can assume a uniform error with range 1, when you could do a maximum-likelihood fit (and watch out for the non-standard distribution theory).
On 27 Aug 2003, Sven Garbade wrote:
Hi all, suppose I've got a vector y with some data (from a repeated measure design) observed given the conditions in f1 and f2. I've got a model with two unknown fix constants a and b which tries to predict y with respect to the values in f1 and f2. Here is an exsample # "data" y <- c(runif(10, -1,0), runif(10,0,1)) # f1 f1 <- rep(c(-1.4, 1.4), rep(10,2)) # f2 f2 <- rep(c(-.5, .5), rep(10,2)) Suppose my simple model looks like y = a/f1 + b*f2
Is there a function in R which can compute the estimates for a and b? And is it possible to test the model, eg how "good" the fits of the model are?
Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595