search for: unconstraint

...test appears to work for linear regression only (see: http://en.wikipedia.org/wiki/Chow_test). Another option I was thinking of is to estimate an alternative model in which the coefficients for x1 and x2 are constraint to be equal and to compare the fit of such a constraint model with the one of an unconstraint one. But again I'm not sure how this can be done using coxph. Could anyone help me out on this please? Thanks, Michael Michael Haenlein Associate Professor of Marketing ESCP Europe Paris, France [[alternative HTML version deleted]]

...if 2 5-parameter (A, B, xmid, scal and H) logistic curves are parallel based on residual sum of squares. What's usually done is to first fit the 2 curves using a constraint (or global) model where all parameters are kept the same except for "xmid"; then fit 2 independent curves using unconstraint models where all 5 parameters are allowed for change. The extra residual sum of squares can then be used to test parallelism using either Chi-square or F test. Now, fitting 2 separate curves are straight forward using nls(), but how to fit a global model to only allow "xmid" to be differ...

Dear list, I am trying to follow an example that estimates a 2x2 markov transition matrix across several periods from aggregate data using restricted least squares. I seem to be making headway using solve.QP(quadprog) as the unrestricted solution matches the example I am following, and I can specify simple equality and inequality constraints. However, I cannot correctly specify a constraint

I'm having difficulty figuring out how to implement the following set of constraints in Quadprog: 1). x1+x2+x3+x4=a1 2). x1+x2+x5+x6=a2 3). x1+x3+x5+x7=a3 4). x1+x2=b1 5). x1+x3=b2 6). x1+x5=b3 for the problem: MIN (x1-c1)2+(x2-c2)2+...+(x8-c8)2. As far a I understand, "solve.QP(Dmat, dvec, Amat, bvec, meq=0, factorized=FALSE)" reads contraints using an element-by-element

Dear list members, I am trying to get information on how to fit a linear regression with constrained parameters. Specifically, I have 8 predictors , their coeffiecients should all be non-negative and add up to 1. I understand it is a quadratic programming problem but I have no experience in the subject. I searched the archives but the results were inconclusive. Could someone provide suggestions

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off