Description Usage Arguments Value References Examples
This function computes the constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative, as described in Fraser and Massam (1989).
1 | clse(dat.obj)
|
dat.obj |
A list with the following format, |
The returned value is a list with format, list(dat.obj, beta.vec, yhat)
. Here dat.obj
is the input of the function, beta.vec
gives the estimated regression coefficient, and yhat
is the vector for the fitted response values.
Fraser, D. A. S. and H. Massam (1989). A mixed primal-dual bases algorithm for regression under inequality constraints. Application to concave regression. Scandinavian Journal of Statistics 16, 65-74.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | #generate a dataset for illustration.
x=seq(1,10,,100)
y=x^2+rnorm(length(x))
#generate spline bases.
tmp=MIC.splines.basis.fast(x=x, df = 10, knots = NULL, boundary.knots=NULL,
type="Is",degree = 3,delta=0.001,eq.alloc=FALSE)
#plot the spline bases.
plot(tmp)
#generate the data object for the clse function.
dat.obj=list(y=y, mat=cbind(1, tmp$mat), lam=c(0, rep(1, ncol(tmp$mat))))
#fit clse.
fit=clse(dat.obj=dat.obj)
#visualize fitted results.
plot(x, y, pch=16)
lines(x, fit$yhat, lwd=3, col=2)
|
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