miscF (version 0.1-4)

curve.polynomial.rjmcmc: Curve Fitting Using Piecewise Polynomials with Unknown Number and Location of Knots

Description

Fit a variety of curves by a sequence of piecewise polynomials. The number and location of knots are determined by the Reversible Jump MCMC method.

Usage

curve.polynomial.rjmcmc(y, x, lambda, l, l0, c=0.4,
                          gamma.shape=1e-3, gamma.rate=1e-3,
                          maxit=10000, err=1e-8, verbose=TRUE)

Arguments

y

a vector of values of a response variable.

x

a vector of values of the corresponding explanatory variable.

lambda

the parameter of the Poisson prior for the number of knots.

l

the order of polynomials.

l0

the degree of continuity at the knots.

c

the parameter controlling the rate of changing dimension. The default value is 0.4.

gamma.shape

the parameter shape of the gamma prior for the error precision. The default value is 1e-3.

gamma.rate

the parameter rate of the gamma prior for the error precision. The default value is 1e-3.

maxit

the maximum number of iterations. The default value is 10000.

err

the iteration stops when consecutive difference in percentage of mean-squared error reaches this bound. The default value is 1e-8.

verbose

logical. If TRUE, then indicate the level of output after every 1000 iterations. The default is TRUE.

Value

knots.save

a list of location of knots. One component per iteration.

betas.save

a list of estimates of regression parameters \(\beta\)s. One component per iteration.

fitted.save

a matrix of fitted values. One column per iteration.

sigma2.save

a vector of estimate of error variance. One component per iteration.

Details

The method is based on Denison et al. (1998). It can be used to fit both smooth and unsmooth curves. When both l0 and l are set to 3, it fits curves using cubic spline.

References

Denison, D. G. T., Mallick, B. K., Smith, A. F. M.(1998) Automatic Bayesian Curve Fitting JRSSB vol. 60, no. 2 333-350

Dimatteo, I., Genovese, C. R., Kass, R. E.(2001) Bayesian Curve-fitting with Free-knot Splines Biometrika vol. 88, no. 4 1055-1071

See Also

sm.spline

Examples

# NOT RUN {
   #Example 1: smooth curve
   #example 3.1. (b) in Denison et al.(1998)

   x <- runif(200)
   xx <- -2 + 4*x
   y.truth <- sin(2*xx) + 2*exp(-16*xx^2)
   y <- y.truth + rnorm(200, mean=0, sd=0.3)

   results <- curve.polynomial.rjmcmc(y, x, lambda=1, l=2, l0=1)

   plot(sort(x), y.truth[order(x)], type="l")
   lines(sort(x), rowMeans(results$fitted.save), col="red")

   #Example 2: unsmooth curve
   #blocks in Denison et al.(1998)
   tj <- c(0.1, 0.13, 0.15, 0.23, 0.25, 0.4, 0.44, 0.65, 0.76, 0.78, 0.81)
   hj <- c(4, -5, 3, -4, 5, -4.2, 2.1, 4.3, -3.1, 2.1, -4.2)

   t <- seq(0, 1, len=2048)
   Ktmtj <- outer(t, tj, function(a,b) ifelse(a-b > 0, 1, -1))
   ft <- rowSums(Ktmtj %*% diag(hj))
   x <- t
   y <- ft + rnorm(2048, 0, 1)

   results <- curve.polynomial.rjmcmc(y, x, lambda=5, l=2, l0=1)

   plot(x, ft, type="s")
   lines(x, rowMeans(results$fitted.save), col="red")

   #Example 3: unsmooth curve
   #bumps in Denison et al.(1998)
   tj <- c(0.1, 0.13, 0.15, 0.23, 0.25, 0.4, 0.44, 0.65, 0.76, 0.78, 0.81)
   hj <- c(4, 5, 3, 4, 5, 4.2, 2.1, 4.3, 3.1, 5.1, 4.2)*10
   wj <- c(0.005, 0.005, 0.006, 0.01, 0.01, 0.03, 0.01, 0.01, 0.005, 0.008, 0.005)

   t <- seq(0, 1, len=2048)
   ft <- rowSums((1 + abs(outer(t, tj ,"-") %*% diag(1/wj)))^(-4) %*% diag(hj))
   y <- ft + rnorm(2048, 0, 1)

   results <- curve.polynomial.rjmcmc(y, t, lambda=5, l=2, l0=1)

   plot(t, ft, type="s")
   lines(t, rowMeans(results$fitted.save), col="red")
  
# }

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