turbo: Methods for objects of class "turbo"

In turboEM: A Suite of Convergence Acceleration Schemes for EM, MM and Other Fixed-Point Algorithms

Description

The turbo class represents results from parameter estimation in fixed-point mapping problems. The turboem function outputs objects of class turbo.

Usage

## S3 method for class 'turbo'
print(x, ...)
## S3 method for class 'turbo'
pars(x, ...)
## S3 method for class 'turbo'
error(x, ...)
## S3 method for class 'turbo'
plot(x, which.methods = seq_along(x$method), 
method.names = x$method[which.methods], xlim, ylim, ...)
## S3 method for class 'turbo'
grad(x, objfn=x$objfn, which.methods = seq_along(x$method), 
  method.names = x$method[which.methods], ...)
## S3 method for class 'turbo'
hessian(x, objfn=x$objfn, which.methods = seq_along(x$method), 
  method.names = x$method[which.methods], ...)
## S3 method for class 'turbo'
stderror(x, objfn=x$objfn, which.methods = seq_along(x$method), 
  method.names = x$method[which.methods], ...)

Arguments

x	An object of class turbo, typically the output of a call to turboem.
which.methods	A vector identifying for which subset of algorithms results are desired.
method.names	A vector of unique identifiers for the algorithms for which results are being provided.
xlim	Optional range for the x-axis of the trace plot.
ylim	Optional range for the y-axis of the trace plot.
objfn	Objective function. Usually this is taken to be the appropriate component of a turbo object.
...	Additional arguments.

Value

print	Shows a brief summary of the results from fitting the acceleration schemes.
pars	Prints the fixed-point values across acceleration schemes at termination of the algorithms.
error	Prints any error messages from running the acceleration schemes
plot	Shows a trace plot of the objective function value over iterations. This method is only available if the call to turboem had the argumentcontrol.run[["keep.objfval"]]=TRUE
grad	Calculates the gradient of the objective function evaluated at the fixed-point across acceleration schemes. Uses numerical methods from the package numDeriv.
hessian	Calculates the Hessian of the objective function evaluated at the fixed-point across acceleration schemes. Uses numerical methods from the package numDeriv.
stderror	Provides estimates of the standard error of the fixed-point across acceleration schemes.

See Also

turboem

Examples

###########################################################################
# Also see the vignette by typing:
#  vignette("turboEM")
#
# EM algorithm for Poisson mixture estimation 

fixptfn <- function(p,y) {
# The fixed point mapping giving a single E and M step of the EM algorithm
# 
pnew <- rep(NA,3)
i <- 0:(length(y)-1)
zi <- p[1]*exp(-p[2])*p[2]^i / (p[1]*exp(-p[2])*p[2]^i + (1 - p[1])*exp(-p[3])*p[3]^i)
pnew[1] <- sum(y*zi)/sum(y)
pnew[2] <- sum(y*i*zi)/sum(y*zi)
pnew[3] <- sum(y*i*(1-zi))/sum(y*(1-zi))
p <- pnew
return(pnew)
}

objfn <- function(p,y) {
# Objective function whose local minimum is a fixed point 
# negative log-likelihood of binary poisson mixture
i <- 0:(length(y)-1)
loglik <- y*log(p[1]*exp(-p[2])*p[2]^i/exp(lgamma(i+1)) + 
		(1 - p[1])*exp(-p[3])*p[3]^i/exp(lgamma(i+1)))
return ( -sum(loglik) )
}

# Real data from Hasselblad (JASA 1969)
poissmix.dat <- data.frame(death=0:9, freq=c(162,267,271,185,111,61,27,8,3,1))
y <- poissmix.dat$freq

# Use a preset seed so the example is reproducable. 
require("setRNG")
old.seed <- setRNG(list(kind="Mersenne-Twister", normal.kind="Inversion",
    seed=1))

p0 <- c(runif(1),runif(2,0,4))  # random starting value

# Basic EM algorithm, SQUAREM, and parabolic EM, with default settings
res1 <- turboem(par=p0, y=y, fixptfn=fixptfn, objfn=objfn, method=c("EM", "squarem", "pem"))

# Apply methods for class "turbo"
res1
pars(res1)
grad(res1)
hessian(res1)
stderror(res1)
error(res1)

# We get an error for Dynamic ECME (decme) if we do not specify the boundary function
res2 <- turboem(par=p0, y=y, fixptfn=fixptfn, objfn=objfn, 
  method=c("EM", "squarem", "pem", "decme"))
res2
error(res2)

# we can't plot the results, because we did not store the objective function value at each iteration
# Changing the options to store the objective function values, we can:
res1keep <- turboem(par=p0, y=y, fixptfn=fixptfn, objfn=objfn, method=c("EM", "squarem", "pem"), 
  control.run=list(keep.objfval=TRUE))
plot(res1keep, xlim=c(0.001, 0.02))

turboEM documentation built on Aug. 5, 2021, 9:09 a.m.