Description Usage Arguments Details Value Note Author(s) References See Also Examples
Based on independent intervals X_i = [L_i,R_i], where Inf < L_i <= R_i <= Inf, compute the maximum likelihood estimator of a (sub)probability density phi and the remaining mass p0 at infinity (also known as cure parameter) under the assumption that the former is logconcave. Computation is based on an EM algorithm. For further information see Duembgen, Rufibach, and Schuhmacher (2013, preprint).
1 2 3 4 5 6 7  logcon(x, adapt.p0=FALSE, p0=0, knot.prec=IQR(x[x<Inf])/75, reduce=TRUE,
control=lc.control())
logConCens(x, adapt.p0=FALSE, p0=0, knot.prec=IQR(x[x<Inf])/75, reduce=TRUE,
control=lc.control())
logconcure(x, p0=0, knot.prec=IQR(x[x<Inf])/75, reduce=TRUE, control=lc.control())

x 
a twocolumn matrix of n >= 2 rows containing the data intervals, or a vector of length n >= 2 containing the exact data points. 
adapt.p0 

p0 
a number from 0 to 1 specifying the mass at infinity. If the algorithm is allowed to adapt p0, this argument only specifies the starting value. Otherwise it is assumed that the true cure parameter p0 is equal to this number. In particular, for the default setting of 0, a proper probability density phi is estimated. 
knot.prec 
the maximal distance between two consecutive grid points, where knots (points at which the resulting logsubdensity phi may change slope) can be positioned. See details. 
reduce 

control 
a list of control parameters for the more technical aspects of the algorithm; usually the result of a call
to 
Based on the data intervals X_i = [L_i,R_i] described above, function logcon
computes a concave, piecewise linear function phi and a probability p0 which satisfy int exp(phi(x)) dx = 1p0 and jointly maximize the (normalized) loglikelihood.
l(φ, p_0) = (1/n) ∑_{i=1}^n [ 1{L_i = R_i} phi(X_i) + 1{L_i < R_i} log ( int_{L_i}^{R_i} exp phi(x) \, dx + 1{R_i = Inf} p_0 ) ].
If x
is a twocolumn matrix, it is assumed to contain the left and right interval endpoints in the correct order. Intervals may have length zero (both endpoints equal) or be unbounded to the right (right endpoint is Inf
). Computation is based on an EM algorithm, where the Mstep uses an active set algorithm for computing the logconcave MLE for exact data with weights. The active set algorithm was described in Duembgen, Huesler, and Rufibach (2007) and Duembgen and Rufibach (2011) and is available in the R package logcondens
. It has been reimplemented in C for the current package because of speed requirements. The whole algorithm for censored data has been indicated in Duembgen, Huesler, and Rufibach (2007) and was elaborated in Duembgen, Schuhmacher, and Rufibach (2013, preprint).
If x
is a vector argument, it is assumed to contain the exact data points. In this case the active set algorithm is accessed directly.
In order to obtain a finite dimensional optimization problem the (supposed) domain of phi is subdivided by a grid. Stretches between interval endpoints where for theoretical reasons no knots (points where the slope of phi changes) can lie are left out. The argument kink.prec
gives the maximal distance we allow between consecutive grid points in stretches where knots can lie. Say plotint(x)
to see the grid.
The EM algorithm works only for fixed dimensionality of the problem, but the domain of the function phi is not a priori known. Therefore there is an outer loop starting with the largest possible domain, given by the minimal and maximal endpoints of all the intervals, and decreasing the domain as soon as the EM steps let phi become very small towards the boundary. “Very small” means that the integral of exp o phi over the first or last stretch between interval endpoints within the current domain falls below a certain threshold red.thresh
, which can be controlled via lc.control
.
Domain reduction tends to be rather conservative. If the computed solution has a suspiciously steep slope at any of the domain boundaries, the recommended strategy is to enforce a smaller domain by increasing the parameters domind1l
and/or domind2r
via lc.control
. The function loglike
may be used to compare the (normalized) loglikelihoods of the results.
logConCens
is an alias for logcon
. It is introduced to provide unified naming with the main functions in the packages logcondens
and logcondiscr
.
logconcure
is the same as logcon
with adapt.p0 = TRUE
fixed.
An object of class lcdensity
for which reasonable plot
, print
, and summary
methods are available.
If the argument x
is a twocolumn matrix (censored data case), such an object has the following components.
basedOn 
the string 
status 
currently only 
x 
the data entered. 
tau 
the ordered vector of different interval endpoints. 
domind1, domind2 
the indices of the 
tplus 
the grid vector. 
isKnot 

phi 
the vector of phivalues at the finite elements of 
phislr 
if sup(dom(phi)) = Inf, the slope of phi after the last knot. Otherwise Inf. 
phislr.range 
a vector of length 2 specifying a range of possible values for 
cure 
the cure parameter. Either the original argument 
cure.range 
a vector of length 2 specifying a range of possible values for 
Fhat 
the vector of values of the distribution function F of exp o phi at the finite elements of 
Fhatfin 
the computed value of lim_{t to Inf} F(t). 
If x
is a vector, this function does the same as the function logConDens
in the package
logcondens
. The latter package offers additional features such as gridbased computation with weights
(for high numerical stability) and
smoothing of the estimator, as well as nicer plotting. For exact data we recommend using
logConDens
for
everyday data analysis. logcon
with a vector argument is to be preferred if time is of the essence (for
data sets with several thousands of points or repeated optimization in iterative algorithms) or
if an additional slope functionality is required.
Two other helpful packages for logconcave density estimation based on exact data are logcondiscr
for estimating a discrete distribution and LogConcDEAD
for estimating a multivariate continuous distribution.
Dominic Schuhmacher [email protected]
Kaspar Rufibach [email protected]
Lutz Duembgen [email protected]
Duembgen, L., Huesler, A., and Rufibach, K. (2007). Active set and EM algorithms for logconcave densities based on complete and censored data. Technical Report 61. IMSV, University of Bern. http://arxiv.org/abs/0707.4643
Duembgen, L. and Rufibach, K., (2011). logcondens: Computations Related to Univariate LogConcave Density Estimation. Journal of Statistical Software, 39(6), 128. http://www.jstatsoft.org/v39/i06
Duembgen, L., Rufibach, K., and Schuhmacher, D. (2013, preprint). Maximum likelihood estimation of a logconcave density based on censored data. http://arxiv.org/pdf/1311.6403v2.pdf
lc.control
, lcdensitymethods
, loglike
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163  # A function for artificially censoring exact data
censor < function(y, timemat) {
tm < cbind(0,timemat,Inf)
n < length(y)
res < sapply(1:n, function(i){
return( c( max(tm[i,][tm[i,] < y[i]]), min(tm[i,][tm[i,] >= y[i]]) ) ) } )
return(t(res))
}
# 
# interval censored data
# 
set.seed(20)
n < 100
# generate exact data:
y < rgamma(n,3)
# generate matrix of inspection times:
itimes < matrix(rexp(10*n),n,10)
itimes < t(apply(itimes,1,cumsum))
# transform exact data to interval data
x < censor(y, itimes)
# plot both
plotint(x, imarks=y)
# Compute censored logconcave MLE
# (assuming only the censored data is available to us)
res < logcon(x)
plot(res)
# Compare it to the logconcave MLE for the exact data
# and to the true Gamma(3,1) logdensity
res.ex < logcon(y)
lines(res.ex$x, res.ex$phi, lwd=2.5, lty=2)
xi < seq(0,14,0.05)
lines(xi,log(dgamma(xi,3,1)), col=3, lwd=2)
# 
# censored data with cure
# 
## Not run:
set.seed(21)
n < 100
# generate exact data:
y < rgamma(n,3)
cured < as.logical(rbinom(n,1,0.3))
y[cured] < Inf
# generate matrix of inspection times:
itimes < matrix(rexp(6*n),n,6)
itimes < t(apply(itimes,1,cumsum))
# transform exact data to interval data
x < censor(y, itimes)
# plot both
plotint(x, imarks=y)
# Compute censored logconcave MLE including cure parameter
# (assuming only the censored data is available to us)
res < logcon(x, adapt.p0=TRUE)
plot(res)
# There is a tradeoff between righthand slope and cure parameter here
# (seen by the grey area on the right), but the margin is very small:
res$cure.range
# Compare the corresponding CDF to the true CDF
plot(res, type="CDF")
xi < seq(0,14,0.05)
lines(xi,0.7*pgamma(xi,3,1), col=3, lwd=2)
# Note that the tradeoff for the righthand slope is not visible anymore
# (in terms of the CDF the effect is too small)
## End(Not run)
# 
# real right censored data with cure
# 
# Look at data set ovarian from package survival
# Gives survival times in days for 26 patients with advanced ovarian carcinoma,
# ignoring the covariates
# Bring data to right format and plot it
## Not run:
library(survival)
data(ovarian)
sobj < Surv(ovarian$futime, ovarian$fustat)
x < cbind(sobj[,1], ifelse(as.logical(sobj[,2]),sobj[,1],Inf))
plotint(x)
# Compute censored logconcave MLE including cure parameter
res < logcon(x, adapt.p0=TRUE)
# Compare the corresponding survival function to the KaplanMeier estimator
plot(res, type="survival")
res.km < survfit(sobj ~ 1)
lines(res.km, lwd=1.5)
## End(Not run)
# 
# current status data
# 
## Not run:
set.seed(22)
n < 200
# generate exact data
y < rweibull(n,2,1)
# generate vector of inspection times
itime < matrix(rexp(n),n,1)
# transform exact data to interval data
x < censor(y, itime)
# plot both
plotint(x, imarks=y)
# Compute censored logconcave MLE
# (assuming only the censored data is available to us)
res < logcon(x)
plot(res, type="CDF")
# Compare it to the true Weibull(2,1) c.d.f.
xi < seq(0,3,0.05)
lines(xi,pweibull(xi,2,1), col=3, lwd=2)
## End(Not run)
# 
# rounded/binned data
# 
## Not run:
set.seed(23)
n < 100
# generate data in [0,1] rounded to one digit
y < round(rbeta(n,2,3),1)
# bring data to right format and plot it
x < cbind(y0.05,y+0.05)
plotint(x)
# Compute censored logconcave MLE
res < logcon(x)
plot(res, type="density", xlim=c(0,1))
# Compare it to the true Beta(2,3) density
xi < seq(0,1,0.005)
lines(xi,dbeta(xi,2,3), col=3, lwd=2)
# The peaks in the estimated density are often considered unsatisfactory
# However, they are barely noticeable in the c.d.f.
plot(res, type="CDF", xlim=c(0,1))
lines(xi,pbeta(xi,2,3), col=3, lwd=2)
# To get rid of them in the density apply the smoothing
# proposed in the package logcondens (to be implemented here)
## End(Not run)

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