Package 'pkmon' reference manual

Title:	Least-Squares Estimator under k-Monotony Constraint for Discrete Functions
Description:	We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.
Authors:	Jade Giguelay
Maintainer:	Francois Deslandes <[email protected]>
License:	CC BY 4.0
Version:	1.1
Built:	2024-12-06 06:34:41 UTC
Source:	CRAN

Least-squares estimator under k-monotony constraint for discrete functions

Description

Description: This package implements two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008). The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013), and from mixtures of splines.

Author(s)

Jade Giguelay

References

Giguelay J. (2016) Estimation of a discrete distribution under k-monotony constraint, in revision, (arXiv:1608.06541)

Lefevre C., Loisel S. (2013) <DOI:10.1239/jap/1378401239> On multiply monotone distributions, continuous or discrete, with applications, Journal of Applied Probability, 50, 827–847.

Groeneboom P., Jongbloed G. Wellner J. A. (2008) <DOI:10.1111/j.1467-9469.2007.00588.x> The Support Reduction Algorithm for Computing Non-Parametric Function Estimates in Mixture Models, Scandinavian Journal of Statistics, 35, 385–399

Examples


####################
# Example 1
# one triangular function T_j=Q_j^2, for j=supp=20 and for k=2 and k=3
n=30;
k1=2;
k2=3;
l=2;
supp=20;
p=dSpline(supp, k=l);
X=rSpline(n=n, supp, k=l);
ptilde=pEmp(X);
phat1=pMonotone(ptilde$freq, k=k1);
phat2=pMonotone(ptilde$freq, k=k2);

x.limits=c(0, max(supp+1, phat1$Spi+1, phat2$Spi+1));
y.limits=range(p, ptilde$freq, phat1$p, phat2$p);

plot(NULL, xlim=x.limits, ylim=y.limits, xlab="Counts", ylab="Frequencies");
points(0:supp, p, pch=16, col=1, lwd=2); 
points(ptilde$supp, ptilde$freq, pch=4, col=2, lwd=2);
points(0:max(phat1$Spi), phat1$p, pch=8, col=3, lwd=2);
points(0:max(phat2$Spi), phat2$p, pch=2, col=4, lwd=2);
legend("topright", pch=c(16, 4, 8, 2), col=c(1, 2, 3, 4),
	legend=c("p", expression(tilde("p")), expression(hat("p")*" - k = 2"), 
	expression(hat("p")*" - k = 3")));

####################
# Example 2
# mixture of 3 splines Q_j^3 and for k=4 and k=3
n=30;
k1=4;
k2=3;
l=3;
supp=c(5, 10, 20);
prob=c(0.5, 0.3, 0.2);
p=dmixSpline(supp, k=l, prob=prob);
X=rmixSpline(n=n, supp, k=l, prob=prob);
ptilde=pEmp(X);
phat1=pMonotone(ptilde$freq, k=k1);
phat2=pMonotone(ptilde$freq, k=k2);

x.limits=c(0, max(supp+1, phat1$Spi+1, phat2$Spi+1));
y.limits=range(p, ptilde$freq, phat1$p, phat2$p);

plot(NULL, xlim=x.limits, ylim=y.limits, xlab="Counts", ylab="Frequencies");
points(0:max(supp), p, pch=16, col=1, lwd=2); 
points(ptilde$supp, ptilde$freq, pch=4, col=2, lwd=2);
points(0:max(phat1$Spi), phat1$p, pch=8, col=3, lwd=2);
points(0:max(phat2$Spi), phat2$p, pch=2, col=4, lwd=2);
legend("topright", pch=c(16, 4, 8, 2), col=c(1, 2, 3, 4),
	legend=c("p", expression(tilde("p")), expression(hat(p)* " - k = 4"), 
	expression(hat(p)* " - k = 3")));

####################
# Example 3
# Poisson density
n=30;
k1=2;
k2=3;
supp=10;
p=dpois(0:supp, lambda=1);
X=rpois(n, lambda=1);
ptilde=pEmp(X);
phat1=pMonotone(ptilde$freq, k=k1);
phat2=pMonotone(ptilde$freq, k=k2);

x.limits=c(0, max(supp, phat1$Spi+1, phat2$Spi+1));
y.limits=range(p, ptilde$freq, phat1$p, phat2$p);

plot(NULL, xlim=x.limits, ylim=y.limits, xlab="Counts", ylab="Frequencies");
points(0:max(supp), p, pch=16, col=1, lwd=2);
points(ptilde$supp, ptilde$freq, pch=4, col=2, lwd=2);
points(0:max(phat1$Spi), phat1$p, pch=8, col=3, lwd=2);
points(0:max(phat2$Spi), phat2$p, pch=2, col=4, lwd=2);
legend("topright", pch=c(16, 4, 8, 2), col=c(1, 2, 3, 4),
       legend=c("p", expression(tilde("p")), expression(hat(p)* " - k = 2"),
                expression(hat(p)* " - k = 3")));

## Not run: 
####################
# Simulation for comparing ptilde and pHat (p is 3-monotone, k=3)
#
#OUTPUT
#
# cvge : percentage of non-convergence of the algorithm
# r.emp : L2-risk for the empirical estimator
# r.Hat : L2-risk for the estimator under k-monotony constraint


nSim=500;

n=30;
k=3;
l=3;
supp=20;
p=dSpline(supp, k=l);

result <- matrix(nrow=nSim,ncol=3);
dimnames(result)[[2]] <- c("cvge","r.emp","r.Hat");
for (i in 1:nSim) {
  X=rSpline(n=n, supp, k=l);
  ptilde=pEmp(X);
  phat=pMonotone(ptilde$freq, k=k);
  m <- max(supp+1,length(ptilde$freq)+1,phat$Spi+1)
  pV=c(p,rep(0,m-length(p)))
  pHat=c(phat$p,rep(0,m-length(phat$p)))
  ptilde=c(ptilde$freq,rep(0,m-length(ptilde$freq)))
  result[i,] <- c(phat$cvge,sum((pV-ptilde)**2),
                  sum((pV-pHat)**2))
}

apply(result,2,mean)

#Example with set.seed(0)
#cvge       r.emp       r.Hat 
#0.000000000 0.030682552 0.004984899

## End(Not run)
####################
# Example 1
# one triangular function T_j=Q_j^2, for j=supp=20 and for k=2 and k=3
n=30;
k1=2;
k2=3;
l=2;
supp=20;
p=dSpline(supp, k=l);
X=rSpline(n=n, supp, k=l);
ptilde=pEmp(X);
phat1=pMonotone(ptilde$freq, k=k1);
phat2=pMonotone(ptilde$freq, k=k2);

x.limits=c(0, max(supp+1, phat1$Spi+1, phat2$Spi+1));
y.limits=range(p, ptilde$freq, phat1$p, phat2$p);

plot(NULL, xlim=x.limits, ylim=y.limits, xlab="Counts", ylab="Frequencies");
points(0:supp, p, pch=16, col=1, lwd=2); 
points(ptilde$supp, ptilde$freq, pch=4, col=2, lwd=2);
points(0:max(phat1$Spi), phat1$p, pch=8, col=3, lwd=2);
points(0:max(phat2$Spi), phat2$p, pch=2, col=4, lwd=2);
legend("topright", pch=c(16, 4, 8, 2), col=c(1, 2, 3, 4),
	legend=c("p", expression(tilde("p")), expression(hat("p")*" - k = 2"), 
	expression(hat("p")*" - k = 3")));

####################
# Example 2
# mixture of 3 splines Q_j^3 and for k=4 and k=3
n=30;
k1=4;
k2=3;
l=3;
supp=c(5, 10, 20);
prob=c(0.5, 0.3, 0.2);
p=dmixSpline(supp, k=l, prob=prob);
X=rmixSpline(n=n, supp, k=l, prob=prob);
ptilde=pEmp(X);
phat1=pMonotone(ptilde$freq, k=k1);
phat2=pMonotone(ptilde$freq, k=k2);

x.limits=c(0, max(supp+1, phat1$Spi+1, phat2$Spi+1));
y.limits=range(p, ptilde$freq, phat1$p, phat2$p);

plot(NULL, xlim=x.limits, ylim=y.limits, xlab="Counts", ylab="Frequencies");
points(0:max(supp), p, pch=16, col=1, lwd=2); 
points(ptilde$supp, ptilde$freq, pch=4, col=2, lwd=2);
points(0:max(phat1$Spi), phat1$p, pch=8, col=3, lwd=2);
points(0:max(phat2$Spi), phat2$p, pch=2, col=4, lwd=2);
legend("topright", pch=c(16, 4, 8, 2), col=c(1, 2, 3, 4),
	legend=c("p", expression(tilde("p")), expression(hat(p)* " - k = 4"), 
	expression(hat(p)* " - k = 3")));

####################
# Example 3
# Poisson density
n=30;
k1=2;
k2=3;
supp=10;
p=dpois(0:supp, lambda=1);
X=rpois(n, lambda=1);
ptilde=pEmp(X);
phat1=pMonotone(ptilde$freq, k=k1);
phat2=pMonotone(ptilde$freq, k=k2);

x.limits=c(0, max(supp, phat1$Spi+1, phat2$Spi+1));
y.limits=range(p, ptilde$freq, phat1$p, phat2$p);

plot(NULL, xlim=x.limits, ylim=y.limits, xlab="Counts", ylab="Frequencies");
points(0:max(supp), p, pch=16, col=1, lwd=2);
points(ptilde$supp, ptilde$freq, pch=4, col=2, lwd=2);
points(0:max(phat1$Spi), phat1$p, pch=8, col=3, lwd=2);
points(0:max(phat2$Spi), phat2$p, pch=2, col=4, lwd=2);
legend("topright", pch=c(16, 4, 8, 2), col=c(1, 2, 3, 4),
       legend=c("p", expression(tilde("p")), expression(hat(p)* " - k = 2"),
                expression(hat(p)* " - k = 3")));

## Not run: 
####################
# Simulation for comparing ptilde and pHat (p is 3-monotone, k=3)
#
#OUTPUT
#
# cvge : percentage of non-convergence of the algorithm
# r.emp : L2-risk for the empirical estimator
# r.Hat : L2-risk for the estimator under k-monotony constraint


nSim=500;

n=30;
k=3;
l=3;
supp=20;
p=dSpline(supp, k=l);

result <- matrix(nrow=nSim,ncol=3);
dimnames(result)[[2]] <- c("cvge","r.emp","r.Hat");
for (i in 1:nSim) {
  X=rSpline(n=n, supp, k=l);
  ptilde=pEmp(X);
  phat=pMonotone(ptilde$freq, k=k);
  m <- max(supp+1,length(ptilde$freq)+1,phat$Spi+1)
  pV=c(p,rep(0,m-length(p)))
  pHat=c(phat$p,rep(0,m-length(phat$p)))
  ptilde=c(ptilde$freq,rep(0,m-length(ptilde$freq)))
  result[i,] <- c(phat$cvge,sum((pV-ptilde)**2),
                  sum((pV-pHat)**2))
}

apply(result,2,mean)

#Example with set.seed(0)
#cvge       r.emp       r.Hat 
#0.000000000 0.030682552 0.004984899

## End(Not run)

Normalized spline basis

Description

Computes the k-monotone discrete splines from Lefevre and Loisel (2013).

Usage

BaseNorm(k, J)
BaseNorm(k, J)

Arguments

`k`	Degree of monotony
`J`	maximum support of the splines

Value

matrix $Q$ with J+1 rows and J+1 columns with $Q(i,j)=Q_j^k(i-1)=C_{j-i+k-1}^{k-1}$ , where $C$ represents the binomial coefficient.

Author(s)

Jade Giguelay

References

Giguelay, J., (2016), Estimation of a discrete distribution under k-monotony constraint, in revision, (arXiv:1608.06541)

Lefevre C., Loisel S. (2013) <DOI:10.1239/jap/1378401239> On multiply monotone distributions, continuous or discrete, with applications, Journal of Applied Probability, 50, 827–847.

Examples

# Computing 3-monotone splines with maximum support 8
Q=BaseNorm(3, 8)
matplot(Q, type="l", main="3-monotone splines with maximum support 8");
# Computing 3-monotone splines with maximum support 8
Q=BaseNorm(3, 8)
matplot(Q, type="l", main="3-monotone splines with maximum support 8");

Discrete laplacian

Description

Computes the laplacians of a discrete function

Usage

Delta(k, L, p)
Delta(k, L, p)

Arguments

`k`	Maximum order of the laplacian
`L`	Support of the function
`p`	Discrete function represented as a vector

Value

Returns a matrix with the laplacians $(-1)^j\Delta^j (p(l))$ of vector $p$ for $j$ in $1,\ldots,k$ and $l$ in $0,\ldots,L$ .

Author(s)

Jade Giguelay

References

Knopp K. (1925), <DOI:10.1007/BF01479598> Mehrfach monotone Zahlenfolgen, Mathematische Zeitschrift, 22, 75–85

Giguelay, J., (2016), Estimation of a discrete distribution under k-monotony constraint, in revision, (arXiv:1608.06541)

Examples

p=dSpline(k=3, supp=20)
M=Delta(3, 20, p)
p=dSpline(k=3, supp=20)
M=Delta(3, 20, p)

Estimators of discrete probabilities under k-monotony constraint

Description

Estimators of discrete probabilities under k-monotony constraint. Estimation can be done on the set of k-monotone functions or on the set of k-monotone probabilities.

Usage

pMonotone(ptild, t.zero = 1e-10, t.P = 1e-08, max.sn = NULL, k, verbose = FALSE)
fMonotone(ptild, t.zero = 1e-10, t.P = 1e-08, max.sn = NULL, k, verbose = FALSE)
pMonotone(ptild, t.zero = 1e-10, t.P = 1e-08, max.sn = NULL, k, verbose = FALSE)
fMonotone(ptild, t.zero = 1e-10, t.P = 1e-08, max.sn = NULL, k, verbose = FALSE)

Arguments

`ptild`	Empirical estimator
`t.zero`	Threshold for the precision of the directionnal derivatives. (see OUTPUT below)
`t.P`	Threshold for the precision on the stopping criterion. (see OUTPUT below)
`max.sn`	The maximum support for the evaluation of the estimator
`k`	Degree of monotony
`verbose`	if TRUE, print for each iteration on the maximum support : pi, Psi and sumP (see OUTPUT below)

Details

The thresholds t.P and t.zero are used for the precision in the algorithm : in Step one (See REFERENCES below) the algorithm computes the directionnal derivatives of the current estimator and stops if all the directionnal derivarives are null that is to say if they are smaller than t.zero. In Step two (See REFERENCES below) the algorithm computes a stopping criterion and stops if and only if the stopping criterion is verified that is to say if some quantities are non-negative that is to say bigger than -t.P.

Value

`cvge`	cvge = 0 if the criterion Psi decreases with the support of pi. cvge = 1 if Psi increases. cvge = 2 if maximum number of iterations reached
`Spi`	Support of the positive measure pi at the last iteration
`pi`	Values of the positive measure pi at the last iteration
`p`	Values of pHat
`Psi`	Scalar value of the criterion to be minimised
`sumP`	`sum(pHat)` at convergence
`history`	Data frame with components
`L`	The maximum of the support of pi
`Psi`	Value of the criterion for the value L
`SumP`	Value of `sum(pHat)`

Author(s)

Jade Giguelay

References

Giguelay, J., (2016), Estimation of a discrete distribution under k-monotony constraint, in revision, (arXiv:1608.06541)

Examples

x=rSpline(n=50, 20, k=4)
ptild=pEmp(x);
res=pMonotone(ptild$freq, k=4)
x=rSpline(n=50, 20, k=4)
ptild=pEmp(x);
res=pMonotone(ptild$freq, k=4)

k-Knot

Description

k-Knots of a discrete function.

Usage

kKnot(p, k)
kKnot(p, k)

Arguments

`p`	Vector
`k`	Degree of the knots

Details

An integer i is a k-knot of p if $\Delta^k p(i) >0$ , where $\Delta^k$ is the k-th Laplacian of the sequence p.

Value

Vector with the k-knots of p.

Author(s)

Jade Giguelay

References

Knopp K. (1925), <DOI:10.1007/BF01479598> Mehrfach monotone Zahlenfolgen, Mathematische Zeitschrift, 22, 75–85

Giguelay, J., (2016), Estimation of a discrete distribution under k-monotony constraint, in revision, (arXiv:1608.06541)

Examples

p=dmixSpline(c(5, 10, 20), k=3, c(0.5, 0.25, 0.25))
knots=kKnot(p, 3) 
p=dmixSpline(c(5, 10, 20), k=3, c(0.5, 0.25, 0.25))
knots=kKnot(p, 3)

Empirical estimator of a discrete function

Description

Empirical estimator of a discrete function

Usage

pEmp(X)
pEmp(X)

Arguments

`X`	A random sample from a discrete probability.

Details

The empirical estimator is defined as $p(j)=\Sigma_{i=1}^n \bold{1}_{x_j=j}$ .

Value

`support`	The points of the support of the estimator
`count`	The counts of the sample
`freq`	The normalized counts

Author(s)

Jade Giguelay

Examples

x=rpois(100, lambda=0.3)
ptild=pEmp(x)
x=rpois(100, lambda=0.3)
ptild=pEmp(x)

Random generation and distribution function of k-monotone densities

Description

Random generation and distribution function for the spline of the basis from Lefevre and Loisel (2013), and mixtures of splines.

Usage

rSpline(n=1, supp, k)
dSpline(supp, k)
rmixSpline(n=1, supp, k,prob)
dmixSpline(supp, k, prob)
rSpline(n=1, supp, k)
dSpline(supp, k)
rmixSpline(n=1, supp, k,prob)
dmixSpline(supp, k, prob)

Arguments

`supp`	Support of the spline, or vector of the supports of the splines for the mixture of splines
`n`	Number of random values to return
`k`	Degree of monotony
`prob`	Vector of probabilities for the mixture of splines

Details

See BaseNorm for details on the spline basis.

Value

rSpline and rmixSpline generates random deviates from the splines and mixtures of splines.

dSpline and dmixSpline gives the distribution function.

Author(s)

Jade Giguelay

References

Giguelay, J., (2016), Estimation of a discrete distribution under k-monotony constraint, in revision, (arXiv:1608.06541)

Lefevre C., Loisel S. (2013) <DOI:10.1239/jap/1378401239> On multiply monotone distributions, continuous or discrete, with applications, Journal of Applied Probability, 50, 827–847.

Examples

x=rSpline(n=100, 20, 3)
p=dSpline(20, 3)
xmix=rmixSpline(n=100, c(5, 20), 3, c(0.5, 0.5))
pmix=dmixSpline(c(5, 20), 3, c(0.5, 0.5))
par(mfrow=c(1, 2))
hist(x, freq=FALSE)
lines(p, col="red")
hist(xmix, freq=FALSE)
lines(pmix, col="red")
x=rSpline(n=100, 20, 3)
p=dSpline(20, 3)
xmix=rmixSpline(n=100, c(5, 20), 3, c(0.5, 0.5))
pmix=dmixSpline(c(5, 20), 3, c(0.5, 0.5))
par(mfrow=c(1, 2))
hist(x, freq=FALSE)
lines(p, col="red")
hist(xmix, freq=FALSE)
lines(pmix, col="red")

Package 'pkmon'

Help Index

Least-squares estimator under k-monotony constraint for discrete functions

Description

Author(s)

References

See Also

Examples

Normalized spline basis

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Discrete laplacian

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Estimators of discrete probabilities under k-monotony constraint

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

k-Knot

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Empirical estimator of a discrete function

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Random generation and distribution function of k-monotone densities

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples