Package 'pendensity'

Title: Density Estimation with a Penalized Mixture Approach
Description: Estimation of univariate (conditional) densities using penalized B-splines with automatic selection of optimal smoothing parameter.
Authors: Christian Schellhase
Maintainer: Christian Schellhase <[email protected]>
License: GPL (>= 2)
Version: 0.2.13
Built: 2024-11-11 07:19:43 UTC
Source: CRAN

Help Index

The package 'pendensity' offers routines for estimating penalized unconditional and conditional (on factor groups) densities.

Description

The package 'pendensity' offers routines for estimating penalized unconditional and conditional (on factor groups) densities. For details see the description of the function pendensity.

Details

Package: pendensity
Type: Package
Version: 0.2.12
Date: 2019-04-07
License: GPL (>= 2) LazyLoad: yes

The packages contributes the function 'pendensity()' for estimating densities using penalized splines techniques.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Examples

y <- rnorm(100)
test <- pendensity(y~1)
plot(test)

#################

#second simple example
#with covariate

x <- rep(c(0,1),200)
y <- rnorm(400,x*0.2,1)
test <- pendensity(y~as.factor(x),lambda0=2e+07)
plot(test)

#################

#calculate the value at some (maybe not observed) value yi=c(0,1) of the estimated density

plot(test,val=c(0,1))

#################

#density-example of the stock exchange Allianz in 2006

data(Allianz)
time.Allianz <- strptime(Allianz[,1],form="%d.%m.%y")

#looking for all dates in 2006
data.Allianz <- Allianz[which(time.Allianz$year==106),2]

#building differences of first order
d.Allianz <- diff(data.Allianz)

#estimating the density, choosing a special start value for lambda
density.Allianz <- pendensity(d.Allianz~1,lambda0=90000)
plot(density.Allianz)

Daily final prices (DAX) of the German stock Allianz in the years 2006 and 2007

Description

Containing the daily final prices of the German stock Allianz in the years 2006 and 2007.

Usage

data(Allianz)

Format

A data frame with 507 observations of the following 2 variables.

Date

Date

ClosingPrice

ClosingPrice

Examples

data(Allianz)

form<-'%d.%m.%y'

time.Allianz <- strptime(Allianz[,1],form)

#looking for all dates in 2006
data.Allianz <- Allianz[which(time.Allianz$year==106),2]

#building differences of first order
d.Allianz <- diff(data.Allianz)

#estimating the density
density.Allianz <- pendensity(d.Allianz~1,)
plot(density.Allianz)

Calculating the bias of the parameter beta

Description

Calculating the bias of the parameter beta.

Usage

bias.par(penden.env)

Arguments

penden.env

Containing all information, environment of pendensity()

Details

The bias of the parameter beta is calculated as the product of the penalty parameter lambda, the penalized second order derivative of the log likelihood function w.r.t. beta 'Derv.pen', the penalty matrix 'Dm' and the parameter set 'beta'.

Bias(β)=−λDerv2.pen(β)−1DmβBias(\beta)= - \lambda {Derv2.pen(\beta)}^{-1} D_m \beta

The needed values are saved in the environment.

Value

Returning the bias of the parameter beta.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the actual weights ck

Description

Calculating the actual weights ck for each factor combination of the covariates combinations.

Usage

ck(penden.env, beta.val)

Arguments

penden.env

Containing all information, environment of pendensity()
beta.val

actual parameter beta

Details

The weights in depending of the covariate 'x' are calculated as follows.

ck(x,β)=exp⁡(Z(x)βk)∑j=−KKexp⁡(Z(x)βj)c_k(x,\beta)=\frac{\exp(Z(x)\beta_k)}{\sum_{j=-K}^{K}\exp(Z(x)\beta_j)}

For estimations without covariates, Z doesn't appear in calculations.

ck(β)=exp⁡(βk)∑k=−KKexp⁡(βk)c_k(\beta)=\frac{\exp(\beta_k)}{\sum_{k=-K}^{K}\exp(\beta_k)}

Starting density calculation, the groupings of the covariates are indexed in the main program. The groupings are saved in 'x.factor', the index which response belongs to which group is saved in 'Z.index'. Therefore, one can link to the rows in 'x.factor' to calculate the weights 'ck'.

The needed values are saved in the environment.

Value

Returning the actual weights ck, depending on the actual parameter beta in a matrix with rows.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the penalty matrix

Description

calculating the penalty matrix depending on the number of covariates 'p', the order of differences to be penalized 'm', the corresponding difference matrix 'L' of order 'm', the covariate matrix 'Z', the number of observations 'n' and the number of knots 'K'.

Usage

D.m(penden.env)

Arguments

penden.env

Containing all information, environment of pendensity()

Details

The penalty matrix is calculated as

Dm=(LT⊗Ip)(IK−m⊗ZTZn)(L⊗Ip)D_m=(L^T \otimes I_p) (I_{K-m} \otimes \frac{Z^T Z}{n}) (L \otimes I_p)

The needed values are saved in the environment.

Value

Returning the penalty matrix.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the first derivative of the pendensity likelihood function w.r.t. parameter beta

Description

Calculating the first derivative of the pendensity likelihood function w.r.t. parameter beta.

Usage

Derv1(penden.env)

Arguments

penden.env

Containing all information, environment of pendensity()

Details

We calculate the first derivative of the pendensity likelihood function w.r.t. the parameter beta. The calculation of the first derivative of the pendensity likelihood function w.r.t. parameter beta is done in four steps. The first derivative equals in the case of covariates

s(β)=∂l(β)/∂β=∑i=1nsi(β)s(\beta)= {\partial l(\beta)}/{\partial \beta}= \sum_{i=1}^n s_i(\beta)

where

si(β)=ZT(xi)CT(xi,β)ϕ~if^(yi∣xi)s_i(\beta) = {{\bf \mathcal{Z}}^T(x_i)} {{\bf \mathcal{C}}^T (x_i,\beta)} \frac{\tilde{\boldsymbol\phi}_i}{\hat{f}(y_i|x_i)}


Without covariates, the matrix 'Z' doesn't appear. Starting density calculation, the groupings of the covariates are indexed in the main program. The groupings are saved in 'x.factor'. Creating an index that reports which response belongs to which covariate group, saving in 'Z.index'. Therefore, one can link to the rows in the object 'ck' to calculate the matrix 'C.bold', which depends only on the grouping of the covariate. Without any covariate, 'C.bold' is equal for every observation.
The calculation of the first derivative of the pendensity likelihood function w.r.t. parameter beta is done in four steps. Firstly, we calculate the matrix 'C.bold', depending on the groups of 'x.factor'.
Secondly, for calculating we need the fitted values of each observation, 'f.hat'. These values are calculated for the actual parameter set beta in the program 'f.hat'. Of course, we need the value of the base for each observation, ϕ[i]\phi[i].
Moreover, for the case of conditional density estimation, we need a Z-Matrix, due to the rules for derivations of the function 'exp()'. This Z-matrix doesn't appear directly in the calculations. We construct the multiplication with this Z-matrix with using an outer product between the corresponding grouping in 'x.factor' and the product of the corresponding values 'C.bold' and 'base.den', divided by the fitted value 'f.hat'. Finally, we add some penalty on the derivative, which is calculated in the fourth step. The penalty equals −λDmβ-\lambda D_m \beta.
For later use, we save the unpenalized first derivative as a matrix, in which the i-th column contains the first derivative of the pendensity likelihood function, evaluated for the i-th value of the response. The needed values are saved in the environment.

Value

Derv1.cal

matrix, in which the i-th column contains the first derivative, evaluated for the i-th value of the response variable without penalty. Needed for calculating the second order derivative, called s(β)s(\beta)
Derv1.pen

first order derivation of the penalized likelihood function w.r.t. parameter beta, called

sp(β)s_p(\beta)

f.hat.val

fitted values of the response for actual parameter beta, called f^\hat{f}

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the second order derivative with and without penalty

Description

Calculating the second order derivative of the likelihood function of the pendensity approach w.r.t. the parameter beta. Thereby, for later use, the program returns the second order derivative with and without the penalty.

Usage

Derv2(penden.env, lambda0)

Arguments

penden.env

Containing all information, environment of pendensity()
lambda0

smoothing parameter lambda

Details

We approximate the second order derivative in this approach with the negative fisher information.

J(β)=−∂2l(β)∂β ∂βT≈∑i=1nsi(β)siT(β).J(\beta)= - \frac{\partial^2 l(\beta)}{\partial \beta \ \partial \beta^T} \approx \sum_{i=1}^n s_i(\beta) s_i^T(\beta) .

Therefore we construct the second order derivative of the i-th observation w.r.t. beta with the outer product of the matrix Derv1.cal and the i-th row of the matrix Derv1.cal.
The penalty is computed as

λDm\lambda D_m

.

Value

Derv2.pen

second order derivative w.r.t. beta with penalty
Derv2.cal

second order derivative w.r.t. beta without penalty. Needed for calculating of e.g. AIC.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Daily final prices (DAX) of the German stock Deutsche Bank in the years 2006 and 2007

Description

Containing the daily final prices of the German stock Deutsche Bank in the years 2006 and 2007.

Usage

data(DeutscheBank)

Format

A data frame with 507 observations of the following 2 variables.

Date

Date

ClosingPrice

ClosingPrice

Examples

data(DeutscheBank)

form<-'%d.%m.%y'

time.DeutscheBank <- strptime(DeutscheBank[,1],form)

#looking for all dates in 2006
data.DeutscheBank <- DeutscheBank[which(time.DeutscheBank$year==106),2]

#building differences of first order
d.DeutscheBank <- diff(data.DeutscheBank)

#estimating the density
density.DeutscheBank <- pendensity(d.DeutscheBank~1)
plot(density.DeutscheBank)

These functions are used for calculating the empirical and theoretical distribution functions.

Description

These functions cooperate with each other for calculating the distribution functions. 'distr.func' is the main program, calling 'distr.func.help',generating an environment with needed values for calculating the distribution of each interval between two neighbouring knots. 'distr.func' returns analytical functions of the distribution of each interval between two neighbouring knots. Therefore the function 'poly.part' is needed to construct these functions. 'cal.int' evaluates these integrals, considering if the whole interval should be evaluated or if any discrete value 'yi' is of interest.

Usage

distr.func(yi = NULL, obj, help.env=distr.func.help(obj))
distr.func.help(obj)
cal.int(len.b, q, help.env, knots.val)
poly.part(i,j,knots.val,help.env,q, yi=NULL, poly=FALSE)

Arguments

yi

if the distribution at any discrete point is of interest, you can call for it. Default=NULL doesn't consider any discrete point

obj

a object of class pendensity
help.env

object is generated with calling distr.func.help(obj)
len.b

length of B-Spline
q

order of the B-Spline
knots.val

values of the used knots
poly

TRUE/FALSE
i

internal values for calculating the polynomials of each B-Spline
j

internal values for calculating the polynomials of each B-Spline

Value

distr.func

returns analytical functions of the distributions between each two neighbouring intervals
distr.func.help

creating environment 'help.env', creating help points between each two neighbouring knots and calculates the polynomial-coefficients of each base part
cal.int

evaluating the result of distr.func. Thereby it's possible to call for an explicit distribution values F(yi)
poly.part

using in 'distr.func' for creating the polynomial functions of each interval of each two neighbouring knots

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the fitted density or distribution

Description

Calculating the fitted density or distribution.

Usage

dpendensity(x,val)
ppendensity(x,val)

Arguments

x

Object of class pendensity
val

Vector of values where to calculate the density

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the actual fitted values 'f.hat' of the estimated density function f for the response y

Description

Calculating the actual fitted values of the response, depending on the actual parameter set beta

Usage

f.hat(penden.env,ck.temp=NULL)

Arguments

penden.env

Containing all information, environment of pendensity()
ck.temp

actual weights, depending on the actual parameter set beta. If NULL, the beta parameter is caught in th environment

Details

Calculating the actual fitted values of the response, depending on the actual parameter set beta. Multiplying the actual set of parameters ckc_k with the base 'base.den' delivers the fitted values, depending on the group of covariates, listed in 'x.factor'.

Value

The returned value is a vector of the fitted value for each observation of y.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculates the difference matrix of order m

Description

Calculating the differences matrix 'L' of order 'm', depending on the number of knots 'k'.

Usage

L.mat(penden.env)

Arguments

penden.env

Environment of pendensity()

Value

Returns the difference matrix of order 'm' for given number of knots 'K'.

The needed values are saved in the environment.

Note

Right now, the difference matrix is implemented for m=1,2,3,4.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the marginal likelihood

Description

Calculating the marginal likelihood.

Usage

marg.likelihood(penden.env,pen.likelihood)

Arguments

penden.env

Containing all information, environment of pendensity()
pen.likelihood

penalized log likelihood

Details

Calculating is done using a Laplace approximation for the integral of the marginal likelihood.

The needed values are saved in the environment.

Value

Returning the marginal likelihood.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the AIC value

Description

Calculating the AIC value of the density estimation. Therefore, we add the unpenalized log likelihood of the estimation and the degree of freedom, which are

Usage

my.AIC(penden.env, lambda0, opt.Likelihood = NULL)

Arguments

penden.env

Containing all information, environment of pendensity()
lambda0

penalty parameter lambda
opt.Likelihood

optimal unpenalized likelihood of the density estimation

Details

AIC is calculated as AIC(λ)=−l(β^)+df(λ)AIC(\lambda)= - l(\hat{\beta}) + df(\lambda)

Value

myAIC

sum of the negative unpenalized log likelihood and mytrace
mytrace

calculated mytrace as the sum of the diagonal matrix df, which results as the product of the inverse of the penalized second order derivative of the log likelihood with the unpenalized second order derivative of the log likelihood

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

my.bspline

Description

Integrates the normal B-Spline Base to a value of one. The dimension of the base depends on the input of number of knots 'k' and of the order of the B-Spline base 'q'.

Usage

my.bspline(h, q, knots.val, y, K, plot.bsp)

Arguments

h

if equidistant knots are just (default in pendensity()), h is the distance between two neighbouring knots
q

selected order of the B-Spline base
knots.val

selected values for the knots
y

values of the response variable y
K

the number of knots K for the construction of the base
plot.bsp

Indicator variable TRUE/FALSE if the integrated B-Spline base should be plotted

Details

Firstly, the function constructs the B-Spline base to the given number of knots 'K' and the given locations of the knots 'knots.val\$val. Due to the recursive construction of the B-Spline, for all orders greater than 2, the dimension of the B-Spline base of given K grows up with help.degree=q-2. Avoiding open B-Splines at the boundary, we simulate 6 extra knots at both ends of the support, saved in knots.val\$all. Therefore, we get normal B-Splines at the given knots 'knots.val\$val'. For these knots, we construct the B-Spline base of order 'q' and for order 'q+1' (using for calculation the distribution). Additionally, we save q-1 knots at both ends of the support of 'knots.val\$val'. After construction, we get a base of dimension K=K+help.degree. So, we define our value K and cut our B-Spline base at both ends to get the adequate base due to the order 'q' and the number of knots 'K'. For the base of order 'q+1', we need to get an additional base, due to the construction of the B-Splines. Due to the fact, that we use equidistant knots, we can integrate our B-Splines very simple to the value of 1. The integration is done by the well-known factor q/(knots.val\$help[i+q]-knots.val\$help[i]). This results the standardization coefficients 'stand.num' for each B-spline (which are identically for equidistant knots). Moreover, one can draw the integrated base and, if one calls this function with the argument 'plot.bsp=TRUE'.

Value

base.den

The integrated B-Spline base of order q
base.den2

The integrated B-Spline base of order q+1
stand.num

The coefficients for standardization of the ordinary B-Spline base
knots.val

This return is a list. It consider of the used knots 'knots.val\$val', the help knots 'knots.val\$help' and the additional knots 'knots.val\$all', used for the construction of the base and the calculation of the distribution function of each B-Spline.
K

The transformed value of K, due to used order 'q' and the input of 'K'
help.degree

Due to the recursive construction of the B-Spline, for all orders greater than 2, the dimension of the B-Spline base of given K grows up with 'help.degree=q-2'. This value is returned for later use.

Note

This functions uses the fda-package for the construction of the B-Spline Base.

Author(s)

Christian Schellhase <[email protected]>

my.positive.definite.solve

Description

Reverses a quadratic positive definite matrix.

Usage

my.positive.definite.solve(A, eps = 1e-15)

Arguments

A

quadratic positive definite matrix
eps

level of the lowest eigenvalue to consider

Details

The program makes an eigenvalue decomposition of the positive definite matrix A and searches all eigenvalues greater than eps. The value of return is the inverse matrix of A, constructed with the matrix product of the corresponding eigenvalues and eigenvectors.

Value

The return is the inverse matrix of A.

Author(s)

Christian Schellhase <[email protected]>

Calculating the new parameter beta

Description

Calculating the direction of the Newton-Raphson step for the known beta and iterate a step size bisection to control the maximizing of the penalized likelihood.

Usage

new.beta.val(llold, penden.env)

Arguments

llold

log likelihood of the algorithm one step before
penden.env

Containing all information, environment of pendensity()

Details

We terminate the search for the new beta, when the new log likelihood is smaller than the old likelihood and the step size is smaller or equal 1e-3. We calculate the direction of the Newton Raphson step for the known betatbeta_t and iterate a step size bisection to control the maximizing of the penalized likelihood

lp(βt,λ0)l_p(\beta_t,\lambda_0)

. This means we set

βt+1=βt−2−v{sp(βt,λ0)⋅(−Jp(βt,λ0))−1}\beta_{t+1}=\beta_t - 2^{-v} \{s_p(\beta_t,\lambda_0) \cdot (-J_p(\beta_t,\lambda_0))^{-1}\}

with sps_p as penalized first order derivative and JpJ_p as penalized second order derivative. We begin with v=0v=0. Not yielding a new maximum for a current v, we increase v step by step respectively bisect the step size. We terminate the iteration, if the step size is smaller than some reference value epsilon (eps=1e-3) without yielding a new maximum. We iterate for new parameter beta until the new log likelihood depending on the new estimated parameter beta differ less than 0.1 log-likelihood points from the log likelihood estimated before.

Value

Likelie

corresponding log likelihood
step

used step size

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating new penalty parameter lambda

Description

Calculating new penalty parameter lambda.

Usage

new.lambda(penden.env,lambda0)

Arguments

penden.env

Containing all information, environment of pendensity()
lambda0

actual penalty parameter lambda

Details

Iterating for the lambda is stopped, when the changes between the old and the new lambda is smaller than 0.01*lambda0. If this criterion isn't reached, the iteration is terminated after 11 iterations.

The iteration formulae is

λ−1=β^TDmβ^df(λ^)−(m−1).\lambda^{-1}=\frac{\hat{\beta}^T D_m \hat{\beta}}{df(\hat{\lambda})-(m-1)}.

Value

Returning the new lambda.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the log likelihood

Description

Calculating the considered log likelihood. If one chooses lambda0=0, one gets the (actual) unpenalized log likelihood. Otherwise, one gets the penalized log likelihood for the used fitted values of the response y and the actual parameter set beta.

Usage

pen.log.like(penden.env,lambda0,f.hat.val=NULL,beta.val=NULL)

Arguments

penden.env

Containing all information, environment of pendensity()
lambda0

penalty parameter lambda
f.hat.val

matrix contains the fitted values of the response, if NULL the matrix is caught in the environment
beta.val

actual parameter set beta, if NULL the vector is caught in the environment

Details

The calculation depends on the fitted values of the response as well as on the penalty parameter lambda and the penalty matrix Dm.

l(β)=∑i=1n[log⁡{∑k=−KKck(xi,β)ϕk(yi)}]−12λβTDmβl(\beta)=\sum_{i=1}^{n} \left[ \log \{\sum_{k=-K}^K c_k(x_i,\beta) \boldsymbol\phi_k(y_i)\} \right]- \frac 12 \lambda \beta^T D_m \beta

.

The needed values are saved in the environment.

Value

Returns the log likelihood depending on the penalty parameter lambda.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Formula interpretation and data transfer

Description

Function 'pendenForm' interprets the input 'form' of the function pendensity(),transfers the response and covariates data back to the main program and categorize the values to groupings.

Usage

pendenForm(penden.env)
string.help(string, star = " ")

Arguments

penden.env

environment used in pendensity()
string

string of the formula
star

separating letter

Value

Returning the values and the structure of response and covariates.

Author(s)

Christian Schellhase <[email protected]>

Calculating penalized density

Description

Main program for estimation penalized densities. The estimation can be done for response with or without any covariates. The covariates have to be factors. The response is called 'y', the covariates 'x'. We estimate densities using penalized splines. This done by using a number of knots and a penalty parameter, which are sufficient large. We penalize the m-order differences of the beta-coefficients to estimate the weights 'ck' of the used base functions.

Usage

pendensity(form, base, no.base, max.iter, lambda0, q, sort, with.border, m, data,eps)

Arguments

form

formula describing the density, the formula is

y∼x1+x2+…xny \sim x_1 + x_2 + \dots x_n

where 'x' have to be factors.
base

supported bases are "bspline" or "gaussian"
no.base

how many knots 'K', following the approach to use 2'no.base'+1 knots, if 'no.base' is NULL, default is K=41.
max.iter

maximum number of iteration, the default is max.iter=20.
lambda0

start penalty parameter, the default is lambda0=500
q

order of B-Spline base, the default is 'q=3'
sort

TRUE or FALSE, if TRUE the response and the covariates should be sorted. Default is TRUE.
with.border

determining the number of additional knots on the left and the right of the support of the response. The number of knots 'no.base' is not influenced by this parameter. The amount of knots 'no.base' are placed on the support of the response. The amount of knots determined in 'with.border' is placed outside the support and reduce the amount of knots on the support about its value. Default is NULL.
m

m-th order difference for penalization. Default is m=q.
data

reference to the data. Default reference is the data=parent.frame().
eps

Level of percentage to determine calculation of optimal lambda, default=0.01

Details

pendensity() begins with setting the parameters for the estimation. Checking the formula and transferring the data into the program, setting the knots and creating the base, depending on the chosen parameter 'base'. Moreover the penalty matrix is constructed. At the beginning of the first iteration the beta parameter are set equal to zero. With this setup, the first log likelihood is calculated and is used for the first iteration for a new beta parameter.
The iteration for a new beta parameter is done with a Newton-Raphson-Iteration and implemented in the function 'new.beta.val'. We calculate the direction of the Newton Raphson step for the known betatbeta_t and iterate a step size bisection to control the maximizing of the penalized likelihood

lp(βt,λ0)l_p(\beta_t,\lambda_0)

. This means we set

βt+1=βt−2−v{sp(βt,λ0)⋅(−Jp(βt,λ0))−1}\beta_{t+1}=\beta_t - 2^{-v} \{s_p(\beta_t,\lambda_0) \cdot (-J_p(\beta_t,\lambda_0))^{-1}\}

with sps_p as penalized first order derivative and JpJ_p as penalized second order derivative. We begin with v=0v=0. Not yielding a new maximum for a current v, we increase v step by step respectively bisect the step size. We terminate the iteration, if the step size is smaller than some reference value epsilon (eps=1e-3) without yielding a new maximum. We iterate for new parameter beta until the new log likelihood depending on the new estimated parameter beta differ less than 0.1 log-likelihood points from the log likelihood estimated before.
After reaching the new parameter beta, we iterate for a new penalty parameter lambda. This iteration is done by the function 'new.lambda'. The iteration formula is

λ−1=β^TDmβ^df(λ^)−p(m−1){\lambda}^{-1}=\frac{\hat{\beta}^T D_m \hat{\beta}}{df(\hat{\lambda})-p(m-1)}


The iteration for the new lambda is terminated, if the approximate degree of freedom minus p*(m-1) is smaller than some epsilon2 (eps2=0.01). Moreover, we terminate the iteration if the new lambda is approximatively converted, i.e. the new lambda differs only 0.001*old lambda (*) from the old lambda. If these both criteria doesn't fit, the lambda iteration is terminated after eleven iterations.
We begin a new iteration with the new lambda, restarting with parameter beta setting equal to zero again. This procedure is repeated until convergence of lambda, i.e. that the new lambda fulfills the criteria (*). If this criteria is not fulfilled after 20 iterations, the total iteration terminates.
After terminating all iterations, the final AIC, ck and beta are saved in the output.
For speediness, all values, matrices, vectors etc. are saved in an environment called 'penden.env'. Most of the used programs get only this environment as input.

Value

Returning an object of class pendensity. The class pendensity consists of the "call" and three main groups "values", "splines" and "results".

call: the formula prompted for calculation of the penalized density.

#####

\$values contains: y: the values of the response variable x: the values of the covariate(s) sort: TRUE/FALSE if TRUE the response (and covariates) have been sorted in increasing order of the response

\$values\$covariate contains Z: matrix Z levels: existing levels of each covariate how.levels: number of existing levels of all covariates how.combi: number of combination of levels x.factor: list of all combination of levels

#####

\$splines contains K: number of knots N: number of coefficients estimated for each base, depending on the number of covariates. MeanW: values of the knots used for splines and means of the Gaussian densities Stand.abw: values of the standard deviance of the Gaussian densities h: distance between the equidistant knots m: used difference order for penalization q: used order of the B-Spline base stand.num: calculated values for standardization getting B-Spline densities base: used kind of base, "bspline" or "gaussian" base.den: values of the base of order q created with knots=knots.val$val base.den2: values of the base of order q+1 created with knots=knots.val$val. Used for calculating the distribution function(s). L: used difference matrix Dm: used penalty matrix, depending on lambda0, L (,Z) and n=number of observations help.degree: additional degree(s) depending on the number of knots K and the used order q

\$splines\$knots.val contains: val: list of the used knots in the support of the response all: list of the used knots extended with additional knots used for constructing

#####

results contains: ck: final calculated weights ck beta.val: final calculated parameter beta lambda0: final calculated lambda0 fitted: fitted values of the density f(y) variance.par: final variances of the parameter beta bias.par: final bias of the parameter beta

results\$AIC contains: my.AIC: final AIC value my.trace: trace component of the final AIC

results\$Derv contains: Derv2.pen: final penalized second order derivation Derv2.cal: final non-penalized second order derivation Derv1.cal: final non-penalized first order derivation

results\$iterations contains: list.opt.results: list of the final results of each iteration of new beta + new lambda all.lists: list of lists. Each list contains the result of one iteration

results\$likelihood contains: pen.Likelihood: final penalized log likelihood opt.Likelihood: final log likelihood marg.Likelihood: final marginal likelihood

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Plotting estimated penalized densities

Description

Plotting estimated penalized densities, need object of class 'pendensity'.

Usage

## S3 method for class 'pendensity'
plot(x, plot.val = 1, val=NULL, latt = FALSE, kernel = FALSE, confi = TRUE,
 main = NULL, sub = NULL, xlab = NULL, ylab = NULL, plot.base = FALSE,
 lwd=NULL,legend.txt=NULL,plot.dens=TRUE,...)

Arguments

x

object of class pendensity
plot.val

if plot.val=1 the density is plotted, if plot.val=2 the distribution function of the observation values is plotted, if plot.val=3 the distribution function is plotted as function
val

vector of y, at which the estimated density should be calculated. If plot.val=2, the calculated values of distribution are returned and the values are pointed in the distribution function of the observed values.
latt

TRUE/FALSE, if TRUE the lattice interface should be used for plotting, default=FALSE
kernel

TRUE/FALSE, if TRUE a kernel density estimation should be added to the density plots, default=FALSE
confi

TRUE/FALSE, if TRUE confidence intervals should be added to the density plots, default=TRUE

main

Main of the density plot, if NULL main contains settings 'K', 'AIC' and 'lambda0' of the estimation
sub

sub of the density plot, if NULL sub contains settings used base 'base' and used order of B-Spline 'q'
xlab

xlab of the density plot, if NULL xlab contains 'y'
ylab

ylab of the density plot, if NULL ylab contains 'density'
plot.base

TRUE/FALSE, if TRUE the weighted base should be added to the density plot, default=FALSE
lwd

lwd of the lines of density plot, if NULL lwd=3, the confidence bands are plotted with lwd=2
legend.txt

if FALSE no legend is plotted, legend.txt can get a vector of characters with length of the groupings. legend.txt works only for plot.val=1
plot.dens

TRUE/FALSE, if the estimated density should be plotted. Default=TRUE. Interesting for evaluating densities in values 'val', while this special plot is not needed.
...

further arguments

Details

Each grouping of factors is plotted. Therefore, equidistant help values are constructed in the support of the response for each grouping of factors. Weighting these help values with knots weights ck results in the density estimation for each grouping of factors. If asked for, pointwise confidence intervals are computed and plotted.

Value

If the density function is plotted, function returns two values

help.env

Contains the constructed help values for the response, the corresponding values for the densities and if asked for the calculated confidence intervals
combi

list of all combinations of the covariates

If additionally the function is called with a valid argument for 'val', a list returns with

y

values at which the estimated density has been calculated
fy

calculated density values in y
sd.up.y.val

the values of the upper confidence interval of y
sd.down.y.val

the values of the lower confidence interval of y

If the empirical distribution function is plotted, the function returns

y

containing the observed values y
sum

containing the empirical distribution of each observation y

If the theoretical distribution function is plotted, the function returns an environment. For plotting the theoretical distributions, each interval between two knots is evaluated at 100 equidistant simulated points between the two knots considered. These points are saved in the environment with the name "paste("x",i,sep="")" for each interval i, the calculated distribution is save with the name "paste("F(x)",i,sep="")" for each interval i. For these points, the distribution is calculated.

Note

For plotting the density and e.g. the empirical distributions, use e.g. 'X11()' before calling the second plot to open a new graphic device.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Examples

y <- rnorm(100)
test <- pendensity(y~1)
plot(test)

#distribution
plot(test,plot.val=2)

Printing the main results of the (conditional) penalized density estimation

Description

Printing the call of the estimation, the resulting weights, the final lambda0 and the corresponding value of AIC. Need an object of class pendensity.

Usage

## S3 method for class 'pendensity'
print(x, ...)

Arguments

x

x has to be object of class pendensity
...

further arguments

Author(s)

Christian Schellhase <[email protected]>

Testing pairwise equality of densities

Description

Every group of factor combination is tested pairwise for equality to all other groups.

Usage

test.equal(obj)

Arguments

obj

object of class pendensity

Details

We consider the distribution of the integrated B-Spline density base. This is saved in the program in the object named 'mat1'. Moreover, we use the variance 'var.par' of the estimation, the weights and some matrices 'C' of the two compared densities to construct the matrix 'W'. We simulate the distribution of the test statistic using a spectral decomposition of W.

Value

Returning a list of p-values for testing pairwise for equality.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating the variance of the parameters

Description

Calculating the variance of the parameters of the estimation, depending on the second order derivative and the penalized second order derivative of the density estimation.

Usage

variance.par(penden.env)

Arguments

penden.env

Containing all information, environment of pendensity()

Details

The variance of the parameters of the estimation results as the product of the inverse of the penalized second order derivative times the second order derivative without penalization time the inverse of the penalized second order derivative.

V(β,λ0)=Ip−1(β,λ)Ip(β,λ=0)Ip−1(β,λ)V(\beta, \lambda_0)=I_p^{-1}(\beta, \lambda) I_p(\beta, \lambda=0) I_p^{-1}(\beta, \lambda) with Ip(β−1,λ)=Ef(y){Jp(β,λ)}I_p(\beta^{-1}, \lambda)=E_{f(y)}\bigl\{J_p(\beta, \lambda)\bigr\}

The needed values are saved in the environment.

Value

The return is a variance matrix of the dimension (K-1)x(K-1).

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

Calculating variance and standard deviance of each observation.

Description

Calculating the variance and standard deviance of each observation. Therefore we use the variance of the parameter set beta, called 'var.par'.

Usage

variance.val(base.den, var.par, weight, K, x, list.len, Z, x.factor, y.val=NULL)

Arguments

base.den

base values
var.par

variance of the parameter set beta
weight

weights ck
K

number of knots
x

covariates
list.len

number of covariate combinations
Z

covariate matrix
x.factor

list of covariate combinations
y.val

optimal values for calculating the variance in any point yi in the case of a factorial density

Value

Returning a vector with the standard deviance of each observation.

Author(s)

Christian Schellhase <[email protected]>

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.