Nothing
#
#
# Script for adjustment of lambda
#
#
library(aws)
############################################################
#
# univariate
#
############################################################
# Gaussian models
y <- rnorm(10000)
# local const alpha < 0.01
yhat <- aws(y,hmax=10000,ladjust=1,u=0,testprop=TRUE,graph=TRUE)#11.3
# local polynomial degree 1 alpha=0.05
yhatInf <- lpaws(y,degree=1,hmax=1000,ladjust=1e10,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)
yhat <- lpaws(y,degree=1,hmax=1000,ladjust=1,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)#6
max(yhat@mae/yhatInf@mae-1)
# local polynomial degree 0 alpha=0.05
yhatInf <- lpaws(y,degree=0,hmax=1000,ladjust=1e10,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)
yhat <- lpaws(y,degree=0,hmax=1000,ladjust=1,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)#6
max(yhat@mae/yhatInf@mae-1)
# local polynomial degree 2 alpha=0.05
yhatInf <- lpaws(y,degree=2,hmax=1000,ladjust=1e10,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)
yhat <- lpaws(y,degree=2,hmax=1000,ladjust=1,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)#10.4
max(yhat@mae/yhatInf@mae-1)
# irregular design alpha=0.08
x <- sort(runif(10000,0,10000))
yhatInf <- aws.irreg(y,x,hmax=1000,nbins=1000,ladjust=1e10,graph=TRUE)
yhat <- aws.irreg(y,x,hmax=1000,nbins=1000,ladjust=1,graph=TRUE)#14.2
sd(yhat@theta)/sd(yhatInf@theta)-1
# local const with variance model alpha < 0.01
yhatInf <- aws.gaussian(y,hmax=1000,ladjust=1e10,u=0,graph=TRUE,homogen=FALSE)
yhat <- aws.gaussian(y,hmax=1000,ladjust=1,u=0,graph=TRUE,homogen=FALSE)#11.3
max(yhat@mae/yhatInf@mae-1)
# local const Bernoulli alpha = 0.07
y <- rbinom(10000,1,.05) # extreme values are more critical
yhat <- aws(y,hmax=10000,ladjust=1,u=0.05,family="Bernoulli",testprop=TRUE,graph=TRUE)#9.6
# local const Poisson alpha = 0.01
y <- rpois(10000,1)
yhat <- aws(y,hmax=10000,ladjust=1,u=1,family="Poisson",testprop=TRUE,graph=TRUE)#9.6
# local const Exponential alpha < 0.01
y <- rexp(10000,1)
yhat <- aws(y,hmax=4000,ladjust=1,u=1,family="Exponential",testprop=TRUE,graph=TRUE)#14.2
# local const Volatility alpha =0.02
y <- rnorm(10000)
yhat <- aws(y,hmax=4000,ladjust=1,u=1,family="Volatility",testprop=TRUE,graph=TRUE)#10
# local const Variance alpha = 0.05
y <- rnorm(10000)^2
yhat <- aws(y,hmax=4000,ladjust=1,u=1,family="Variance",testprop=TRUE,graph=TRUE,shape=1)#12.8
############################################################
#
# bivariate
#
############################################################
# Gaussian models
y <- matrix(rnorm(512^2),512,512)
# local const alpha = 0.07
yhat <- aws(y,hmax=10,ladjust=1,u=0,testprop=TRUE,graph=TRUE)#6.1
# local polynomial degree 1 alpha=0.07
yhatInf <- lpaws(y,degree=1,hmax=15,ladjust=1e10,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)
yhat <- lpaws(y,degree=1,hmax=15,ladjust=1,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)#11.3
max(yhat@mae/yhatInf@mae-1)
# local polynomial degree 2 alpha=0.09
yhatInf <- lpaws(y,degree=2,hmax=20,ladjust=1e10,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)
yhat <- lpaws(y,degree=2,hmax=20,ladjust=1,u=0,graph=TRUE,homogen=FALSE,earlystop=FALSE)# 27
max(yhat@mae/yhatInf@mae-1)
# irregular design alpha=0.05
y <- rnorm(10000)
x <- cbind(runif(10000,0,1),runif(10000,0,1))
yhatInf <- aws.irreg(y,x,hmax=25,nbins=200,ladjust=1e10,graph=TRUE)
yhat <- aws.irreg(y,x,hmax=25,nbins=200,ladjust=1,graph=TRUE)#9.1
sd(yhat@theta)/sd(yhatInf@theta)-1
# local const with variance model alpha = 0.025
y <- matrix(rnorm(512^2),512,512)
yhatInf <- aws.gaussian(y,hmax=10,ladjust=1e10,u=0,graph=TRUE,homogen=FALSE)
yhat <- aws.gaussian(y,hmax=10,ladjust=1,u=0,graph=TRUE,homogen=FALSE)#7.6
max(yhat@mae/yhatInf@mae-1)
# local const Bernoulli alpha = 0.025
y <- matrix(rbinom(512^2,1,.05),512,512) # extreme values are more critical
yhat <- aws(y,hmax=10,ladjust=1,u=0.05,family="Bernoulli",testprop=TRUE,graph=TRUE)#7.6
# local const Poisson alpha = 0.02
y <- matrix(rpois(512^2,1),512,512)
yhat <- aws(y,hmax=10,ladjust=1,u=1,family="Poisson",testprop=TRUE,graph=TRUE)#7.6
# local const Exponential alpha = 0.03
y <- matrix(rexp(512^2,1),512,512)
yhat <- aws(y,hmax=10,ladjust=1,u=1,family="Exponential",testprop=TRUE,graph=TRUE)#6.8
# local const Volatility alpha =0.05
y <- matrix(rnorm(512^2),512,512)
yhat <- aws(y,hmax=10,ladjust=1,u=1,family="Volatility",testprop=TRUE,graph=TRUE)#6.1
# local const Variance alpha = 0.04
y <- matrix(rnorm(512^2)^2,512,512)
yhat <- aws(y,hmax=10,ladjust=1,u=1,family="Variance",testprop=TRUE,graph=TRUE,shape=1)#6.1
############################################################
#
# 3D
#
############################################################
# Gaussian models
y <- array(rnorm(64^3),c(64,64,64))
# local const alpha = 0.05
yhat <- aws(y,hmax=5,ladjust=1,u=0,testprop=TRUE,graph=TRUE)#6.2
# local const Bernoulli alpha = 0.02
y <- array(rbinom(64^3,1,.05),c(64,64,64)) # extreme values are more critical
yhat <- aws(y,hmax=5,ladjust=1,u=0.05,family="Bernoulli",testprop=TRUE,graph=TRUE)#6.9
# local const Poisson alpha = 0.02
y <- array(rpois(64^3,1),c(64,64,64))
yhat <- aws(y,hmax=5,ladjust=1,u=1,family="Poisson",testprop=TRUE,graph=TRUE)#6.9
# local const Exponential alpha = 0.04
y <- array(rexp(64^3,1),c(64,64,64))
yhat <- aws(y,hmax=5,ladjust=1,u=1,family="Exponential",testprop=TRUE,graph=TRUE)#6.1
# local const Volatility alpha =0.045
y <- array(rnorm(64^3),c(64,64,64))
yhat <- aws(y,hmax=5,ladjust=1,u=1,family="Volatility",testprop=TRUE,graph=TRUE)#6.1
# local const Variance alpha = 0.04
y <- array(rnorm(64^3)^2,c(64,64,64))
yhat <- aws(y,hmax=5,ladjust=1,u=1,family="Variance",testprop=TRUE,graph=TRUE,shape=1)#6.1
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