Nothing
expectreg.ls <-
function(formula, data = NULL,estimate = c("laws", "restricted", "bundle", "sheets"),
smooth = c("schall", "ocv", "gcv", "cvgrid", "aic", "bic", "lcurve", "fixed"),
lambda = 1, expectiles = NA, ci = FALSE, LAWSmaxCores = 1, ...)
{
dot_in <- list(...)
delta_garrote <- NULL
if(!is.null(dot_in) && length(dot_in) > 0 && "delta_garrote" %in% names(dot_in)) {
delta_garrote <- dot_in$delta_garrote
}
build_onemodel <- FALSE
if(!is.null(dot_in) && length(dot_in) > 0 && "list_models" %in% names(dot_in) && length(dot_in$list_models) > 0) {
list_models <- dot_in$list_models
build_onemodel <- TRUE
}
smooth = match.arg(smooth)
estimate = match.arg(estimate)
if(estimate != "laws" && build_onemodel) stop("build_onemodel only possible for laws")
smooth_orig <- smooth
if(!is.na(charmatch(expectiles[1], "density")) && charmatch(expectiles[1], "density") > 0) {
pp <- seq(0.01, 0.99, by = 0.01)
} else if(any(is.na(expectiles)) || !is.vector(expectiles) || any(expectiles > 1) || any(expectiles < 0)) {
pp <- c(0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.98, 0.99)
} else {
pp <- expectiles
}
np <- length(pp)
yy = eval(as.expression(formula[[2]]), envir = data, enclos = environment(formula))
attr(yy, "name") = deparse(formula[[2]])
m = length(yy)
design = list()
x = list()
types = list()
bnd = list()
Zspathelp = list()
nb = vector()
nbp = vector()
nbunp = vector()
krig.phi = list()
center = TRUE
varying = list()
Blist = list()
Plist = list()
terms_formula <- labels(terms(formula))
if (formula[[3]] == "1") {
design[[1]] = rb(matrix(1, nrow = m, ncol = 1), "parametric", center = F)
smooth = "fixed"
design[[1]]$xname <- "(Intercept)"
} else if(formula[[3]] == ".") {
design[[1]] = rb(data[,names(data) != all.vars(formula[[2]])],"parametric")
smooth = "fixed"
} else {
for(i in 1:length(terms_formula)) {
types[[i]] = strsplit(terms_formula,"(",fixed=TRUE)[[i]][1]
temp_formula <- terms_formula[i]
if(types[[i]] == terms_formula[i]) {
temp_formula <- paste("rb(",terms_formula[i],", type = 'parametric')")
}
design[[i]] = eval(parse(text=temp_formula),envir=data,enclos=environment(formula))
}
}
nterms = length(design)
varying[[1]] = design[[1]][[9]]
if(any(!is.na(varying[[1]]))) {
B = design[[1]][[1]] * varying[[1]]
Blist[[1]] = design[[1]][[1]] * varying[[1]]
} else {
B = design[[1]][[1]]
Blist[[1]] = design[[1]][[1]]
}
DD = as.matrix(design[[1]][[2]])
Plist[[1]] = DD
x[[1]] = design[[1]][[3]]
names(x)[1] = design[[1]]$xname_orig[1]
types[[1]] = design[[1]][[4]]
bnd[[1]] = design[[1]][[5]]
Zspathelp[[1]] = design[[1]][[6]]
nb[1] = ncol(design[[1]][[1]])
nbp[1] <- design[[1]]$nbp
nbunp[1] <- design[[1]]$nbunp
krig.phi[[1]] = design[[1]][[7]]
center = center && design[[1]][[8]]
constmat = as.matrix(design[[1]]$constraint)
########### Begin: testing for not well defined combinations of parameters #####################
vec_s_xname <- rep(NA,times=length(design))
vec_s_xname_orig <- rep(NA,times=length(design))
vec_s_type <- rep(NA,times=length(design))
vec_s_B_size <- rep(NA,times=length(design))
for(i in 1:length(design)) {
zzzzz <- design[[i]]
if(!"regbase" %in% class(zzzzz)) {
if(grepl(x=class(zzzzz), pattern="smooth.spec")) {
stop("In expectreg smooth term are defined via rb() and not s() as in mgcv!")
} else {
stop("Wrong definition of covariates!")
}
}
vec_s_xname [i] <- zzzzz$xname[1]
vec_s_xname_orig [i] <- zzzzz$xname_orig
vec_s_type [i] <- zzzzz$type
vec_s_B_size [i] <- zzzzz$B_size
#if(vec_s_Alternative[i] == 2 && vec_s_split[i]) {
# stop("Model selection with Alternative == 2 and split == T is not well defined, \n
# due to the fact that with Alternative 2 the decomposition is not linear + penalized term")
# }
}
unique_xname <- unique(vec_s_xname_orig)
for(u in unique_xname) {
indices <- which(vec_s_xname_orig == u)
if(length(vec_s_type[indices]) != 1 & length(unique(vec_s_type[indices])) == 1) {
stop("Duplicated covariate!")
}
if(length(unique(vec_s_B_size[indices])) != 1) {stop("Do not mix different B_size types for one covariate!")}
if(length(vec_s_type[indices]) != 1) {
if(!(length(unique(vec_s_type[indices])) == 2 & identical(sort(vec_s_type[indices]), sort(c("parametric","penalizedpart_pspline"))))) {
stop("Do not mix different types for one covariate!")
}
}
}
########### End: testing for not well defined combinations of parameters #####################
if(length(design) > 1)
for(i in 2:length(terms_formula))
{
varying[[i]] = design[[i]][[9]]
if(any(!is.na(varying[[i]]))) {
B = cbind(B, design[[i]][[1]] * varying[[i]])
Blist[[i]] = design[[i]][[1]] * varying[[i]]
} else {
B = cbind(B, design[[i]][[1]])
Blist[[i]] = design[[i]][[1]]
}
design[[i]][[2]] = as.matrix(design[[i]][[2]])
Plist[[i]] = design[[i]][[2]]
DD = rbind(cbind(DD,matrix(0,nrow=nrow(DD),ncol=ncol(design[[i]][[2]]))),
cbind(matrix(0,nrow=nrow(design[[i]][[2]]),ncol=ncol(DD)),design[[i]][[2]]))
constmat = rbind(cbind(constmat,matrix(0,nrow=nrow(constmat),ncol=ncol(design[[i]]$constraint))),
cbind(matrix(0,nrow=nrow(design[[i]]$constraint),ncol=ncol(constmat)),design[[i]]$constraint))
x[[i]] = design[[i]][[3]]
names(x)[i] = design[[i]]$xname_orig[1]
types[[i]] = design[[i]][[4]]
bnd[[i]] = design[[i]][[5]]
Zspathelp[[i]] = design[[i]][[6]]
nb[i] = ncol(design[[i]][[1]])
nbp[i] <- design[[i]]$nbp
nbunp[i] <- design[[i]]$nbunp
krig.phi[[i]] = design[[i]][[7]]
center = center && design[[i]][[8]]
}
for(i in 1:length(design)) {
if(nb[i] != (nbunp[i]+nbp[i])) stop("Major error of implementation: \n\r Number of effects does not fit to sum of number of (un)penalized effects")
}
if(center) {
B = cbind(1,B)
DD = rbind(0,cbind(0,DD))
constmat = rbind(0,cbind(0,constmat))
}
if(!build_onemodel) {
if(estimate == "laws"){
if(all(constmat == 0)) {
coef.vector = laws(B,DD,yy,pp,lambda,smooth,nb,nbp,nbunp,center,types,LAWSmaxCores=LAWSmaxCores)
} else {
coef.vector = laws_constmat(B,DD,yy,pp,lambda,smooth,nb,center,constmat,types,LAWSmaxCores=LAWSmaxCores)
}
} else {
if(estimate == "restricted") {
coef.vector = restricted(B, DD, yy, pp, lambda, smooth, nb, center, constmat, types)
trend.coef = coef.vector[[4]]
residual.coef = coef.vector[[5]]
asymmetry = coef.vector[[6]]
} else {
if(estimate == "bundle") {
coef.vector = bundle(B,DD,yy,pp,lambda,smooth,nb,center,constmat,types)
trend.coef = coef.vector[[4]]
residual.coef = coef.vector[[5]]
asymmetry = coef.vector[[6]]
} else {
if(estimate == "sheets") {
coef.vector = sheets(Blist,Plist,yy,pp,lambda,smooth,nb,center,types)
}
}
}
}
vector.a.ma.schall = coef.vector[[1]]
lala = coef.vector[[2]]
diag.hat = coef.vector[[3]]
}
## Build one model out of list of selected models
if(build_onemodel) {
ci <- FALSE
vector.a.ma.schall <- matrix(0,nrow=ncol(B),ncol=np)
lala <- matrix(1,nrow=length(design),ncol=np)
diag.hat <- matrix(0,nrow=nrow(design[[1]]$B),ncol=length(pp))
Inter_ind <- 0
if(center){
Inter_ind <- 1
for(tt in 1:np) {
vector.a.ma.schall[1,tt] <- list_models[[tt]]$intercept
}
}
if(length(nb) != length(design)) {stop("length(np) != length(design)")}
for(k in 1:length(nb)) {
partbasis <- (sum(nb[0:(k-1)])+1):(sum(nb[0:k])) + Inter_ind
if(1 %in% partbasis){ stop("EEE") }
names_partbasis <- design[[k]]$xname[1]
for(tt in 1:np) {
if(length(list_models) == np) {
if(!is.null(list_models[[tt]]$coefficients[[names_partbasis]])){
vector.a.ma.schall[partbasis,tt] <- list_models[[tt]]$coefficients[[names_partbasis]][,1]
lala[k,tt] <- list_models[[tt]]$lambda[[names_partbasis]]
}
} else {stop("wrong list_models")}
}
}
for(tt in 1:np) {
diag.hat[,tt] <- list_models[[tt]]$diag.hatma
}
}
######################
# manipulate vector.a.ma.schall with given delta_garrote
if(!is.null(delta_garrote)) {
if(!(inherits(delta_garrote,c("list","numeric")))) {stop("class of delta_garrote does not fit")}
if((inherits(delta_garrote,"list"))) {
if(!(length(delta_garrote) %in% c(1,np))) {stop("length of list delta_garrote does not fit")}
if(length(delta_garrote) == np) {
for(i in 1:np) {
if(length(delta_garrote[[i]]) != length(nb)){ stop("length of delta_garrote does not fit to number of covariates")}
}
}
if(length(delta_garrote) == 1) {
if(length(delta_garrote[[1]]) != length(nb)){ stop("length of list delta_garrote does not fit to number of covariates")}
}
}
if(inherits(delta_garrote,"numeric")) {
if(length(delta_garrote) != length(nb)) {stop("length of vector delta_garrote does not fit to number of covariates")}
}
Inter_ind <- 0
if(center) {
Inter_ind <- 1
}
if(length(nb) != length(design)) {stop("length(np) != length(design)")}
for(k in 1:length(nb)) {
partbasis = (sum(nb[0:(k-1)])+1):(sum(nb[0:k]))+Inter_ind
names_partbasis <- design[[k]]$xname[1]
for(i in 1:np) {
if(inherits(delta_garrote,"list")) {
if(length(delta_garrote) == np) {
vector.a.ma.schall[partbasis,i] <- vector.a.ma.schall[partbasis,i] * delta_garrote[[i]][which(gsub(pattern=" (fixed)",replacement="",x=names(delta_garrote[[i]]),fixed=T) == names_partbasis)]
}
if(length(delta_garrote) == 1) {
vector.a.ma.schall[partbasis,i] <- vector.a.ma.schall[partbasis,i] * delta_garrote[[1]][which(gsub(pattern=" (fixed)",replacement="",x=names(delta_garrote[[1]]),fixed=T) == names_partbasis)]
}
}
if(inherits(delta_garrote,"numeric")) {
vector.a.ma.schall[partbasis,i] <- vector.a.ma.schall[partbasis,i] * delta_garrote[which(gsub(pattern=" (fixed)",replacement="",x=names(delta_garrote),fixed=T) == names_partbasis)]
}
}
}
}
######################
covariance = NULL
##############################
if(ci) {
W = list()
covariance = list()
for(i in 1:np) {
W = as.vector(ifelse(yy > B %*% vector.a.ma.schall[,i], pp[i], 1 - pp[i]))
square.dev = (yy - B %*% vector.a.ma.schall[,i])^2
correct = 1/(1-diag.hat[,i])
if(any(is.na(W))) {
correct[!is.na(W)] = correct[1:(length(correct)-length(which(is.na(W))))]
correct[is.na(W)] = 1
W[which(is.na(W))] = 1
square.dev[which(is.na(square.dev))] = 0
}
lahmda = rep(lala[,i],times=nb)
if(center)
lahmda = c(0,lahmda)
K = lahmda * t(DD) %*% DD
helpmat = solve(t(W * B) %*% B + K)
covariance[[i]] = helpmat %*% (t(B * (W^2 * (correct^2* square.dev)[,1])) %*% B) %*% helpmat
}
}
##############################
Z <- list()
coefficients <- list()
final.lambdas <- list()
helper <- list()
fitted = B %*% vector.a.ma.schall
if(center)
{
intercept = vector.a.ma.schall[1,]
B = B[,-1,drop=FALSE]
vector.a.ma.schall = vector.a.ma.schall[-1,,drop=FALSE]
} else
intercept = rep(0,np)
for(k in 1:length(design)) {
final.lambdas[[k]] = lala[k, ]
names(final.lambdas)[k] = design[[k]]$xname
partbasis = (sum(nb[0:(k-1)])+1):(sum(nb[0:k]))
if(types[[k]] == "pspline" || types[[k]] == "penalizedpart_pspline" || types[[k]] == "tp")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = NA
for(i in 1:np)
{
Z[[k]][,i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
} else if(types[[k]] == "markov")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = list(bnd[[k]],Zspathelp[[k]])
for(i in 1:np)
{
Z[[k]][,i] = design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
} else if(types[[k]] == "2dspline")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = NA
for(i in 1:np)
{
Z[[k]][,i] = design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
} else if(types[[k]] == "radial")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = Zspathelp[[k]]
for(i in 1:np)
{
Z[[k]][,i] = design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
} else if(types[[k]] == "krig")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = list(krig.phi[[k]],Zspathelp[[k]])
for(i in 1:np)
{
Z[[k]][,i] = design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
} else if(types[[k]] == "random")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = NA
for(i in 1:np)
{
Z[[k]][,i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
} else if(types[[k]] == "ridge")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = NA
for(i in 1:np)
{
Z[[k]][,i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
} else if(types[[k]] == "parametric")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = NA
for(i in 1:np)
{
Z[[k]][,i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
} else if(types[[k]] == "special")
{
Z[[k]] <- matrix(NA, m, np)
coefficients[[k]] = matrix(NA,nrow=nb[k],ncol=np)
helper[[k]] = NA
for(i in 1:np)
{
Z[[k]][,i] <- design[[k]][[1]] %*% vector.a.ma.schall[partbasis,i,drop=FALSE] + intercept[i]
coefficients[[k]][,i] = vector.a.ma.schall[partbasis,i,drop=FALSE]
}
}
names(Z)[k] = design[[k]]$xname[1]
names(coefficients)[k] = design[[k]]$xname[1]
names(final.lambdas)[k] = design[[k]]$xname[1]
names(design)[k] = design[[k]]$xname[1]
}
desmat = B
if(center)
desmat = cbind(1,B)
result = list("lambda"=final.lambdas, "intercepts"=intercept,
"coefficients"=coefficients, "values"=Z, "response"=yy,
"covariates"=x, "formula"=formula, "asymmetries"=pp,
"effects"=types, "helper"=helper, "design"=desmat,
"bases"=design, "fitted"=fitted, "covmat"=covariance,
"diag.hatma" = diag.hat, "data"=data, "smooth_orig"=smooth_orig,
"delta_garrote" = delta_garrote)
if (estimate == "restricted" || estimate == "bundle") {
result$trend.coef = trend.coef
result$residual.coef = residual.coef
result$asymmetry.coef = asymmetry
}
result$predict <- function(newdata = NULL, with_intercept = T) {
BB = list()
values = list()
bmat = NULL
for(k in 1:length(coefficients))
{
BB[[k]] = predict(design[[k]],newdata=newdata)
values[[k]] <- BB[[k]] %*% coefficients[[k]]
if(with_intercept) {
values[[k]] = t(apply(values[[k]],1,function(x) { x + intercept } ))
}
bmat = cbind(bmat,BB[[k]])
}
if (center) {
bmat = cbind(1, bmat)
vector.a.ma.schall = rbind(intercept, vector.a.ma.schall)
}
fitted = bmat %*% vector.a.ma.schall
names(values) = names(coefficients)
list("fitted"=fitted,"values"=values)
}
if (formula[[3]] == "1")
{
result$intercepts <- as.vector(coefficients[[1]])
result$coefficients <- NULL
result$covariates <- NULL
}
class(result) = c("expectreg",estimate,smooth)
result
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.