#' @templateVar name_model_full Pareto/NBD
#' @template template_class_clvmodelstaticcov
#'
#' @seealso Other clv model classes \linkS4class{clv.model}, \linkS4class{clv.model.pnbd.no.cov}, \linkS4class{clv.model.pnbd.dynamic.cov}
#' @seealso Classes using its instance: \linkS4class{clv.fitted.transactions.static.cov}
#'
#' @include all_generics.R class_clv_model_pnbd.R
setClass(Class = "clv.model.pnbd.static.cov", contains = "clv.model.pnbd.no.cov",
slots = list(
start.param.cov = "numeric"),
# Prototype is labeled not useful anymore, but still recommended by Hadley / Bioc
prototype = list(
start.param.cov = numeric(0)))
#' @importFrom methods new
clv.model.pnbd.static.cov <- function(){
# optimx.args stay the same as for nocov
return(new("clv.model.pnbd.static.cov",
clv.model.pnbd.no.cov(),
# Overwrite nocov name
name.model = "Pareto/NBD with Static Covariates",
start.param.cov = 0.1))
}
clv.model.pnbd.static.cov.get.alpha_i <- function(clv.fitted){
alpha_i <- NULL
dt.alpha_i <- clv.fitted@cbs[, "Id"]
m.cov.data.trans <- clv.data.get.matrix.data.cov.trans(clv.data=clv.fitted@clv.data, correct.row.names=dt.alpha_i$Id,
correct.col.names=names(clv.fitted@prediction.params.trans))
dt.alpha_i[, alpha_i := pnbd_staticcov_alpha_i(alpha_0 = clv.fitted@prediction.params.model[["alpha"]],
vCovParams_trans = clv.fitted@prediction.params.trans,
mCov_trans = m.cov.data.trans)]
return(dt.alpha_i)
}
clv.model.pnbd.static.cov.get.beta_i <- function(clv.fitted){
beta_i <- NULL
dt.beta_i <- clv.fitted@cbs[, "Id"]
m.cov.data.life <- clv.data.get.matrix.data.cov.life(clv.data=clv.fitted@clv.data, correct.row.names=dt.beta_i$Id,
correct.col.names=names(clv.fitted@prediction.params.life))
dt.beta_i[, beta_i := pnbd_staticcov_beta_i(beta_0 = clv.fitted@prediction.params.model[["beta"]],
vCovParams_life = clv.fitted@prediction.params.life,
mCov_life = m.cov.data.life)]
return(dt.beta_i)
}
# Methods --------------------------------------------------------------------------------------------------------------------------------
# .clv.model.check.input.args ------------------------------------------------------------------------------------------------------------
# use nocov, no static cov checks
# . clv.model.put.estimation.input ------------------------------------------------------------------------------------------------------------
# Nothing specific required, use nocov
# . clv.model.transform.start.params.cov ------------------------------------------------------------------------------------------------------------
setMethod(f = "clv.model.transform.start.params.cov", signature = signature(clv.model="clv.model.pnbd.static.cov"), definition = function(clv.model, start.params.cov){
# no transformation needed
return(start.params.cov)
})
# . clv.model.backtransform.estimated.params.cov -----------------------------------------------------------------------------------------------------
setMethod(f = "clv.model.backtransform.estimated.params.cov", signature = signature(clv.model="clv.model.pnbd.static.cov"), definition = function(clv.model, prefixed.params.cov){
# no transformation needed
return(prefixed.params.cov)
})
# . clv.model.prepare.optimx.args -----------------------------------------------------------------------------------------------------
#' @importFrom utils modifyList
setMethod(f = "clv.model.prepare.optimx.args", signature = signature(clv.model="clv.model.pnbd.static.cov"),
definition = function(clv.model, clv.fitted, prepared.optimx.args){
# Do not call the no.cov function as the LL is different
# Everything to call the LL function
optimx.args <- modifyList(prepared.optimx.args,
list(
obj = clv.fitted,
LL.function.sum = pnbd_staticcov_LL_sum,
LL.function.ind = pnbd_staticcov_LL_ind, # if doing correlation
# For cpp static cov the param order is: model, life, trans
LL.params.names.ordered = c(clv.model@names.prefixed.params.model,
clv.fitted@names.prefixed.params.after.constr.life,
clv.fitted@names.prefixed.params.after.constr.trans),
vX = clv.fitted@cbs$x,
vT_x = clv.fitted@cbs$t.x,
vT_cal = clv.fitted@cbs$T.cal,
mCov_life = clv.data.get.matrix.data.cov.life(clv.data = clv.fitted@clv.data, correct.row.names=clv.fitted@cbs$Id,
correct.col.names=clv.data.get.names.cov.life(clv.fitted@clv.data)),
mCov_trans = clv.data.get.matrix.data.cov.trans(clv.data = clv.fitted@clv.data, correct.row.names=clv.fitted@cbs$Id,
correct.col.names=clv.data.get.names.cov.trans(clv.fitted@clv.data))),
keep.null = TRUE)
return(optimx.args)
})
# . clv.model.vcov.jacobi.diag -----------------------------------------------------------------------------------------------------
setMethod(f = "clv.model.vcov.jacobi.diag", signature = signature(clv.model="clv.model.pnbd.static.cov"),
definition = function(clv.model, clv.fitted, prefixed.params){
# Get corrections from nocov model
m.diag.model <- callNextMethod()
# No transformations for static covs: Set diag to 1 for all static cov params
# Gather names of cov param
names.cov.prefixed.params <- c(clv.fitted@names.prefixed.params.free.life,
clv.fitted@names.prefixed.params.free.trans)
if(clv.fitted@estimation.used.constraints)
names.cov.prefixed.params <- c(names.cov.prefixed.params, clv.fitted@names.prefixed.params.constr)
# Set to 1
m.diag.model[names.cov.prefixed.params,
names.cov.prefixed.params] <- diag(x = 1,
nrow = length(names.cov.prefixed.params),
ncol = length(names.cov.prefixed.params))
return(m.diag.model)
})
# . clv.model.predict -----------------------------------------------------------------------------------------------------
setMethod("clv.model.predict", signature(clv.model="clv.model.pnbd.static.cov"), definition = function(clv.model, clv.fitted, dt.predictions, verbose, continuous.discount.factor, ...){
# cran silence
period.length <- CET <- i.CET <- x <- t.x <- T.cal <- PAlive <- i.PAlive <- i.DERT <- DERT <- NULL
predict.number.of.periods <- dt.predictions[1, period.length]
# To ensure sorting, do everything in a single table
dt.result <- copy(clv.fitted@cbs[, c("Id", "x", "t.x", "T.cal")])
data.cov.mat.life <- clv.data.get.matrix.data.cov.life(clv.data = clv.fitted@clv.data, correct.row.names=dt.result$Id,
correct.col.names=names(clv.fitted@prediction.params.life))
data.cov.mat.trans <- clv.data.get.matrix.data.cov.trans(clv.data = clv.fitted@clv.data, correct.row.names=dt.result$Id,
correct.col.names=names(clv.fitted@prediction.params.trans))
# Add CET
dt.result[, CET := pnbd_staticcov_CET(r = clv.fitted@prediction.params.model[["r"]],
alpha_0 = clv.fitted@prediction.params.model[["alpha"]],
s = clv.fitted@prediction.params.model[["s"]],
beta_0 = clv.fitted@prediction.params.model[["beta"]],
dPeriods = predict.number.of.periods,
vX = x,
vT_x = t.x,
vT_cal = T.cal,
vCovParams_trans = clv.fitted@prediction.params.trans,
vCovParams_life = clv.fitted@prediction.params.life,
mCov_trans = data.cov.mat.trans,
mCov_life = data.cov.mat.life)]
# Add PAlive
dt.result[, PAlive := pnbd_staticcov_PAlive(r = clv.fitted@prediction.params.model[["r"]],
alpha_0 = clv.fitted@prediction.params.model[["alpha"]],
s = clv.fitted@prediction.params.model[["s"]],
beta_0 = clv.fitted@prediction.params.model[["beta"]],
vX = x,
vT_x = t.x,
vT_cal = T.cal,
vCovParams_trans = clv.fitted@prediction.params.trans,
vCovParams_life = clv.fitted@prediction.params.life,
mCov_trans = data.cov.mat.trans,
mCov_life = data.cov.mat.life)]
# Add DERT
dt.result[, DERT := pnbd_staticcov_DERT(r = clv.fitted@prediction.params.model[["r"]],
alpha_0 = clv.fitted@prediction.params.model[["alpha"]],
s = clv.fitted@prediction.params.model[["s"]],
beta_0 = clv.fitted@prediction.params.model[["beta"]],
continuous_discount_factor = continuous.discount.factor,
vX = x,
vT_x = t.x,
vT_cal = T.cal,
mCov_life = data.cov.mat.life,
mCov_trans = data.cov.mat.trans,
vCovParams_life = clv.fitted@prediction.params.life,
vCovParams_trans = clv.fitted@prediction.params.trans)]
# Add results to prediction table, by matching Id
dt.predictions[dt.result, CET := i.CET, on = "Id"]
dt.predictions[dt.result, PAlive := i.PAlive, on = "Id"]
dt.predictions[dt.result, DERT := i.DERT, on = "Id"]
return(dt.predictions)
})
# . clv.model.expectation -----------------------------------------------------------------------------------------------------
setMethod("clv.model.expectation", signature(clv.model="clv.model.pnbd.static.cov"), function(clv.model, clv.fitted, dt.expectation.seq, verbose){
r <- s <- alpha_i <- beta_i <- i.alpha_i <- i.beta_i <- T.cal <- t_i <- NULL
#calculate alpha_i, beta_i
params_i <- clv.fitted@cbs[, c("Id", "T.cal", "date.first.actual.trans")]
dt.alpha_i <- clv.model.pnbd.static.cov.get.alpha_i(clv.fitted)
dt.beta_i <- clv.model.pnbd.static.cov.get.beta_i(clv.fitted)
params_i[dt.alpha_i, alpha_i := i.alpha_i, on="Id"]
params_i[dt.beta_i, beta_i := i.beta_i, on="Id"]
# To caluclate expectation at point t for customers alive in t, given in params_i.t
fct.expectation <- function(params_i.t) {
return(drop(pnbd_staticcov_expectation(r = clv.fitted@prediction.params.model[["r"]],
s = clv.fitted@prediction.params.model[["s"]],
vAlpha_i = params_i.t$alpha_i,
vBeta_i = params_i.t$beta_i,
vT_i = params_i.t$t_i)))}
return(DoExpectation(dt.expectation.seq = dt.expectation.seq, params_i = params_i,
fct.expectation = fct.expectation, clv.time = clv.fitted@clv.data@clv.time))
})
# . clv.model.pmf -----------------------------------------------------------------------------------------------------
setMethod("clv.model.pmf", signature=(clv.model="clv.model.pnbd.static.cov"), function(clv.model, clv.fitted, x){
Id <- T.cal <- pmf.x <- alpha_i <- beta_i <- i.alpha_i <- i.beta_i <- NULL
dt.res <- clv.fitted@cbs[, list(Id, T.cal)]
dt.alpha_i <- clv.model.pnbd.static.cov.get.alpha_i(clv.fitted)
dt.beta_i <- clv.model.pnbd.static.cov.get.beta_i(clv.fitted)
dt.res[dt.alpha_i, alpha_i := i.alpha_i, on="Id"]
dt.res[dt.beta_i, beta_i := i.beta_i, on="Id"]
dt.res[, pmf.x := pnbd_staticcov_PMF(r = clv.fitted@prediction.params.model[["r"]],
s = clv.fitted@prediction.params.model[["s"]],
vAlpha_i = alpha_i,
vBeta_i = beta_i,
vT_i = T.cal,
x = x)]
dt.res <- dt.res[, list(Id, pmf.x)]
setnames(dt.res, "pmf.x", paste0("pmf.x.", x))
return(dt.res)
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.