R/f_interface_latentattrition.R
latentAttrition.RdFit latent attrition models for transaction behavior, using a formula to specify the covariates.
latentAttrition(
formula,
family,
data,
optimx.args = list(),
verbose = TRUE,
...
)Formula to select and transform covariates in data. Has to be left empty if data contains no covariates. See Details.
A latentAttrition model. One of pnbd, bgnbd, or ggomnbd.
A clv.data object.
Additional arguments to control the optimization which are forwarded to optimx::optimx.
If multiple optimization methods are specified, only the result of the last method is further processed.
Show details about the running of the function.
Forwarded to model specified in family.
A two-part formula is used to select and transform the covariates stored in data before the model is estimated on it.
May not be given if data contains no covariates.
The formula left hand side (LHS) has to remain empty and may never be specified.
The formula right hand side (RHS) follows a two-part notation using | as separator.
1st part: Which covariates to include for the lifetime process, potentially transforming them and adding interactions. The dot ('.') refers to all lifetime covariates.
2nd part: Which covariates to include for the transaction process, potentially transforming them and adding interactions. The dot ('.') refers to all transaction covariates
e.g: ~ covlife | covtrans
See the example section for illustrations on how to specify the formula parameter.
# \donttest{
data("apparelTrans")
data("apparelStaticCov")
clv.nocov <-
clvdata(apparelTrans, time.unit="w", date.format="ymd")
# Create static covariate data with 2 covariates
clv.staticcov <-
SetStaticCovariates(clv.nocov,
data.cov.life = apparelStaticCov,
names.cov.life = c("Gender", "Channel"),
data.cov.trans = apparelStaticCov,
names.cov.trans = c("Gender", "Channel"))
# Fit models without covariates.
# Note that NO formula may be specified in this case
latentAttrition(formula =, family=pnbd, data=clv.nocov)
#> Starting estimation...
#> Estimation finished!
#> Pareto/NBD Standard Model
#>
#> Call:
#> latentAttrition(family = pnbd, data = clv.nocov)
#>
#> Coefficients:
#> r alpha s beta
#> 1.3768 47.2937 0.6746 62.8325
#> KKT1: TRUE
#> KKT2: TRUE
#>
#> Used Options:
#> Correlation: FALSE
latentAttrition(formula =, family=bgnbd, data=clv.nocov)
#> Starting estimation...
#> Estimation finished!
#> BG/NBD Standard Model
#>
#> Call:
#> latentAttrition(family = bgnbd, data = clv.nocov)
#>
#> Coefficients:
#> r alpha a b
#> 0.4447 14.2235 0.6653 3.2849
#> KKT1: TRUE
#> KKT2: TRUE
latentAttrition(formula =, family=ggomnbd, data=clv.nocov)
#> Starting estimation...
#> Estimation finished!
#> GGompertz/NBD Standard Model
#>
#> Call:
#> latentAttrition(family = ggomnbd, data = clv.nocov)
#>
#> Coefficients:
#> r alpha b s beta
#> 1.376e+00 4.725e+01 4.050e-07 6.743e-01 2.542e-05
#> KKT1: TRUE
#> KKT2: FALSE
# Fit pnbd with start parameters and correlation
# required args are passed as part of '...'
latentAttrition(formula =, family=pnbd, data=clv.nocov,
use.cor=TRUE,
start.params.model=c(r=1, alpha=10, s=2, beta=8))
#> Starting estimation...
#> Estimation finished!
#> Pareto/NBD Standard Model
#>
#> Call:
#> latentAttrition(family = pnbd, data = clv.nocov, use.cor = TRUE,
#> start.params.model = c(r = 1, alpha = 10, s = 2, beta = 8))
#>
#> Coefficients:
#> r alpha s beta
#> 1.2588082 43.0074084 0.4502443 28.1249106
#> Cor(life,trans)
#> -0.0005874
#> KKT1: FALSE
#> KKT2: FALSE
#>
#> Used Options:
#> Correlation: TRUE
# Fit pnbd with all present covariates
latentAttrition(formula=~.|., family=pnbd, data=clv.staticcov)
#> Starting estimation...
#> Estimation finished!
#> Pareto/NBD with Static Covariates Model
#>
#> Call:
#> latentAttrition(formula = ~. | ., family = pnbd, data = clv.staticcov)
#>
#> Coefficients:
#> r alpha s beta life.Gender
#> 1.6943 85.6670 0.6985 56.4854 -0.6634
#> life.Channel trans.Gender trans.Channel
#> 0.5544 0.3004 0.5583
#> KKT1: TRUE
#> KKT2: TRUE
#>
#> Used Options:
#> Correlation: FALSE
#> Constraints: FALSE
#> Regularization: FALSE
# Fit pnbd with selected covariates
latentAttrition(formula=~Gender|Channel+Gender, family=pnbd,
data=clv.staticcov)
#> Starting estimation...
#> Estimation finished!
#> Pareto/NBD with Static Covariates Model
#>
#> Call:
#> latentAttrition(formula = ~Gender | Channel + Gender, family = pnbd,
#> data = clv.staticcov)
#>
#> Coefficients:
#> r alpha s beta life.Gender
#> 1.6988 83.8248 0.6881 42.9313 -0.7195
#> trans.Channel trans.Gender
#> 0.5112 0.2945
#> KKT1: TRUE
#> KKT2: TRUE
#>
#> Used Options:
#> Correlation: FALSE
#> Constraints: FALSE
#> Regularization: FALSE
# Fit pnbd with start parameters for covariates
latentAttrition(formula=~Gender|., family=pnbd,
data=clv.staticcov,
start.params.life = c(Gender = 0.6),
start.params.trans = c(Gender = 0.6, Channel = 0.4))
#> Starting estimation...
#> Estimation finished!
#> Pareto/NBD with Static Covariates Model
#>
#> Call:
#> latentAttrition(formula = ~Gender | ., family = pnbd, data = clv.staticcov,
#> start.params.life = c(Gender = 0.6), start.params.trans = c(Gender = 0.6,
#> Channel = 0.4))
#>
#> Coefficients:
#> r alpha s beta life.Gender
#> 1.7004 83.8950 0.6873 42.7870 -0.7208
#> trans.Gender trans.Channel
#> 0.2944 0.5115
#> KKT1: TRUE
#> KKT2: TRUE
#>
#> Used Options:
#> Correlation: FALSE
#> Constraints: FALSE
#> Regularization: FALSE
# Fit pnbd with transformed covariate data
latentAttrition(formula=~Gender|I(log(Channel+2)), family=pnbd,
data=clv.staticcov)
#> Starting estimation...
#> Estimation finished!
#> Pareto/NBD with Static Covariates Model
#>
#> Call:
#> latentAttrition(formula = ~Gender | I(log(Channel + 2)), family = pnbd,
#> data = clv.staticcov)
#>
#> Coefficients:
#> r alpha
#> 1.6467 160.1213
#> s beta
#> 0.6695 38.2271
#> life.Gender trans.I.log.Channel...2..
#> -0.8432 1.2318
#> KKT1: TRUE
#> KKT2: TRUE
#>
#> Used Options:
#> Correlation: FALSE
#> Constraints: FALSE
#> Regularization: FALSE
# Fit pnbd with all covs and regularization
latentAttrition(formula=~.|., family=pnbd, data=clv.staticcov,
reg.lambdas = c(life=3, trans=8))
#> Starting estimation...
#> Estimation finished!
#> Pareto/NBD with Static Covariates Model
#>
#> Call:
#> latentAttrition(formula = ~. | ., family = pnbd, data = clv.staticcov,
#> reg.lambdas = c(life = 3, trans = 8))
#>
#> Coefficients:
#> r alpha s beta life.Gender
#> 1.383495 47.741272 0.676110 62.860816 -0.006224
#> life.Channel trans.Gender trans.Channel
#> 0.003006 0.003998 0.005636
#> KKT1: TRUE
#> KKT2: TRUE
#>
#> Used Options:
#> Correlation: FALSE
#> Constraints: FALSE
#> Regularization: TRUE
# Fit pnbd with all covs and constraint parameters for Channel
latentAttrition(formula=~.|., family=pnbd, data=clv.staticcov,
names.cov.constr='Channel')
#> Starting estimation...
#> Estimation finished!
#> Pareto/NBD with Static Covariates Model
#>
#> Call:
#> latentAttrition(formula = ~. | ., family = pnbd, data = clv.staticcov,
#> names.cov.constr = "Channel")
#>
#> Coefficients:
#> r alpha s beta life.Gender
#> 1.6938 85.6338 0.6995 56.7011 -0.6637
#> trans.Gender constr.Channel
#> 0.3002 0.5581
#> KKT1: TRUE
#> KKT2: TRUE
#>
#> Used Options:
#> Correlation: FALSE
#> Constraints: TRUE
#> Regularization: FALSE
# }