summary.clv.fitted {CLVTools}  R Documentation 
Summary method for fitted CLV models that provides statistics about the estimated parameters
and information about the optimization process. If multiple optimization methods were used
(for example if specified in parameter optimx.args
), all information here refers to
the last method/row of the resulting optimx
object.
## S3 method for class 'clv.fitted' summary(object, ...) ## S3 method for class 'clv.fitted.transactions.static.cov' summary(object, ...) ## S3 method for class 'summary.clv.fitted' print( x, digits = max(3L, getOption("digits")  3L), signif.stars = getOption("show.signif.stars"), ... )
object 
A fitted CLV model 
... 
Ignored for 
x 
an object of class 
digits 
the number of significant digits to use when printing. 
signif.stars 
logical. If TRUE, ‘significance stars’ are printed for each coefficient. 
This function computes and returns a list of summary information of the fitted model
given in object
. It returns a list of class summary.clv.no.covariates
that contains the
following components:
name.model 
the name of the fitted model. 
call 
The call used to fit the model. 
tp.estimation.start 
Date or POSIXct indicating when the fitting period started. 
tp.estimation.end 
Date or POSIXct indicating when the fitting period ended. 
estimation.period.in.tu 
Length of fitting period in 
time.unit 
Time unit that defines a single period. 
coefficients 
a 
estimated.LL 
the value of the loglikelihood function at the found solution. 
AIC 
Akaike's An Information Criterion for the fitted model. 
BIC 
Schwarz' Bayesian Information Criterion for the fitted model. 
KKT1 
KarushKuhnTucker optimality conditions of the first order, as returned by optimx. 
KKT2 
KarushKuhnTucker optimality conditions of the second order, as returned by optimx. 
fevals 
The number of calls to the loglikelihood function during optimization. 
method 
The last method used to obtain the final solution. 
additional.options 
A list of additional options used for model fitting.

For models fits with static covariates, the list additionally is of class summary.clv.static.covariates
and the list in additional.options
contains the following elements:
additional.options 

The model fitting functions pnbd
.
Function coef
will extract the coefficients
matrix including summary statistics and
function vcov
will extract the vcov
from the returned summary object.
data("apparelTrans") # Fit pnbd standard model, no covariates clv.data.apparel < clvdata(apparelTrans, time.unit="w", estimation.split=40, date.format="ymd") pnbd.apparel < pnbd(clv.data.apparel) # summary about model fit summary(pnbd.apparel) # Add static covariate data data("apparelStaticCov") data.apparel.cov < SetStaticCovariates(clv.data.apparel, data.cov.life = apparelStaticCov, names.cov.life = "Gender", data.cov.trans = apparelStaticCov, names.cov.trans = "Gender", name.id = "Id") # fit model with covariates and regualization pnbd.apparel.cov < pnbd(data.apparel.cov, reg.lambdas = c(life=2, trans=4)) # additional summary about covariate parameters # and used regularization summary(pnbd.apparel.cov)