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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 clv.fitted
summary(object, ...)# S3 method for clv.fitted.transactions.static.cov
summary(object, ...)
# S3 method for summary.clv.fitted
print(
x,
digits = max(3L, getOption("digits") - 3L),
signif.stars = getOption("show.signif.stars"),
...
)
A fitted CLV model
Ignored for summary, forwarded to printCoefmat for print.
an object of class "summary.clv.no.covariates", usually, a result of a call to summary.clv.no.covariates.
the number of significant digits to use when printing.
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:
the name of the fitted model.
The call used to fit the model.
Date or POSIXct indicating when the fitting period started.
Date or POSIXct indicating when the fitting period ended.
Length of fitting period in time.units.
Time unit that defines a single period.
a px4 matrix with columns for the estimated coefficients, its standard error,
the t-statistic and corresponding (two-sided) p-value.
the value of the log-likelihood function at the found solution.
Akaike's An Information Criterion for the fitted model.
Schwarz' Bayesian Information Criterion for the fitted model.
Karush-Kuhn-Tucker optimality conditions of the first order, as returned by optimx.
Karush-Kuhn-Tucker optimality conditions of the second order, as returned by optimx.
The number of calls to the log-likelihood function during optimization.
The last method used to obtain the final solution.
A list of additional options used for model fitting.
Whether the correlation between the purchase and the attrition process was estimated.
Correlation coefficient measuring the correlation between the two processes, if used.
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:
Whether L2 regularization for parameters of contextual factors was used.
The regularization lambda used for the parameters of the Lifetime process, if used.
The regularization lambda used for the parameters of the Transaction process, if used.
Whether any covariate parameters were forced to be the same for both processes.
Name of the covariate parameters which were constraint, if used.
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.
# NOT RUN {
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)
# }
# NOT RUN {
# }
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