View source: R/ChannelAttribution.R
markov_model | R Documentation |
Estimate a k-order Markov model from customer journey data. Differently from markov_model, this function iterates estimation until convergence is reached and enables multiprocessing.
markov_model(Data, var_path, var_conv, var_value=NULL, var_null=NULL,
order=1, nsim_start=1e5, max_step=NULL, out_more=FALSE, sep=">",
ncore=1, nfold=10, seed=0, conv_par=0.05, rate_step_sim=1.5,
verbose=TRUE, flg_adv=TRUE)
Data |
data.frame containing customer journeys data. |
var_path |
column name containing paths. |
var_conv |
column name containing total conversions. |
var_value |
column name containing total conversion value. |
var_null |
column name containing total paths that do not lead to conversions. |
order |
Markov Model order. |
nsim_start |
minimum number of simulations used in computation. |
max_step |
maximum number of steps for a single simulated path. if NULL, it is the maximum number of steps found into Data. |
out_more |
if TRUE, transition probabilities between channels and removal effects will be returned. |
sep |
separator between the channels. |
ncore |
number of threads used in computation. |
nfold |
how many repetitions are used to verify if convergence has been reached at each iteration. |
seed |
random seed. Giving this parameter the same value over different runs guarantees that results will not vary. |
conv_par |
convergence parameter for the algorithm. The estimation process ends when the percentage of variation of the results over different repetions is less than convergence parameter. |
rate_step_sim |
number of simulations used at each iteration is equal to the number of simulations used at previous iteration multiplied by rate_step_sim. |
verbose |
if TRUE, additional information about process convergence will be shown. |
flg_adv |
if TRUE, ChannelAttribution Pro banner is printed. |
An object of class
data.frame
with the estimated number of conversions and the estimated conversion value attributed to each channel.
Davide Altomare (info@channelattribution.io).
## Not run:
library(ChannelAttribution)
data(PathData)
#Estimate a Makov model using total conversions
markov_model(Data, var_path="path", "total_conversions")
#Estimate a Makov model using total conversions and revenues
markov_model(Data, "path", "total_conversions",
var_value="total_conversion_value")
#Estimate a Makov model using total conversions, revenues and paths that do not lead to conversions
markov_model(Data, "path", "total_conversions",
var_value="total_conversion_value", var_null="total_null")
#Estimate a Makov model returning transition matrix and removal effects
markov_model(Data, "path", "total_conversions",
var_value="total_conversion_value", var_null="total_null", out_more=TRUE)
#Estimate a Markov model using 4 threads
markov_model(Data, "path", "total_conversions",
var_value="total_conversion_value", ncore=4)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.