markov_model_mp {ChannelAttribution} | R Documentation |
This function is a multiprocessing version of markov_model function.
markov_model_mp(Data, var_path, var_conv, var_value=NULL, var_null=NULL, order=1, nsim_start=1e5, max_step=NULL, out_more=FALSE, sep=">", ncore=Inf, nfold=10, seed=0, conv_par=0.05, rate_step_sim=1.5, verbose=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. |
out_more |
if TRUE returns the transition probabilities between channels and removal effects. |
sep |
separator between the channels. |
ncore |
number of threads used in computation. Default is number of CPUs available. |
nfold |
how many repetitions are used to verify if convergence is 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 |
show additional information about process convergence. |
An object of class
data.frame
with the estimated number of conversions and the estimated conversion value attributed to each channel.
Davide Altomare (davide.altomare@gmail.com).
## Not run: library(ChannelAttribution) data(PathData) markov_model_mp(Data,"path","total_conversions", var_null="total_null") ## End(Not run)