What is the significance of “removal_effects” in ChannelAttribution R package?

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I am trying to build a Markov model using the ChannelAttribution package available in R.



Sample code to run this model is -



M <- markov_model(Data, var_path, var_conv, var_value=NULL, var_null=NULL,
order=1, nsim=NULL, max_step=NULL, out_more=FALSE, sep=">", seed=NULL)


Source documentation says that out_more = TRUE option gives model's removal_effects. I would like to know the significance of this M$removal_effects.



If these are the weights of the input channels in the Data , then should they sum to 1 ?



I have tried to print the sum of these removal_effects and I get a different value every time I run the model



Source documentation - https://cran.r-project.org/web/packages/ChannelAttribution/ChannelAttribution.pdf




markov-chains hidden-markov-models




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    up vote
    1
    down vote

    favorite












    I am trying to build a Markov model using the ChannelAttribution package available in R.



    Sample code to run this model is -



    M <- markov_model(Data, var_path, var_conv, var_value=NULL, var_null=NULL,
    order=1, nsim=NULL, max_step=NULL, out_more=FALSE, sep=">", seed=NULL)


    Source documentation says that out_more = TRUE option gives model's removal_effects. I would like to know the significance of this M$removal_effects.



    If these are the weights of the input channels in the Data , then should they sum to 1 ?



    I have tried to print the sum of these removal_effects and I get a different value every time I run the model



    Source documentation - https://cran.r-project.org/web/packages/ChannelAttribution/ChannelAttribution.pdf




    markov-chains hidden-markov-models




    share|cite|improve this question





















      up vote
      1
      down vote

      favorite









      up vote
      1
      down vote

      favorite











      I am trying to build a Markov model using the ChannelAttribution package available in R.



      Sample code to run this model is -



      M <- markov_model(Data, var_path, var_conv, var_value=NULL, var_null=NULL,
      order=1, nsim=NULL, max_step=NULL, out_more=FALSE, sep=">", seed=NULL)


      Source documentation says that out_more = TRUE option gives model's removal_effects. I would like to know the significance of this M$removal_effects.



      If these are the weights of the input channels in the Data , then should they sum to 1 ?



      I have tried to print the sum of these removal_effects and I get a different value every time I run the model



      Source documentation - https://cran.r-project.org/web/packages/ChannelAttribution/ChannelAttribution.pdf




      markov-chains hidden-markov-models




      share|cite|improve this question











      I am trying to build a Markov model using the ChannelAttribution package available in R.



      Sample code to run this model is -



      M <- markov_model(Data, var_path, var_conv, var_value=NULL, var_null=NULL,
      order=1, nsim=NULL, max_step=NULL, out_more=FALSE, sep=">", seed=NULL)


      Source documentation says that out_more = TRUE option gives model's removal_effects. I would like to know the significance of this M$removal_effects.



      If these are the weights of the input channels in the Data , then should they sum to 1 ?



      I have tried to print the sum of these removal_effects and I get a different value every time I run the model



      Source documentation - https://cran.r-project.org/web/packages/ChannelAttribution/ChannelAttribution.pdf





      markov-chains hidden-markov-models





      share|cite|improve this question










      share|cite|improve this question




      share|cite|improve this question









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