Transition probabilies: generating and displaying

markovTransition.R

#############################################
#      Simulation          ##################
#############################################
set.seed(225+1600)
num_users <- 1000
#define transition probabilities between states 
start_probs <- c("check" = .7, "save" = .2, "both" = .1)
check <- c("check" = .6, "save" = .05, "both" = .3, "none" = .05)
save <- c("check" = .1, "save" = .8, "both" = .05, "none" = .05)
both <- c("check" = .2, "save" = .2, "both" = .6, "none" = 0)

transition_matrix <- rbind(
  check,
  save,
  both
)

cumulative_transition_matrix <- t(apply(transition_matrix,1,cumsum))


## create data ###
users <- data.frame(
  date = 1,
  id = sample(num_users:(num_users*10),size=num_users,replace=FALSE),
  finst = sample(names(start_probs),size=num_users,prob=start_probs,replace=TRUE),
  stringsAsFactors = FALSE
)
  
## function to generate next day's state
markovTransition <- function(state_vector,trans_prob) {
  # function to take a user and run transition probability
  l = length(state_vector)
  rands = runif(l)
  result = rep(NA,l)
  for (i in 1:l) {
    (state <- state_vector[i])
    result[i] = names(trans_prob[state,])[(sum(trans_prob[state,] < rands[i])+1)]
  }
  return(result)
}

## let's generate for tomorrow ##
usersday2 <- data.frame(
  date = 2,
  id = users$id,
  finst = markovTransition(users$finst,cumulative_transition_matrix),
  stringsAsFactors = FALSE
)

ShowTable.R

#############################################
#      Actual solution     ##################
#############################################

library(dplyr)
combined <- left_join(users,usersday2,by="id") 
table(combined$finst.x,combined$finst.y)[c("check","save","both"),c("check","save","both", "none")]

#      check save both none
# check   415   43  215   37
# save     20  154   10   10
# both     24   15   57    0