I have a vector of sequences with presence (
1) and absence (
0), from were I have calculated the first order Markov Process.
This is how the data looks like:
dataset=c(NA,NA,0,0,0,0,1,0,1,1,0,1,1,NA,NA,NA,NA,NA,0, 1,1,1,1,1,1,0,NA,1,1,1,1,1,1,0,0,NA,NA,0,1,1, NA,NA,0,NA,0,0,0,1,1,1,0,1,1,1,1,0,1,NA,0,1)
For thsi vector I have calculated:
- Probability of Presence (
P1) and Absence (
- Probability of having Presence followed by Presence (
P11), Presence followed by Absence (
P10), Absence followed by Presence (
P01) and Absence followed by Absence (
The transition probabilities were obtained by using a loop the checks for sequence of 2 values: to calculate
P_00 I am using
P_00 : dataset[j]==0 & dataset[j+1]==0 , etc.
These are the results:
P_0=0.3913043 P_1=0.6086957 P_0+P_1=1 P_00=0.1538462 P_01=0.2307692 P_11=0.4615385 P_10=0.1538462 P_00+P_01+P_11+P_10=1
The idea is to populate the NA with presence/absence sequences according to the Probability values obtained for the sequence. The problem is that for now I am not being able to find the best and more adequate process for this problem and I am a newbie with this type of problems. Even if there is already a package that can do this for me, I would prefer to understand how can I do it.