问题
i have a sequence of 1/0's indicating if patient is in remission or not,
assume the records of remission or not were taken at discrete times,
how can i check the markov property for each patient, then summarize the findings, that is the assumption that the probability of remission for any patient at any time depends only if the patient had remission the last time/not remission last time(same as thing as saying probability of remission for any patient at any time depends only if the patient had remission in the previous row, well if not first observation)
P(r=1 at t=t+1|r=1 at t)=p(r=1 at t+1|r=1 at t, r=0 at t=t-1, r=1 at t=t-2, r=1 at t=t-3)
easy to understand if you understand the markov property
this is an exercept of my df
patientId remission
ju67 1
ju67 0
ju67 0
ju88 1
ju88 1
ju23 1
ju23 0
any ideas? subsetting the dataframe with the required condtions then computing probabilities using 'msm' package or (probably better way) just viewing the state transitions table will work but how do i do this, for the subsets of dataframe i would need for example to include only patients with three consecutive 0's in remission(including 0 in remission now) and compare this to subset of a dataframe with two consecutive 0's in remission(including 0 in remission now) –
来源:https://stackoverflow.com/questions/29804288/subseting-dataframe-conditions-on-factorbinary-columnvector-in-r-language