Data consists of 4 variable, id, x1 and x2, continuous variables which are correlated with y, a binary variable. 0 and 1 in the binary variable represent different states. Is it possible to use Markov chain models to calculate and plot state transition probability along the gradient of covariate values for each id and subsequently for the pooled data?
set.seed(1)
id =rep(1, 100)
x1 = rnorm(100)
x2 = rnorm(100)
z = 1 + 2*x1 + 3*x2
pr = 1/(1+exp(-z))
y = rbinom(100,1,pr)
a<-data.frame(id,x1,x2, y)
set.seed(2)
id =rep(2, 100)
x1 = rnorm(100)
x2 = rnorm(100)
z = 1 + 2*x1 + 3*x2
pr = 1/(1+exp(-z))
y = rbinom(100,1,pr)
b<-data.frame(id,x1,x2, y)
set.seed(3)
id =rep(3, 100)
x1 = rnorm(100)
x2 = rnorm(100)
z = 1 + 2*x1 + 3*x2
pr = 1/(1+exp(-z))
y = rbinom(100,1,pr)
c<-data.frame(id,x1,x2, y)
d<-rbind(a,b,c)