[R] Computing and testing transition probabilities?

[Gabor Grothendieck]

Ajay Shah <ajayshah <at> mayin.org> writes:

: 
: Folks, I have a situation with many firms, observed for many years
: (but it's not panel data, so every now and then data for a firm just
: goes missing).
: 
: Let me show you an example. There are 3 firms "a", "b" and "c". There
: are 3 years: 1981, 1982 and 1983. There's a factor f which takes
: values 1, 2 or 3.
: 
: set.seed(5)
: D = data.frame(
:   names=c("a", "a", "a", "b", "b", "c", "c", "c", "d", "d"),
:   year= c( 81,  82,  83,  81,  83,  81,  82,  83,  82,  83),
:   f=    sample(1:3, 10, replace=T)
:   )
: print(D)
: 
: What I'd like to do is locate situations where a firm is observed for
: two consecutive years, and put together conditional probabilities of
: state transition.
: 
: Expressed as counts, putting time $t$ as the rows and time $t+1$ as
: the columms, I'd like to get the table:
: 
:     1   2   3
: 1           1
: 2       1
: 3   1   1   1

I do not get the same answer as you but my understanding from
your description is that this is what you are looking for:

R> set.seed(5)
R> D <- data.frame(
+  names =c("a", "a", "a", "b", "b", "c", "c", "c", "d", "d"),
+  year = c( 81, 82, 83, 81, 83, 81, 82, 83, 82, 83),
+  f= sample(1:3, 10, replace=T)
+ )
R> print(D)
   names year f
1      a   81 1
2      a   82 3
3      a   83 3
4      b   81 1
5      b   83 1
6      c   81 3
7      c   82 2
8      c   83 3
9      d   82 3
10     d   83 1
 
R> MM <- merge(D, cbind(year = D$year + 1, D), by.x = 2:1, by.y = 1:2)
R> tab <- with(MM, table(f.x, f.y))
R> tab
   f.y
f.x 1 2 3
  1 0 0 1
  2 0 0 1
  3 1 1 1
R> prop.table(tab, margin = 1)
   f.y
f.x 1         2         3        
  1 0.0000000 0.0000000 1.0000000
  2 0.0000000 0.0000000 1.0000000
  3 0.3333333 0.3333333 0.3333333