Help on loops

x

# first split the data by 'id'
x.s <- split(x, x$id)
# then process each group
result <- lapply(x.s, function(.grp){

result

Dear R-helpers,

I have a basic question on using loops.

I have a panel data set with different variables measured for "n" firms over "t" time periods. A snapshot of the data is given below

id??? t??? X1??? X2
1??? 1?? ? 4??? 3
1??? 2??? 9??? 2
1??? 3??? 7??? 3
1??? 4??? 6??? 6
2??? 1??? 6??? 4
2??? 2??? 5??? 3
2??? 3??? 1??? 1
3??? 1??? 9??? 6
3??? 2??? 5??? 5

thus total sample n=9

My problem is as follows. I need to do some computations on the data where the first observation for each firm (ie. id=1 and t=1; id=2 and t=1; id=3 and t=1) requires a specific operation on x1 and x2 and the subsequent operations are based on the computed value of the first operation.

For example the pseudocode is as follows

##define output matrix

output <- rep(0,n)

## define coefficient vector
b <- c(1,1)

for (i in 1:number of simulations)
{
?? for (j in 1:id)
??? {
????? for(k in 1:t)
???????? {
?????????? if(Data$t[,2]==1)
????????????? {
???????????????? meanvec <- Data[k,3:4]%*%b
???????????????? output[k] = calc (meanvec) # output from calc is a scalar
????????????? }
??????????? else
????????????????? output[k]= calc(a*output[k-1]+Data[k,3:4]%*%b)
?????????? }
?????? }
??? }

Thus the end result should be a vector "output" with nine observations based on the computations.

I hope the problem is clear and I greatly appreciate any help in solving this problem . Thanks in advance for your help.

Kind Regards

Anup




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