df with max function applied to 6 lags of a variable?!?

Hello everyone!
I have a data frame of 136 variables with 270 observations. I would like to create a new data frame such that each element of that data frame contains the maximum value of the 6 prior lags of the initial data frame. So for example, if my original data frame, A, were
A1=c(7.72, 7.94, 7.56, 7.54, 0.93, 0.59, 7.21, 8.00, 7.19, 7.57)A2=c(4.27, 3.70, 3.80, 3.67, 3.83, 3.95, 4.02, 2.06, 3.28, 2.17)A3=c(4.16, 2.91, 3.89, 3.73, 4.00, 3.81, 3.86, 1.88, 3.74, 3.75)A4=c(3.56, 3.27, 3.42, 3.32, 3.48, 3.54, 3.60, 2.04, 3.38, 3.37)A5=c(8.24, 8.67, 8.49, 0.00, 9.39, 0.00, 8.49, 8.94, 0.00, 0.00)A6=c(4.28, 4.18, 4.20, 4.14, 4.40, 4.20, 4.20, 4.15, 4.17, 4.16)
A <- cbind(A1, A2, A3, A4, A5, A6)
My new data frame, Amax, where each element is amax(i,j)=max[a(i-1,j), a(i-2,j), a(i-3,j), a(i-4,j), a(1-5,j), a(i-6,j)] would be:NA ? NA ? NA ? NA ? NA ? ?NA NA ? NA ? NA ? NA ? NA ? ?NA NA ? NA ? NA ? NA ? NA ? ?NA NA ? NA ? NA ? NA ? NA ? ?NA NA ? NA ? NA ? NA ? NA ? ?NA NA ? NA ? NA ? NA ? NA ? ?NA 7.94
?4.27
?4.16
?3.56
?9.39
?4.40 7.94
?4.02
?4.00
?3.60
?9.39
?4.40 8.00
?4.02
?4.00
?3.60
?9.39
?4.40 8.00
?4.02
?4.00
?3.60
?9.39
?4.40 8.00
?4.02
?4.00
?3.60
?9.39
?4.40
So the question is: what is the most efficient way of doing this in R?
Thanks!Rita ________________________________________ "If you think education is expensive, try ignorance"--Derek Bok

library(zoo)
as.data.frame(rollmax(BOD, 3, na.pad = TRUE, align = "right"))