fit <- a4aSCA(ple4, FLIndices(b=bioidx, a=ple4.index), qmodel=list(~1, ~s(age, k=4)))
Note: The following observations are treated as being missing at random:
fleet year age
a 1997 1
a 1997 2
Predictions will be made for missing observations.
predict(fit)
Error in X %*% b : non-conformable arguments
traceback()
15: FUN(X[[3L]], ...)
14: FUN(X[[3L]], ...)
13: lapply(X = list(catch = <S4 object of class "submodel">, b = <S4 object of class "submodel">,
a = <S4 object of class "submodel">), FUN = function (object,
...)
standardGeneric("predict"))
12: lapply(X = list(catch = <S4 object of class "submodel">, b = <S4 object of class "submodel">,
a = <S4 object of class "submodel">), FUN = function (object,
...)
standardGeneric("predict"))
11: do.call("lapply", lstargs)
10: lapply(object, predict)
9: lapply(object, predict)
8: predict(vm)
7: predict(vm)
6: .local(object, ...)
5: predict(pars(object))
4: predict(pars(object))
3: .local(object, ...)
2: predict(fit)
1: predict(fit)