14.6 Fit the model

We specify this one-to-one in R for MARSS():

makemod <- function(n) {
    B <- matrix(list(0), n + 1, n + 1)
    diag(B)[2:(n + 1)] <- paste0("b", 1:n)
    B[1, 1] <- 1
    A <- "zero"
    Z <- cbind(1, diag(1, n))
    Q <- matrix(list(0), n + 1, n + 1)
    Q[1, 1] <- 1
    diag(Q)[2:(n + 1)] <- paste0("q", 1:n)
    R <- "zero"
    U <- "zero"
    x0 <- "zero"
    mod.list <- list(B = B, A = A, Z = Z, Q = Q, R = R, U = U, 
        x0 = x0, tinitx = 0)
    return(mod.list)
}
mod.list1 <- makemod(3)

Demean the data.

dat2 <- dat - apply(dat, 1, mean) %*% matrix(1, 1, TT)

Fit to that

fit.mod1 <- MARSS(dat2, model = mod.list1)

Success! algorithm run for 15 iterations. abstol and log-log tests passed.
Alert: conv.test.slope.tol is 0.5.
Test with smaller values (<0.1) to ensure convergence.

MARSS fit is
Estimation method: kem 
Convergence test: conv.test.slope.tol = 0.5, abstol = 0.001
Algorithm ran 15 (=minit) iterations and convergence was reached. 
Log-likelihood: -244.5957 
AIC: 501.1914   AICc: 502.2035   
 
     Estimate
B.b1    0.790
B.b2    0.406
B.b3    0.905
Q.q1    1.004
Q.q2   23.042
Q.q3   55.892
Initial states (x0) defined at t=0

Standard errors have not been calculated. 
Use MARSSparamCIs to compute CIs and bias estimates.