MARSS: Multivariate Autoregressive State-Space Modeling

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### code chunk number 10: Cs2_Code1
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# Code to fit the single population model with i.i.d. errors
# Read in data
dat <- t(harborSealWA) # MARSS needs time ACROSS columns
years <- dat[1, ]
n <- nrow(dat) - 1
dat <- dat[2:nrow(dat), ]
legendnames <- (unlist(dimnames(dat)[1]))

# estimate parameters
Z.model <- factor(c(1, 1, 1, 1, 1))
R.model <- "diagonal and equal"
kem1 <- MARSS(dat, model = list(Z = Z.model, R = R.model))

# make figure
graphics::matplot(years, t(dat),
  xlab = "", ylab = "Index of log abundance",
  pch = c("1", "2", "3", "4", "5"), ylim = c(5, 9), bty = "L"
)
lines(years, kem1$states - 1.96 * kem1$states.se,
  type = "l",
  lwd = 1, lty = 2, col = "red"
)
lines(years, kem1$states + 1.96 * kem1$states.se,
  type = "l",
  lwd = 1, lty = 2, col = "red"
)
lines(years, kem1$states, type = "l", lwd = 2)
title("Observations and total population estimate", cex.main = .9)

coef(kem1, type = "vector") # parameter estimates as a vector

# show estimated elements for each parameter matrix as a list
coef(kem1)

kem1$logLik # show the log-likelihood
kem1$AIC # show the AIC