# Select residual model Given a complex vector of movement residuals, will use AIC to select the order of the autocorrelation, i.e. white noise (WN), position autocorrelation (OU), or position and velocity autocorrelation (OUF) ```r selectModel(Z.res, T = NULL, method = c("ar", "like")[1], showtable = FALSE) ``` ## Arguments - `Z.res`: complex vector of residuals - `T`: time vector (only needed for method = 'like') - `method`: One of 'ar' or 'like' - whether to use the AR equivalence (faster, but needs to be regular) or likelihood estimation. - `showtable`: whether to return the AIC values of the respective models ## Returns A character string - `'wn'`, `'ou'` or `'ouf'`. Optionally also the AIC table. ## Examples ```r require(marcher) # white noise example Z1 <- rnorm(100) + 1i*rnorm(100) # OU example T <- 1:100 p.s2 <- c(tau.z = 5, tau.v = 0) S2 <- outer(T, T, getCov, p=p.s2, model="ou") Z2 <- mvrnorm2(n = 1, mu = rep(0,length(T)), S2) + 1i * mvrnorm2(n = 1, mu = rep(0,length(T)), S2) # OUF example p.s3 <- c(tau.z = 5, tau.v = 2) S3 <- outer(T, T, getCov, p=p.s3, model="ouf") Z3 <- mvrnorm2(n = 1, mu = rep(0,length(T)), S3) + 1i * mvrnorm2(n = 1, mu = rep(0,length(T)), S3) # plot all three par(mfrow=c(1,3), mar = c(2,2,2,2)) plot(Z1, asp=1, type="o") plot(Z2, asp=1, type="o") plot(Z3, asp=1, type="o") # select models using 'ar' method (results might vary!) selectModel(Z1, T = T, method = "ar", showtable = TRUE) selectModel(Z2, T = T, method = "ar", showtable = TRUE) selectModel(Z3, T = T, method = "ar", showtable = TRUE) selectModel(Z1, T = T, method = "like", showtable = TRUE) selectModel(Z2, T = T, method = "like", showtable = TRUE) selectModel(Z3, T = T, method = "like", showtable = TRUE) # repeat using irregular times (requiring "like" method) T <- cumsum(rexp(100)) # white noise example p.s1 <- c(tau.z = 0, tau.v = 0) S1 <- outer(T, T, getCov, p=p.s1, model="wn") Z1 <- mvrnorm2(n = 1, mu = rep(0,length(T)), S1) + 1i * mvrnorm2(n = 1, mu = rep(0,length(T)), S1) # OU example p.s2 <- c(tau.z = 5, tau.v = 0) S2 <- outer(T, T, getCov, p=p.s2, model="ou") Z2 <- mvrnorm2(n = 1, mu = rep(0,length(T)), S2) + 1i * mvrnorm2(n = 1, mu = rep(0,length(T)), S2) # OUF example p.s3 <- c(tau.z = 5, tau.v = 2) S3 <- outer(T, T, getCov, p=p.s3, model="ouf") Z3 <- mvrnorm2(n = 1, mu = rep(0,length(T)), S3) + 1i * mvrnorm2(n = 1, mu = rep(0,length(T)), S3) Z.list <- list(Z1, Z2, Z3) # plot par(mfrow=c(1,3), mar = c(2,2,2,2)) lapply(Z.list, function(z) plot(z, asp=1, type="o")) # select model lapply(Z.list, function(z) selectModel(z, T = T, method = "like", showtable = TRUE)) ```

selectModel function