Outliers | R Documentation |

This is a list object containing true outliers, the dataset, and the saved result from running dynr.taste.

data(Outliers)

A data frame with 6000 rows and 6 variables

The true outliers for observed variables are saved in ‘Outliers$generated$shockO’.

id. Six outliers were added for each ID.

time_O. Time points where the outliers were added.

obs. Variable indices where the outliers were added.

shock.O. The magnitude of outliers.

The true outliers for state variables are saved in ‘Outliers$generated$shockL’.

id. Three outliers were added for each ID.

time_L. Time points where the outliers were added.

lat. Variable indices where the outliers were added.

shock.L. The magnitude of outliers.

A dataset simulated based on state-space model including the outliers. The data is saved in ‘Outliers$generated$y’. The variables are as follows:

id. ID of the systems (1 to 100)

times. Time indices (100 time points for each participant)

V1 - V6. observed variables

The detected innovative outliers from dynr.taste for this dataset, which is used for testing whether the dynr.taste replicate the same result. The data is saved in ‘Outliers$detect_O’. The variables are as follows:

id. IDs

time_L. Time points where the outliers were detected

obs. Variable indices for observed variables where the outliers were detected

The detected additive outliers from dynr.taste for this dataset, which is used for testing whether the dynr.taste replicate the same result. The data is saved in ‘Outliers$detect_L’. The variables are as follows:

id. IDs

time_L. Time points where the outliers were detected

obs. Variable indices for latent variables where the outliers were detected

## Not run: #The following was used to generate the data #--------------------------------------- lambda <- matrix(c(1.0, 0.0, 0.9, 0.0, 0.8, 0.0, 0.0, 1.0, 0.0, 0.9, 0.0, 0.8), ncol=2, byrow=TRUE) psi <- matrix(c(0.3, -0.1, -0.1, 0.3), ncol=2, byrow=TRUE) beta <- matrix(c(0.8, -0.2, -0.2, 0.7), ncol=2, byrow=TRUE) theta <- diag(c(0.2, 0.2, 0.2, 0.2, 0.2, 0.2), ncol=6, nrow=6) nlat <- 2; nobs <- 6 mean_0 <- rep(0, nlat) psi_inf <- diag(1, 2*2) - kronecker(beta, beta) psi_inf_inv <- try(solve(psi_inf), silent=TRUE) if("try-error" %in% class(psi_inf_inv)) { psi_inf_inv <- MASS::ginv(psi_inf)} psi_0 <- psi_inf_inv %*% as.vector(psi) dim(psi_0) <- c(2, 2) # measurement error covariance matrix mea_cov <- lambda %*% psi_0 %*% t(lambda) + theta resL <- lapply(1:100, function(subj) { # initial state eta_0 <- mvtnorm::rmvnorm(1, mean=mean_0, sigma=psi_0)#[1,nlat] zeta_0 <- mvtnorm::rmvnorm(1, mean=rep(0, nlat), sigma=psi) eta <- matrix(0, nrow=time, ncol=nlat) eta[1, ] <- beta %*% t(eta_0) + t(zeta_0) zeta <- mvtnorm::rmvnorm(time, mean=rep(0, nlat), sigma=psi) # random shock generation # to avoid shock appearing too early or late (first and last 3) shkLat_time <- sample(4:(time-3), nshockLat) shk_lat <- sample(1:nlat, nshockLat, replace=TRUE) shockLatIdx <- matrix(c(shkLat_time, shk_lat), ncol=2) shockSignL <- sample(c(1,-1), nshockLat, replace=TRUE) colnames(shockLatIdx) <- c("time_L","lat") shockLatV <- shockSignL*( shockMag*sqrt(diag(shockPsi)))[shockLatIdx[,"lat"]] shockLatM <- matrix(0, time, nlat) shockLatM[shockLatIdx] <- shockLatV shkObs_time <- sample(4:(time-3), nshockObs) shk_obs <- sample(1:nobs, nshockObs, replace=TRUE) shockObsIdx <- matrix(c(shkObs_time, shk_obs), ncol=2) shockSignO <- sample(c(1,-1), nshockObs, replace=TRUE) colnames(shockObsIdx) <- c("time_O","obs") shockObsV <- shockSignO*( shockMag*sqrt(diag(mea_cov)) )[shockObsIdx[,"obs"]] shockObsM <- matrix(0, time, nobs) shockObsM[shockObsIdx] <- shockObsV # generate state process WITH shock for (t in 1:(time-1)) { eta[t+1, ] <- shockLatM[t, ] + beta %*% eta[t, ] + zeta[t, ] } # generate observed process y <- shockObsM + eta %*% t(lambda) + mvtnorm::rmvnorm(time, mean=rep(0, nobs), sigma=theta)# epsilon } ## End(Not run)

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