The function removes the initial row by ID if it contains missing values. This process is repeated recursively until the first row per ID no longer has missing observations.
Arguments
- data
Data frame. A data frame object of data for potentially multiple subjects that contain a column of subject ID numbers (i.e., an ID variable), a column indicating subject-specific measurement occasions (i.e., a TIME variable), at least one column of observed values.
- id
Character string. A character string of the name of the ID variable in the data.
- time
Character string. A character string of the name of the TIME variable in the data.
- observed
Character vector. A vector of character strings of the names of the observed variables in the data.
- covariates
Character vector. A vector of character strings of the names of the covariates in the data.
- ncores
Positive integer. Number of cores to use. If
ncores = NULL, use a single core. Consider using multiple cores when number of individuals is large.
See also
Other Dynamic Modeling Utility Functions:
InitialNA(),
InsertNA(),
ScaleByID(),
SubsetByID()
Examples
# prepare parameters
set.seed(42)
## number of individuals
n <- 5
## time points
time <- 5
## dynamic structure
p <- 3
mu0 <- rep(x = 0, times = p)
sigma0 <- 0.001 * diag(p)
sigma0_l <- t(chol(sigma0))
alpha <- rep(x = 0, times = p)
beta <- 0.50 * diag(p)
psi <- 0.001 * diag(p)
psi_l <- t(chol(psi))
library(simStateSpace)
ssm <- SimSSMVARFixed(
n = n,
time = time,
mu0 = mu0,
sigma0_l = sigma0_l,
alpha = alpha,
beta = beta,
psi_l = psi_l,
type = 0
)
data <- as.data.frame(ssm)
# Replace first row with NA
data[1, paste0("y", 1:p)] <- NA
DeleteInitialNA(
data = data,
id = "id",
time = "time",
observed = paste0("y", 1:p),
)
#> id time y1 y2 y3
#> 1 1 1 0.0043989735 -0.0067845275 0.0663098413
#> 2 1 2 -0.0085581649 0.0523870156 0.0359087914
#> 3 1 3 -0.0061817260 0.0483959335 -0.0165906623
#> 4 1 4 -0.0298779752 0.0251096691 -0.0151779919
#> 5 2 0 -0.0061192799 0.0387721989 0.0189961850
#> 6 2 1 -0.0329971795 0.0689297674 0.0346677144
#> 7 2 2 -0.0800860506 0.0005550857 0.0147337653
#> 8 2 3 -0.0139099509 0.0097115321 0.0178024165
#> 9 2 4 -0.0483904352 -0.0332660475 -0.0061693084
#> 10 3 0 0.0162138501 -0.0300663706 -0.0541857866
#> 11 3 1 0.0565002345 -0.0026360905 -0.0148648209
#> 12 3 2 0.0111671458 0.0231926410 0.0174936750
#> 13 3 3 -0.0418041237 0.0081541330 -0.0188789037
#> 14 3 4 -0.0208066072 0.0007748466 -0.0007347123
#> 15 4 0 -0.0417993615 0.0431811476 0.0464821572
#> 16 4 1 -0.0260596182 0.0136457676 0.0062622657
#> 17 4 2 -0.0036136722 0.0120015310 -0.0627221010
#> 18 4 3 0.0425994450 0.0276891614 -0.0135222887
#> 19 4 4 -0.0201818279 -0.0024087753 0.0219790184
#> 20 5 0 0.0002017724 -0.0028082763 0.0114384154
#> 21 5 1 0.0509683787 -0.0109636240 0.0715959928
#> 22 5 2 0.0377919404 0.0306229441 -0.0201126499
#> 23 5 3 0.0673546742 0.0744559593 0.0383459206
#> 24 5 4 0.0046099160 0.0617163188 0.0453289300