Sampling from a time-varying mixed VAR model
Function to sample from a time-varying mixed VAR (mVAR) model
tvmvarsampler(coefarray, lags, thresholds,
sds, type, level, pbar)coefarray |
A p x p x max(level) x max(level) x n_lags x N array, where p are the number of variables, level is the input argument |
lags |
A vector indicating the lags in the mVAR model. E.g. |
thresholds |
A list with p entries, each consisting of a matrix indicating a threshold for each category of the given variable (column) and time point (row). For continuous variable, the matrix has 1 column. |
sds |
A N x p matrix specifying the standard deviation of Gaussian variables (columns) at each time point (rows)If non-Gaussian variables are included in the mVAR model, the corresponding columns are ignored. |
type |
p vector indicating the type of variable for each column in |
level |
p vector indicating the number of categories of each variable. For continuous variables set to 1. |
pbar |
If |
We sample from the mVAR model by separately sampling from its corresponding p conditional distributions.
A list with two entries:
call |
The function call |
data |
The sampled n x p data matrix |
Jonas Haslbeck <jonashaslbeck@gmail.com>
Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Estimating time-varying Mixed Graphical Models in high-dimensional Data. Journal of Statistical Software, 93(8), pp. 1-46. DOI: 10.18637/jss.v093.i08
## Not run:
## We specify a tvmvar model, sample from it and recover it
# a) Set up time-varying mvar model
p <- 6 # Six variables
type <- c("c", "c", "c", "c", "g", "g") # 4 categorical, 2 gaussians
level <- c(2, 2, 4, 4, 1, 1) # 2 categoricals with 2 categories, 2 with 5
max_level <- max(level)
lags <- c(1, 3, 9) # include lagged effects of order 1, 3, 9
n_lags <- length(lags)
N <- 5000
# Specify thresholds
thresholds <- list()
thresholds[[1]] <- matrix(0, ncol=2, nrow=N)
thresholds[[2]] <- matrix(0, ncol=2, nrow=N)
thresholds[[3]] <- matrix(0, ncol=4, nrow=N)
thresholds[[4]] <- matrix(0, ncol=4, nrow=N)
thresholds[[5]] <- matrix(0, ncol=1, nrow=N)
thresholds[[6]] <- matrix(0, ncol=1, nrow=N)
# Specify standard deviations for the Gaussians
sds <- matrix(NA, ncol=6, nrow=N)
sds[,5:6] <- 1
# Create coefficient array
coefarray <- array(0, dim=c(p, p, max_level, max_level, n_lags, N))
# a.1) interaction between continuous 5<-6, lag=3
coefarray[5, 6, 1, 1, 2, ] <- c(rep(.5, N/2), rep(0, N/2))
# a.2) interaction between 1<-3, lag=1
m1 <- matrix(0, nrow=level[2], ncol=level[4])
m1[1, 1:2] <- 1
m1[2, 3:4] <- 1
coefarray[1, 3, 1:level[2], 1:level[4], 1, ] <- m1
# a.3) interaction between 1<-5, lag=9
coefarray[1, 5, 1:level[1], 1:level[5], 3, ] <- c(0, 1)
dim(coefarray)
# b) Sample
set.seed(1)
dlist <- tvmvarsampler(coefarray = coefarray,
lags = lags,
thresholds = thresholds,
sds = sds,
type = type,
level = level,
pbar = TRUE)
# c) Recover: time-varying mVAR model
set.seed(1)
tvmvar_obj <- tvmvar(data = dlist$data,
type = type,
level = level,
lambdaSel = "CV",
lags = c(1, 3, 9),
estpoints = seq(0, 1, length=10),
bandwidth = .05)
tvmvar_obj$wadj[5, 6, 2, ] # parameter goes down, as specified
tvmvar_obj$wadj[1, 3, 1, ]
tvmvar_obj$wadj[1, 5, 3, ]
# For more examples see https://github.com/jmbh/mgmDocumentation
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