if (FALSE) {
## We generate data from a mixed VAR model and then recover the model using mvar()
# 1) Define 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 m=2, 2 categoricals with m=4, two continuous
max_level <- max(level)
lags <- c(1, 3, 9) # include lagged effects of order 1, 3, 9
n_lags <- length(lags)
# Specify thresholds
thresholds <- list()
thresholds[[1]] <- rep(0, level[1])
thresholds[[2]] <- rep(0, level[2])
thresholds[[3]] <- rep(0, level[3])
thresholds[[4]] <- rep(0, level[4])
thresholds[[5]] <- rep(0, level[5])
thresholds[[6]] <- rep(0, level[6])
# Specify standard deviations for the Gaussians
sds <- rep(NULL, p)
sds[5:6] <- 1
# Create coefficient array
coefarray <- array(0, dim=c(p, p, max_level, max_level, n_lags))
# a.1) interaction between continuous 5<-6, lag=3
coefarray[5, 6, 1, 1, 2] <- .4
# 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)
# 2) Sample
set.seed(1)
dlist <- mvarsampler(coefarray = coefarray,
lags = lags,
thresholds = thresholds,
sds = sds,
type = type,
level = level,
N = 200,
pbar = TRUE)
# 3) Recover
set.seed(1)
mvar_obj <- mvar(data = dlist$data,
type = type,
level = level,
lambdaSel = "CV",
lags = c(1, 3, 9),
signInfo = FALSE,
overparameterize = F)
# Did we recover the true parameters?
mvar_obj$wadj[5, 6, 2] # cross-lagged effect of 6 on 2 over lag lags[2]
mvar_obj$wadj[1, 3, 1] # cross-lagged effect of 3 on 1 over lag lags[1]
mvar_obj$wadj[1, 5, 3] # cross-lagged effect of 1 on 5 over lag lags[3]
# How to get the exact parameter estimates?
# Example: the full parameters for the crossed-lagged interaction of 2 on 1 over lag lags[1]
mvar_obj$rawlags[[1]][[1]][[2]]
# 4) Predict / Compute nodewise Error
pred_mvar <- predict.mgm(mvar_obj, dlist$data)
head(pred_mvar$predicted) # first 6 rows of predicted values
pred_mvar$errors # Nodewise errors
# For more examples see https://github.com/jmbh/mgmDocumentation
}
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