networktree: Partitioning of network models — networktree • networktree

Computes a tree model with networks at the end of branches. Can use model-based recursive partitioning or conditional inference.

Wraps the mob() and ctree() functions from the partykit package.

networktree(...)

# S3 method for default
networktree(
  nodevars,
  splitvars,
  method = c("mob", "ctree"),
  model = "correlation",
  transform = c("cor", "pcor", "glasso"),
  na.action = na.omit,
  weights = NULL,
  ...
)

# S3 method for formula
networktree(
  formula,
  data,
  transform = c("cor", "pcor", "glasso"),
  method = c("mob", "ctree"),
  na.action = na.omit,
  model = "correlation",
  ...
)

Arguments

...: additional arguments passed to mob_control (mob) or ctree_control (ctree)
nodevars: the variables with which to compute the network. Can be vector, matrix, or dataframe
splitvars: the variables with which to test split the network. Can be vector, matrix, or dataframe
method: "mob" or "ctree"
model: can be any combination of c("correlation", "mean", "variance") splits are determined based on the specified characteristics
transform: should stored correlation matrices be transformed to partial correlations or a graphical lasso for plotting? Can be set to "cor" (default), "pcor", or "glasso"
na.action: a function which indicates what should happen when the data contain missing values (NAs).
weights: weights
formula: A symbolic description of the model to be fit. This should either be of type y1 + y2 + y3 ~ x1 + x2 with node vectors y1, y2, and y3 or y ~ x1 + x2 with a matrix response y. x1 and x2 are used as partitioning variables.
data: a data frame containing the variables in the model

References

Jones, P.J., Mair, P., Simon, T., Zeileis, A. (2020). Network trees: A method for recursively partitioning covariance structures. Psychometrika, 85(4), 926-945. https://doi.org/10.1007/s11336-020-09731-4

Examples


set.seed(1)
d <- data.frame(trend = 1:200, foo = runif(200, -1, 1))
d <- cbind(d, rbind(
  mvtnorm::rmvnorm(100, mean = c(0, 0, 0),
          sigma = matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), ncol = 3)),
  mvtnorm::rmvnorm(100, mean = c(0, 0, 0),
          sigma = matrix(c(1, 0, 0.5, 0, 1, 0.5, 0.5, 0.5, 1), ncol = 3))
))
colnames(d)[3:5] <- paste0("y", 1:3)

## Now use the function
tree1 <- networktree(nodevars=d[,3:5], splitvars=d[,1:2])

## Formula interface
tree2 <- networktree(y1 + y2 + y3 ~ trend + foo, data=d)

## plot 
plot(tree2)

plot(tree2, terminal_panel = "box")
plot(tree2, terminal_panel = "matrix")

# \donttest{
## Conditional version
tree3 <- networktree(nodevars=d[,3:5], splitvars=d[,1:2], 
                     method="ctree")

## Change control arguments
tree4 <- networktree(nodevars=d[,3:5], splitvars=d[,1:2],
                     alpha=0.01)
# }