Contexts of a VLMC with covariates

This function returns the different contexts present in a VLMC with covariates, possibly with some associated data.

Usage

# S3 method for covlmc
contexts(
  ct,
  sequence = FALSE,
  reverse = FALSE,
  frequency = NULL,
  positions = FALSE,
  local = FALSE,
  metrics = FALSE,
  model = NULL,
  hsize = FALSE,
  merging = FALSE,
  ...
)

Arguments

ct: a fitted covlmc model.
sequence: if TRUE the function returns its results as a data.frame, if FALSE (default) as a list of ctx_node objects. (see details)
reverse: logical (defaults to FALSE). See details.
frequency: specifies the counts to be included in the result data.frame. The default value of NULL does not include anything. "total" gives the number of occurrences of each context in the original sequence. "detailed" includes in addition the break down of these occurrences into all the possible states.
positions: logical (defaults to FALSE). Specify whether the positions of each context in the time series used to build the context tree should be reported in a positions column of the result data frame. The availability of the positions depends on the way the context tree was built. See details for the definition of a position.
local: specifies how the counts reported by frequency are computed. When local is FALSE (default value) the counts include both counts that are specific to the context (if any) and counts from the descendants of the context in the tree. When local is TRUE the counts include only the number of times the context appears without being the last part of a longer context.
metrics: if TRUE, adds predictive metrics for each context (see metrics() for the definition of predictive metrics).
model: specifies whether to include the model associated to a each context. The default result with model=NULL does not include any model. Setting model to "coef" adds the coefficients of the models in a coef column, while "full" include the models themselves (as R objects) in a model column.
hsize: if TRUE, adds a hsize column to the result data frame that gives for each context the size of the history of covariates used by the model.
merging: if TRUE, adds a merged column to the result data frame. For a normal context, the value of merged is FALSE. Contexts that share the same model have a TRUE merged value.
...: additional arguments for the contexts function.

Value

A list of class contexts containing the contexts represented in this tree (as ctx_node_covlmc) or a data.frame.

Details

The default behaviour of the function is to return a list of all the contexts using ctx_node_covlmc objects (as returned by find_sequence.covlmc()). The properties of the contexts can then be explored using adapted functions such as counts(), covariate_memory(), cutoff.ctx_node(), metrics.ctx_node(), model(), merged_with() and positions().

When sequence=TRUE the method returns a data.frame whose first column, named context, contains the contexts as vectors (i.e. the value returned by as_sequence() applied to a ctx_node object). Other columns contain context specific values specified by the additional parameters. Setting any of those parameters to a value that ask for reporting information will toggle the result type of the function to data.frame.

See contexts.ctx_tree() for details about the frequency parameter. When model is non NULL, the resulting data.frame contains the models associated to each context (either the full R model or its coefficients). Other columns are added is the corresponding parameters are set to TRUE.

Positions

A position of a context ctx in the time series x is an index value t such that the context ends with x[t]. Thus x[t+1] is after the context. For instance if x=c(0, 0, 1, 1) and ctx=c(0, 1) (in standard state order), then the position of ctx in x is 3.

State order in a context

Notice that contexts are given by default in the temporal order and not in the "reverse" order used by many VLMC research papers: older values are on the left. For instance, the context c(1, 0) is reported if the sequence 0, then 1 appeared in the time series used to build the context tree. Set reverse to TRUE for the reverse convention which is somewhat easier to relate to the way the context trees are represented by draw() (i.e. recent values at the top the tree).

Examples

pc <- powerconsumption[powerconsumption$week == 5, ]
breaks <- c(0, median(pc$active_power), max(pc$active_power))
dts <- cut(pc$active_power, breaks = breaks)
dts_cov <- data.frame(day_night = (pc$hour >= 7 & pc$hour <= 17))
m_cov <- covlmc(dts, dts_cov, min_size = 5)
## direct representation with ctx_node_covlmc objects
m_cov_ctxs <- contexts(m_cov)
m_cov_ctxs
#> Contexts:
#>  (0,1.34], (0,1.34], (0,1.34]
#>  (1.34,7.54], (0,1.34], (0,1.34]
#>  (1.34,7.54], (0,1.34]
#>  (1.34,7.54]
sapply(m_cov_ctxs, covariate_memory)
#> [1] 3 1 0 0
sapply(m_cov_ctxs, is_merged)
#> [1] FALSE FALSE FALSE FALSE
sapply(m_cov_ctxs, model)
#> [[1]]
#>     (Intercept) day_night_1TRUE day_night_2TRUE day_night_3TRUE 
#>       -2.813042        2.177964       -4.448526        2.417832 
#> 
#> [[2]]
#>     (Intercept) day_night_1TRUE 
#>       -19.56607        18.38741 
#> 
#> [[3]]
#> (Intercept) 
#>   -1.856298 
#> 
#> [[4]]
#> (Intercept) 
#>    2.535411 
#> 
## data.frame interface
contexts(m_cov, model = "coef")
#>        context         coef
#> 1 (0,1.34].... -2.81304....
#> 2 (1.34,7..... -19.5660....
#> 3 (1.34,7..... -1.85629....
#> 4  (1.34,7.54] 2.535411....
contexts(m_cov, model = "full", hsize = TRUE)
#>        context        model hsize
#> 1 (0,1.34].... c(`(Inte....     3
#> 2 (1.34,7..... c(`(Inte....     1
#> 3 (1.34,7..... c(`(Inte....     0
#> 4  (1.34,7.54] c(`(Inte....     0