Package 'stagedtrees'

Description

Convert to an adjacency matrix

Usage

as_adj_matrix(x, ...)

## S3 method for class 'parentslist'
as_adj_matrix(x, ...)

## S3 method for class 'ceg'
as_adj_matrix(x, ignore = x$name_unobserved, endnode = TRUE, ...)

Arguments

Value

the equivalent adjacency matrix

for as_adj_matrix.ceg: the adj matrix corresponding to the CEG.

Description

Convert a staged tree object into an object of class bn from the bnlearn package.

Usage

as_bn(x)

## S3 method for class 'parentslist'
as_bn(x)

## S3 method for class 'sevt'
as_bn(x)

Arguments

Value

an object of class bn from package bnlearn.

Description

Convert to the equivalent representation as list of parents.

Usage

as_parentslist(x, ...)

parentslist(...)

## S3 method for class 'list'
as_parentslist(x, ...)

## S3 method for class 'bn'
as_parentslist(x, order = NULL, ...)

## S3 method for class 'bn.fit'
as_parentslist(x, order = NULL, ...)

## S3 method for class 'sevt'
as_parentslist(x, silent = FALSE, ...)

Arguments

Details

The output of this function is an object of class parentslist which is one of the possible encoding for a directed graph. This is mainly an internal class and its specification can be changed in the future. For example, now it may also include information on the sample space of the variables and the context/partial/local independences.

parentslist() is a simple wrapper of as_parentslist.list(). In particular parentslist(...) is equivalent to as_parentslist(list(...)).

In as_parentslist.sevt, if a context-specific or a local-partial independence is detected a message is printed (if silent = FALSE) and the minimal super-model is returned.

Value

An object of class parentslist for which a print method exists. Basically a list with one entries for each variable with fields:

• parents The parents of the variable.

• context Where context independences are detected.

• partial Where partial independences are detected.

• local Where no context/partial independences are detected, but local independences are present.

• values values for the variable.

See Also

print.parentslist and as.character.parentslist for the parenthesis-encoding of the DAG structure and the asymmetric independences.

Examples

model <- stages_hclust(full(Titanic), k = 2)
pl <- as_parentslist(model)
pl$Age

Description

Convert to an equivalent object of class sevt.

Usage

as_sevt(x, ...)

## S3 method for class 'bn.fit'
as_sevt(x, order = NULL, ...)

## S3 method for class 'bn'
as_sevt(x, order = NULL, values = NULL, ...)

## S3 method for class 'parentslist'
as_sevt(x, order = NULL, values = NULL, ...)

Arguments

Details

In as_sevt.bn.fit the order argument, if provided, must be a topological order of the bn.fit object (no check is performed). If the order is not provided a topological order will be used (the one returned by bnlearn::node.ordering).

In as_sevt.parentslist the order argument, if provided, must be a topological order of the corresponding DAG (no check is performed). If the order is not provided names(x) is used.

The values parameter is used to specify the sample space of each variable. For a parentslist object created with as_parentslist from an object of class sevt, it is, usually, not needed to specify the values parameter, since the sample space is saved in the parentslist object.

Value

the equivalent object of class sevt.

Examples

model <- stages_hclust(full(Titanic), k = 2)
plot(model)
pl <- as_parentslist(model)
model2 <- as_sevt(pl)
plot(model2) ## this is a super-model of the first staged tree
## we can check it with
inclusions_stages(model, model2)

Description

Nice print of a parentslist object

Usage

## S3 method for class 'parentslist'
as.character(x, only_parents = FALSE, ...)

## S3 method for class 'parentslist'
print(x, ...)

Arguments

Value

as.character.parentslist returns a string encoding the associated directed graph and eventually the context specific independences. The encoding is similar to the one returned by modelstring in package bnlearn and package deal. In particular, parents of a variable can be enclosed in:

• ( ) if a partial (conditional) independence is present.

• { } if a context specific independence is present.

• < > if no context specific and partial (conditional) independences are present, but at least a local independence is detected.

If a parent is not enclosed in parenthesis the dependence is full.

If only_parents = TRUE, the simple DAG encoding as in bnlearn is returned.

Examples

model <- stages_hclust(full(Titanic), k = 2)
pl <- as_parentslist(model)
pl
as.character(pl)
as.character(pl, only_parents = TRUE)

Description

Artificial dataset with observations from four variables having a non-symmetrical conditional independence structure.

Usage

Asym

Format

A data frame with 1000 observations of 4 binary variables.

Source

The data has been generated by Federico Carli [email protected].

Description

Create a bar plot visualizing probabilities associated to the different stages of a variable in a staged event tree.

Usage

## S3 method for class 'sevt'
barplot(
  height,
  var,
  ignore = height$name_unobserved,
  beside = TRUE,
  horiz = FALSE,
  legend.text = FALSE,
  col = NULL,
  xlab = ifelse(horiz, "probability", NA),
  ylab = ifelse(!horiz, "probability", NA),
  ...
)

Arguments

Value

As barplot: A numeric vector (or matrix, when beside = TRUE), giving the coordinates of all the bar midpoints drawn, useful for adding to the graph.

Examples

model <- stages_fbhc(full(PhDArticles, lambda = 1))
barplot(model, "Kids", beside = TRUE)

Description

Build the CEG representation from an object of class sevt.

Usage

ceg(object)

Arguments

Details

An object of class ceg is a staged event tree object with additional information on the positions.

Value

an object of class ceg.

Examples

DD <- generate_xor_dataset(3, 100)
model <- stages_bhc(full(DD))
model.ceg <- ceg(model)
model.ceg$positions

Description

Generate the sequence of all the conditional independences matrices of stages for a given variable in the model.

Usage

ci_matrices(object, var)

Arguments

Value

A list with i-1 matrices, where i is the depth of variable var in the tree.

Examples

mod <- sevt(list(A = c("a", "aa"),
                 B = c("b", "bb", "bbb"),
                 C = c("c", "cc")), full = TRUE)
stages(mod)["C", A = "a", B = c("b", "bb")] <- "stage1"
stages(mod)["C", A = "aa"] <- "stage2"
stages(mod)["C", A = "a", B = "bbb"] <- "stage2"

ci_matrices(mod, "C")

Description

Compute the context specific interventional discrepeancy of a staged tree with respect to a reference staged tree.

Usage

cid(object1, object2, FUN = mean)

Arguments

Value

A list with components:

• wrong a stages-like structure which record where object2 wrongly infer the interventional distance with respect to object1.

• cid the value of the computed CID.

References

Leonelli M., Varando G. Context-Specific Causal Discovery for Categorical Data Using Staged Trees, The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023, https://arxiv.org/abs/2106.04416

Examples

model1 <- stages_bhc(full(Titanic))
model2 <- stages_bhc(full(Titanic,
  order = c("Survived", "Sex", "Age", "Class")
))
cid(model1, model2)$cid
cid(model1, model2)$wrong

Description

Compare two staged event trees, return the differences of the stages structure and plot the difference tree. Three different methods to compute the difference tree are available (see Details).

Usage

compare_stages(
  object1,
  object2,
  method = "naive",
  return_tree = FALSE,
  plot = FALSE,
  ...
)

hamming_stages(object1, object2, return_tree = FALSE, FUN = sum)

diff_stages(object1, object2)

Arguments

Details

compare_stages tests if the stage structure of two sevt objects is the same. Three methods are available:

• naive first applies stndnaming to both objects and then simply compares the resulting stage names.

• hamming uses the hamming_stages function that finds a minimal subset of nodes which stages must be changed to obtain the same structure.

• stages uses the diff_stages function that compares stages to check whether the same stage structure is present in both models.

Setting return_tree = TRUE will return the stages difference obtained with the selected method. The stages difference is a list of numerical vectors with same lengths and structure as stages(object1) or stages(object2), where values are 1 if the corresponding node has different (with respect to the selected method) associated stage, and 0 otherwise.

With plot = TRUE the plot of the difference tree is displayed.

If return_tree = FALSE and plot = FALSE the logical output is the same for the three methods and thus the naive method should be used since it is computationally faster.

hamming_stages finds a minimal set of nodes for which the associated stages should be changed to obtain equivalent structures. To do that, a maximum-weight bipartite matching problem between the stages of the two staged trees is solved using the Hungarian method implemented in the solve_LSAP function of the clue package. hamming_stages requires the package clue.

Value

compare_stages: if return_tree = FALSE, logical: TRUE if the two models are exactly equal, otherwise FALSE. Else if return_tree = TRUE, the differences between the two trees, according to the selected method.

hamming_stages: if return_tree = FALSE, integer, the minimum number of situations where the stage should be changed to obtain the same models. If return_tree = TRUE a stages-like structure showing which situations should be modified to obtain the same models.

diff_stages: a stages-like structure marking the situations belonging to stages which are not the exactly equal.

Examples

data("Asym")
mod1 <- stages_bhc(full(Asym, lambda = 1))
mod2 <- stages_fbhc(full(Asym, lambda = 1))
compare_stages(mod1, mod2)

##########
m0 <- full(PhDArticles[, 1:4], lambda = 0)
m1 <- stages_bhc(m0)
m2 <- stages_bj(m0, distance = "totvar", thr = 0.25)
diff_stages(m1, m2)

Description

Confint method for class sevt.

Usage

## S3 method for class 'sevt'
confint(
  object,
  parm,
  level = 0.95,
  method = c("wald", "waldcc", "wilson", "goodman", "quesenberry-hurst"),
  ignore = object$name_unobserved,
  ...
)

Arguments

Details

Compute confidence intervals for staged event trees. Currently five methods are available:

• wald, waldcc: Wald method and with continuity correction.

• wilson, quesenberry-hurst and goodman.

Value

A matrix with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in % (by default 2.5% and 97.5%).

Author(s)

The function is partially inspired by code in the MultinomCI function from the DescTools package, implemented by Andri Signorelli and Pablo J. Villacorta Iglesias.

References

Goodman, L. A. (1965) On Simultaneous Confidence Intervals for Multinomial Proportions Technometrics, 7, 247-254.

Wald, A. Tests of statistical hypotheses concerning several parameters when the number of observations is large, Trans. Am. Math. Soc. 54 (1943) 426-482.

Wilson, E. B. Probable inference, the law of succession and statistical inference, J.Am. Stat. Assoc. 22 (1927) 209-212.

Quesenberry, C., & Hurst, D. (1964). Large Sample Simultaneous Confidence Intervals for Multinomial Proportions. Technometrics, 6(2), 191-195

Examples

m1 <- stages_bj(full(PhDArticles), distance = "kullback", thr = 0.01)
confint(m1, "Prestige", level = 0.90)
confint(m1, "Married", method = "goodman")
confint(m1, c("Married", "Kids"))

Description

Copy a sevt obejct

Usage

copy_sevt(object, extra = NULL)

Arguments

Value

the copied object.

Description

Dataset with observations from four variables (Sex, Age, ICU, death) for 10000 simulated SARS-CoV-2 hospital patients.

Usage

covid_patients

Format

A data frame with 10000 observations of 4 variables. The variables and their levels are as follows:

• Sex: Female, Male

• Age: 0-39, 40-49, 50-59, 60-69, 70-79, 80+

• ICU: yes, no

• death: yes, no

Details

The data are simulated from an event tree where conditional probabilities for ICU and death are taken from the results of Lefrancq et al. (2021). Lefrancq et al. (2021) estimated such probabilities from data on patients, recorded in the SI-VIC database, who started their hospitalization between 13 March and 30 November 2020.

Source

The data has been generated with the code in the Examples section. Conditional probabilities were copied from the tables in the Supplementary materials of Lefrancq et al. (2021). Marginal probabilities of gender and probabilities of age given gender were instead obtained from the linked GitHub repository https://github.com/noemielefrancq/Evolution-Outcomes-COVID19-France.

References

Leonelli, M. and Varando, G. (2023). Context-Specific Causal Discovery for Categorical Data Using Staged Trees. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:8871-8888 Available from https://proceedings.mlr.press/v206/leonelli23a.html.

Lefrancq N., Paireau J., Hozé N., Courtejoie N., Yazdanpanah Y., Bouadma L. (2021). Evolution of outcomes for patients hospitalised during the first 9 months of the SARS-CoV-2 pandemic in France: A retrospective national surveillance data analysis. The Lancet Regional Health - Europe, 5:100087.

Examples

library(stagedtrees)

data_model <- sevt(list(
  Sex = c("Female", "Male"),
  Age = c(
    "0-39", "40-49", "50-59", "60-69",
    "70-79", "80+"
  ),
  ICU = c("yes", "no"),
  death = c("yes", "no")
), full = TRUE)
data_model$prob <- list()
data_model$prob$Sex <- list("1" = c(Female = 0.45185, Male = 0.54815))

dist_age_male <- c(
  0.01616346, # 0 - 39
  0.04159445, # 40 - 49
  0.10130439, # 50 - 59
  0.16825686, # 60 - 69
  0.25217550, # 70 - 79
  0.42050534
) # 80+

dist_age_female <- c(
  0.01688613, # 0 - 39
  0.04271329, # 40 - 49
  0.10131681, # 50 - 59
  0.16841872, # 60 - 69
  0.25289366, # 70 - 79
  0.41777138
) # 80+

names(dist_age_male) <- data_model$tree$Age
names(dist_age_female) <- data_model$tree$Age
data_model$prob$Age <- list(
  "1" = dist_age_female,
  "2" = dist_age_male
)
data_model$prob$ICU <- list(
  "1" = c(yes = 0.125, no = 1 - 0.125), # Female 0-39
  "2" = c(yes = 0.149, no = 1 - 0.149), # Female 40-49
  "3" = c(yes = 0.193, no = 1 - 0.193), # Female 50-59
  "4" = c(yes = 0.225, no = 1 - 0.225), # Female 60-69
  "5" = c(yes = 0.175, no = 1 - 0.175), # Female 70-79
  "6" = c(yes = 0.037, no = 1 - 0.037), # Female 80+
  "7" = c(yes = 0.197, no = 1 - 0.197), # Male 0-39
  "8" = c(yes = 0.2687, no = 1 - 0.2687), # Male 40-49
  "9" = c(yes = 0.3171, no = 1 - 0.3171), # Male 50-59
  "10" = c(yes = 0.3415, no = 1 - 0.3415), # Male 60-69
  "11" = c(yes = 0.274, no = 1 - 0.274), # Male 70-79
  "12" = c(yes = 0.073, no = 1 - 0.073) # Male 80+
)
data_model$prob$death <- list(
  ################### FEMALE ################################
  "1"  = c(yes = 0.077, no = 1 - 0.077), # Female 0-39 ICU
  "2"  = c(yes = 0.004, no = 1 - 0.004), # Female 0-39 no-ICU
  "3"  = c(yes = 0.117, no = 1 - 0.117), # Female 40-49 ICU
  "4"  = c(yes = 0.017, no = 1 - 0.017), # Female 40-49 no-ICU
  "5"  = c(yes = 0.185, no = 1 - 0.185), # Female 50-59 ICU
  "6"  = c(yes = 0.030, no = 1 - 0.030), # Female 50-59 no-ICU
  "7"  = c(yes = 0.239, no = 1 - 0.239), # Female 60-69 ICU
  "8"  = c(yes = 0.058, no = 1 - 0.058), # Female 60-69 no-ICU
  "9"  = c(yes = 0.324, no = 1 - 0.324), # Female 70-79 ICU
  "10" = c(yes = 0.124, no = 1 - 0.124), # Female 70-79 no-ICU
  "11" = c(yes = 0.454, no = 1 - 0.454), # Female 80+ ICU
  "12" = c(yes = 0.266, no = 1 - 0.266), # Female 80+ no-ICU
  ################# MALE ##################################
  "13" = c(yes = 0.079, no = 1 - 0.079), # Male 0-39 ICU
  "14" = c(yes = 0.008, no = 1 - 0.008), # Male 0-39 no-ICU
  "15" = c(yes = 0.098, no = 1 - 0.098), # Male 40-49 ICU
  "16" = c(yes = 0.016, no = 1 - 0.016), # Male 40-49 no-ICU
  "17" = c(yes = 0.171, no = 1 - 0.171), # Male 50-59 ICU
  "18" = c(yes = 0.030, no = 1 - 0.030), # Male 50-59 no-ICU
  "19" = c(yes = 0.278, no = 1 - 0.278), # Male 60-69 ICU
  "20" = c(yes = 0.067, no = 1 - 0.067), # Male 60-69 no-ICU
  "21" = c(yes = 0.383, no = 1 - 0.383), # Male 70-79 ICU
  "22" = c(yes = 0.150, no = 1 - 0.150), # Male 70-79 no-ICU
  "23" = c(yes = 0.478, no = 1 - 0.478), # Male 80+ ICU
  "24" = c(yes = 0.363, no = 1 - 0.363) # Male 80+ no-ICU
)

# covid_patients <- sample_from(data_model, 10000, seed = 123)
# usethis::use_data(covid_patients, overwrite = TRUE)

Description

Extract the dependency subtree of a staged tree with respect to a variable

Usage

depsubtree(object, var, other_stages = c("NA", "indep", "full"))

Arguments

Details

The dependency sub-tree is a staged event tree which is sufficient to describe the conditional distribution of the variable var given its predecessors in the original tree represented by object. In particular the preceding variables are restricted to the parents of var in the minimal-DAG obtained with as_parentslist. This is the minimal set of variables which contexts are sufficient to fully represent the conditional distribution of var. Stages for variables different from var are either set to NA, or to the full or indep model, depending on other_stages.

Value

an object of class sevt representing the dependency sub-tree.

Examples

mod <- stages_kmeans(full(Titanic), k = 2)
par(mfrow = c(1, 2))
plot(mod, main = "staged tree")
plot(depsubtree(mod, "Age"), main = "dependency subtree for Age")
par(mfrow = c(1, 1))

Description

Data simulated by Shenvi et al. (2019) describing an intervention to reduce falls among the elderly.

Usage

falls_long

Format

A data frame with 50000 observations of 6 binary variables.

Details

As described in Walley et al (2023): "Under this intervention, a certain proportion of individuals aged over 65 would be assessed and classified as low or high risk. Those assessed to be at a high risk are referred to a falls clinic for an advanced assessment. All those who are referred, 50% of other high risk individuals, and 10% of low risk individuals go on to receive treatment. It is assumed that those who are not assessed receive neither referral nor treatment."

Source

The original data have been downloaded from the cegpy github repository and is also available as supplementary materials of Walley et al (2023). In the original data pairs of variables Housing-Assessment and Referred-Treatment are encoded in variables HousingAssessment and Treatment respectively. Function colsplit from package reshape2 was used to split those variables.

References

Walley G., Shenvi A., Strong P., Kobalczyk K. (2023), cegpy: Modelling with chain event graphs in Python, Knowledge-Based Systems, Volume 274.

Shenvi, A., Smith, J.Q., Walton, R., Eldridge, S. (2019). Modelling with Non-stratified Chain Event Graphs. In: Argiento, R., Durante, D., Wade, S. (eds) Bayesian Statistics and New Generations. BAYSM 2018. Springer Proceedings in Mathematics & Statistics, vol 296.

Description

find roots in graphs

Usage

find_roots(x)

Arguments

Value

nodes with 0 incoming edges, that is with 0 in-degree.

Description

Build fitted staged event tree from data.

Usage

full(
  data,
  order = NULL,
  join_unobserved = TRUE,
  lambda = 0,
  name_unobserved = "UNOBSERVED"
)

## S3 method for class 'table'
full(
  data,
  order = names(dimnames(data)),
  join_unobserved = TRUE,
  lambda = 0,
  name_unobserved = "UNOBSERVED"
)

## S3 method for class 'data.frame'
full(
  data,
  order = colnames(data),
  join_unobserved = TRUE,
  lambda = 0,
  name_unobserved = "UNOBSERVED"
)

indep(
  data,
  order = NULL,
  join_unobserved = TRUE,
  lambda = 0,
  name_unobserved = "UNOBSERVED"
)

## S3 method for class 'table'
indep(
  data,
  order = names(dimnames(data)),
  join_unobserved = TRUE,
  lambda = 0,
  name_unobserved = "UNOBSERVED"
)

## S3 method for class 'data.frame'
indep(
  data,
  order = colnames(data),
  join_unobserved = TRUE,
  lambda = 0,
  name_unobserved = "UNOBSERVED"
)

Arguments

Details

Functions to create full or independent staged tree models from data. The full (or saturated) staged tree is the model where every situation is in a different stage, and thus the model has the maximum number of parameters. Conversely, the independent staged tree (indep) assigns all the situations related to the same variable to the same stage, thus it is equivalent to the independence factorization.

Examples

## full model
DD <- generate_xor_dataset(4, 100)
model_full <- full(DD, lambda = 1)

## independence model (data.frame)
DD <- generate_xor_dataset(4, 100)
model <- indep(DD, lambda = 1)
model

Description

Randomly generate a simple classification problem.

Usage

generate_linear_dataset(
  p,
  n,
  eps = 1.2,
  gamma = runif(1, min = -p, max = p),
  alpha = runif(p, min = -p, max = p)
)

Arguments

Value

A data.frame with n independent random variables and one class variable C computed as sign(sum(x * alpha) + runif(1, -eps, eps) + gamma).

Examples

DD <- generate_linear_dataset(p = 5, n = 1000)

Description

Randomly generate a data.frame of independent binary variables.

Usage

generate_random_dataset(p, n)

Arguments

Value

A data.frame with n independent random variables.

Examples

DD <- generate_random_dataset(p = 5, n = 1000)

Description

Generate a xor dataset

Usage

generate_xor_dataset(p, n, eps = 1.2)

Arguments

Value

The xor dataset with n + 1 variables, where the first one is the class variable C computed as a noisy xor.

Examples

DD <- generate_xor_dataset(p = 5, n = 1000, eps = 1.2)

Description

Utility functions to obtain stages from paths and paths from stages.

Usage

get_stage(object, path)

get_path(object, var, stage)

Arguments

Value

get_stage returns the stage name(s) for given path(s).

get_path returns a data.frame containing the paths corresponding to the given stage(s).

Examples

model <- stages_fbhc(full(PhDArticles))
get_stage(model, c("0", "male"))
paths <- expand.grid(model$tree[2:1])[, 2:1]
get_stage(model, paths)
get_path(model, "Kids", "5")
get_path(model, "Gender", "2")
get_path(model, "Kids", c("5", "6"))

Description

Obtain the graph representation of a staged tree or a CEG as an object from the igraph package.

Usage

get_edges(x, ignore = x$name_unobserved, ...)

## S3 method for class 'sevt'
get_edges(x, ignore = x$name_unobserved, ...)

get_vertices(x, ignore = x$name_unobserved, ...)

## S3 method for class 'sevt'
get_vertices(x, ignore = x$name_unobserved, ...)

## S3 method for class 'ceg'
get_edges(x, ignore = x$name_unobserved, ...)

## S3 method for class 'ceg'
get_vertices(x, ignore = x$name_unobserved, ...)

as_igraph(x, ignore = x$name_unobserved, ...)

## S3 method for class 'sevt'
as_igraph(x, ignore = x$name_unobserved, ...)

## S3 method for class 'ceg'
as_igraph(x, ignore = x$name_unobserved, ...)

## S3 method for class 'parentslist'
as_igraph(x, ...)

Arguments

Details

Functions to translate the graph structure of a sevt or ceg object to a graph object from the igraph package. Additional functions that extract the edge lists and the vertices are available. This can be useful, for example to plot the staged tree with igraph or additional packages (see the examples).

Value

for get_edges: the edges list corresponding to the graph associated to x.

for get_vertices: the vertices list corresponding to the graph associated to x.

for as.igraph: a graph object from the igraph package.

Examples

mod <- stages_bhc(full(Titanic))
get_edges(mod)
get_vertices(mod)
## Not run: 
library(igraph)
library(ggraph)
######## sevt example ########
## convert to igraph object
g <- as_igraph(mod)
## plot with igraph directly
plot(g, layout = layout_with_sugiyama)
## plot with ggraph
ggraph(g, "sugiyama") +
  geom_edge_fan(
    aes(
      label = label,
      label_pos = 0.5 + runif(length(label), -0.1, 0.1)
    ),
    angle_calc = "along", show.legend = FALSE, check_overlap = FALSE,
    end_cap = circle(0.02, "npc"),
    arrow = grid::arrow(
      angle = 25,
      length = unit(0.025, "npc"),
      type = "closed"
    )
  ) +
  geom_node_point(aes(x = x, y = y, color = stage),
    size = 5,
    show.legend = FALSE
  ) +
  ggforce::theme_no_axes() + coord_flip() + scale_y_reverse()

######## ceg example ########
g.ceg <- as_igraph(ceg(mod))
### igraph plotting functions can be used
plot(g.ceg, layout = layout.sugiyama)
### igraph object can be also plotted with ggplot2 and ggraph
ggraph(g.ceg, "sugiyama") +
  geom_edge_fan(
    aes(
      label = label,
      color = label,
      label_pos = 0.5 + runif(length(label), -0.1, 0.1)
    ),
    angle_calc = "along", show.legend = FALSE, check_overlap = FALSE,
    end_cap = circle(0.02, "npc"),
    arrow = grid::arrow(
      angle = 25,
      length = unit(0.025, "npc"),
      type = "closed"
    )
  ) +
  geom_node_point(aes(x = x, y = y, color = stage), size = 3, show.legend = FALSE) +
  ggforce::theme_no_axes() + coord_flip() + scale_y_reverse()

## End(Not run)

Description

Display the relationship between two staged tree models over the same variables.

Usage

inclusions_stages(object1, object2)

Arguments

Details

Computes the relations between the stages structures of the two models.

The relations between stages of the same variable are stored in a data frame with three columns where each row represent a relation between a stage of the first model (s1) and a stage of the second model (s2). The relation can be one of the following: inclusion (s1 < s2 or s1 > s2; equal (s1 = s2); not-equal (s1 != s2).

Value

a list with inclusion relations between stage structures for each variable in the models.

Examples

mod1 <- stages_bhc(full(PhDArticles[, 1:5], lambda = 1))
mod2 <- stages_fbhc(full(PhDArticles[, 1:5], lambda = 1))
inclusions_stages(mod1, mod2)

Description

join positions in a staged tree model

Usage

join_positions(model, var, s1, s2)

Arguments

Details

this functions works similarly to the join_stages function in the stagedtrees package, but it also joins downstream stages to make nodes with stages s1,s2 in the same position. This function works properly only when downstream variables from var have full stages vectors.

Description

Join situations with no observations

Usage

join_unobserved(
  object,
  fit = TRUE,
  trace = 0,
  name = "UNOBSERVED",
  scope = sevt_varnames(object)[-1],
  lambda = object$lambda
)

Arguments

Details

It takes as input a (fitted) staged event tree object and it joins, in the same stage, all the situations with zero recorded observations. Since such joining does not change the log-likelihood of the model, it is a useful (time-wise) pre-processing prior to others model selection algorithms.

Unobserved situations can be joined directly in full or indep, by setting join_unobserved = TRUE.

Value

a staged event tree with at most one stage per variable with no observations. If, as default, fit=TRUE the model will be re-fitted, if fit=FALSE probabilities in the output model are not estimated.

Examples

DD <- generate_xor_dataset(p = 5, n = 10)
model_full <- full(DD, lambda = 1, join_unobserved = FALSE)
model <- join_unobserved(model_full)
logLik(model_full)
logLik(model)
BIC(model_full, model)

Description

Compute, or extract the log-likelihood of a staged event tree.

Usage

## S3 method for class 'sevt'
logLik(object, ...)

Arguments

Value

An object of class logLik.

Examples

data("PhDArticles")
mod <- indep(PhDArticles)
logLik(mod)

Description

Function to perform likelihood ratio test between two or multiple staged event tree models.

Usage

lr_test(object, ...)

Arguments

Details

If a single object of class sevt is passed as argument, it computes the likelihood-ratio test with respect to the independence model. If multiple objects are passed, likelihood-ratio tests between the first object and the followings are computed. In the latter case the function checks automatically if the first model is nested in the additional ones, via inclusions_stages, and throws an error if not.

Value

An object of class anova which contains the log-likelihood, degrees of freedom, difference in degrees of freedom, likelihood ratio statistics and corresponding p values.

Examples

data(PhDArticles)
order <- c("Gender", "Kids", "Married", "Articles")
phd.mod1 <- stages_hc(indep(PhDArticles, order))
phd.mod2 <- stages_hc(full(PhDArticles, order))

## compare two nested models
lr_test(phd.mod1, phd.mod2)

## compare a single model vs the independence model
lr_test(phd.mod1)

Description

Number of publications of 915 PhD biochemistry students during the 1950’s and 1960’s.

Usage

PhDArticles

Format

A data frame with 915 rows and 6 variables:

Articles

Number of articles during the last 3 years of PhD: either 0, 1-2 or >2.

Gender

male or female.

Kids

yes if the student has at least one kid 5 or younger, no otherwise.

Married

yes or no.

Mentor

Number of publications of the student's mentor: low between 0 and 3, medium between 4 and 10, high otherwise.

Prestige

low if the student is at a low-prestige university, high otherwise.

Source

The data has been modified from the Rchoice package.

References

Long, J. S. (1990). The origins of sex differences in science. Social Forces, 68(4), 1297-1316.

Description

igraph's plotting for CEG

Usage

## S3 method for class 'ceg'
plot(x, col = NULL, ignore = x$name_unobserved, layout = NULL, ...)

Arguments

Details

This function is a simple wrapper around igraph's plot.igraph. The ceg object is converted to an igraph object with as_igraph. If not specified, the default layout used is a rotated layout.sugiyama.

We use palette() as palette for the igraph plotting, while plot.igraph uses as default a different palette. This is to allow matching stages colors between plot.ceg and plot.sevt.

Examples

## Not run: 
 model <- stages_bhc(full(Titanic))
 model.ceg <- ceg(model)
 plot(model.ceg, edge.arrow.size = 0.1, vertex.label.dist = -2)
 
## End(Not run)

Description

igraph plotting of DAGs (parentslist)

Usage

## S3 method for class 'parentslist'
plot(
  x,
  col = c(context = "red", `context-partial` = "orange", partial = "blue", local =
    "purple", total = "black"),
  ...
)

Arguments

Details

This function is a simple wrapper around igraph's plot.igraph. The parentslist object is converted to an igraph object with as_igraph and then passed to plot.igraph. Additional arguments ... are passed to the igraph plotting function, allowing customization.

Examples

## Not run: 
 model <- stages_bhc(full(Titanic))
 pl <- as_parentslist(model, silent = TRUE)
 plot(model, edge.label = NA)
 
## End(Not run)

Description

Plot method for staged event tree objects. It allows easy plotting of staged event trees with some options (see Examples).

Usage

## S3 method for class 'sevt'
plot(
  x,
  y = 10,
  limit = y,
  xlim = c(0, 1),
  ylim = c(0, 1),
  main = NULL,
  sub = NULL,
  asp = 1,
  cex_label_nodes = 0,
  cex_label_edges = 1,
  cex_nodes = 2,
  cex_tree_y = 0.9,
  col = NULL,
  col_edges = "black",
  var_names = TRUE,
  ignore = x$name_unobserved,
  pch_nodes = 16,
  lwd_nodes = 1,
  lwd_edges = 1,
  ...
)

make_stages_col(x, col = NULL, ignore = x$name_unobserved, limit = NULL)

Arguments

Examples

data("PhDArticles")
mod <- stages_bj(full(PhDArticles, join_unobserved = TRUE))

### simple plotting
plot(mod)

### labels in nodes
plot(mod, cex_label_nodes = 1, cex_nodes = 0)

### reduce nodes size
plot(mod, cex_nodes = 0.5)

### change line width and nodes style
plot(mod, lwd_edges = 3, pch_nodes = 5)

### changing palette
plot(mod, col = function(s) heat.colors(length(s)))

### or changing global palette
palette(hcl.colors(10, "Harmonic"))
plot(mod)
palette("default") ##

### forcing plotting of unobserved stages
plot(mod, ignore = NULL)

### use function to specify colors
plot(mod, col = function(stages) {
  hcl.colors(n = length(stages))
})

### manually give stages colors
### as an example we will assign colors only to the stages of two variables
### Gender (one stage named "1") and Mentor (six stages)
col <- list(
  Gender = c("1" = "blue"),
  Mentor = c(
    "UNOBSERVED" = "grey",
    "2" = "red",
    "3" = "purple",
    "10" = "pink",
    "18" = "green",
    "22" = "brown"
  )
)
### by setting ignore = NULL we will plot also the UNOBSERVED stage for Mentor
plot(mod, col = col, ignore = NULL)

Description

Demographic information of a population of possible Pokemon Go users.

Usage

Pokemon

Format

A data frame with 999 rows and 5 variables:

Use

Y if the individual used the app, N otherwise

Age

>30 if the individual is older than 30, <=30 otherwise

Degree

Yes if the individual completed a Higher Education degree, No otherwise

Gender

Male or Female

Activity

Yes if the individual was physically active (i.e. had a walk longer than 30 mins, went for a run or had a bike ride to get some exercise) in the past week before the experiment, No otherwise

Source

https://osf.io/xy5g6/

References

Gabbiadini, Alessandro, Christina Sagioglou, and Tobias Greitemeyer. "Does Pokémon Go lead to a more physically active life style?." Computers in Human Behavior 84 (2018): 258-263.

Description

Predict class values from a staged event tree model.

Usage

## S3 method for class 'sevt'
predict(object, newdata = NULL, class = NULL, prob = FALSE, log = FALSE, ...)

Arguments

Details

Predict the most probable a posterior value for the class variable given all the other variables in the model. Ties are broken at random and if, for a given vector of predictor variables, all conditional probabilities are 0, NA is returned.

if prob = TRUE, a matrix with number of rows equals to the number of rows in the newdata and number of columns as the number of levels of the class variable is returned. if log = TRUE, log-probabilities are returned.

if prob = FALSE, a vector of length as the number of rows in the newdata with the level with higher estimated probability for each new observations is returned.

Value

A vector of predictions or the corresponding matrix of probabilities.

Examples

DD <- generate_xor_dataset(p = 4, n = 600)
order <- c("C", "X1", "X2", "X3", "X4")
train <- DD[1:500, order]
test <- DD[501:600, order]
model <- full(train)
model <- stages_bhc(model)
pr <- predict(model, newdata = test, class = "C")
table(pr, test$C)
# class values:
predict(model, newdata = test, class = "C")
# probabilities:
predict(model, newdata = test, class = "C", prob = TRUE)
# log-probabilities:
predict(model, newdata = test, class = "C", prob = TRUE, log = TRUE)

Description

Print a staged event tree

Usage

## S3 method for class 'sevt'
print(x, ..., max = 5)

Arguments

Details

The order of the variables in the staged tree is printed (from root). In addition the number of levels of each variable is shown in square brackets. If available the log-likelihood of the model is printed.

Value

An invisible copy of x.

Examples

DD <- generate_xor_dataset(5, 100)
model <- full(DD, lambda = 1)
print(model)

Description

Compute (marginal and/or conditional) probabilities of elementary events with respect to the probability encoded in a staged event tree.

Usage

prob(object, x, conditional_on = NULL, log = FALSE, na0 = TRUE)

Arguments

Details

Computes probabilities related to a vector or a data.frame of observations.

Optionally, conditional probabilities can be obtained by specifying the conditioning event in conditional_on. This can be done either with a single named vector or with a data.frame object with the same number of rows of x. In the former, the same conditioning is used for all the computed probabilities (if x has multiple rows); while with the latter different conditioning events (but on the same variables) can be specified for each row of x.

Value

the probabilities to observe each observation in x, possibly conditional on the event(s) in conditional_on.

Examples

data(Titanic)
model <- full(Titanic, lambda = 1)
samples <- expand.grid(model$tree[c(1, 4)])
pr <- prob(model, samples)
## probabilities sum up to one
sum(pr)
## print observations with probabilities
print(cbind(samples, probability = pr))

## compute one probability
prob(model, c(Class = "1st", Survived = "Yes"))

## compute conditional probability
prob(model, c(Survived = "Yes"), conditional_on = c(Class = "1st"))

## compute conditional probabilities with different conditioning set
prob(model, data.frame(Age = rep("Adult", 8)),
  conditional_on = expand.grid(model$tree[2:1])
)
## the above should be the same as
summary(model)$stages.info$Age

Description

generate a random DAG coded as parentslist object.

Usage

random_parentslist(n, k = 2, maxp = n)

Arguments

Details

For each variable a subset of random cardinality (maximum maxp) of the preceding variables is randomly selected as parents set. The possible levels of each variables are randomly selected in 2,...,k.

Value

a parentslist object.

Examples

random_parentslist(5, 3, 2)

## we can generate the associated staged tree
pl <- random_parentslist(4, 2, 2)
plot(as_sevt(pl), main = as.character(pl))

Description

Generate a random sevt from a DAG or a tree. Probabilities are also randomly generated.

Usage

random_sevt(x, q = 0.5, rfun = rexp)

## S3 method for class 'list'
random_sevt(x, q = 0.5, rfun = rexp)

## S3 method for class 'parentslist'
random_sevt(x, q = 0.5, rfun = rexp)

## S3 method for class 'sevt'
random_sevt(x, q = 0.5, rfun = rexp)

Arguments

Details

The generated staged tree is obtained by randomly joining stages with probability q.

For random_sevt.list, x should be a list representing an event tree, same format as lists provided to sevt.list. The random generated sevt will be obtained by randomly joining stages starting from a full staged event tree.

For random_sevt.parentslist, x should be a parentslist object representing a DAG, this could be obtained with as_parentslist or with random_parentslist. The random generated sevt will be obtained by randomly joining stages starting from a the staged tree equivalent to the DAG.

For random_sevt.sevt, x should be a sevt. The random generated sevt will be obtained by randomly joining stages starting from the provided sevt object.

     Stages (conditional) probabilities are sampled from
     the corresponding probability simplex by generating
     a vector with the user-defined function \code{rfun} and
     normalizing it to sum up to one.
     Absolute value is applied to assure non-negativity.
     The default \code{rfun = rexp} induces a uniform sampling
     from the probability simplex.

Value

A randomly generated fitted sevt object.

Examples

model_gt <- random_sevt(list(
  X = c("a", "b"), Y = c("c", "d", "e"),
  Z = c("1", "2", "3"), W = c("yes", "no")
))

## sample data from model_gt and estimate a staged tree
data <- sample_from(model_gt, 100)
model_est <- stages_bhc(full(data))

## compare true and estimated model
hamming_stages(model_gt, model_est)
compare_stages(model_gt, model_est, method = "hamming", plot = TRUE)

Description

Change the name of a stage in a staged event tree.

Usage

rename_stage(object, var, stage, new)

Arguments

Details

No internal checks are performed and as side effect stages can be joined, if e.g. new is equal to the name of a stage for variable var.

Value

a staged event tree object where stages stage have been renamed to new.

Description

Generate a random sample from the distribution encoded in a staged event tree object.

Usage

sample_from(object, size = 1, seed = NULL)

Arguments

Details

It samples size observations according to the transition probabilities (object$prob) in the model.

Value

A data frame containing size observations from the variables in object.

Examples

model <- stages_fbhc(full(PhDArticles, lambda = 1))
sample_from(model, 10)

Description

Find the optimal staged event tree with a dynamic programming approach.

Usage

search_best(
  data,
  alg = stages_bhc,
  search_criterion = BIC,
  lambda = 0,
  join_unobserved = TRUE,
  ...
)

Arguments

Details

This function is an implementation of the dynamic programming approach of Silander and Leong (2013). If the search_criterion is decomposable the returned model attains the best value among all possible orders.

Value

The estimated staged event tree model.

References

Silander T., Leong TY. A Dynamic Programming Algorithm for Learning Chain Event Graphs. In: Fürnkranz J., Hüllermeier E., Higuchi T. (eds) Discovery Science. DS 2013. Lecture Notes in Computer Science, vol 8140. Springer, Berlin, Heidelberg. 2013.

Cowell R and Smith J. Causal discovery through MAP selection of stratified chain event graphs. Electronic Journal of Statistics, 8(1):965–997, 2014.

Examples

## default search using BIC score
model <- search_best(Titanic, alg = stages_kmeans)

## use df as search_criterion
model1 <- search_best(Titanic, alg = stages_bhc,
                      search_criterion = function(m) attr(logLik(m), "df"))

Description

Search the optimal staged event tree with a greedy heuristic.

Usage

search_greedy(
  data,
  alg = stages_bhc,
  search_criterion = BIC,
  lambda = 0,
  join_unobserved = TRUE,
  ...
)

Arguments

Details

The greedy approach implemented in this function iteratively adds variables to the staged tree that better improve the search_criterion.

Value

The estimated staged event tree model.

Examples

model <- search_greedy(Titanic, alg = stages_fbhc)
print(model)

Description

Structure and usage of S3 class sevt, used to store a staged event tree.

Usage

sevt(x, full = FALSE, order = NULL)

## S3 method for class 'table'
sevt(x, full = FALSE, order = names(dimnames(x)))

## S3 method for class 'data.frame'
sevt(x, full = FALSE, order = colnames(x))

## S3 method for class 'list'
sevt(x, full = FALSE, order = names(x))

Arguments

Details

A staged event tree object is a list with components:

• tree (required): A named list with one component for each variable in the model, a character vector with the names of the levels for that variable. The order of the variables in tree is the order of the event tree.

• stages (required): A named list with one component for each variable but the first, a character vector storing the stages for the situations related to path ending in that variable.

• ctables: A named list with one component for each variable, the flat contingency table of that variable given the previous variables.

• lambda: The smoothing parameter used to compute probabilities.

• name_unobserved: The stage name for unobserved situations.

• prob: The conditional probability tables for every variable and stage. Stored in a named list with one component for each variable, a list with one component for each stage.

• ll: The log-likelihood of the estimated model. If present, logLik.sevt will return this value instead of computing the log-likelihood.

The tree structure is never defined explicitly, instead it is implicitly defined by the list tree containing the order of the variables and the names of their levels. This is sufficient to define a complete symmetric tree where an internal node at a depth related to a variable v has a number of children equal to the cardinality of the levels of v. The stages information is instead stored as a list of vectors, where each vector is indexed as the internal nodes of the tree at a given depth.

To define a staged tree from data (data frame or table) the user can call either full or indep which both construct the staged tree object, attach the data in ctables and compute probabilities. After, one of the available model selection algorithm can be used, see for example stages_hc, stages_bhc or stages_hclust. If, mainly for development, only the staged tree structure is needed (without data or probabilities) the basic sevt constructor can be used.

Value

A staged event tree object, an object of class sevt.

Examples

######### from table
model.titanic <- sevt(Titanic, full = TRUE)

######### from data frame
DD <- generate_random_dataset(p = 4, n = 1000)
model.indep <- sevt(DD)
model.full <- sevt(DD, full = TRUE)

######### from list
model <- sevt(list(
  X = c("good", "bad"),
  Y = c("high", "low")
))

Description

Return an updated staged event tree with one additional variable at the end of the tree.

Usage

sevt_add(object, var, data, join_unobserved = TRUE, useNA = "ifany")

Arguments

Details

This function update a staged event tree object with an additional variable. The stages structure of the new variable is initialized as in the saturated model.

Value

An object of class sevt representing a staged event tree model with var added as last variable.

Examples

model <- full(Titanic, order = c("Age", "Class"))
print(model)
model <- sevt_add(model, "Survived", Titanic)
print(model)

Description

Return the number of parameters of the model.

Usage

sevt_df(x)

Arguments

Value

integer, degrees of freedom of the staged event tree.

Description

Estimate transition probabilities in a staged event tree from data. Probabilities are estimated with the relative frequencies plus, eventually, an additive (Laplace) smoothing.

Usage

sevt_fit(
  object,
  data = NULL,
  lambda = NULL,
  scope = NULL,
  compute_logLik = TRUE
)

Arguments

Details

The data in form of contingency tables and the log-likelihood of the model is (eventually) stored in the returned staged event tree. Partial re-fit of a model can be performed with the scope argument. Partial re-fit can only be done over a fully fitted model, e.g. when changing the stages structure of one of the variables. In case of a partial re-fit, the data and lambda arguments will be ignored and the data and lambda value stored in the sevt object will be used (a warning is issued if such arguments are supplied).

Value

A fitted staged event tree, that is an object of class sevt with ctables and prob components. Additionally the chosen lambda is stored in the returned object and eventually the log-likelihood of the model is saved in the ll field.

Examples

#########
model <- sevt(list(
  X = c("good", "bad"),
  Y = c("high", "low")
))
D <- data.frame(
  X = c("good", "good", "bad"),
  Y = c("high", "low", "low")
)
model.fit <- sevt_fit(model, data = D, lambda = 1)

Description

Estimate transition probabilities in a staged event tree from data containing missing data using the hard (imputed) Expectation-Maximization algorithm.

Usage

sevt_fit_em(
  object,
  data = object$data_raw,
  lambda = NULL,
  scope = NULL,
  max_iter = 5,
  chain_impute = FALSE
)

Arguments

Value

A fitted staged event tree, See the return field of sevt_fit.

Description

Utility returning the number of variables in a staged event tree model.

Usage

sevt_nvar(object)

Arguments

Value

integer, the number of variables.

Description

Function to simplify a staged tree model.

Usage

sevt_simplify(object, fit = TRUE)

Arguments

Details

The simplify function will produce the corresponding simple staged tree, that is a staged tree where stages and positions are equivalent. To do so the function ceg is used to compute positions, and then the stages' vectors are replaced with the positions' vectors. The model is the re-fitted if the input was a fitted staged tree. Despite the name, the simplified staged tree has always a number of stages greater or equal to the initial staged tree, thus it is a more complex statistical model.

Value

an object of class sevt representing the simplified model. The returned model will be fitted if the input model was.

Examples

mod <- stages_kmeans(full(Titanic), k = 2)
simpl <- sevt_simplify(mod)
plot(simpl)

Description

Utility returning variable-names in a staged event tree model.

Usage

sevt_varnames(object)

Arguments

Value

A character vector.

Description

Algorithms to create, learn, fit and explore staged event tree models. Functions to compute probabilities, make predictions from the fitted models and to plot, analyze and manipulate staged event trees.

Details

A staged event tree is a representation of a particular factorization of a joint probability over a product space. In particular, given a vector of categorical random variables $X1, X2, \ldots$, a staged event tree represents the factorization $P(X1, X2, X3, \ldots) = P(X1)P(X2 | X1) P(X3 | X1, X2) \ldots$. Additionally, the stages structure indicates which conditional probabilities are equal.

Model selection algorithms:

• full model full

• independence model indep

• Hill-Climbing stages_hc

• Backward Hill-Climbing stages_bhc

• Fast Backward Hill-Climbing stages_fbhc

• Backward Hill-Climbing Random stages_bhcr

• Backward joining stages_bj

• Simple Backward Hill-Climbing stages_simplebhc

• Hierarchical Clustering stages_hclust

• K-Means Clustering stages_kmeans

• Optimal order search search_best

• Greedy order search search_greedy

Probabilities, log-likelihood and predictions:

• Marginal/Conditional probabilities prob

• Log-Likelihood logLik.sevt

• Predict method predict.sevt

• Confidence intervals confint.sevt

Plot, explore and compare:

• Plot plot.sevt

• Compare compare_stages

• Stages inclusion inclusions_stages

• Stages info summary.sevt

• List of parents as_parentslist

• Barplot construction barplot.sevt

• Likelihood-ratio test lr_test

• Context-specific interventional distance cid

Modify models:

• Join and isolate unobserved situations join_unobserved

• Join two stages join_stages

• Join two positions join_positions

• Rename a stage rename_stage

Author(s)

Maintainer: Gherardo Varando [email protected] (ORCID)

Authors:

• Federico Carli

• Manuele Leonelli (ORCID)

• Eva Riccomagno

References

Collazo R. A., Görgen C. and Smith J. Q. Chain event graphs. CRC Press, 2018.

Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. Discovery of statistical equivalence classes using computer algebra. International Journal of Approximate Reasoning, vol. 95, pp. 167-184, 2018.

Barclay L. M., Hutton J. L. and Smith J. Q. Refining a Bayesian network using a chain event graph. International Journal of Approximate Reasoning, vol. 54, pp. 1300-1309, 2013.

Smith J. Q. and Anderson P. E. Conditional independence and chain event graphs. Artificial Intelligence, vol. 172, pp. 42-68, 2008.

Thwaites P. A., Smith, J. Q. A new method for tackling asymmetric decision problems. International Journal of Approximate Reasoning, vol. 88, pp. 624–639, 2017.

See Also

Useful links:

• https://github.com/stagedtrees/stagedtrees

• Report bugs at https://github.com/stagedtrees/stagedtrees/issues

Examples

data("PhDArticles")
mf <- full(PhDArticles, join_unobserved = TRUE)
mod <- stages_fbhc(mf)
plot(mod)

Description

Functions to get or set the stages of an object of class sevt.

Usage

stages(object)

## S3 method for class 'sevt'
stages(object)

## S3 method for class 'sevt.stgs'
print(x, ..., max = 5)

stages(object) <- value

## S3 method for class 'sevt.stgs'
x[i, ...]

## S3 replacement method for class 'sevt.stgs'
x[i, ..., fit = TRUE] <- value

## S3 method for class 'sevt.stgs'
x[[...]]

## S3 replacement method for class 'sevt.stgs'
x[[..., fit = TRUE]] <- value

Arguments

Details

This functions are the preferred way to access and modify directly the stages of an object of class sevt. In particular the indexing and replacing methods for the object extracted with the function stages() take care of checking the stages sanity and refit the object probabilities when needed. This is useful for manually setting some independence statements (see the Examples).

Value

For stages(): returns an object of class sevt.stgs which encode the stages of object. Objects of class sevt.stgs have dedicated method for sub-setting and replacing.

Stages indexing

Stages can be indexed, retrieved and replaced by the corresponding variables names and/or by paths or contexts.

In particular, stages(object)[[var]] extracts the stages vector corresponding to variable var (similarly to object$stages[[var]]. Alternatively stages(object)[[path]] indexes a stage via the corresponding path from root (similar to get_stage); a path is recognized as such if named or if of length > 2.

stages(object)[var, context] extracts multiple stages corresponding to a variable and eventually filtered by a specific context on the preceding variables.

Examples

# start with full model
mod <- full(Titanic)

# impose the context independence Survived indep Sex, Age | Class = 1st
stages(mod)["Survived", Class = "1st"] <- "C1"

# impose Survived indep Class | Class in (2nd 3rd)
stages(mod)["Survived", Class = "3rd"] <- stages(mod)["Survived", Class = "2nd"]

# impose Age indep Class | Sex
stages(mod)["Age", Sex = "Female"] <- "S-female"
stages(mod)["Age", Sex = "Male"] <- "S-male"

# stages of Survived
stages(mod)[["Survived"]]

# stages of Survived and Age
stages(mod)[c("Survived", "Age")]

# stages of Survived in the context Class 2nd or 3rd
stages(mod)["Survived", Class = c("2nd", "3rd")]

# check independencies
as_parentslist(mod)

Description

Greedy search of staged event trees with iterative joining of stages.

Usage

stages_bhc(
  object,
  score = function(x) {
     return(-BIC(x))
 },
  max_iter = Inf,
  scope = NULL,
  ignore = object$name_unobserved,
  trace = 0
)

Arguments

Details

For each variable the algorithm tries to join stages and moves to the best model that increases the score. When no increase is possible it moves to the next variable.

Value

The final staged event tree obtained.

Examples

DD <- generate_xor_dataset(p = 4, n = 100)
model <- stages_bhc(full(DD), trace = 2)
summary(model)

Description

Randomly try to join stages. This is a pretty-useless function, used for comparisons.

Usage

stages_bhcr(
  object,
  score = function(x) {
     return(-BIC(x))
 },
  max_iter = 100,
  trace = 0
)

Arguments

Details

At each iteration a variable and two of its stages are randomly selected. If joining the stages increases the score, the model is updated. The procedure is repeated until the number of iterations reaches max_iter.

Value

an object of class sevt.

Examples

DD <- generate_xor_dataset(p = 4, n = 100)
model <- stages_bhcr(full(DD), trace = 2)
summary(model)

Description

Join stages from more complex to simpler models using a distance and a threshold value.

Usage

stages_bj(
  object,
  distance = "kullback",
  thr = 0.1,
  scope = NULL,
  ignore = object$name_unobserved,
  trace = 0
)

Arguments

Details

For each variable in the model stages are joined iteratively. At each iteration the two stages with minimum distance are selected and joined if their distance is less than thr.

Available distances are: manhattan (manhattan), euclidean (euclidean), Renyi divergence (reny), Kullback-Liebler (kullback), total-variation (totvar), squared Hellinger (hellinger), Bhattacharyya (bhatt), Chan-Darwiche (chandarw). See also probdist.

Value

The final staged event tree obtained.

Examples

DD <- generate_xor_dataset(p = 5, n = 1000)
model <- stages_bj(full(DD, lambda = 1), trace = 2)
summary(model)

Description

Greedy search of staged event trees with iterative joining of stages.

Usage

stages_csbhc(
  object,
  score = function(x) {
     return(-BIC(x$ll))
 },
  max_iter = Inf,
  scope = NULL,
  ignore = object$name_unobserved
)

Arguments

Details

For each variable the algorithm tries to join stages , by adding context specific independences, and moves to the best model that increases the score. When no increase is possible it moves to the next variable.

Value

The final staged event tree obtained.

Examples

model <- stages_csbhc(full(Titanic))
summary(model)

Description

Structural EM for stages structure

Usage

stages_em(
  object,
  data = object$data_raw,
  stages_alg = stages_bhc,
  max_iter_em = 5,
  incremental = FALSE,
  chain_impute = FALSE,
  ...
)

Arguments

Value

The final staged event tree obtained.

Description

Greedy search of staged event trees with iterative joining of stages.

Usage

stages_fbhc(
  object,
  score = function(x) {
     return(-BIC(x))
 },
  max_iter = Inf,
  scope = NULL,
  ignore = object$name_unobserved,
  trace = 0
)

Arguments

Details

For each variable the algorithm tries to join stages and moves to the first model that increases the score. When no increase is possible it moves to the next variable.

Value

The final staged event tree obtained.

Examples

DD <- generate_xor_dataset(p = 5, n = 100)
model <- stages_fbhc(full(DD), trace = 2)
summary(model)

Description

Greedy search of staged event trees with iterative moving of nodes between stages.

Usage

stages_hc(
  object,
  score = function(x) {
     return(-BIC(x))
 },
  max_iter = Inf,
  scope = NULL,
  ignore = object$name_unobserved,
  trace = 0
)

Arguments

Details

For each variable node-moves that best increases the score are performed until no increase is possible. A node-move is either changing the stage associate to a node or move the node to a new stage.

The ignore argument can be used to specify stages that should not be affected during the search, that is left untouched. This is useful for preserving structural zeroes and to speed-up computations.

Value

The final staged event tree obtained.

Examples

start <- indep(PhDArticles[, 1:5], join_unobserved = TRUE)
model <- stages_hc(start)

Description

Build a stage event tree with k stages for each variable by clustering stage probabilities with hierarchical clustering.

Usage

stages_hclust(
  object,
  distance = "totvar",
  k = NA,
  method = "complete",
  ignore = object$name_unobserved,
  limit = length(object$tree),
  scope = NULL,
  score = function(x) {
     return(-BIC(x))
 }
)

Arguments

Details

hclust_sevt performs hierarchical clustering of the initial stage probabilities in object and it aggregates them into the specified number of stages (k). A different number of stages for the different variables in the model can be specified by supplying a (named) vector via the argument k. If k is NA for some variables, all possible number of stages will be checked and the one that maximize the score will be selected.

Value

A staged event tree object.

Examples

data("Titanic")
model <- stages_hclust(full(Titanic, join_unobserved = TRUE, lambda = 1), k = 2)
summary(model)

### or search k via BIC minimization
model1 <- stages_hclust(full(Titanic), k = NA)

Description

Build a stage event tree with k stages for each variable by clustering (transformed) probabilities with k-means.

Usage

stages_kmeans(
  object,
  k = length(object$tree[[1]]),
  algorithm = "Hartigan-Wong",
  transform = sqrt,
  ignore = object$name_unobserved,
  limit = length(object$tree),
  scope = NULL,
  nstart = 1
)

Arguments

Details

kmenas_sevt performs k-means clustering to aggregate the stage probabilities of the initial staged tree object. Different values for k can be specified by supplying a (named) vector to k. kmeans from the stats package is used internally and arguments algorithm and nstart refer to the same arguments as kmeans.

Value

A staged event tree.

Examples

data("Titanic")
model <- stages_kmeans(full(Titanic, join_unobserved = TRUE, lambda = 1), k = 2)
summary(model)

Description

Greedy search of simple staged event trees with iterative joining of positions.

Usage

stages_simplebhc(
  object,
  score = function(x) {
     return(-BIC(x))
 },
  scope = NULL,
  max_iter = Inf,
  ignore = object$name_unobserved
)

Arguments

Details

This function is similar to the classical backward hill-climbing implemented in stages_bhc, but instead of joining stages it consider joining of positions via join_positions. Thus, the search is in the space of simple staged tree models if the initial stage tree is simple. See the references for additional details.

Value

an object of class sevt, the simple staged tree resulting from the search.

References

Leonelli M, Varando G. Structural Learning of Simple Staged Trees, arXiv preprint arXiv:2203.04390v1

See Also

join_positions() sevt_simplify()

Examples

mod <- stages_simplebhc(full(Titanic))
plot(mod)

Description

Rename all stages in a staged event tree.

Usage

stndnaming(
  object,
  uniq = FALSE,
  prefix = FALSE,
  ignore = object$name_unobserved
)

Arguments

Value

a staged event tree object with stages named with consecutive integers.

Examples

model <- stages_fbhc(full(PhDArticles, join_unobserved = TRUE))
model$stages
model1 <- stndnaming(model)
model1$stages

### unique stage names in all tree
model2 <- stndnaming(model, uniq = TRUE)
model2$stages

### prefix stage names with variable name
model3 <- stndnaming(model, prefix = TRUE)
model3$stages

### manuallty select stage names left untouched
model4 <- stndnaming(model, ignore = c("2", "6"), prefix = TRUE)
model4$stages

Description

Extract subtree

Usage

subtree(object, path)

Arguments

Details

Returns the subtree of the staged event tree, starting from path.

Value

A staged event tree object corresponding to the subtree.

Examples

DD <- generate_random_dataset(4, 100)
model <- sevt(DD, full = TRUE)
plot(model)
model1 <- subtree(model, path = c("-1", "1"))
plot(model1)

Description

Summary method for class sevt.

Usage

## S3 method for class 'sevt'
summary(object, ...)

## S3 method for class 'summary.sevt'
print(x, max = 10, ...)

Arguments

Details

Print model information and summary of stages.

Value

An object of class summary.sevt for which a print method exist.

Examples

model <- stages_fbhc(full(PhDArticles, lambda = 1))
summary(model)

Description

Add text to a staged event tree plot

Usage

## S3 method for class 'sevt'
text(x, y = ylim[1], limit = 10, xlim = c(0, 1), ylim = c(0, 1), ...)

Arguments

Description

Topological sorting of parentslist

Usage

topo_sort_parentslist(x, all = FALSE)

all_topo_sort(x)

Arguments

Details

This function will return a topological order of the graph encoded in x if the graph is a DAG, otherwise a partial order will be returned.

Object x should represent a DAG, otherwise if cycles are present, the output will not be complete orders of the graph.

Value

a matrix where every row is a topological ordeing of the DAG.

Examples

pl <- list(A = list(), B = list(), C = list(parents = c("A", "B")),
D = list(parents = c("C")),
E = list(parents = c("A")),
F = list(parents = c("B", "E")))
topo_sort_parentslist(pl)
pl <- list(A = list(), B = list(), C = list(parents = c("A", "B")),
D = list(parents = c("C")),
E = list(parents = c("A")),
F = list(parents = c("B", "E")))
all_topo_sort(pl)

Description

Generated dataset with observations from five variables (SEX, AGE, ICU, RSP, OUT) describing imaginary patients' trajectories in a hospital.

Usage

trajectories

Format

A data frame with 10000 observations of 5 variables.

Source

The data has been generated with the code in the Examples section.

Examples

library("stagedtrees")

tree <- list(SEX = c("male", "female"),
             AGE = c("child", "adult", "elder"),
             ICU = c("0", "1"),
             RSP = c("intub", "mask", "no"),
             OUT = c("death", "survived"))

model <- sevt(tree, full = TRUE)

stages(model)["ICU", AGE = "child"] <- "ICUchild"

stages(model)["ICU", SEX = "male", AGE = "elder"] <-
  stages(model)["ICU", SEX = "female", AGE = "elder"]

stages(model)["RSP", AGE = c("child"), ICU = "0"] <- "childnoICU"
stages(model)["RSP", AGE = c("child"), ICU = "1"] <- "childICU"

stages(model)["RSP", AGE = c("adult")] <- stages(model)["RSP", AGE = c("elder")]


stages(model)["OUT", AGE = "adult",
              SEX = "female",
              ICU = "1",
              RSP = c("intub", "mask")] <- "femaleICUresp"

stages(model)["OUT", AGE = "child",
                     ICU = "1",
                     RSP = "intub"] <- "childICUintub"

stages(model)["OUT", AGE = "child",
              ICU = "1",
              RSP = "mask"] <- "childICUmask"

stages(model)["OUT", AGE = "child",
              ICU = "1",
              RSP = "no"] <- "childICUno"

stages(model)["OUT", AGE = "adult", SEX = "male"] <-
  stages(model)["OUT", AGE = "elder", SEX = "female"]

stages(model)["OUT", ICU = "0", RSP = "intub"] <- "UNOBS"
stages(model)["OUT", ICU = "0", RSP = "intub"] <- "UNOBS"
stages(model)["OUT", AGE = "child", ICU = "0"] <- "UNOBS"

model$prob <- list()
model$prob$SEX <- list( "NA" = c(male = 0.4, female = 0.6))
model$prob$AGE <- list("1" = c("child" = 0.1, "adult" = 0.5, "elder" = 0.4),
                       "2" = c("child" = 0.1, "adult" = 0.3, "elder" = 0.6))

model$prob$ICU <- list("ICUchild" = c("0" = 0, "1" = 1),
                       "2" = c("0" = 0.4, "1" = 0.6), ## male adult
                       "5" = c("0" = 0.2, "1" = 0.8), ## female adult
                       "6" = c("0" = 0.7, "1" = 0.3)) ## elder

model$prob$RSP <- list("childnoICU" = c("intub" = NA, "mask" = NA, "no" = NA),
                       "childICU" = c("intub" = 0.1, "mask" = 0.7, "no" = 0.2),
                       "5" = c("intub" = 0, "mask" = 0.7, "no" = 0.3), # male noICU
                       "6" = c("intub" = 0.4, "mask" = 0.5, "no" = 0.1), # male ICU
                       "11" = c("intub" = 0, "mask" = 0.5, "no" = 0.5), # female noICU
                       "12" = c("intub" = 0.4, "mask" = 0.5, "no" = 0.1)) # female ICU


model$prob$OUT <- list("UNOBS" = c("death" = NA, "survived" = NA),
                       "childICUintub" = c("death" = 0.03, "survived" = 0.97),
                       "childICUmask" = c("death" = 0.02, "survived" = 0.98),
                       "childICUno" = c("death" = 0.01, "survived" = 0.99),
                       ### male adult and female elder ICU = 0 :
                       "32" = c("death" = 0.05, "survived" = 0.95), ## mask
                       "33" = c("death" = 0.01, "survived" = 0.99), ## no
                       ### male adult and female elder ICU = 1 :
                       "34" = c("death" = 0.15, "survived" = 0.85), ## intub
                       "35" = c("death" = 0.08, "survived" = 0.92), ## mask
                       "36" = c("death" = 0.04, "survived" = 0.96), ## no
                       ##############
                       "14" = c("death" = 0.2, "survived" = 0.8), # male elder 0 mask
                       "15" = c("death" = 0.1, "survived" = 0.9), # male elder 0 no
                       "16" = c("death" = 0.3, "survived" = 0.7), # male elder 1 intub
                       "17" = c("death" = 0.25, "survived" = 0.75), # male elder 1 mask
                       "18" = c("death" = 0.3, "survived" = 0.7), # male elder 1 no
                       ##############
                       "26" = c("death" = 0.1, "survived" = 0.9), # female adult 0 mask
                       "27" = c("death" = 0.15, "survived" = 0.85), # female adult 0 no
                       "30" = c("death" = 0.2, "survived" = 0.8),  # female adult 1 no
                       ##############
                       "femaleICUresp" = c("death" = 0.1, "survived" = 0.9)
                       )

# trajectories <- sample_from(model, 10000, seed = 1)
# usethis::use_data(trajectories, overwrite = TRUE)