Path data class (R6 class)

Class containing information about state paths and subjects.

Details

It is not recommended for users to try to create data using the constructor of this class. Rather use df_to_pathdata.

Public fields

subject_df

Data frame with one row per subject. Must have one row for each subject, and subject_id and all covariates as columns.

path_df

Data frame of actual paths. Each row corresponds to one time point. Must have path_id, state, time, and trans_idx as columns. These are

path_id: path identifier
state: integer index of the state at the time point, and until the next time point
time: time point
trans_idx: Integer indicating which transition this time point corresponds to. If the time point is not a state transition, this should be 0.

link_df

Links the path and subject data frames. Must have one row for each path, and path_id, draw_idx, rep_idx, and subject_id as columns.

covs

Covariate column names.

transmat

A TransitionMatrix describing the system to which the paths belong.

Methods

Method `new()`

Initialize

Usage

PathData$new(subject_df, path_df, link_df, transmat, covs = NULL)

Arguments

subject_df

Data frame with one row per subject. Must have one row for each subject, and subject_id and all covariates as columns.

path_df

Data frame of actual paths. Each row corresponds to one time point. Must have path_id, state, time, and trans_idx as columns. These are

path_id: path identifier
state: integer index of the state at the time point, and until the next time point
time: time point
trans_idx: Integer indicating which transition this time point corresponds to. If the time point is not a state transition, this should be 0.

link_df

Links the path and subject data frames. Must have one row for each path, and path_id, draw_idx, rep_idx, and subject_id as columns.

transmat

A TransitionMatrix describing the system to which the paths belong.

covs

Covariate column names.

Method `unique_subjects()`

Get unique subject ids

Usage

PathData$unique_subjects()

Method `truncate()`

Truncate paths after terminal events

Usage

PathData$truncate()

Returns

A new PathData object with only the paths data frame edited.

Method `covariate_names()`

Get names of covariates

Usage

PathData$covariate_names()

Returns

a character vector

Method `subset_covariates()`

Create a new PathData with a subset of covariates

Usage

PathData$subset_covariates(covs, renamed_old = NULL, renamed_new = NULL)

Arguments

covs: Names of all new covariates
renamed_old: Name of an old covariate to rename
renamed_new: Name of new covariate where old one is copied

Method `state_at()`

For each path, get the state it is in at time t

Usage

PathData$state_at(t)

Arguments

t: time

Returns

a data frame with one row for each path

Method `transition_times()`

Get all transition times

Usage

PathData$transition_times(trans_inds = NULL)

Arguments

trans_inds: Indices of transition whose occurrence times are requested. Default is NULL, in which case all transitions are considered.

Returns

a numeric vector

Method `n_paths()`

Get number of paths

Usage

PathData$n_paths()

Returns

an integer

Method `state_names()`

Get names of all states

Usage

PathData$state_names()

Returns

character vector

Method `get_event_state_names()`

Get names of all states to which transitioning can be a transition event

Usage

PathData$get_event_state_names()

Returns

character vector

Method `terminal_states()`

Get names of terminal states

Usage

PathData$terminal_states()

Returns

character vector

Method `print()`

Print info

Usage

PathData$print()

Returns

nothing

Method `get_path_df()`

Get path data frame

Usage

PathData$get_path_df(truncate = FALSE)

Arguments

truncate: Remove rows after terminal events?

Method `as_data_frame()`

Convert to one long data frame

Usage

PathData$as_data_frame(covariates = NULL, truncate = FALSE)

Arguments

covariates: Which covariates to include?
truncate: Remove rows after terminal events?

Method `full_link()`

Full link data frame

Usage

PathData$full_link(covariates = NULL)

Arguments

covariates: Which covariates to include

Returns

A data.frame with same number of rows as link_df, including also the covariate columns and subject_id

Method `as_transitions()`

Data frame in transitions format

Usage

PathData$as_transitions(covariates = NULL, truncate = FALSE)

Arguments

covariates: Which covariates to include?
truncate: Remove rows after terminal events first?

Returns

A data.frame

Method `as_transitions_alt()`

Data frame in alternative transitions format

Usage

PathData$as_transitions_alt(covariates = NULL, truncate = FALSE)

Arguments

covariates: Which covariates to include?
truncate: Remove rows after terminal events first?

Returns

A data.frame

Method `as_msdata()`

Convert to format used by the 'mstate' package

Usage

PathData$as_msdata(covariates = NULL)

Arguments

covariates: Which covariates to include?

Returns

An msdata object

Method `plot_paths()`

Step plot of the paths

Usage

PathData$plot_paths(n_paths = NULL, alpha = 0.5, truncate = FALSE)

Arguments

n_paths: Number of paths to subsample for plotting.
alpha: opacity
truncate: truncate after terminal events?

Method `prop_matrix()`

Transition proportion matrix

Usage

PathData$prop_matrix(include_censor = TRUE)

Arguments

include_censor: Include censoring (no event) proportion in the matrix

Returns

a table

Method `plot_graph()`

Visualize the transition proportion matrix as a graph

Usage

PathData$plot_graph(digits = 3, ...)

Arguments

digits: Max number of digits to show in numbers
...: Arguments passed to qgraph

Returns

qgraph plot

Method `fit_coxph()`

Fit Cox proportional hazards model

Usage

PathData$fit_coxph(covariates = NULL, ...)

Arguments

covariates: Covariates to include.
...: Arguments passed to survival::coxph.

Method `fit_mstate()`

Fit frequentist 'mstate' model

Usage

PathData$fit_mstate(covariates = NULL, ...)

Arguments

covariates: Covariates to include.
...: Arguments passed to survival::coxph.

Method `filter()`

Filter based on subject id, creates new object

Usage

PathData$filter(subject_ids_keep)

Arguments

subject_ids_keep: Subject ids to keep

Method `clone()`

The objects of this class are cloneable with this method.

Usage

PathData$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Details

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Method unique_subjects()

Usage

Method truncate()

Usage

Returns

Method covariate_names()

Usage

Returns

Method subset_covariates()

Usage

Arguments

Method state_at()

Usage

Arguments

Returns

Method transition_times()

Usage

Arguments

Returns

Method n_paths()

Usage

Returns

Method state_names()

Usage

Returns

Method get_event_state_names()

Usage

Returns

Method terminal_states()

Usage

Returns

Method print()

Usage

Returns

Method get_path_df()

Usage

Arguments

Method as_data_frame()

Usage

Arguments

Method full_link()

Usage

Arguments

Returns

Method as_transitions()

Usage

Arguments

Returns

Method as_transitions_alt()

Usage

Arguments

Returns

Method as_msdata()

Usage

Arguments

Returns

Method plot_paths()

Usage

Arguments

Method prop_matrix()

Usage

Arguments

Returns

Method plot_graph()

Usage

Arguments

Returns

Method fit_coxph()

Usage

Arguments

Method fit_mstate()

Usage

Method `new()`

Method `unique_subjects()`

Method `truncate()`

Method `covariate_names()`

Method `subset_covariates()`

Method `state_at()`

Method `transition_times()`

Method `n_paths()`

Method `state_names()`

Method `get_event_state_names()`

Method `terminal_states()`

Method `print()`

Method `get_path_df()`

Method `as_data_frame()`

Method `full_link()`

Method `as_transitions()`

Method `as_transitions_alt()`

Method `as_msdata()`

Method `plot_paths()`

Method `prop_matrix()`

Method `plot_graph()`

Method `fit_coxph()`

Method `fit_mstate()`

Method `filter()`

Method `clone()`