Package 'maskedhaz'

Title: Masked-Cause Likelihood Models for Series Systems with Arbitrary Hazard Components
Description: Likelihood-based inference for series systems with masked component cause of failure, using arbitrary dynamic failure rate component distributions. Computes log-likelihood, score, Hessian, and maximum likelihood estimates for masked data satisfying conditions C1, C2, C3 under general component hazard functions. Implements the 'series_md' protocol defined in the 'maskedcauses' package.
Authors: Alexander Towell [aut, cre]
Maintainer: Alexander Towell <[email protected]>
License: GPL (>= 3)
Version: 0.1.0
Built: 2026-05-24 09:28:42 UTC
Source: https://github.com/queelius/maskedhaz

Help Index

Assumptions for masked-cause DFR series systems

Description

Assumptions for masked-cause DFR series systems

Usage

## S3 method for class 'dfr_series_md'
assumptions(model, ...)

Arguments

model

A dfr_series_md object.
...

Additional arguments (unused).

Value

Character vector of model assumptions.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2)
))
assumptions(model)

Marginal cause-of-failure probability for DFR series systems

Description

Method for cause_probability that returns a closure computing P(K=j∣θ)P(K=j \mid \theta) for each component, marginalized over the system failure time T via Monte Carlo integration. By Theorem 5, this equals ET[P(K=j∣T,θ)]E_T[P(K=j \mid T, \theta)].

Usage

## S3 method for class 'dfr_series_md'
cause_probability(model, ...)

Arguments

model

A dfr_series_md object.
...

Additional arguments passed to the returned closure.

Value

A function with signature function(par, ...) returning an m-vector where element j gives P(K=j | theta).

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2), dfr_exponential(0.3)
))
cp_fn <- cause_probability(model)
set.seed(1)
cp_fn(par = c(0.1, 0.2, 0.3), n_mc = 2000)

Component hazard for a masked-cause DFR series system

Description

Component hazard for a masked-cause DFR series system

Usage

## S3 method for class 'dfr_series_md'
component_hazard(x, j, ...)

Arguments

x

A dfr_series_md object.
j

Component index.
...

Additional arguments passed to the closure.

Value

A closure computing component j's hazard.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2)
))
h1 <- component_hazard(model, 1)
h1(t = 5, par = 0.1)   # 0.1 (constant exponential hazard)

Conditional cause-of-failure probability for DFR series systems

Description

Method for conditional_cause_probability that returns a closure computing P(K=j∣T=t,θ)P(K=j \mid T=t, \theta) for each component. By Theorem 6 of the foundational paper, this equals hj(t;θ)/∑lhl(t;θ)h_j(t; \theta) / \sum_l h_l(t; \theta).

Usage

## S3 method for class 'dfr_series_md'
conditional_cause_probability(model, ...)

Arguments

model

A dfr_series_md object.
...

Additional arguments passed to the returned closure.

Value

A function with signature function(t, par, ...) returning an n x m matrix where column j gives P(K=j | T=t, theta).

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2), dfr_exponential(0.3)
))
ccp_fn <- conditional_cause_probability(model)
ccp_fn(t = c(1, 5, 10), par = c(0.1, 0.2, 0.3))

Masked-Cause Likelihood Model for DFR Series Systems

Description

Constructs a likelihood model for series systems with masked component cause of failure, where components are arbitrary dfr_dist distributions. Supports exact, right-censored, left-censored, and interval-censored observations with candidate sets satisfying C1-C2-C3.

Usage

dfr_series_md(
  series = NULL,
  components = NULL,
  par = NULL,
  n_par = NULL,
  lifetime = "t",
  lifetime_upper = "t_upper",
  omega = "omega",
  candset = "x"
)

Arguments

series

A dfr_dist_series object. Ignored if components is provided.
components

A list of dfr_dist objects. If provided, a dfr_dist_series is built from these.
par

Optional concatenated parameter vector.
n_par

Optional integer vector of parameter counts per component.
lifetime

Column name for system lifetime (default "t").
lifetime_upper

Column name for interval upper bound (default "t_upper").
omega

Column name for observation type (default "omega").
candset

Column prefix for candidate set indicators (default "x").

Details

The model computes the masked-cause log-likelihood for series systems where the system lifetime is the minimum of independent component lifetimes, and the causing component is partially observed through candidate sets.

Observation types (stored in the omega column):

"exact"

Failed at time t, cause masked among candidates

"right"

Right-censored: survived past time t

"left"

Left-censored: failed before time t

"interval"

Failed in interval (t, t_upper)

Masking conditions:

C1

Failed component is in candidate set with probability 1

C2

Uniform probability for candidate sets given component cause

C3

Masking probabilities independent of system parameters

Value

An object of class c("dfr_series_md", "series_md", "likelihood_model").

See Also

is_dfr_series_md for the type predicate, dfr_dist_series for the series distribution, loglik for the likelihood interface

Examples

library(flexhaz)
library(serieshaz)

# From components
model <- dfr_series_md(components = list(
    dfr_exponential(0.1),
    dfr_exponential(0.2),
    dfr_exponential(0.3)
))

# From pre-built series
sys <- dfr_dist_series(list(
    dfr_weibull(shape = 2, scale = 100),
    dfr_exponential(0.05)
))
model2 <- dfr_series_md(series = sys)

MLE fitting for masked-cause DFR series systems

Description

Returns a solver function that finds the maximum likelihood estimates for component parameters given masked series system data.

Usage

## S3 method for class 'dfr_series_md'
fit(object, ...)

Arguments

object

A dfr_series_md object.
...

Additional arguments (currently unused).

Details

Uses optim to maximize the log-likelihood. The score function (gradient) is computed from the same loglik closure via grad, and the Hessian at the MLE via hessian. One-parameter problems auto-upgrade from Nelder-Mead to BFGS with a warning, because Nelder-Mead is unreliable in one dimension.

Value

A solver function with signature function(df, par, method = "Nelder-Mead", ..., control = list()) that returns a fisher_mle object.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2)
))
set.seed(1)
df <- rdata(model)(theta = c(0.1, 0.2), n = 200, tau = 10, p = 0)
solver <- fit(model)
result <- solver(df, par = c(0.15, 0.15))
coef(result)

Hessian of log-likelihood for masked-cause DFR series systems

Description

Returns a Hessian function computed via numerical differentiation of the log-likelihood using hessian.

Usage

## S3 method for class 'dfr_series_md'
hess_loglik(model, ...)

Arguments

model

A dfr_series_md object.
...

Additional arguments (currently unused).

Value

A function with signature function(df, par, ...) returning the Hessian matrix.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2)
))
set.seed(1)
df <- rdata(model)(theta = c(0.1, 0.2), n = 50, tau = 10, p = 0.3)
H_fn <- hess_loglik(model)
H_fn(df, par = c(0.1, 0.2))

Test whether an object is a dfr_series_md

Description

Test whether an object is a dfr_series_md

Usage

is_dfr_series_md(x)

Arguments

x

Object to test.

Value

Logical scalar.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2)
))
is_dfr_series_md(model)    # TRUE
is_dfr_series_md(42)       # FALSE

Log-likelihood for masked-cause DFR series systems

Description

Returns a log-likelihood function for a series system with masked component cause of failure. Supports four observation types: exact failures, right-censored, left-censored, and interval-censored.

Usage

## S3 method for class 'dfr_series_md'
loglik(model, ...)

Arguments

model

A dfr_series_md object.
...

Additional arguments (currently unused).

Details

Log-likelihood contributions by observation type:

Exact (ω=\omega = "exact")

log⁡Li=log⁡(∑j∈Cihj(ti))−Hsys(ti)\log L_i = \log(\sum_{j \in C_i} h_j(t_i)) - H_{sys}(t_i)

Right-censored (ω=\omega = "right")

log⁡Li=−Hsys(ti)\log L_i = -H_{sys}(t_i)

Left-censored (ω=\omega = "left")

log⁡Li=log⁡∫0ti[∑j∈Cihj(u)]Ssys(u) du\log L_i = \log \int_0^{t_i} [\sum_{j \in C_i} h_j(u)] S_{sys}(u) \, du

Interval-censored (ω=\omega = "interval")

log⁡Li=log⁡∫titupper,i[∑j∈Cihj(u)]Ssys(u) du\log L_i = \log \int_{t_i}^{t_{upper,i}} [\sum_{j \in C_i} h_j(u)] S_{sys}(u) \, du

The exact and right-censored paths use vectorized hazard / cumulative hazard calls. Left and interval censoring require per-row numerical integration via integrate.

The returned closure caches validated and decoded masked-data extracted from the data frame across repeated calls with the same df, so that the O(n) validation cost is paid only once per optim/numDeriv sweep. The cache is per-closure, kept in the closure's enclosing environment. This is safe for sequential use; if you share the same closure object across forked workers (e.g. parallel::mcparallel), concurrent writes to the cache are possible but only affect performance, not correctness.

Value

A function with signature function(df, par, ...) that computes the log-likelihood.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2)
))
set.seed(1)
df <- rdata(model)(theta = c(0.1, 0.2), n = 50, tau = 10, p = 0.3)
ll_fn <- loglik(model)
ll_fn(df, par = c(0.1, 0.2))

Number of components in a masked-cause DFR series system

Description

Number of components in a masked-cause DFR series system

Usage

## S3 method for class 'dfr_series_md'
ncomponents(x, ...)

Arguments

x

A dfr_series_md object.
...

Additional arguments (unused).

Value

Integer, the number of components.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2), dfr_exponential(0.3)
))
ncomponents(model)   # 3

Print method for dfr_series_md

Description

Print method for dfr_series_md

Usage

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

Arguments

x

A dfr_series_md object.
...

Additional arguments (unused).

Value

Invisibly returns x.

Examples

model <- dfr_series_md(components = list(
  dfr_weibull(shape = 2, scale = 100),
  dfr_exponential(0.05)
))
print(model)

Random data generation for masked-cause DFR series systems

Description

Returns a function that generates random masked series system data from the model's data-generating process (DGP). Uses sample_components for component lifetimes and applies right-censoring and masking satisfying C1-C2-C3.

Usage

## S3 method for class 'dfr_series_md'
rdata(model, ...)

Arguments

model

A dfr_series_md object.
...

Additional arguments (currently unused).

Value

A function with signature function(theta, n, tau = Inf, p = 0, ...) that returns a data frame with columns for lifetime, observation type, and candidate sets.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2)
))
set.seed(1)
df <- rdata(model)(theta = c(0.1, 0.2), n = 20, tau = 10, p = 0.3)
head(df)

Score function for masked-cause DFR series systems

Description

Returns a score (gradient) function computed via numerical differentiation of the log-likelihood using grad.

Usage

## S3 method for class 'dfr_series_md'
score(model, ...)

Arguments

model

A dfr_series_md object.
...

Additional arguments (currently unused).

Value

A function with signature function(df, par, ...) returning the gradient vector.

Examples

model <- dfr_series_md(components = list(
  dfr_exponential(0.1), dfr_exponential(0.2)
))
set.seed(1)
df <- rdata(model)(theta = c(0.1, 0.2), n = 50, tau = 10, p = 0.3)
s_fn <- score(model)
s_fn(df, par = c(0.1, 0.2))