rga: Reliability Growth Analysis.

In ReliaGrowR: Reliability Growth Analysis

Reliability Growth Analysis.

Description

This function performs reliability growth analysis using the Crow-AMSAA model by Crow (1975) https://apps.dtic.mil/sti/citations/ADA020296 or piecewise NHPP model by Guo et al. (2010) doi:10.1109/RAMS.2010.5448029. It fits a log-log linear regression of cumulative failures versus cumulative time. The function accepts either two numeric vectors (times, failures) or a data frame containing both. The ⁠Piecewise NHPP⁠ model can automatically detect change points or use user-specified breakpoints.

Usage

rga(
  times,
  failures,
  model_type = "Crow-AMSAA",
  breaks = NULL,
  conf_level = 0.95
)

Arguments

times	Either a numeric vector of cumulative failure times or a data frame containing both failure times and failure counts. If a data frame is provided, it must contain two columns: times and failures. The times column contains cumulative failure times, and the failures column contains the number of failures at each corresponding time.
failures	A numeric vector of the number of failures at each corresponding time in times. Must be the same length as times if both are vectors. All values must be positive and finite. Ignored if times is a data frame.
model_type	The model type. Either Crow-AMSAA (default) or ⁠Piecewise NHPP⁠ with change point detection.
breaks	An optional vector of breakpoints for the ⁠Piecewise NHPP⁠ model.
conf_level	The desired confidence level, which defaults to 95%. The confidence level is the probability that the confidence interval contains the true mean response.

Details

The scaling relationship between the size of input data (numbers of observations) and speed of algorithm execution is approximately linear (O(n)). The function is efficient and can handle large data sets (e.g., thousands of observations) quickly. The function uses the segmented package for piecewise regression, which employs an iterative algorithm to estimate breakpoints. The number of iterations required for convergence may vary depending on the data and initial values. In practice, the function typically converges within a few iterations for most data sets. However, in some cases, especially with complex data or poor initial values, it may take more iterations.

Value

The function returns an object of class rga that contains:

times	The input cumulative failure times.
failures	The input number of failures.
n_obs	The number of observations (failures).
cum_failures	Cumulative failures.
model	The fitted model object (lm (linear model) or segmented).
residuals	Model residuals on the log-log scale. These represent deviations of the observed log cumulative failures from the fitted values and are useful for diagnostic checking.
logLik	The log-likelihood of the fitted model. The log-likelihood is a measure of model fit, with higher values indicating a better fit.
AIC	Akaike Information Criterion (AIC). AIC is a measure used for model selection, with lower values indicating a better fit.
BIC	Bayesian Information Criterion(BIC). BIC is another criterion for model selection
breakpoints	Breakpoints (log scale) if applicable.
fitted_values	Fitted cumulative failures on the original scale.
lower_bounds	Lower confidence bounds (original scale).
upper_bounds	Upper confidence bounds (original scale).
betas	Estimated beta(s). Betas are the slopes of the log-log plot.
betas_se	Standard error(s) of the estimated beta(s).
growth_rate	Estimated growth rate(s). Growth rates are calculated as 1 - beta.
lambdas	Estimated lambda(s). Lambdas are the intercepts of the log-log plot.

See Also

Other Reliability Growth Analysis: plot.rga(), print.rga()

Examples

times <- c(100, 200, 300, 400, 500)
failures <- c(1, 2, 1, 3, 2)
result1 <- rga(times, failures)
print(result1)

df <- data.frame(times = times, failures = failures)
result2 <- rga(df)
print(result2)

result3 <- rga(times, failures, model_type = "Piecewise NHPP")
print(result3)

result4 <- rga(times, failures, model_type = "Piecewise NHPP", breaks = c(450))
print(result4)

ReliaGrowR documentation built on Nov. 7, 2025, 9:06 a.m.