## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
library(GammaFrailtySPC)

## ----dist-example-------------------------------------------------------------
# Calculate density and survival probability
y_val <- 15
d_val <- dgamma_aft(x = y_val, beta0 = 4.12, beta1 = -0.15, covariates = 3,
                    shape_kappa = 1.72, frailty_lambda = 1.17)
s_val <- pgamma_aft(q = y_val, beta0 = 4.12, beta1 = -0.15, covariates = 3,
                    shape_kappa = 1.72, frailty_lambda = 1.17, lower.tail = FALSE)

cat("Density at y =", y_val, ":", d_val, "\n")
cat("Survival probability at y =", y_val, ":", s_val, "\n")

## ----fit-example--------------------------------------------------------------
# Load textile industry dataset
data("textile_data")
p1_data <- subset(textile_data, phase == "PhaseI")

# Fit Weibull AFT Gamma Frailty Model
fit <- spc_gamma_frailty_fit(y = p1_data$y, x = p1_data$x, delta = p1_data$delta)
summary(fit)

## ----monitoring-example-------------------------------------------------------
p2_data <- subset(textile_data, phase == "PhaseII")

# Run master SPC function
spc_res <- spc_gamma_frailty(
  y = p2_data$y,
  x = p2_data$x,
  delta = p2_data$delta,
  fit = fit,
  alpha = 0.005,
  omega = 0.05,
  ewma_lcl = -2.5,
  upsilon = 0.95,
  cusum_lcl = -2.5
)

# Display summary and plot charts
summary(spc_res)
plot(spc_res)