Calculate statistics for a normal distribution control chart

Usage

stats_n(data, alpha = 0.0027, mu = NULL, sd = NULL, ...)

Arguments

data: The data matrix
alpha: The significance level
mu: The mean of the distribution (optional)
sd: The standard deviation of the distribution (optional)
...: Additional arguments to be passed to the optimization algorithm

Value

A list containing the estimated parameters, control limits, alpha_hat, ARL, MRL, SDRL, convergence status, and value of the objective function

Examples

set.seed(0)
x <- r_n(lots = 1000, n = 10, mu = 1, sd = 1.7)

# Estimate for maximum likelihood
stats_n(data = x, alpha = 0.0027)
#> $mu_hat
#> [1] 1.017761
#> 
#> $sd_hat
#> [1] 1.671162
#> 
#> $alpha_hat
#> [1] 0.003
#> 
#> $ARL
#> [1] 333.3333
#> 
#> $MRL
#> [1] 230.7023
#> 
#> $SDRL
#> [1] 332.833
#> 
#> $li
#> [1] -0.5676303
#> 
#> $ls
#> [1] 2.603152
#> 
#> $convergence
#> [1] 0
#> 
#> $value
#> [1] 781.165
#> 

# Useing the true parameters
stats_n(data = x, alpha = 0.0027, mu = 1, sd = 1.7)
#> $mu_hat
#> [1] 1
#> 
#> $sd_hat
#> [1] 1.7
#> 
#> $alpha_hat
#> [1] 0.003
#> 
#> $ARL
#> [1] 333.3333
#> 
#> $MRL
#> [1] 230.7023
#> 
#> $SDRL
#> [1] 332.833
#> 
#> $li
#> [1] -0.6127492
#> 
#> $ls
#> [1] 2.612749
#> 
#> $convergence
#> NULL
#> 
#> $value
#> NULL
#>