Overview

IQCC focuses on control charts whose classical normal limits may be poorly calibrated because the monitored statistic is discrete, skewed, bounded, or strongly non-normal. The package combines three ideas:

compute limits on the original measurement scale;
evaluate actual false-alarm probabilities whenever possible;
validate formulas against independent calculations and published examples.

Binomial p chart

For

\[ X\sim\operatorname{Binomial}(n,p),\qquad \hat p=X/n, \]

\[ E(\hat p)=p,\qquad \operatorname{Var}(\hat p)=\frac{p(1-p)}{n}. \]

The normal limits use

\[ p\pm z\sqrt{\frac{p(1-p)}{n}}. \]

The first Cornish-Fisher correction adds

\[ \frac{(z^2-1)(1-2p)}{6n} \]

to the normal quantile on the original p scale.

pchart_table <- do.call(
  rbind,
  lapply(c("normal", "cf1", "cf2"), function(method) {
    lim <- pchart_limits(p = 0.015, n = 20, type = method)
    risk <- pchart_alpha_risk(
      p = 0.015,
      n = 20,
      lcl = lim$lcl,
      ucl = lim$ucl
    )
    data.frame(
      method = method,
      lcl = lim$lcl,
      center = lim$center,
      ucl = lim$ucl,
      alpha = risk,
      arl0 = ifelse(risk == 0, Inf, 1 / risk)
    )
  })
)
pchart_table
#>   method lcl center        ucl        alpha       arl0
#> 1 normal   0  0.015 0.09653924 0.0357458712   27.97526
#> 2    cf1   0  0.015 0.16120479 0.0002023458 4942.03542
#> 3    cf2   0  0.015 0.13031923 0.0031780828  314.65511

For unequal subgroup sizes, IQCC estimates the in-control proportion using the pooled estimator

\[ \hat p_0=\frac{\sum_i X_i}{\sum_i n_i}. \]

Poisson u chart

Let

\[ X\sim\operatorname{Poisson}(\lambda n),\qquad U=X/n. \]

Then

\[ E(U)=\lambda,\qquad \operatorname{Var}(U)=\frac{\lambda}{n}. \]

u_table <- do.call(
  rbind,
  lapply(c("normal", "cf1", "cf2"), function(method) {
    lim <- uchart_limits(lambda = 0.15, n = 20, type = method)
    risk <- uchart_alpha_risk(
      lambda = 0.15,
      n = 20,
      lcl = lim$lcl,
      ucl = lim$ucl
    )
    data.frame(
      method = method,
      lcl = lim$lcl,
      center = lim$center,
      ucl = lim$ucl,
      alpha = risk,
      arl0 = ifelse(risk == 0, Inf, 1 / risk)
    )
  })
)
u_table
#>   method lcl center       ucl       alpha     arl0
#> 1 normal   0   0.15 0.4098056 0.003802992 262.9509
#> 2    cf1   0   0.15 0.4764711 0.001102488 907.0392
#> 3    cf2   0   0.15 0.4668489 0.001102488 907.0392

Double-sampling np chart

Let

\[ D_1\sim\operatorname{Binomial}(n_1,p),\qquad D_2\sim\operatorname{Binomial}(n_2,p), \]

independently. Define

\[ a=\lfloor WL\rfloor,\qquad b=\lfloor UCL_1\rfloor+1,\qquad c=\lfloor UCL_2\rfloor. \]

The decision rule is:

accept at stage 1 when \(D_1\leq a\);
signal at stage 1 when \(D_1\geq b\);
continue when \(a<D_1<b\);
accept at stage 2 when \(D_1+D_2\leq c\).

Thus

\[ ARL(p)=\frac{1}{1-P_T(p)}, \qquad ASS(p)=n_1+n_2P_2(p). \]

prob <- dsnp_prob_accept(
  p = 0.005,
  n1 = 34,
  n2 = 162,
  wl = 1.5,
  ucl1 = 2.5,
  ucl2 = 4.5
)

data.frame(
  pt = prob$pt,
  p_signal = prob$p_signal,
  arl0 = dsnp_arl(0.005, 34, 162, 1.5, 2.5, 4.5)$arl,
  arl1 = dsnp_arl(0.0075, 34, 162, 1.5, 2.5, 4.5)$arl,
  ass0 = dsnp_ass(0.005, 34, 162, 1.5, 2.5)$ass
)
#>          pt    p_signal     arl0     arl1     ass0
#> 1 0.9987553 0.001244692 803.4114 193.2229 35.93534

Generalized variance chart

Let \(S\) be the sample covariance matrix from a p-variate normal subgroup of size \(n\). Then

\[ (n-1)S\sim W_p(n-1,\Sigma). \]

The monitored statistic is

\[ G=|S|. \]

Bartlett’s decomposition gives

\[ G\overset{d}{=} \frac{|\Sigma|}{(n-1)^p} \prod_{j=1}^{p}Y_j, \qquad Y_j\sim\chi^2_{n-j}, \]

with independent factors.

gv_table <- do.call(
  rbind,
  lapply(c("normal", "cf", "exact"), function(method) {
    lim <- gv_limits(
      n = 10,
      p = 2,
      det_sigma = 0.5320,
      type = method
    )
    risk <- gv_alpha_risk(
      n = 10,
      p = 2,
      det_sigma = 0.5320,
      type = method
    )
    data.frame(
      method = method,
      lcl = lim$lcl,
      center = lim$center,
      ucl = lim$ucl,
      alpha = risk$alpha,
      arl0 = risk$arl0
    )
  })
)
gv_table
#>   method lcl    center      ucl       alpha      arl0
#> 1 normal   0 0.4728889 1.428685 0.020789525  48.10115
#> 2     cf   0 0.4728889 2.160305 0.002651853 377.09479
#> 3  exact   0 0.4728889 2.153629 0.002700000 370.37037

set.seed(321)
x <- array(rnorm(10 * 8 * 2), dim = c(10, 8, 2))
cchart.GV(x, Sigma = diag(2), type = "exact", plot = TRUE)

#> Generalized Variance Control Chart
#>   Dimension: p = 2 ; subgroup size n = 8 
#>   Subgroups: 10 (Phase I: 10 ; Phase II: 0 )
#>   Limits: exact / upper ; nominal alpha = 0.0027 
#>   Covariance: supplied Sigma 
#>   LCL = 0 ; center = 0.8571 ; UCL = 4.622 
#>   Signals: 0

For dimension two, the exact distribution simplifies to

\[ G\overset{d}{=} \frac{|\Sigma|}{4(n-1)^2} \left(\chi^2_{2n-4}\right)^2. \]

For higher dimensions, IQCC offers simulation using the Bartlett factors. The package does not claim a generic Meijer-G implementation. Published exact quantiles are used only in explicitly supported dimension-three cases.

Auxiliary trace chart

The generalized variance chart is sensitive to determinant changes in the covariance matrix. A complementary diagnostic monitors the trace of the standardized covariance matrix,

\[ T=(n-1)\operatorname{tr}(\Sigma_0^{-1}S). \]

If the process is multivariate normal and \(\Sigma=\Sigma_0\), then

\[ T\sim\chi^2_{p(n-1)}. \]

This gives an exact upper control limit for the trace chart:

trv_limits(n = 8, p = 2, alpha = 0.0027)
#> $lcl
#> [1] 0
#> 
#> $ucl
#> [1] 33.19518
#> 
#> $center
#> [1] 14
#> 
#> $type
#> [1] "chisq"
#> 
#> $alpha
#> [1] 0.0027
#> 
#> $n
#> [1] 8
#> 
#> $p
#> [1] 2
#> 
#> $df
#> [1] 14
#> 
#> $nsim
#> [1] 1e+05
#> 
#> $seed
#> NULL

Published Table 3

The report defines \(N=n-1\) in Section 3.3, but the numerical rows of its Table 3 use the displayed \(N\) as subgroup size \(n\). The probability headings also appear to be reversed: the values printed under 0.9980 match exact 0.9973 chi-square quantiles, while those under 0.9973 match exact 0.9980 quantiles. IQCC keeps the mathematical tail convention rather than reproducing the apparent label error.

table3 <- data.frame(
  report_row = rep(c(3, 16, 30), each = 2),
  printed_heading = rep(c(0.9980, 0.9973), 3),
  nominal_probability = rep(c(0.9973, 0.9980), 3),
  published = c(20.07, 20.79, 75.88, 77.17, 128.18, 129.83)
)
table3$df <- 3 * (table3$report_row - 1)
table3$calculated <- vapply(
  seq_len(nrow(table3)),
  function(i) trv_limits(
    n = table3$report_row[i],
    p = 3,
    alpha = 1 - table3$nominal_probability[i]
  )$ucl,
  numeric(1)
)
table3$tolerance <- 0.03
table3$tolerance_ratio <- abs(
  table3$calculated - table3$published
) / table3$tolerance
table3
#>   report_row printed_heading nominal_probability published df calculated
#> 1          3          0.9980              0.9973     20.07  6   20.06190
#> 2          3          0.9973              0.9980     20.79  6   20.79117
#> 3         16          0.9980              0.9973     75.88 45   75.89011
#> 4         16          0.9973              0.9980     77.17 45   77.17949
#> 5         30          0.9980              0.9973    128.18 87  128.19861
#> 6         30          0.9973              0.9980    129.83 87  129.83960
#>   tolerance tolerance_ratio
#> 1      0.03      0.26993425
#> 2      0.03      0.03892391
#> 3      0.03      0.33696773
#> 4      0.03      0.31639022
#> 5      0.03      0.62024948
#> 6      0.03      0.32005572

Published Case B: partial reproduction

The report’s three-dimensional bolt example uses \(n=15\), 30 Phase I subgroups, and 40 Phase II subgroups. In Case B, the covariance parameters in the last row of Table 8 are increased by 20% for Phase II. Tables 9 and 10 publish rounded Phase I mean covariance matrices whose generalized variances remain close:

case_a_sbar <- matrix(
  c(4.2366, 1.4773, 1.1929,
    1.4773, 6.1264, 2.3399,
    1.1929, 2.3399, 3.9335),
  nrow = 3, byrow = TRUE
)
case_b_sbar <- matrix(
  c(4.0112, 1.7865, 1.2484,
    1.7865, 5.8933, 1.7188,
    1.2484, 1.7188, 3.8909),
  nrow = 3, byrow = TRUE
)
data.frame(
  case = c("A", "B"),
  published_determinant = c(69.8438, 66.1893),
  determinant_from_rounded_matrix = c(det(case_a_sbar), det(case_b_sbar))
)
#>   case published_determinant determinant_from_rounded_matrix
#> 1    A               69.8438                        69.84308
#> 2    B               66.1893                        66.19151
data.frame(
  figure = "9c",
  published_trv_ucl = 72.01,
  calculated_trv_ucl = trv_limits(n = 15, p = 3, alpha = 0.0027)$ucl
)
#>   figure published_trv_ucl calculated_trv_ucl
#> 1     9c             72.01           71.99455

The published Case B figure shows that the generalized-variance chart does not cross its exact or Cornish-Fisher limit, whereas the auxiliary tr(V) chart has Phase II signals. This is a partial, qualitative reproduction: the report prints only subgroup rows 1, 2, 3, and 30 plus summaries, not all 70 generated subgroups or an RNG seed. Consequently, the published signal sequence cannot be recomputed without inventing observations. The deterministic example below is therefore retained and explicitly labeled synthetic.

The trace chart complements cchart.GV() because covariance changes can preserve the determinant while changing the sum of standardized eigenvalues. The following synthetic deterministic example has two subgroup covariance matrices with the same determinant; |S| is unchanged, while tr(V) increases.

base <- sqrt(3 / 4) * rbind(
  c(1, 1), c(1, -1), c(-1, 1), c(-1, -1)
)
g0 <- base %*% chol(diag(2))
g1 <- base %*% chol(diag(c(4, 0.25)))

data.frame(
  generalized_variance = gv_stat(list(g0, g1)),
  trace_statistic = trv_stat(list(g0, g1), Sigma0 = diag(2))
)
#>   generalized_variance trace_statistic
#> 1                    1            6.00
#> 2                    1           12.75

cat("<!-- IQCC_EXECUTED_FOUNDATIONS -->\n")

Validation strategy

IQCC combines algebraic property tests, independently coded numerical oracles, and reproduction of published numerical examples. This is particularly important because a plausible formula may still use a different tail convention, integer threshold, or parameter-estimation rule than the intended method.

References

Joekes, S. and Barbosa, E. P. (2013). An improved attribute control chart for monitoring non-conforming proportion in high quality processes. Control Engineering Practice, 21, 407-412.
Joekes, S., Smrekar, M. and Barbosa, E. P. (2015). Extending a double sampling control chart for non-conforming proportion in high quality processes to the case of small samples. Statistical Methodology, 23, 35-49.
Barbosa, E. P., Gneri, M. A. and Meneguetti, A. Improving Shewhart-type Generalized Variance Control Charts for Multivariate Process Variability Monitoring using Cornish-Fisher Quantile Correction, Meijer-G Function and Other Tools.

Statistical Foundations of IQCC