tcc: Trellis Control Charts
In qicharts: Quality Improvement Charts

Description Usage Arguments Details Value References Examples

View source: R/tcc.R

Run and control charts for multivariate data i trellis (grid) layout.

tcc(
  n,
  d,
  x,
  g1,
  g2,
  breaks,
  notes,
  data,
  chart = c("run", "i", "mr", "xbar", "s", "t", "p", "c", "u", "g"),
  multiply = 1,
  freeze = NULL,
  exclude,
  target = NA,
  n.sum = FALSE,
  y.neg = TRUE,
  y.percent = FALSE,
  y.expand = NULL,
  x.pad = 1,
  x.date.format = NULL,
  cl.lab = TRUE,
  cl.decimals = NULL,
  main,
  xlab = "Subgroup",
  ylab = "Value",
  subtitle = NULL,
  caption = NULL,
  cex = 1,
  pex = 1,
  prime = TRUE,
  flip = FALSE,
  dots.only = FALSE,
  print.summary = FALSE,
  ...
)

`n`	Numerator, numeric vector of counts or measures to plot. Mandatory.
`d`	Denominator, numeric vector of subgroup sizes. Mandatory for P and U charts.
`x`	Subgrouping vector used for aggregating data by subgroup and making x-labels. Mandatory for Xbar and S charts.
`g1`	Grouping vector 1 used for trellis layout (facets).
`g2`	Grouping vector 2 used for trellis layout (facets).
`breaks`	Numeric vector of break points. Useful for splitting graph in two or more sections with separate center line and control limits.
`notes`	Character vector of notes to be added to individual. data points.
`data`	Data frame containing variables.
`chart`	Type of control chart. Possible types are: "run": run chart (default). "i": individuals chart. "mr": moving range chart. "xbar": sample average chart. "s": sample standard deviation chart. "t": time between events chart. "p": proportions chart. "c": counts chart. "u": rates chart. "g": cases between events chart.
`multiply`	Integer indicating a number to multiply y axis by, e.g. 100 for percents rather than proportions. See also `y.percent` argument.
`freeze`	Number identifying the last data point to include in calculations of center and limits (ignored if `breaks` argument is given).
`exclude`	Numeric vector of data points to exclude from runs analysis and calculations of center and control lines (same for each facet).
`target`	Numeric value indicating a target value to be plotted as a horizontal line (same for each facet).
`n.sum`	Logical value indicating whether the mean (default) or sum of numerator (n argument) per subgroup should be plotted. Only relevant for run, C, and I charts with multiple counts per subgroup.
`y.neg`	Logical value. If TRUE (default), the y axis is allowed to be negative (only relevant for I and Xbar charts).
`y.percent`	Logical. If TRUE, formats y axis labels as percent.
`y.expand`	Numeric value to include in y axis. Useful e.g. for beginning y axis at zero.
`x.pad`	Number indicating expansion of x axis to make room for center line labels.
`x.date.format`	Date format of x axis labels. See `?strftime()` for possible date formats.
`cl.lab`	Logical value. If TRUE (default), plots center line labels.
`cl.decimals`	Number of decimals on center line labels.
`main`	Character string specifying the title of the plot.
`xlab`	Character string specifying the x axis label.
`ylab`	Character string specifying the y axis label.
`subtitle`	Character string specifying the subtitle.
`caption`	Character string specifying the caption.
`cex`	Number indicating the amount by which text should be magnified.
`pex`	Number indicating the amount by which plotting symbols should be magnified.
`prime`	Logical value, If TRUE (default unless `dots.only = TRUE`), control limits incorporate between-subgroup variation as proposed by Laney (2002). Only relevant for P and U charts.
`flip`	Logical. If TRUE rotates the plot 90 degrees.
`dots.only`	Logical value. If TRUE, data points are not connected by lines, prime is forced to be FALSE, and runs analysis is not performed. Useful for comparison and funnel plots.
`print.summary`	Logical. If TRUE, prints summary of tcc object.
`...`	Further arguments to ggplot function.

tcc() is a wrapper function for ggplot2() that makes multivariate run and control charts. It takes up to two grouping variables for multidimensional trellis plots.

Note that, in contrast to the qic() function, the prime argument defaults to TRUE, which means that control limits of P and U charts by default incorporate between-subgroup variation as proposed by Laney (2002).

An object of class ggplot.

Runs analysis:

Jacob Anhoej, Anne Vingaard Olesen (2014). Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non-Random Variation in Health Care Processes. PLoS ONE 9(11): e113825. doi: 10.1371/journal.pone.0113825 .
Jacob Anhoej (2015). Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series. PLoS ONE 10(3): e0121349. doi: 10.1371/journal.pone.0121349
Mark F. Schilling (2012). The Surprising Predictability of Long Runs. Math. Mag. 85, 141-149.
Zhenmin Chen (2010). A note on the runs test. Model Assisted Statistics and Applications 5, 73-77.

Calculation of control limits:

Douglas C. Montgomery (2009). Introduction to Statistical Process Control, Sixth Edition, John Wiley & Sons.
James C. Benneyan (2001). Number-Between g-Type Statistical Quality Control Charts for Monitoring Adverse Events. Health Care Management Science 4, 305-318.
Lloyd P. Provost, Sandra K. Murray (2011). The Health Care Data Guide: Learning from Data for Improvement. San Francisco: John Wiley & Sons Inc.
David B. Laney (2002). Improved control charts for attributes. Quality Engineering, 14(4), 531-537.

# Run chart of 24 random normal variables
tcc(rnorm(24))

# Build data frame for examples
d <- data.frame(x = rep(1:24, 4),
                mo = (rep(seq(as.Date('2014-1-1'),
                              length.out = 24,
                              by = 'month'),
                          4)),
                n = rbinom(4 * 24, 100, 0.5),
                d = round(runif(4 * 24, 90, 110)),
                g1 = rep(c('a', 'b'), each = 48),
                g2 = rep(c('A', 'B'), each = 24))

# Run chart with two grouping variables
tcc(n, d, mo, g1 = g1, g2 = g2, data = d)

# P chart
tcc(n, d, mo, g1 = g1, g2 = g2, data = d, chart = 'p')

# P chart with baseline fixed to the first 12 data points
tcc(n, d, mo, g1 = g1, g2 = g2, data = d, chart = 'p', freeze = 12)

# P chart with two breaks and summary output
tcc(n, d, mo, g1 = g1, g2 = g2, data = d, chart = 'p',
 breaks = c(12, 18), print.summary = TRUE)

qicharts will no longer be maintained. Please consider moving to qicharts2: https://anhoej.github.io/qicharts2/.
   g1 g2 breaks n.obs n.useful        cl       lcl       ucl limits.signal
1   a  A      1    12       12 0.5041667 0.3103965 0.6979368         FALSE
2   a  A      2     6        6 0.5035336 0.3506604 0.6564068         FALSE
3   a  A      3     6        6 0.4907563 0.2063913 0.7751213         FALSE
4   a  B      1    12       12 0.4706362 0.2496553 0.6916172         FALSE
5   a  B      2     6        6 0.5149502 0.3971633 0.6327371         FALSE
6   a  B      3     6        6 0.5244519 0.4017140 0.6471899         FALSE
7   b  A      1    12       12 0.4784768 0.2581644 0.6987892         FALSE
8   b  A      2     6        6 0.5207987 0.3476214 0.6939760         FALSE
9   b  A      3     6        6 0.5495652 0.3789125 0.7202179         FALSE
10  b  B      1    12       12 0.4899329 0.3303875 0.6494783         FALSE
11  b  B      2     6        6 0.4754902 0.2782886 0.6726918         FALSE
12  b  B      3     6        6 0.4614122 0.3173339 0.6054904         FALSE
   runs.signal longest.run longest.run.max n.crossings n.crossings.min
1        FALSE           4               7           5               3
2        FALSE           2               6           3               1
3        FALSE           3               6           3               1
4        FALSE           3               7           8               3
5        FALSE           4               6           1               1
6        FALSE           2               6           4               1
7        FALSE           3               7           5               3
8        FALSE           4               6           1               1
9        FALSE           2               6           4               1
10       FALSE           3               7           7               3
11       FALSE           2               6           3               1
12       FALSE           3               6           3               1