qcc
.
Different functions are available for the different types of control charts.
stats.xbar(data, sizes) stats.s(data, sizes) stats.R(data, sizes) stats.p(data, sizes) stats.np(data, sizes) stats.c(data, sizes) stats.u(data, sizes) stats.ma(data, sizes = 2) stats.ms(data, sizes = 2) stats.mR(data, sizes = 2) stats.ewma(data, sizes = 1, wt = 0.25)
data
may be a matrix or
list. For p, np, c, and u charts,
data
must be a vector
or matrix with one column.
For ma, ms, mR and ewma data is a
vector or one column matrix.
sizes
corresponds to the number of units examined.
These functions are important to computing the
statistics
and
center
components of a
"qcc"
object in preparation to plotting a Shewhart or a
cusum quality control chart. The
sizes
argument is passed in from
qcc
so must be provided if these functions are called separately.
The values for
statistics
and
center
for each type of chart are computed
as follows:
statistics <- apply(data, 1, mean) center <- sum(sizes * statistics)/sum(sizes)When
data
is a list
sapply
is used instead of
apply
.
statistics <- sqrt(apply(data, 1, var)) center <- sum(sizes * statistics)/sum(sizes)When
data
is a list
sapply
is used instead of
apply
.
statistics <- apply(data, 1, range) statistics <- statistics[2, ] - statistics[1, ] center <- sum(sizes * statistics)/sum(sizes)When
data
is a list
sapply
is used instead of
apply
.
statistics <- data center <- sum(sizes * data)/sum(sizes)
statistics <- data center <- sizes * sum(data)/sum(sizes)
statistics <- data center <- mean(statistics)
sizes
is assumed equal to 1 (one).
statistics <- data center <- sum(sizes * statistics)/sum(sizes)
statistics <- moving.ave(data, span = sizes) sizes <- statistics$sizes statistics <- statistics$aves center <- sum(sizes * statistics)/sum(sizes)
statistics <- moving.sigma(data, span = sizes) sizes <- statistics$sizes statistics <- statistics$sigmas center <- sum(sizes * statistics)/sum(sizes)
statistics <- moving.range(data, span = sizes) sizes <- statistics$sizes statistics <- statistics$ranges center <- sum(sizes * statistics)/sum(sizes)
center <- mean(data) statistics <- numeric(length(data) + 1) statistics[1] <- center for(i in seq(length(data))) statistics[i + 1] <- wt * data[i] + (1 - wt) * statistics[i]
type
argument of
qcc
. You can use these
functions as templates. Be sure, however, to include either a
sizes
argument
or the
...
argument, since
qcc
will pass in
sizes
in the call to your
function.