Description Usage Arguments Details Value Methods (by class) References Examples
Test on deviceevents using the Shewhart xbar control chart. Includes the first 4 Western Electric rules common to statistical process control.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 
df 
Required input data frame of class

... 
Further arguments passed onto 
ts_event 
Required if Default: Example: 
analysis_of 
Optional string indicating the English description of what
was analyzed. If specified, this will override the name of the
Default: Example: 
eval_period 
Optional positive integer indicating the number of unique times counting in reverse chronological order to assess. This will be used to establish the process mean and moving range. Default: 
zero_rate 
Required maximum proportion of Default: 
we_rule 
Required integer from Default: 
mu 
Optional value of the incontrol process mean, typically measured from historical data. Default: 
sigma 
Optional value of the incontrol process standard deviation, typically measured from historical data. Default: 
Function shewhart()
is an implementation of the xbar
Control Chart test from the family of statistical process control tests
originally proposed by Walter Shewhart.
we_rule
has four possible values: 1
is one point over the
3sigma limit. 2
is two out of three consecutive points over the
2sigma limit. 3
is four of five consecutive points over the 1sigma
limit. 4
is nine consecutive points over the process mean.
A named list of class mdsstat_test
object, as follows:
Name of the test run
English description of what was analyzed
Named boolean of whether the test was run. The name contains the run status.
A standardized list of test run results: statistic
for the test statistic, lcl
and ucl
for the 95
confidence bounds, p
for the pvalue, signal
status, and
signal_threshold
.
The test parameters
The data on which the test was run
mds_ts
: Shewhart on mds_ts data
default
: Shewhart on general data
Montgomery, Douglas C. Introduction to Statistical Quality Control by Douglas C. Montgomery, 5th Edition: Study Guide. Cram101, 2013.
1 2 3 4 5 6 7 8 9  # Basic Example
# data < data.frame(time=c(1:25), event=as.integer(stats::rnorm(25, 100, 25)))
# a1 < shewhart(data)
# Example using an mds_ts object
# a2 < shewhart(mds_ts[[3]])
# Example using a derived rate as the "event"
# data < mds_ts[[3]]
# data$rate < ifelse(is.na(data$nA), 0, data$nA) / data$exposure
# a3 < shewhart(data, c(Rate="rate"))

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