qcc.formula: Formula interface to qcc

In West-End-Statistics/r-library-vakdr: Miscellanious helper functions

Description

This function provides a simplified interface to the qcc function. The main goal is to remove the need for using the qcc groups function. This allows for much simpler use of the function within piping functions/map.

Usage

qcc.formula(formula, data, type, plot = FALSE, cutoff = NULL, ...)

Arguments

formula	a formula describing the outcome variable and the grouping variable
data	a data frame
type	a character string specifying the group statistics to compute. Available methods are: Statistic charted Chart description "xbar" mean means of a continuous process variable "R" range ranges of a continuous process variable "S" standard deviation standard deviations of a continuous variable "xbar.one" mean one-at-time data of a continuous process variable "p" proportion proportion of nonconforming units "np" count number of nonconforming units "c" count nonconformities per unit "u" count average nonconformities per unit "g" count number of non-events between events Furthermore, a user specified type of chart, say "newchart", can be provided. This requires the definition of "stats.newchart", "sd.newchart", and "limits.newchart". As an example, see stats.xbar.
plot	logical. If TRUE a Shewhart chart is plotted.
cutoff	a cutoff date for new data
...	further arguments passed to qcc:qcc()

Value

a qcc object

Examples

data("pistonrings", package = 'qcc')
diameter <- qcc::qcc.groups(pistonrings$diameter, pistonrings$sample)

# Without Cutoff
qcc_obj <- qcc::qcc(diameter[1:25,], type="xbar")
qcc_obj2 <- qcc.formula(diameter ~ sample, data = dplyr::filter(pistonrings, sample <= 25), type = "xbar", plot = TRUE)

# With Cutoff
qcc_new <-  qcc::qcc(diameter[1:25,], newdata = diameter[24:40,], type="xbar")
qcc_new2 <- qcc.formula(diameter ~ sample, data = pistonrings, type = "xbar", plot = TRUE, cutoff = 25)

# Dates and p chart
## use POSIXct, not Date object to create date
start <- as.POSIXct('2017-01-01')
end <- as.POSIXct('2017-01-15')
samp_dates <- seq(from = start, to = end, length.out = 15)
samp_dates <- sample(samp_dates, 100, replace = TRUE)
old_process <- samp_dates < '2017-01-08'
vals <- ifelse(old_process,rbinom(sum(old_process), 1 , p = 0.5), rbinom(sum(!old_process), 1,  p = 0.2) )
d <- data.frame(samp_dates, vals)

## Run the graphs with plot = TRUE
qcc.formula(vals ~ samp_dates, data = d, type = "p", plot = TRUE)
qcc.formula(vals ~ samp_dates, data = d, type = "p", plot = TRUE, cutoff = '2017-01-08')

West-End-Statistics/r-library-vakdr documentation built on Dec. 18, 2021, 7:16 p.m.