| Title: | Easier CUSUM Control Charts |
|---|---|
| Description: | Create CUSUM (cumulative sum) statistics from a vector or dataframe. Also create single or faceted CUSUM control charts, with or without control limits. Accepts vector, dataframe, tibble or data.table inputs. |
| Authors: | John MacKintosh [aut, cre] |
| Maintainer: | John MacKintosh <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.9000 |
| Built: | 2024-10-21 03:01:27 UTC |
| Source: | https://github.com/johnmackintosh/cusumcharter |
Calculates cusum statistics and control limits based on input parameters. If no target value is supplied the mean of the x value will be used.
cusum_control( x, target = NULL, std_dev = NULL, desired_shift = 1, k = 0.5, h = 4 )cusum_control( x, target = NULL, std_dev = NULL, desired_shift = 1, k = 0.5, h = 4 )
x |
input vector |
target |
target value for comparison, the mean of x will be used if missing |
std_dev |
Defaults to the screened moving range of x. A known or desired value for standard deviation can be supplied instead. |
desired_shift |
how many standard deviations do you want to detect? This value is typically between 0.5 to 1. Defaults to 1. |
k |
allowable slack - defaults to half the standard deviation multiplied by desired shift |
h |
action limits - usually between 4 and 5, defaults to 4. The standard deviation is multiplied by this value to determine the upper and lower limits on the chart |
data.frame showing original inputs and calculated control limits
test_vec3 <- c(1,1,2,3,5,7,11,7,5,7,8,9,5) controls <- cusum_control(test_vec3, target = 4)test_vec3 <- c(1,1,2,3,5,7,11,7,5,7,8,9,5) controls <- cusum_control(test_vec3, target = 4)
Calculates cusum statistics and control limits based on input parameters. If no target value is supplied the median of the x value will be used.
cusum_control_median( x, target = NULL, std_dev = NULL, desired_shift = 1, k = 0.5, h = 4 )cusum_control_median( x, target = NULL, std_dev = NULL, desired_shift = 1, k = 0.5, h = 4 )
x |
input vector |
target |
target value for comparison, the median of x will be used if missing |
std_dev |
Defaults to the screened moving range of x. A known or desired value for standard deviation can be supplied instead. |
desired_shift |
how many standard deviations do you want to detect? This value is typically between 0.5 to 1. Defaults to 1. |
k |
allowable slack - defaults to half the standard deviation multiplied by desired shift |
h |
action limits - usually between 4 and 5, defaults to 4. The standard deviation is multiplied by this value to determine the upper and lower limits on the chart |
data.frame showing original inputs and calculated control limits
test_vec3 <- c(1,1,2,3,5,7,11,7,5,7,8,9,5) controls <- cusum_control_median(test_vec3, target = 4) controls_median <- cusum_control_median(test_vec3)test_vec3 <- c(1,1,2,3,5,7,11,7,5,7,8,9,5) controls <- cusum_control_median(test_vec3, target = 4) controls_median <- cusum_control_median(test_vec3)
Returns a ggplot2 object showing calculated control limits. This function should be used after calculating the control limits with either cusum_control or cusum_control_median.
cusum_control_plot( df, xvar, show_below = FALSE, pos_col = "#385581", centre_col = "black", neg_col = "#6dbac6", highlight_col = "#c9052c", facet_var = NULL, facet_scales = "free_y", scale_type = NULL, datebreaks = NULL, title_text = NULL, ... )cusum_control_plot( df, xvar, show_below = FALSE, pos_col = "#385581", centre_col = "black", neg_col = "#6dbac6", highlight_col = "#c9052c", facet_var = NULL, facet_scales = "free_y", scale_type = NULL, datebreaks = NULL, title_text = NULL, ... )
df |
input data frame generated by cusum_control function |
xvar |
the variable on the x axis, typically an obervation number or date/time |
show_below |
whether to highlight points below the LCL, default is FALSE |
pos_col |
line and point colour for positive values |
centre_col |
line colour for centre line |
neg_col |
line nd point colour for negative values |
highlight_col |
|
facet_var |
|
facet_scales |
defaults to "free_y", but any of the usual ggplot2 facet values can be supplied e.g. "fixed" or "free_x" |
scale_type |
if you need a date or datetime scale, specify either "date" or "datetime" here. Otherwise, leave as NULL and ggplot2 will pick an appropriate scale for you |
datebreaks |
a character string specifying the breaks as text e.g "2 days" or "3 weeks". See ggplot2 date_breaks for further details |
title_text |
optional title for chart |
... |
further arguments passed on to ggplot2 |
ggplot2 object suited for further amendments if required.
test_vec3 <- c(1,1,2,3,5,7,11,7,5,7,8,9,5) controls <- cusum_control(test_vec3, target = 4) cusum_control_plot(controls, xvar = obs)test_vec3 <- c(1,1,2,3,5,7,11,7,5,7,8,9,5) controls <- cusum_control(test_vec3, target = 4) cusum_control_plot(controls, xvar = obs)
Calculates the cumulative sum statistic of a vector of values, centred on either the mean of the data, or a supplied target value.
cusum_single(x, target = NULL)cusum_single(x, target = NULL)
x |
a numeric vector from which to calculate the cumulative sum statistics |
target |
value to compare each element of x to. If not provided, the mean of x will be calculated and used as a target value |
a vector of the cumulative sum statistic, centred on the target value
test_vec <- c(0.175, 0.152, 0.15, 0.207, 0.136, 0.212, 0.166) cusum_single(test_vec)test_vec <- c(0.175, 0.152, 0.15, 0.207, 0.136, 0.212, 0.166) cusum_single(test_vec)
Provides substantially more information than cusum_single.
Outputs a data.frame with the original values, target, the variance, the cumulative sum of the variance, and the cumulative sum centered on the target value. This centering is achieved by adding the target value to the cumulative sum.
cusum_single_df(x, target = NULL)cusum_single_df(x, target = NULL)
x |
a numeric vector from which to calculate the cumulative sum statistics |
target |
value to compare each element of x to. If not provided, the mean of x will be calculated and used as a target value |
a dataframe with the original values, target, the variance, the cumulative sum of the variance, and the cumulative sum centered on the target value. This centering is achieved by adding the target value to the cumulative sum.
test_vec <- c(0.175, 0.152, 0.15, 0.207, 0.136, 0.212, 0.166) cusum_single_df(test_vec, target = 0.16)test_vec <- c(0.175, 0.152, 0.15, 0.207, 0.136, 0.212, 0.166) cusum_single_df(test_vec, target = 0.16)
Calculates the cumulative sum statistic of a vector of values, centred on either the median of the data, or a supplied target value.
cusum_single_median(x, target = NULL)cusum_single_median(x, target = NULL)
x |
a numeric vector from which to calculate the cumulative sum statistics |
target |
value to compare each element of x to. If not provided, the median value of x will be calculated and used as a target value |
a vector of the cumulative sum statistic, centred on the target value
test_vec <- c(0.175, 0.152, 0.15, 0.207, 0.136, 0.212, 0.166) cusum_single_median(test_vec)test_vec <- c(0.175, 0.152, 0.15, 0.207, 0.136, 0.212, 0.166) cusum_single_median(test_vec)
Provides substantially more information than cusum_single_median.
Outputs a data.frame with the original values, target, the variance, the cumulative sum of the variance, and the cumulative sum centered on the target value. This centering is achieved by adding the target value to the cumulative sum.
cusum_single_median_df(x, target = NULL)cusum_single_median_df(x, target = NULL)
x |
a numeric vector from which to calculate the cumulative sum statistics |
target |
value to compare each element of x to. If not provided, the median value of x will be calculated and used as a target value |
a dataframe with the original values, target, the variance, the cumulative sum of the variance, and the cumulative sum centered on the target value. This centering is achieved by adding the target value to the cumulative sum.
test_vec <- c(0.175, 0.152, 0.15, 0.207, 0.136, 0.212, 0.166) cusum_single_median_df(test_vec, target = 0.16) cusum_single_median_df(test_vec)test_vec <- c(0.175, 0.152, 0.15, 0.207, 0.136, 0.212, 0.166) cusum_single_median_df(test_vec, target = 0.16) cusum_single_median_df(test_vec)