R/03_Summay_FUNs.R
In ggmtech/ggQC: Quality Control Charts for 'ggplot'
Defines functions
QC_Lines
ylines_indv
Documented in
QC_Lines
##############################
# Copyright 2018 Kenith Grey #
##############################
# Copyright Notice --------------------------------------------------------
# This file is part of ggQC.
#
# ggQC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ggQC is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ggQC. If not, see <http://www.gnu.org/licenses/>.
# 2nd Order Functions ------------------------------------------------------
# Report Lines for XmR chart
ylines_indv <- function(y, n=1, method = "XmR", na.rm = FALSE){
switch(method,
"mR" = {
QC_indv_functions <- list(
mR_LCL = ZERO,
mR = mR, mR_UCL = mR_UCL,
xBar_one_LCL = xBar_one_LCL,
mean = mean,
xBar_one_UCL = xBar_one_UCL
)},
"XmR" = {
QC_indv_functions <- list(
xBar_one_LCL = xBar_one_LCL,
mean = mean,
xBar_one_UCL = xBar_one_UCL,
mR_LCL = ZERO,mR = mR, mR_UCL = mR_UCL,
sigma = function(y=y, n=n, na.rm=na.rm){
sigma_est_symetric(y = y, na.rm = na.rm,
center = mean,
threesigma = xBar_one_UCL)
}
)},
"c" = {
QC_indv_functions <- list(
cBar_LCL = cBar_LCL,
cBar = mean,
cBar_UCL = cBar_UCL,
sigma = function(y=y, n=n, na.rm=na.rm){
sigma_est_symetric(y = y, na.rm = na.rm, n=n,
center = mean,
threesigma = cBar_UCL)
}
)},
"np" = {
QC_indv_functions <- list(
npBar_LCL = npBar_LCL,
npBar = npBar,
npBar_UCL = npBar_UCL,
sigma = function(y=y, n=n, na.rm=na.rm){
sigma_est_symetric(y = y, na.rm = na.rm, n=n,
center = npBar,
threesigma = npBar_UCL)
}
)},
"p" = {
p_chart_data <- data.frame(
pBar_LCL = pBar_LCL(y, n, na.rm=na.rm),
pBar = pBar(y, n, na.rm=na.rm),
pBar_UCL = pBar_UCL(y, n, na.rm=na.rm)
# Does not make sense to calculate a chart sigma here
)
return(p_chart_data)
},
"u" = {
u_chart_data <- data.frame(
uBar_LCL = uBar_LCL(y, n, na.rm=na.rm ),
uBar = uBar(y, n, na.rm=na.rm),
uBar_UCL = uBar_UCL(y, n, na.rm=na.rm)
# Does not make sense to calculate a chart sigma here
)
return(u_chart_data)
}
)
#print(n)
as.data.frame.list(
unlist(lapply(QC_indv_functions,
FUN = function(f){f(y, n=n, na.rm = na.rm)}))
)
}
#Report lines for subgroup plots
#' @export
#' @title Calculate QC Limits
#' @description Calculates QC chart lines for the following chart types and reports in a dataframe:
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian,
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#' @param data vector or dataframe, as indicated below for each chart type
#' \itemize{
#' \item \bold{Individuals & Attribute Charts}: vector of values;
#' \item \bold{Studentized & Dispersion Charts}: dataframe
#' }
#' @param value string, \bold{Studentized Charts} and \bold{Dispersion Charts}, numeric vector in dataframe with values of interest
#' @param grouping string, \bold{Studentized Charts} and \bold{Dispersion Charts}: single factor/variable to split
#' the dataframe "values" by
#' @param formula \bold{Studentized Charts} and \bold{Dispersion Charts}: a formula,
#' such as y ~ x1 + x2, where the y variable is
#' numeric data to be split into groups according to the grouping x
#' factors/variables
#' @param n number or vector as indicated below for each chart type.
#' \itemize{
#' \item \bold{Individuals Charts}: No effect
#' \item \bold{Attribute Charts}: (p and u) vector, indicating sample area of opportunity.
#' \item \bold{Attribute Charts}: (np) number, indicating constant sampling area of opportunity.
#' \item \bold{Studentized Charts}: number, user specified subgroup size.
#' \item \bold{Dispersion Charts}: No effect
#' }
#' @param method string, calling the following methods:
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#' @param na.rm a logical value indicating whether NA values should be
#' stripped before the computation proceeds.
#' @return a dataframe,
#' \itemize{
#' \item \bold{Attribute Data:} (p and u) Center Line, Upper Control Limit and Lower Control limit for each point.
#' \item \bold{Other Data}: single line dataframe, with relevant control limits noted in column headings.
#' }
#' @note If using the \bold{formula} argument do not use \bold{value} and \bold{group} arguments.
#' @references Wheeler, DJ, and DS Chambers. Understanding Statistical Process Control, 2nd Ed. Knoxville, TN: SPC, 1992. Print.
#' @examples
#' #############################################
#' # Example 1: Charts other than "p" or "u" #
#' #############################################
#'
#'# Load Libraries ----------------------------------------------------------
#' require(ggQC)
#' require(plyr)
#' require(ggplot2)
#'
#'# Setup Data --------------------------------------------------------------
#' set.seed(5555)
#' Process1 <- data.frame(processID = as.factor(rep(1,100)),
#' metric_value = rnorm(100,0,1),
#' subgroup_sample=rep(1:20, each=5),
#' Process_run_id = 1:100)
#' set.seed(5555)
#' Process2 <- data.frame(processID = as.factor(rep(2,100)),
#' metric_value = rnorm(100,5, 1),
#' subgroup_sample=rep(1:10, each=10),
#' Process_run_id = 101:200)
#'
#' Both_Processes <- rbind(Process1, Process2)
#'
#'# QC Values For Individuals -----------------------------------------------
#' # All Together
#' QC_Lines(data = Both_Processes$metric_value, method = "XmR")
#'
#'
#' # For Each Process
#' ddply(Both_Processes, .variables = "processID",
#' .fun =function(df){
#' QC_Lines(data = df$metric_value, method = "XmR")
#' }
#' )
#'
#'# QC Values For Studentized Runs-------------------------------------------
#' # All Together
#' QC_Lines(data = Both_Processes,
#' formula = metric_value ~ subgroup_sample)
#'
#'
#' # For Each Process
#' ddply(Both_Processes, .variables = "processID",
#' .fun =function(df){
#' QC_Lines(data = df, formula = metric_value ~ subgroup_sample)
#' }
#' )
#'
#'
#'########################
#'# Example 2 "p" data #
#'########################
#'
#'# Setup p Data ------------------------------------------------------------
#' set.seed(5555)
#' bin_data <- data.frame(
#' trial = 1:30,
#' Num_Incomplete_Items = rpois(n = 30, lambda = 30),
#' Num_Items_in_Set = runif(n = 30, min = 50, max = 100))
#'
#' bin_data$Proportion_Incomplete <- bin_data$Num_Incomplete_Items/bin_data$Num_Items_in_Set
#'
#'# QC_Lines for "p" data ---------------------------------------------------
#' QC_Lines(data = bin_data$Proportion_Incomplete,
#' n = bin_data$Num_Items_in_Set, method="p")
#'
#'
#'########################
#'# Example 3 "u" data #
#'########################
#'
#'# Setup u Data ------------------------------------------------------------
#' set.seed(5555)
#' bin_data <- data.frame(
#' trial=1:30,
#' Num_of_Blemishes = rpois(n = 30, lambda = 30),
#' Num_Items_Inspected = runif(n = 30, min = 50, max = 100))
#'
#' bin_data$Blemish_Rate <- bin_data$Num_of_Blemishes/bin_data$Num_Items_Inspected
#'
#'
#'# QC Lines for "u" data ---------------------------------------------------
#' QC_Lines(data = bin_data$Blemish_Rate,
#' n = bin_data$Num_Items_Inspected, method="u")
QC_Lines <- function(data=NULL, value=NULL, grouping=NULL, formula=NULL, n=NULL, method="xBar.rBar", na.rm = FALSE){
switch(method,
"mR" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = 1, method = "mR", na.rm = na.rm))
}else{
return(message("Error: method 'mR' requires a vector not a dataframe in data arg."))
}
},
"XmR" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = 1, method = "XmR", na.rm = na.rm))
}else{
return(message("Error: method 'XmR' requires a vector not a dataframe in data arg."))
}
},
"c" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = 1, method = "c", na.rm = na.rm))
}else{
return(message("Error: method 'c' requires a vector not a dataframe in data arg."))
}
},
"np" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = n, method = "np", na.rm = na.rm))
}else{
return(message("Error: method 'np' requires a vector not a dataframe in data arg."))
}
},
"p" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = n, method = "p", na.rm = na.rm))
}else{
return(message("Error: method 'p' requires a vector not a dataframe in data arg."))
}
},
"u" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = n, method = "u", na.rm = na.rm))
}else{
return(message("Error: method 'u' requires a vector not a dataframe in data arg."))
}
},
"xBar.rBar" = {
Lines <- list(rBar_LCL = rBar_LCL , rBar = rBar, rBar_UCL = rBar_UCL,
d2_N = NFUN,
xBar_rBar_LCL = xBar_rBar_LCL,
xBar_Bar = xBar_Bar,
xBar_rBar_UCL = xBar_rBar_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xBar_Bar,
threesigma = xBar_rBar_UCL)}
)
},
"xBar.rMedian" = {
Lines <- list(rMedian_LCL = rMedian_LCL, rMedian = rMedian,
rMedian_UCL = rMedian_UCL, d4_N = NFUN,
xBar_rMedian_LCL = xBar_rMedian_LCL,
xBar_Bar = xBar_Bar,
xBar_rMedian_UCL = xBar_rMedian_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xBar_Bar,
threesigma = xBar_rMedian_UCL)})
},
"xBar.sBar" = {
Lines <- list(sBar_LCL = sBar_LCL,
sBar = sBar,
sBar_UCL = sBar_UCL, c4_N = NFUN,
xBar_sBar_LCL = xBar_sBar_LCL,
xBar_Bar = xBar_Bar,
xBar_sBar_UCL = xBar_sBar_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xBar_Bar,
threesigma = xBar_sBar_UCL)})
},
"xMedian.rBar" = {
Lines <- list(rBar_LCL = rBar_LCL , rBar = rBar, rBar_UCL = rBar_UCL,
b2_N = NFUN,
xMedian_rBar_LCL = xMedian_rBar_LCL,
xMedian_Bar = xMedian_Bar,
xMedian_rBar_UCL = xMedian_rBar_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xMedian_Bar,
threesigma = xMedian_rBar_UCL)})
},
"xMedian.rMedian" = {
Lines <- list(rMedian_LCL = rMedian_LCL, rMedian = rMedian,
rMedian_UCL = rMedian_UCL, b4_N = NFUN,
xMedian_rMedian_LCL = xMedian_rMedian_LCL,
xMedian_Bar = xMedian_Bar,
xMedian_rMedian_UCL = xMedian_rMedian_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xMedian_Bar,
threesigma = xMedian_rMedian_UCL)})
},
"rBar" = {
Lines <- list(N = NFUN, N = NFUN,
xBar_Bar = xBar_Bar,
N = NFUN,
rBar_LCL = rBar_LCL,
rBar = rBar,
rBar_UCL = rBar_UCL)},
"rMedian" = {
Lines <- list(N = NFUN, N = NFUN,
xBar_Bar = xBar_Bar, N = NFUN,
rMedian_LCL = rMedian_LCL,
rMedian = rMedian,
rMedian_UCL = rMedian_UCL)},
"sBar" = {
Lines <- list(N = NFUN, N = NFUN,
xBar_Bar = xBar_Bar, N = NFUN,
sBar_LCL = sBar_LCL,
sBar = sBar,
sBar_UCL = sBar_UCL)}
)
as.data.frame.list(
unlist(lapply(Lines,
FUN = function(f){f(data=data, value=value, grouping=grouping, formula=formula, n=n)}))
)
}
ggmtech/ggQC documentation built on Dec. 20, 2021, 10:43 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions QC_Lines ylines_indv
Documented in QC_Lines
##############################
# Copyright 2018 Kenith Grey #
##############################
# Copyright Notice --------------------------------------------------------
# This file is part of ggQC.
#
# ggQC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ggQC is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ggQC. If not, see <http://www.gnu.org/licenses/>.
# 2nd Order Functions ------------------------------------------------------
# Report Lines for XmR chart
ylines_indv <- function(y, n=1, method = "XmR", na.rm = FALSE){
switch(method,
"mR" = {
QC_indv_functions <- list(
mR_LCL = ZERO,
mR = mR, mR_UCL = mR_UCL,
xBar_one_LCL = xBar_one_LCL,
mean = mean,
xBar_one_UCL = xBar_one_UCL
)},
"XmR" = {
QC_indv_functions <- list(
xBar_one_LCL = xBar_one_LCL,
mean = mean,
xBar_one_UCL = xBar_one_UCL,
mR_LCL = ZERO,mR = mR, mR_UCL = mR_UCL,
sigma = function(y=y, n=n, na.rm=na.rm){
sigma_est_symetric(y = y, na.rm = na.rm,
center = mean,
threesigma = xBar_one_UCL)
}
)},
"c" = {
QC_indv_functions <- list(
cBar_LCL = cBar_LCL,
cBar = mean,
cBar_UCL = cBar_UCL,
sigma = function(y=y, n=n, na.rm=na.rm){
sigma_est_symetric(y = y, na.rm = na.rm, n=n,
center = mean,
threesigma = cBar_UCL)
}
)},
"np" = {
QC_indv_functions <- list(
npBar_LCL = npBar_LCL,
npBar = npBar,
npBar_UCL = npBar_UCL,
sigma = function(y=y, n=n, na.rm=na.rm){
sigma_est_symetric(y = y, na.rm = na.rm, n=n,
center = npBar,
threesigma = npBar_UCL)
}
)},
"p" = {
p_chart_data <- data.frame(
pBar_LCL = pBar_LCL(y, n, na.rm=na.rm),
pBar = pBar(y, n, na.rm=na.rm),
pBar_UCL = pBar_UCL(y, n, na.rm=na.rm)
# Does not make sense to calculate a chart sigma here
)
return(p_chart_data)
},
"u" = {
u_chart_data <- data.frame(
uBar_LCL = uBar_LCL(y, n, na.rm=na.rm ),
uBar = uBar(y, n, na.rm=na.rm),
uBar_UCL = uBar_UCL(y, n, na.rm=na.rm)
# Does not make sense to calculate a chart sigma here
)
return(u_chart_data)
}
)
#print(n)
as.data.frame.list(
unlist(lapply(QC_indv_functions,
FUN = function(f){f(y, n=n, na.rm = na.rm)}))
)
}
#Report lines for subgroup plots
#' @export
#' @title Calculate QC Limits
#' @description Calculates QC chart lines for the following chart types and reports in a dataframe:
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian,
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#' @param data vector or dataframe, as indicated below for each chart type
#' \itemize{
#' \item \bold{Individuals & Attribute Charts}: vector of values;
#' \item \bold{Studentized & Dispersion Charts}: dataframe
#' }
#' @param value string, \bold{Studentized Charts} and \bold{Dispersion Charts}, numeric vector in dataframe with values of interest
#' @param grouping string, \bold{Studentized Charts} and \bold{Dispersion Charts}: single factor/variable to split
#' the dataframe "values" by
#' @param formula \bold{Studentized Charts} and \bold{Dispersion Charts}: a formula,
#' such as y ~ x1 + x2, where the y variable is
#' numeric data to be split into groups according to the grouping x
#' factors/variables
#' @param n number or vector as indicated below for each chart type.
#' \itemize{
#' \item \bold{Individuals Charts}: No effect
#' \item \bold{Attribute Charts}: (p and u) vector, indicating sample area of opportunity.
#' \item \bold{Attribute Charts}: (np) number, indicating constant sampling area of opportunity.
#' \item \bold{Studentized Charts}: number, user specified subgroup size.
#' \item \bold{Dispersion Charts}: No effect
#' }
#' @param method string, calling the following methods:
#' \itemize{
#' \item \bold{Individuals Charts}: mR, XmR,
#' \item \bold{Attribute Charts}: c, np, p, u,
#' \item \bold{Studentized Charts}: xBar.rBar, xBar.rMedian, xBar.sBar, xMedian.rBar,
#' xMedian.rMedian
#' \item \bold{Dispersion Charts}: rBar, rMedian, sBar.
#' }
#' @param na.rm a logical value indicating whether NA values should be
#' stripped before the computation proceeds.
#' @return a dataframe,
#' \itemize{
#' \item \bold{Attribute Data:} (p and u) Center Line, Upper Control Limit and Lower Control limit for each point.
#' \item \bold{Other Data}: single line dataframe, with relevant control limits noted in column headings.
#' }
#' @note If using the \bold{formula} argument do not use \bold{value} and \bold{group} arguments.
#' @references Wheeler, DJ, and DS Chambers. Understanding Statistical Process Control, 2nd Ed. Knoxville, TN: SPC, 1992. Print.
#' @examples
#' #############################################
#' # Example 1: Charts other than "p" or "u" #
#' #############################################
#'
#'# Load Libraries ----------------------------------------------------------
#' require(ggQC)
#' require(plyr)
#' require(ggplot2)
#'
#'# Setup Data --------------------------------------------------------------
#' set.seed(5555)
#' Process1 <- data.frame(processID = as.factor(rep(1,100)),
#' metric_value = rnorm(100,0,1),
#' subgroup_sample=rep(1:20, each=5),
#' Process_run_id = 1:100)
#' set.seed(5555)
#' Process2 <- data.frame(processID = as.factor(rep(2,100)),
#' metric_value = rnorm(100,5, 1),
#' subgroup_sample=rep(1:10, each=10),
#' Process_run_id = 101:200)
#'
#' Both_Processes <- rbind(Process1, Process2)
#'
#'# QC Values For Individuals -----------------------------------------------
#' # All Together
#' QC_Lines(data = Both_Processes$metric_value, method = "XmR")
#'
#'
#' # For Each Process
#' ddply(Both_Processes, .variables = "processID",
#' .fun =function(df){
#' QC_Lines(data = df$metric_value, method = "XmR")
#' }
#' )
#'
#'# QC Values For Studentized Runs-------------------------------------------
#' # All Together
#' QC_Lines(data = Both_Processes,
#' formula = metric_value ~ subgroup_sample)
#'
#'
#' # For Each Process
#' ddply(Both_Processes, .variables = "processID",
#' .fun =function(df){
#' QC_Lines(data = df, formula = metric_value ~ subgroup_sample)
#' }
#' )
#'
#'
#'########################
#'# Example 2 "p" data #
#'########################
#'
#'# Setup p Data ------------------------------------------------------------
#' set.seed(5555)
#' bin_data <- data.frame(
#' trial = 1:30,
#' Num_Incomplete_Items = rpois(n = 30, lambda = 30),
#' Num_Items_in_Set = runif(n = 30, min = 50, max = 100))
#'
#' bin_data$Proportion_Incomplete <- bin_data$Num_Incomplete_Items/bin_data$Num_Items_in_Set
#'
#'# QC_Lines for "p" data ---------------------------------------------------
#' QC_Lines(data = bin_data$Proportion_Incomplete,
#' n = bin_data$Num_Items_in_Set, method="p")
#'
#'
#'########################
#'# Example 3 "u" data #
#'########################
#'
#'# Setup u Data ------------------------------------------------------------
#' set.seed(5555)
#' bin_data <- data.frame(
#' trial=1:30,
#' Num_of_Blemishes = rpois(n = 30, lambda = 30),
#' Num_Items_Inspected = runif(n = 30, min = 50, max = 100))
#'
#' bin_data$Blemish_Rate <- bin_data$Num_of_Blemishes/bin_data$Num_Items_Inspected
#'
#'
#'# QC Lines for "u" data ---------------------------------------------------
#' QC_Lines(data = bin_data$Blemish_Rate,
#' n = bin_data$Num_Items_Inspected, method="u")
QC_Lines <- function(data=NULL, value=NULL, grouping=NULL, formula=NULL, n=NULL, method="xBar.rBar", na.rm = FALSE){
switch(method,
"mR" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = 1, method = "mR", na.rm = na.rm))
}else{
return(message("Error: method 'mR' requires a vector not a dataframe in data arg."))
}
},
"XmR" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = 1, method = "XmR", na.rm = na.rm))
}else{
return(message("Error: method 'XmR' requires a vector not a dataframe in data arg."))
}
},
"c" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = 1, method = "c", na.rm = na.rm))
}else{
return(message("Error: method 'c' requires a vector not a dataframe in data arg."))
}
},
"np" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = n, method = "np", na.rm = na.rm))
}else{
return(message("Error: method 'np' requires a vector not a dataframe in data arg."))
}
},
"p" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = n, method = "p", na.rm = na.rm))
}else{
return(message("Error: method 'p' requires a vector not a dataframe in data arg."))
}
},
"u" = {
if(is.vector(data)){
return(ylines_indv(y = data, n = n, method = "u", na.rm = na.rm))
}else{
return(message("Error: method 'u' requires a vector not a dataframe in data arg."))
}
},
"xBar.rBar" = {
Lines <- list(rBar_LCL = rBar_LCL , rBar = rBar, rBar_UCL = rBar_UCL,
d2_N = NFUN,
xBar_rBar_LCL = xBar_rBar_LCL,
xBar_Bar = xBar_Bar,
xBar_rBar_UCL = xBar_rBar_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xBar_Bar,
threesigma = xBar_rBar_UCL)}
)
},
"xBar.rMedian" = {
Lines <- list(rMedian_LCL = rMedian_LCL, rMedian = rMedian,
rMedian_UCL = rMedian_UCL, d4_N = NFUN,
xBar_rMedian_LCL = xBar_rMedian_LCL,
xBar_Bar = xBar_Bar,
xBar_rMedian_UCL = xBar_rMedian_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xBar_Bar,
threesigma = xBar_rMedian_UCL)})
},
"xBar.sBar" = {
Lines <- list(sBar_LCL = sBar_LCL,
sBar = sBar,
sBar_UCL = sBar_UCL, c4_N = NFUN,
xBar_sBar_LCL = xBar_sBar_LCL,
xBar_Bar = xBar_Bar,
xBar_sBar_UCL = xBar_sBar_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xBar_Bar,
threesigma = xBar_sBar_UCL)})
},
"xMedian.rBar" = {
Lines <- list(rBar_LCL = rBar_LCL , rBar = rBar, rBar_UCL = rBar_UCL,
b2_N = NFUN,
xMedian_rBar_LCL = xMedian_rBar_LCL,
xMedian_Bar = xMedian_Bar,
xMedian_rBar_UCL = xMedian_rBar_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xMedian_Bar,
threesigma = xMedian_rBar_UCL)})
},
"xMedian.rMedian" = {
Lines <- list(rMedian_LCL = rMedian_LCL, rMedian = rMedian,
rMedian_UCL = rMedian_UCL, b4_N = NFUN,
xMedian_rMedian_LCL = xMedian_rMedian_LCL,
xMedian_Bar = xMedian_Bar,
xMedian_rMedian_UCL = xMedian_rMedian_UCL,
sigma = function(data=data, value=value, grouping=grouping,
formula=formula, n=n){
sigma_est_symetric_grouping(
data=data, value=value, grouping=grouping,
formula=formula, n=n,
center = xMedian_Bar,
threesigma = xMedian_rMedian_UCL)})
},
"rBar" = {
Lines <- list(N = NFUN, N = NFUN,
xBar_Bar = xBar_Bar,
N = NFUN,
rBar_LCL = rBar_LCL,
rBar = rBar,
rBar_UCL = rBar_UCL)},
"rMedian" = {
Lines <- list(N = NFUN, N = NFUN,
xBar_Bar = xBar_Bar, N = NFUN,
rMedian_LCL = rMedian_LCL,
rMedian = rMedian,
rMedian_UCL = rMedian_UCL)},
"sBar" = {
Lines <- list(N = NFUN, N = NFUN,
xBar_Bar = xBar_Bar, N = NFUN,
sBar_LCL = sBar_LCL,
sBar = sBar,
sBar_UCL = sBar_UCL)}
)
as.data.frame.list(
unlist(lapply(Lines,
FUN = function(f){f(data=data, value=value, grouping=grouping, formula=formula, n=n)}))
)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.