Package 'r6qualitytools'

Description

Performs the Anderson-Darling test for univariate distributions with an option for bootstrap-based p-value determination. It also allows p-value determination using tabled critical values.

Usage

adSim(x, distribution = "normal", b = 10000)

Arguments

Details

The function first estimates the parameters for the tested distribution, typically using Maximum-Likelihood Estimation (MLE) via the FitDistr function. For normal and log-normal distributions, parameters are estimated by the mean and standard deviation. Cauchy distribution parameters are fitted by the sums of the weighted order statistic when using tabled critical values. The Anderson-Darling statistic is then calculated based on these estimated parameters.

Parametric bootstrapping generates the distribution of the Anderson-Darling test statistic, which is used to determine the p-value. This simulation-based Anderson-Darling distribution and its corresponding critical values for selected quantiles can be printed. If simulation is not performed, a p-value is obtained from tabled critical values, although no exact expressions exist except for the log-normal, normal, and exponential distributions.

Value

A list containing the following components:

distribution

The distribution for which the Anderson-Darling test was applied.

parameter_estimation

The estimated parameters for the distribution.

Anderson_Darling

The value of the Anderson-Darling test statistic.

p_value

The corresponding p-value, either simulated or from tabled values.

critical_values

The critical values corresponding to the 0.75, 0.90, 0.95, 0.975, and 0.99 quantiles, either simulated or from tables.

simAD

The bootstrap-based Anderson-Darling distribution.

Examples

x <- rnorm(25, 32, 2)
adSim(x)
adSim(x, "logistic", 2000)
adSim(x, "cauchy", NA)
adSim(x, "exponential", 2000)
adSim(x, "gumbel", 2000)

Description

This function generates an alias table for a factorial design object.

Usage

aliasTable(fdo, degree, print = TRUE)

Arguments

Value

The function aliasTable returns a matrix indicating the aliased effects.

See Also

fracDesign, fracChoose

Examples

# Create a fractional factorial design
dfrac <- fracDesign(k = 3, gen = "C = AB")
# Display the alias table for the fractional factorial design
aliasTable(dfrac)

Description

Converts an object of class facDesign.c into a data frame.

Usage

as.data.frame_facDesign(dfac)

Arguments

Value

The function as.data.frame_facDesign returns a data frame.

Examples

fdo <- fracDesign(k = 2, replicates = 3, centerCube = 4)
as.data.frame_facDesign(fdo)

Description

Blocks a given factorial or response surface design.

Usage

blocking(fdo, blocks, random.seed, useTable = "rsm", gen)

Arguments

Value

The function blocking returns an object of class facDesign.c with blocking structure.

See Also

facDesign.

Examples

# Example 1
#Create a 2^3 full factorial design
fdo <- facDesign(k = 3)
# Apply blocking to the design with 2 blocks
blocking(fdo, 2)

# Example 2
#Create a response surface design for 3 factors
fdo <- rsmDesign(k = 3)
# Apply blocking to the design with 3 blocks (1 block for star part and 2 blocks for the cube part)
blocking(fdo, 3)

Description

Function visualize the given values of measurement in a run chart and in a histogram. Furthermore the centralized Gage potential index Cg and the non-centralized Gage Capability index Cgk are calculated and displayed.

Usage

cg(
  x,
  target,
  tolerance,
  ref.interval,
  facCg,
  facCgk,
  n = 0.2,
  col,
  pch,
  xlim,
  ylim,
  conf.level = 0.95
)

Arguments

Details

The calculation of the potential and actual gage capability are based on the following formula:

• Cg = (facCg * tolerance[2]-tolerance[1])/ref.interval

• Cgk = (facCgk * abs(target-mean(x))/(ref.interval/2)

If the usage of the historical process variation is preferred the values for the tolerance tolerance must be adjusted manually. That means in case of the 6 sigma methodology for example, that tolerance = 6 * sigma[process].

Value

The function cg returns a list of numeric values. The first element contains the calculated centralized gage potential index Cg and the second contains the non-centralized gage capability index Cgk.

See Also

cg_RunChart, cg_HistChart, cg_ToleranceChart.

Examples

x <- c(9.991, 10.013, 10.001, 10.007, 10.010, 10.013, 10.008,9.992,
       10.017, 10.005, 10.005, 10.002, 10.017, 10.005, 10.002, 9.996,
       10.011, 10.009, 10.006, 10.008, 10.003, 10.002, 10.006, 10.010, 10.013)

cg(x = x, target = 10.003, tolerance = c(9.903, 10.103))

Description

Function visualize the given values of measurement in a histogram

Usage

cg_HistChart(
  x,
  target,
  tolerance,
  ref.interval,
  facCg,
  facCgk,
  n = 0.2,
  col,
  xlim,
  ylim,
  main,
  conf.level = 0.95,
  cgOut = TRUE
)

Arguments

Details

The calculation of the potential and actual gage capability are based on the following formulae:

• Cg = (facCg * tolerance[2]-tolerance[1])/ref.interval

• Cgk = (facCgk * abs(target-mean(x))/(ref.interval/2)

If the usage of the historical process variation is preferred the values for the tolerance tolerance must be adjusted manually. That means in case of the 6 sigma methodology for example, that tolerance = 6 * sigma[process].

Value

The function cg_HistChart returns a list of numeric values. The first element contains the calculated centralized gage potential index Cg and the second contains the non-centralized gage capability index Cgk.

See Also

cg_RunChart, cg_ToleranceChart, cg

Examples

x <- c(9.991, 10.013, 10.001, 10.007, 10.010, 10.013, 10.008,9.992,
       10.017, 10.005, 10.005, 10.002, 10.017, 10.005, 10.002, 9.996,
       10.011, 10.009, 10.006, 10.008, 10.003, 10.002, 10.006, 10.010, 10.013)

cg_HistChart(x = x, target = 10.003, tolerance = c(9.903, 10.103))

Description

Function visualize the given values of measurement in a Run Chart

Usage

cg_RunChart(
  x,
  target,
  tolerance,
  ref.interval,
  facCg,
  facCgk,
  n = 0.2,
  col = "black",
  pch = 19,
  xlim = NULL,
  ylim = NULL,
  main = "Run Chart",
  conf.level = 0.95,
  cgOut = TRUE
)

Arguments

Details

The calculation of the potential and actual gage capability are based on the following formulae:

• Cg = (facCg * tolerance[2]-tolerance[1])/ref.interval

• Cgk = (facCgk * abs(target-mean(x))/(ref.interval/2)

If the usage of the historical process variation is preferred the values for the tolerance tolerance must be adjusted manually. That means in case of the 6 sigma methodology for example, that tolerance = 6 * sigma[process].

Value

The function cg_RunChart returns a list of numeric values. The first element contains the calculated centralized gage potential index Cg and the second contains the non-centralized gage capability index Cgk.

See Also

cg_HistChart, cg_ToleranceChart, cg

Examples

x <- c(9.991, 10.013, 10.001, 10.007, 10.010, 10.013, 10.008,9.992,
       10.017, 10.005, 10.005, 10.002, 10.017, 10.005, 10.002, 9.996,
       10.011, 10.009, 10.006, 10.008, 10.003, 10.002, 10.006, 10.010, 10.013)

cg_RunChart(x = x, target = 10.003, tolerance = c(9.903, 10.103))

Description

Function visualize the given values of measurement in a Tolerance View.

Usage

cg_ToleranceChart(
  x,
  target,
  tolerance,
  ref.interval,
  facCg,
  facCgk,
  n = 0.2,
  col,
  pch,
  xlim,
  ylim,
  main,
  conf.level = 0.95,
  cgOut = TRUE
)

Arguments

Details

The calculation of the potential and actual gage capability are based on the following formulae:

• Cg = (facCg * tolerance[2]-tolerance[1])/ref.interval

• Cgk = (facCgk * abs(target-mean(x))/(ref.interval/2)

If the usage of the historical process variation is preferred the values for the tolerance tolerance must be adjusted manually. That means in case of the 6 sigma methodology for example, that tolerance = 6 * sigma[process].

Value

The function cg_ToleranceChart returns a list of numeric values. The first element contains the calculated centralized gage potential index Cg and the second contains the non-centralized gage capability index Cgk.

See Also

cg_RunChart, cg_HistChart, cg

Examples

x <- c(9.991, 10.013, 10.001, 10.007, 10.010, 10.013, 10.008,9.992,
       10.017, 10.005, 10.005, 10.002, 10.017, 10.005, 10.002, 9.996,
       10.011, 10.009, 10.006, 10.008, 10.003, 10.002, 10.006, 10.010, 10.013)

cg_ToleranceChart(x = x, target = 10.003, tolerance = c(9.903, 10.103))

Description

Function to calculate the real value of a coded value.

Usage

code2real(low, high, codedValue)

Arguments

Value

The function return a real value of a coded value

Examples

code2real(160, 200, 0)

Description

Function to display confounded effects of a fractional factorial design in a human readable way.

Usage

confounds(x, depth = 2)

Arguments

Value

The function returns a summary of the factors confounded.

Examples

vp.frac = fracDesign(k = 4, gen = "D=ABC")
confounds(vp.frac,depth=5)

Description

Creates a contour diagram for an object of class facDesign.c.

Usage

contourPlot(
  x,
  y,
  z,
  data = NULL,
  xlim,
  ylim,
  main,
  xlab,
  ylab,
  form = "fit",
  col = 1,
  steps,
  fun,
  plot = TRUE,
  show.scale = TRUE
)

Arguments

Value

The function contourPlot returns an invisible list containing:

• x - locations of grid lines for x at which the values in z are measured.

• y - locations of grid lines for y at which the values in z are measured.

• z - a matrix containing the values of z to be plotted.

• plot - The generated plot.

See Also

wirePlot, paretoChart

Examples

fdo = rsmDesign(k = 3, blocks = 2)
fdo$.response(data.frame(y = rnorm(fdo$nrow())))

#I - display linear fit
contourPlot(A,B,y, data = fdo, form = "linear")
#II - display full fit (i.e. effect, interactions and quadratic effects
contourPlot(A,B,y, data = fdo, form = "full")
#III - display a fit specified before
fdo$set.fits(fdo$lm(y ~ B + I(A^2)))
contourPlot(A,B,y, data = fdo, form = "fit")
#IV - display a fit given directly
contourPlot(A,B,y, data = fdo, form = "y ~ A*B + I(A^2)")
#V - display a fit using a different colorRamp
contourPlot(A,B,y, data = fdo, form = "full", col = 2)
#VI - display a fit using a self defined colorRamp
myColour = colorRampPalette(c("green", "gray","blue"))
contourPlot(A,B,y, data = fdo, form = "full", col = myColour)

Description

This function creates a ternary plot (contour plot) for mixture designs (i.e. object of class mixDesign).

Usage

contourPlot3(
  x,
  y,
  z,
  response,
  data = NULL,
  main,
  xlab,
  ylab,
  zlab,
  form = "linear",
  col = 1,
  col.text,
  axes = TRUE,
  steps,
  plot = TRUE,
  show.scale = TRUE
)

Arguments

Value

The function contourPlot3 returns an invisible list containing:

• mat - A matrix containing the response values as NA's and numerics.

• plot - The generated plot.

See Also

mixDesign.c, mixDesign, wirePlot3.

Examples

mdo = mixDesign(3,2, center = FALSE, axial = FALSE, randomize = FALSE,
                replicates  = c(1,1,2,3))
mdo$names(c("polyethylene", "polystyrene", "polypropylene"))
mdo$units("percent")
elongation = c(11.0, 12.4, 15.0, 14.8, 16.1, 17.7, 16.4, 16.6, 8.8, 10.0, 10.0,
               9.7, 11.8, 16.8, 16.0)
mdo$.response(elongation)
contourPlot3(A, B, C, elongation, data = mdo, form = "linear")
contourPlot3(A, B, C, elongation, data = mdo, form = "quadratic", col = 2)
contourPlot3(A, B, C, elongation, data = mdo,
             form = "elongation ~ I(A^2) - B:A + I(C^2)",
             col = 3, axes = FALSE)
contourPlot3(A, B, C, elongation, data = mdo,
             form = "quadratic",
             col = c("yellow", "white", "red"),
             axes = FALSE)

Description

Creates desirability functions for use in the optimization of multiple responses.

Usage

desirability(
  response,
  low,
  high,
  target = "max",
  scale = c(1, 1),
  importance = 1
)

Arguments

Details

For a product to be developed, different values of responses are desired, leading to multiple response optimization. Minimization, maximization, as well as a specific target value, are defined using desirability functions. A desirability function transforms the values of a response into [0,1], where 0 stands for a non-acceptable value of the response and 1 for values where higher/lower (depending on the direction of the optimization) values of the response have little merit. This function builds upon the desirability functions specified by Harrington (1965) and the modifications by Derringer and Suich (1980) and Derringer (1994). Castillo, Montgomery, and McCarville (1996) further extended these functions, but these extensions are not implemented in this version.

Value

This function returns a desirability.c object.

See Also

overall, optimum

Examples

# Example 1: Maximization of a response
# Define a desirability for response y where higher values of y are better
# as long as the response is smaller than high
d = desirability(y, low = 6, high = 18, target = "max")
# Show and plot the desirability function
d
plot(d)

# Example 2: Minimization of a response including a scaling factor
# Define a desirability for response y where lower values of y are better
# as long as the response is higher than low
d = desirability(y, low = 6, high = 18, scale = c(2), target = "min")
# Show and plot the desirability function
d
plot(d)

# Example 3: Specific target of a response is best including a scaling factor
# Define a desirability for response y where desired value is at 8 and
# values lower than 6 as well as values higher than 18 are not acceptable
d = desirability(y, low = 6, high = 18, scale = c(0.5, 2), target = 12)
# Show and plot the desirability function
d
plot(d)

# Example 4:
y1 <- c(102, 120, 117, 198, 103, 132, 132, 139, 102, 154, 96, 163, 116, 153,
        133, 133, 140, 142, 145, 142)
y2 <- c(470, 410, 570, 240, 640, 270, 410, 380, 590, 260, 520, 380, 520, 290,
        380, 380, 430, 430, 390, 390)
d1 <- desirability(y1, 120, 170, scale = c(1, 1), target = "max")
d3 <- desirability(y2, 400, 600, target = 500)
d1
plot(d1)
d3
plot(d3)

Description

A class representing the desirability metrics for responses in a design.

Public fields

Methods

Public methods

• desirability.c$new()

• desirability.c$print()

• desirability.c$plot()

• desirability.c$clone()

Method new()

Initializes a new desirability.c object with specified parameters.

Usage

desirability.c$new(
  response = NULL,
  low = NULL,
  high = NULL,
  target = NULL,
  scale = NULL,
  importance = NULL
)

Arguments

Method print()

Prints the details of a desirability.c object.

Usage

desirability.c$print()

Method plot()

Plots the desirability functions based on the specified parameters.

Usage

desirability.c$plot(scale, main, xlab, ylab, line.width, col, numPoints = 500)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

desirability.c$clone(deep = FALSE)

Arguments

See Also

desirability, overall, optimum

Description

The desOpt class represents an object that stores optimization results for factorial design experiments. It includes coded and real factors, responses, desirabilities, overall desirability, and the design object.

Public fields

Methods

Public methods

• desOpt$as.data.frame()

• desOpt$print()

• desOpt$clone()

Method as.data.frame()

Convert the object to a data frame.

Usage

desOpt$as.data.frame()

Method print()

Print a summary of the object.

Usage

desOpt$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

desOpt$clone(deep = FALSE)

Arguments

See Also

optimum, facDesign, desirability

Description

Density function, distribution function, and quantile function for the Gamma distribution.

Usage

dgamma3(x, shape, scale, threshold)

Arguments

Details

The Gamma distribution with scale parameter alpha, shape parameter c, and threshold parameter zeta has a density given by:

$f(x) = \frac{c}{\alpha}\left(\frac{x-\zeta}{\alpha}\right)^{c-1}\exp\left(-\left(\frac{x-\zeta}{\alpha}\right)^c\right)$

The cumulative distribution function is given by:

$F(x) = 1 - \exp\left(-\left(\frac{x-\zeta}{\alpha}\right)^c\right)$

Value

dgamma3 gives the density, pgamma3 gives the distribution function, and qgamma3 gives the quantile function.

Examples

dgamma3(x = 1, scale = 1, shape = 5, threshold = 0)
temp <- pgamma3(q = 1, scale = 1, shape = 5, threshold = 0)
temp
qgamma3(p = temp, scale = 1, shape = 5, threshold = 0)

Description

R6 Class for Distribution Objects

Public fields

Methods

Public methods

• Distr$new()

• Distr$plot()

• Distr$clone()

Method new()

Initialize the fiels of the 'Distribution' object

Usage

Distr$new(x, name, parameters, sd, n, loglik)

Arguments

Method plot()

Plot the distribution with histogram and fitted density curve.

Usage

Distr$plot(
  main = NULL,
  xlab = NULL,
  xlim = NULL,
  xlim.t = TRUE,
  ylab = NULL,
  line.col = "red",
  fill.col = "lightblue",
  border.col = "black",
  box = TRUE,
  line.width = 1
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Distr$clone(deep = FALSE)

Arguments

See Also

distribution, FitDistr, DistrCollection

Examples

# Normal
set.seed(123)
data1 <- rnorm(100, mean = 5, sd = 2)
parameters1 <- list(mean = 5, sd = 2)
distr1 <- Distr$new(x = data1, name = "normal", parameters = parameters1,
                    sd = 2, n = 100, loglik = -120)
distr1$plot()

# Log-normal
data2 <- rlnorm(100, meanlog = 1, sdlog = 0.5)
parameters2 <- list(meanlog = 1, sdlog = 0.5)
distr2 <- Distr$new(x = data2, name = "log-normal", parameters = parameters2,
                    sd = 0.5, n = 100, loglik = -150)
distr2$plot()

# Geometric
data3 <- rgeom(100, prob = 0.3)
parameters3 <- list(prob = 0.3)
distr3 <- Distr$new(x = data3, name = "geometric", parameters = parameters3,
                    sd = sqrt((1 - 0.3) / (0.3^2)), n = 100, loglik = -80)
distr3$plot()

# Exponential
data4 <- rexp(100, rate = 0.2)
parameters4 <- list(rate = 0.2)
distr4 <- Distr$new(x = data4, name = "exponential", parameters = parameters4,
                    sd = 1 / 0.2, n = 100, loglik = -110)
distr4$plot()

# Poisson
data5 <- rpois(100, lambda = 3)
parameters5 <- list(lambda = 3)
distr5 <- Distr$new(x = data5, name = "poisson", parameters = parameters5,
                    sd = sqrt(3), n = 100, loglik = -150)
distr5$plot()

# Chi-square
data6 <- rchisq(100, df = 5)
parameters6 <- list(df = 5)
distr6 <- Distr$new(x = data6, name = "chi-squared", parameters = parameters6,
                    sd = sqrt(2 * 5), n = 100, loglik = -130)
distr6$plot()

# Logistic
data7 <- rlogis(100, location = 0, scale = 1)
parameters7 <- list(location = 0, scale = 1)
distr7 <- Distr$new(x = data7, name = "logistic", parameters = parameters7,
                    sd = 1 * sqrt(pi^2 / 3), n = 100, loglik = -140)
distr7$plot()

# Gamma
data8 <- rgamma(100, shape = 2, rate = 0.5)
parameters8 <- list(shape = 2, rate = 0.5)
distr8 <- Distr$new(x = data8, name = "gamma", parameters = parameters8,
                    sd = sqrt(2 / (0.5^2)), n = 100, loglik = -120)
distr8$plot()

# f
data9 <- rf(100, df1 = 5, df2 = 10)
parameters9 <- list(df1 = 5, df2 = 10)
df1 <- 5
df2 <- 10
distr9 <- Distr$new(x = data9, name = "f", parameters = parameters9,
                    sd = sqrt(((df2^2 * (df1 + df2 - 2)) /
                               (df1 * (df2 - 2)^2 * (df2 - 4)))),
                    n = 100, loglik = -150)
distr9$plot()

# t
data10 <- rt(100, df = 10)
parameters10 <- list(df = 10)
distr10 <- Distr$new(x = data10, name = "t", parameters = parameters10,
                     sd = sqrt(10 / (10 - 2)), n = 100, loglik = -120)
distr10$plot()

# negative binomial
data11 <- rnbinom(100, size = 5, prob = 0.3)
parameters11 <- list(size = 5, prob = 0.3)
distr11 <- Distr$new(x = data11, name = "negative binomial",
                     parameters = parameters11,
                     sd = sqrt(5 * (1 - 0.3) / (0.3^2)),
                     n = 100, loglik = -130)
distr11$plot()

Description

R6 Class for Managing a Collection of Distribution Objects

Public fields

Methods

Public methods

• DistrCollection$new()

• DistrCollection$add()

• DistrCollection$get()

• DistrCollection$print()

• DistrCollection$summary()

• DistrCollection$plot()

• DistrCollection$clone()

Method new()

Initialize the fields of the DistrCollection object.

Usage

DistrCollection$new()

Method add()

Add a Distr object to the collection.

Usage

DistrCollection$add(distr)

Arguments

Method get()

Get a Distr object from the collection by its index.

Usage

DistrCollection$get(i)

Arguments

Returns

A Distr object.

Method print()

Print the summary of all distributions in the collection.

Usage

DistrCollection$print()

Method summary()

Summarize the goodness of fit for all distributions in the collection.

Usage

DistrCollection$summary()

Returns

A data frame with distribution names, Anderson-Darling test statistics, and p-values.

Method plot()

Plot all distributions in the collection.

Usage

DistrCollection$plot(
  xlab = NULL,
  ylab = NULL,
  xlim = NULL,
  ylim = NULL,
  line.col = "red",
  fill.col = "lightblue",
  border.col = "black",
  line.width = 1,
  box = TRUE
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

DistrCollection$clone(deep = FALSE)

Arguments

See Also

Distr, distribution, FitDistr

Examples

set.seed(123)
data1 <- rnorm(100, mean = 5, sd = 2)
parameters1 <- list(mean = 5, sd = 2)
distr1 <- Distr$new(x = data1, name = "normal",
                    parameters = parameters1, sd = 2,
                    n = 100, loglik = -120)

data2 <- rpois(100, lambda = 3)
parameters2 <- list(lambda = 3)
distr2 <- Distr$new(x = data2, name = "poisson",
                    parameters = parameters2, sd = sqrt(3),
                    n = 100, loglik = -150)
collection <- DistrCollection$new()
collection$add(distr1)
collection$add(distr2)
collection$summary()
collection$plot()

Description

Calculates the most likely parameters for a given distribution.

Usage

distribution(x = NULL, distrib = "weibull", ...)

Arguments

Value

distribution() returns an object of class DistrCollection.

See Also

Distr, DistrCollection

Examples

data1 <- rnorm(100, mean = 5, sd = 2)
distribution(data1, distrib = "normal")

Description

Density function, distribution function, and quantile function for the Lognormal distribution.

Usage

dlnorm3(x, meanlog, sdlog, threshold)

Arguments

Details

The Lognormal distribution with meanlog parameter zeta, sdlog parameter sigma, and threshold parameter theta has a density given by:

$f(x) = \frac{1}{\sqrt{2\pi}\sigma(x-\theta)}\exp\left(-\frac{(\log(x-\theta)-\zeta)^2}{2\sigma^2}\right)$

The cumulative distribution function is given by:

$F(x) = \Phi\left(\frac{\log(x-\theta)-\zeta}{\sigma}\right)$

where $\Phi$ is the cumulative distribution function of the standard normal distribution.

Value

dlnorm3 gives the density, plnorm3 gives the distribution function, and qlnorm3 gives the quantile function.

Examples

dlnorm3(x = 2, meanlog = 0, sdlog = 1/8, threshold = 1)
temp <- plnorm3(q = 2, meanlog = 0, sdlog = 1/8, threshold = 1)
temp
qlnorm3(p = temp, meanlog = 0, sdlog = 1/8, threshold = 1)

Description

An R6 class representing a factor in a design of experiments (DOE).

Public fields

Methods

Public methods

• doeFactor$attributes()

• doeFactor$.low()

• doeFactor$.high()

• doeFactor$.type()

• doeFactor$.unit()

• doeFactor$names()

• doeFactor$print()

• doeFactor$clone()

Method attributes()

Get the attributes of the factor.

Usage

doeFactor$attributes()

Method .low()

Get and set the lower bound for the factor.

Usage

doeFactor$.low(value)

Arguments

Method .high()

Get and set the upper bound for the factor.

Usage

doeFactor$.high(value)

Arguments

Method .type()

Get and set the type of the factor.

Usage

doeFactor$.type(value)

Arguments

Method .unit()

Get and set the unit of measurement for the factor.

Usage

doeFactor$.unit(value)

Arguments

Method names()

Get and set the name of the factor.

Usage

doeFactor$names(value)

Arguments

Method print()

Print the characteristics of the factors.

Usage

doeFactor$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

doeFactor$clone(deep = FALSE)

Arguments

See Also

taguchiFactor

Description

Creates a dot plot. For data in groups, the dot plot can be displayed stacked or in separate regions.

Usage

dotPlot(
  x,
  group,
  xlim,
  ylim,
  col,
  xlab,
  ylab,
  pch,
  cex,
  breaks,
  stacked = TRUE,
  main,
  showPlot = TRUE
)

Arguments

Details

Values in x are assigned to the bins defined by breaks. The binning is performed using hist.

Value

A list cointaining:

• An invisible matrix containing NAs and numeric values representing values in a bin. The number of bins is given by the number of columns of the matrix.

• The graphic.

Examples

# Create some data and grouping
set.seed(1)
x <- rnorm(28)
g <- rep(1:2, 14)

# Dot plot with groups and no stacking
dotPlot(x, group = g, stacked = FALSE, pch = c(19, 20), main = "Non stacked dot plot")

# Dot plot with groups and stacking
dotPlot(x, group = g, stacked = TRUE, pch = c(19, 20), main = "Stacked dot plot")

Description

Density function, distribution function, and quantile function for the Weibull distribution with a threshold parameter.

Usage

dweibull3(x, shape, scale, threshold)

Arguments

Details

The Weibull distribution with the scale parameter alpha, shape parameter c, and threshold parameter zeta has a density function given by:

$f(x) = \frac{c}{\alpha} \left(\frac{x - \zeta}{\alpha}\right)^{c-1} \exp\left(-\left(\frac{x - \zeta}{\alpha}\right)^c\right)$

The cumulative distribution function is given by:

$F(x) = 1 - \exp\left(-\left(\frac{x - \zeta}{\alpha}\right)^c\right)$

Value

dweibull3 returns the density, pweibull3 returns the distribution function, and qweibull3 returns the quantile function for the Weibull distribution with a threshold.

Examples

dweibull3(x = 1, scale = 1, shape = 5, threshold = 0)
temp <- pweibull3(q = 1, scale = 1, shape = 5, threshold = 0)
temp
qweibull3(p = temp, scale = 1, shape = 5, threshold = 0)

Description

Generates a 2^k full factorial design.

Usage

facDesign(
  k = 3,
  p = 0,
  replicates = 1,
  blocks = 1,
  centerCube = 0,
  random.seed = 1234
)

Arguments

Value

The function facDesign returns an object of class facDesign.c.

See Also

fracDesign, fracChoose, rsmDesign, pbDesign, taguchiDesign

Examples

# Example 1
vp.full <- facDesign(k = 3)
vp.full$.response(rnorm(2^3))
vp.full$summary()

# Example 2
vp.rep <- facDesign(k = 2, replicates = 3, centerCube = 4)
vp.rep$names(c("Name 1", "Name 2"))
vp.rep$unit(c("min", "F"))
vp.rep$lows(c(20, 40, 60))
vp.rep$highs(c(40, 60, 80))
vp.rep$summary()

# Example 3
dfac <- facDesign(k = 3, centerCube = 4)
dfac$names(c('Factor 1', 'Factor 2', 'Factor 3'))
dfac$names()
dfac$lows(c(80, 120, 1))
dfac$lows()
dfac$highs(c(120, 140, 2))
dfac$highs()
dfac$summary()

Description

The facDesign.c class is used to represent factorial designs, including their factors, responses, blocks, and design matrices. This class supports various experimental designs and allows for the storage and manipulation of data related to the design and analysis of factorial experiments.

Public fields

Methods

Public methods

• facDesign.c$nrow()

• facDesign.c$ncol()

• facDesign.c$print()

• facDesign.c$.clear()

• facDesign.c$names()

• facDesign.c$as.data.frame()

• facDesign.c$get()

• facDesign.c$lows()

• facDesign.c$highs()

• facDesign.c$.nfp()

• facDesign.c$identity()

• facDesign.c$summary()

• facDesign.c$.response()

• facDesign.c$effectPlot()

• facDesign.c$lm()

• facDesign.c$desires()

• facDesign.c$set.fits()

• facDesign.c$types()

• facDesign.c$unit()

• facDesign.c$.star()

• facDesign.c$.blockGen()

• facDesign.c$.block()

• facDesign.c$.centerCube()

• facDesign.c$.centerStar()

• facDesign.c$.generators()

• facDesign.c$clone()

Method nrow()

Get the number of rows Design.

Usage

facDesign.c$nrow()

Method ncol()

Get the number of columns Design.

Usage

facDesign.c$ncol()

Method print()

Prints a formatted representation of the factorial design object, including design matrices and responses.

Usage

facDesign.c$print()

Method .clear()

Clears the factorial design object.

Usage

facDesign.c$.clear()

Method names()

Get or set the names of the factors in the factorial design.

Usage

facDesign.c$names(value)

Arguments

Method as.data.frame()

Converts the factorial design object to a data frame.

Usage

facDesign.c$as.data.frame()

Method get()

Retrieves elements from the factorial design object.

Usage

facDesign.c$get(i, j)

Arguments

Method lows()

Get or set the lower bounds of the factors in the factorial design.

Usage

facDesign.c$lows(value)

Arguments

Method highs()

Get or set the upper bounds of the factors in the factorial design.

Usage

facDesign.c$highs(value)

Arguments

Method .nfp()

Prints a summary of the factors attributes including their low, high, name, unit, and type.

Usage

facDesign.c$.nfp()

Method identity()

Returns the factorial design object itself, used to verify or return the object.

Usage

facDesign.c$identity()

Method summary()

Summarizes the factorial design object.

Usage

facDesign.c$summary()

Method .response()

Get or set the response data in the factorial design object.

Usage

facDesign.c$.response(value)

Arguments

Method effectPlot()

Plots the effects of factors on the response variables.

Usage

facDesign.c$effectPlot(
  factors,
  fun = mean,
  response = NULL,
  lty,
  xlab,
  ylab,
  main,
  ylim
)

Arguments

Method lm()

Fits a linear model to the response data in the factorial design object.

Usage

facDesign.c$lm(formula)

Arguments

Method desires()

Get or set the desirability values for the response variables.

Usage

facDesign.c$desires(value)

Arguments

Method set.fits()

Set the fits for the response variables in the factorial design object.

Usage

facDesign.c$set.fits(value)

Arguments

Method types()

Get or set the types of designs used in the factorial design object.

Usage

facDesign.c$types(value)

Arguments

Method unit()

Get or set the units for the factors in the factorial design object.

Usage

facDesign.c$unit(value)

Arguments

Method .star()

Get or set the star points in the factorial design object.

Usage

facDesign.c$.star(value)

Arguments

Method .blockGen()

Get or set the block generators in the factorial design object.

Usage

facDesign.c$.blockGen(value)

Arguments

Method .block()

Get or set the blocks in the factorial design object.

Usage

facDesign.c$.block(value)

Arguments

Method .centerCube()

Get or set the center points in the cube portion of the factorial design.

Usage

facDesign.c$.centerCube(value)

Arguments

Method .centerStar()

Get or set the center points in the star portion of the factorial design.

Usage

facDesign.c$.centerStar(value)

Arguments

Method .generators()

Get or set the generators for the factorial design.

Usage

facDesign.c$.generators(value)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

facDesign.c$clone(deep = FALSE)

Arguments

See Also

mixDesign.c, taguchiDesign.c.

Description

Maximum-likelihood fitting of univariate distributions, allowing parameters to be held fixed if desired.

Usage

FitDistr(x, densfun, start, ...)

Arguments

Details

For the Normal, log-Normal, geometric, exponential and Poisson distributions the closed-form MLEs (and exact standard errors) are used, and 'start' should not be supplied.

For all other distributions, direct optimization of the log-likelihood is performed using 'optim'. The estimated standard errors are taken from the observed information matrix, calculated by a numerical approximation. For one-dimensional problems the Nelder-Mead method is used and for multi-dimensional problems the BFGS method, unless arguments named 'lower' or 'upper' are supplied (when 'L-BFGS-B' is used) or 'method' is supplied explicitly.

For the '"t"' named distribution the density is taken to be the location-scale family with location 'm' and scale 's'.

For the following named distributions, reasonable starting values will be computed if 'start' is omitted or only partially specified: '"cauchy"', '"gamma"', '"logistic"', '"negative binomial"' (parametrized by mu and size), '"t"' and '"weibull"'. Note that these starting values may not be good enough if the fit is poor: in particular they are not resistant to outliers unless the fitted distribution is long-tailed.

There are 'print', 'coef', 'vcov' and 'logLik' methods for class '"FitDistr"'.

Value

The function 'FitDistr' returns an object of class 'fitdistr', which is a list containing:

See Also

distribution, Distr, DistrCollection.

Examples

set.seed(123)
x = rgamma(100, shape = 5, rate = 0.1)
FitDistr(x, "gamma")

# Now do this directly with more control.
FitDistr(x, dgamma, list(shape = 1, rate = 0.1), lower = 0.001)

set.seed(123)
x2 = rt(250, df = 9)
FitDistr(x2, "t", df = 9)

# Allow df to vary: not a very good idea!
FitDistr(x2, "t")

# Now do fixed-df fit directly with more control.
mydt = function(x, m, s, df) dt((x-m)/s, df)/s
FitDistr(x2, mydt, list(m = 0, s = 1), df = 9, lower = c(-Inf, 0))

set.seed(123)
x3 = rweibull(100, shape = 4, scale = 100)
FitDistr(x3, "weibull")

Description

Designs displayed are the classic minimum abberation designs. Choosing a design is done by clicking with the mouse into the appropriate field.

Usage

fracChoose()

Value

fracChoose returns an object of class facDesign.c.

See Also

fracDesign, facDesign, rsmChoose, rsmDesign

Examples

fracChoose()

Description

Generates a 2^k-p fractional factorial design.

Usage

fracDesign(
  k = 3,
  p = 0,
  gen = NULL,
  replicates = 1,
  blocks = 1,
  centerCube = 0,
  random.seed = 1234
)

Arguments

Value

The function fracDesign returns an object of class facDesign.c.

See Also

facDesign, fracChoose, rsmDesign, pbDesign, taguchiDesign

Examples

#Example 1
#Returns a 2^4-1 fractional factorial design. Factor D will be aliased with
vp.frac = fracDesign(k = 4, gen = "D=ABC")
#the three-way-interaction ABC (i.e. I = ABCD)
vp.frac$.response(rnorm(2^(4-1)))
# summary of the fractional factorial design
vp.frac$summary()

#Example 2
#Returns a full factorial design with 3 replications per factor combination and 4 center points
vp.rep = fracDesign(k = 3, replicates = 3, centerCube = 4)
#Summary of the replicated fractional factorial design
vp.rep$summary()

Description

Function visualize the linearity of a gage by plotting the single and mean bias in one plot and intercalate them with a straight line. Furthermore the function deliver some characteristic values of linearity studies according to MSA (Measurement System Analysis).

Usage

gageLin(
  object,
  conf.level = 0.95,
  ylim,
  col,
  pch,
  lty = c(1, 2),
  stats = TRUE,
  plot = TRUE
)

Arguments

Value

The function returns an object of class MSALinearity which can be used with e.g. plot or summary.

See Also

cg, gageRR, gageLinDesign, MSALinearity.

Examples

# Results of single runs
A=c(2.7,2.5,2.4,2.5,2.7,2.3,2.5,2.5,2.4,2.4,2.6,2.4)
B=c(5.1,3.9,4.2,5,3.8,3.9,3.9,3.9,3.9,4,4.1,3.8)
C=c(5.8,5.7,5.9,5.9,6,6.1,6,6.1,6.4,6.3,6,6.1)
D=c(7.6,7.7,7.8,7.7,7.8,7.8,7.8,7.7,7.8,7.5,7.6,7.7)
E=c(9.1,9.3,9.5,9.3,9.4,9.5,9.5,9.5,9.6,9.2,9.3,9.4)

# create Design
test=gageLinDesign(ref=c(2,4,6,8,10),n=12)
# create data.frame for results
results=data.frame(rbind(A,B,C,D,E))
# enter results in Design
test$response(results)
test$summary()

# no plot and no return
MSALin=gageLin(test,stats=FALSE,plot=FALSE)

# plot only
plot(MSALin)
MSALin$plot()

# summary
MSALin$summary()

Description

Function generates an object that can be used with the function gageLin.

Usage

gageLinDesign(ref, n = 5)

Arguments

Value

The function returns an object of class MSALinearity.

See Also

MSALinearity, gageLin.

Examples

# results of run A-E
A=c(2.7,2.5,2.4,2.5,2.7,2.3,2.5,2.5,2.4,2.4,2.6,2.4)
B=c(5.1,3.9,4.2,5,3.8,3.9,3.9,3.9,3.9,4,4.1,3.8)
C=c(5.8,5.7,5.9,5.9,6,6.1,6,6.1,6.4,6.3,6,6.1)
D=c(7.6,7.7,7.8,7.7,7.8,7.8,7.8,7.7,7.8,7.5,7.6,7.7)
E=c(9.1,9.3,9.5,9.3,9.4,9.5,9.5,9.5,9.6,9.2,9.3,9.4)

# create Design
test=gageLinDesign(ref=c(2,4,6,8,10),n=12)
# create data.frame for results
results=data.frame(rbind(A,B,C,D,E))
# enter results in Design
test$response(results)

Description

Performs a Gage R&R analysis for an object of class gageRR.c.

Usage

gageRR(
  gdo,
  method = "crossed",
  sigma = 6,
  alpha = 0.25,
  tolerance = NULL,
  dig = 3,
  print = TRUE
)

Arguments

Value

The function gageRR returns an object of class gageRR.c and shows typical Gage Repeatability and Reproducibility Output including Process to Tolerance Ratios and the number of distinctive categories (i.e. ndc) the measurement system is able to discriminate with the tested setting.

See Also

gageRR.c, gageRRDesign, gageLin, cg.

Examples

# Create de gageRR Design
design <- gageRRDesign(Operators = 3, Parts = 10, Measurements = 3,
                       method = "crossed", sigma = 6, randomize = TRUE)
design$response(rnorm(nrow(design$X), mean = 10, sd = 2))

# Results of de Design
result <- gageRR(gdo = design, method = "crossed", sigma = 6, alpha = 0.25)
class(result)
result$plot()

Description

R6 Class for Gage R&R (Repeatability and Reproducibility) Analysis

Public fields

Methods

Public methods

• gageRR.c$new()

• gageRR.c$print()

• gageRR.c$subset()

• gageRR.c$summary()

• gageRR.c$get.response()

• gageRR.c$response()

• gageRR.c$names()

• gageRR.c$as.data.frame()

• gageRR.c$get.tolerance()

• gageRR.c$set.tolerance()

• gageRR.c$get.sigma()

• gageRR.c$set.sigma()

• gageRR.c$plot()

• gageRR.c$errorPlot()

• gageRR.c$whiskersPlot()

• gageRR.c$averagePlot()

• gageRR.c$compPlot()

• gageRR.c$clone()

Method new()

Initialize the fiels of the gageRR object

Usage

gageRR.c$new(
  X,
  ANOVA = NULL,
  RedANOVA = NULL,
  method = NULL,
  Estimates = NULL,
  Varcomp = NULL,
  Sigma = NULL,
  GageName = NULL,
  GageTolerance = NULL,
  DateOfStudy = NULL,
  PersonResponsible = NULL,
  Comments = NULL,
  b = NULL,
  a = NULL,
  y = NULL,
  facNames = NULL,
  numO = NULL,
  numP = NULL,
  numM = NULL
)

Arguments

Method print()

Return the data frame containing the measurement data (X)

Usage

gageRR.c$print()

Method subset()

Return a subset of the data frame that containing the measurement data (X)

Usage

gageRR.c$subset(i, j)

Arguments

Method summary()

Summarize the information of the fields of the gageRR object.

Usage

gageRR.c$summary()

Method get.response()

Get or get the response for a gageRRDesign object.

Usage

gageRR.c$get.response()

Method response()

Set the response for a gageRRDesign object.

Usage

gageRR.c$response(value)

Arguments

Method names()

Methods for function names in Package base.

Usage

gageRR.c$names()

Method as.data.frame()

Methods for function as.data.frame in Package base.

Usage

gageRR.c$as.data.frame()

Method get.tolerance()

Get the tolerance for an object of class gageRR.

Usage

gageRR.c$get.tolerance()

Method set.tolerance()

Set the tolerance for an object of class gageRR.

Usage

gageRR.c$set.tolerance(value)

Arguments

Method get.sigma()

Get the sigma for an object of class gageRR.

Usage

gageRR.c$get.sigma()

Method set.sigma()

Set the sigma for an object of class gageRR.

Usage

gageRR.c$set.sigma(value)

Arguments

Method plot()

This function creates a customized plot using the data from the gageRR.c object.

Usage

gageRR.c$plot(main = NULL, xlab = NULL, ylab = NULL, col, lwd, fun = mean)

Arguments

Examples

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$plot()

Method errorPlot()

The data from an object of class gageRR can be analyzed by running 'Error Charts' of the individual deviations from the accepted rference values. These 'Error Charts' are provided by the function errorPlot.

Usage

gageRR.c$errorPlot(main, xlab, ylab, col, pch, ylim, legend = TRUE)

Arguments

Examples

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$errorPlot()

Method whiskersPlot()

In a Whiskers Chart, the high and low data values and the average (median) by part-by-operator are plotted to provide insight into the consistency between operators, to indicate outliers and to discover part-operator interactions. The Whiskers Chart reminds of boxplots for every part and every operator.

Usage

gageRR.c$whiskersPlot(main, xlab, ylab, col, ylim, legend = TRUE)

Arguments

Examples

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$whiskersPlot()

Method averagePlot()

averagePlot creates all x-y plots of averages by size out of an object of class gageRR. Therfore the averages of the multiple readings by each operator on each part are plotted with the reference value or overall part averages as the index.

Usage

gageRR.c$averagePlot(main, xlab, ylab, col, single = FALSE)

Arguments

Examples

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$averagePlot()

Method compPlot()

compPlot creates comparison x-y plots of an object of class gageRR. The averages of the multiple readings by each operator on each part are plotted against each other with the operators as indices. This plot compares the values obtained by one operator to those of another.

Usage

gageRR.c$compPlot(main, xlab, ylab, col, cex.lab, fun = NULL)

Arguments

Examples

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$compPlot()

Method clone()

The objects of this class are cloneable with this method.

Usage

gageRR.c$clone(deep = FALSE)

Arguments

Examples

#create gageRR-object
gdo <- gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
#vector of responses
y <- c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

#appropriate responses
gdo$response(y)
# perform and gageRR
gdo <- gageRR(gdo)

# Using the plots
gdo$plot()

## ------------------------------------------------
## Method `gageRR.c$plot`
## ------------------------------------------------

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$plot()

## ------------------------------------------------
## Method `gageRR.c$errorPlot`
## ------------------------------------------------

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$errorPlot()

## ------------------------------------------------
## Method `gageRR.c$whiskersPlot`
## ------------------------------------------------

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$whiskersPlot()

## ------------------------------------------------
## Method `gageRR.c$averagePlot`
## ------------------------------------------------

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$averagePlot()

## ------------------------------------------------
## Method `gageRR.c$compPlot`
## ------------------------------------------------

# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
      -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
      1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
      1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
      -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
      -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
      -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)

# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)

gdo$compPlot()