- [Week 5 Gage R&R](#Week 5 Gage R&R)
- [Prompt for GPT](#Prompt for GPT)
- [Response from GPT](#Response from GPT)
- [Fix bug](#Fix bug)
Week 5 Gage R&R
This assignment performs a Gage R&R analysis on the data height_recording.csv collected in class.
Prompt for GPT
Dataset *height_recording.csv* has three columns: `Operator`, `Part`, and `Response`. The `Operator` column contains the name of the operator who took the measurement, the `Part` column contains the part number, and the `Response` column contains the height of the water in the glass. Please write codes by using the SixSigma::ss.rr function in R language to perform a Gage R&R analysis on the dataset `height_recording.csv`. The usage of the function SixSigma::ss.rr is as follows:
Gage R & R (Measurement System Assessment)
Description
Performs Gage R&R analysis for the assessment of the measurement system of a process. Related to the Measure phase of the DMAIC strategy of Six Sigma.
Usage
ss.rr(
var,
part,
appr,
lsl = NA,
usl = NA,
sigma = 6,
tolerance = usl - lsl,
data,
main = "Six Sigma Gage R&R Study",
sub = "",
alphaLim = 0.05,
errorTerm = "interaction",
digits = 4,
method = "crossed",
print_plot = TRUE,
signifstars = FALSE
)
Arguments
var
Measured variable
part
Factor for parts
appr
Factor for appraisers (operators, machines, ...)
lsl
Numeric value of lower specification limit used with USL to calculate Study Variation as %Tolerance
usl
Numeric value of upper specification limit used with LSL to calculate Study Variation as %Tolerance
sigma
Numeric value for number of std deviations to use in calculating Study Variation
tolerance
Numeric value for the tolerance
data
Data frame containing the variables
main
Main title for the graphic output
sub
Subtitle for the graphic output (recommended the name of the project)
alphaLim
Limit to take into account interaction
errorTerm
Which term of the model should be used as error term (for the model with interation)
digits
Number of decimal digits for output
method
Character to specify the type of analysis to perform, "crossed" (default) or "nested"
print_plot
if TRUE (default) the plots are printed. Change to FALSE to avoid printing plots.
signifstars
if FALSE (default) the significance stars are ommitted. Change to TRUE to allow printing stars.
Details
Performs an R&R study for the measured variable, taking into account part and appraiser factors. It outputs the sources of Variability, and six graphs: bar chart with the sources of Variability, plots by appraiser, part and interaction and x-bar and R control charts.
Value
Analysis of Variance Table/s. Variance composition and %Study Var. Graphics.
anovaTable
The ANOVA table of the model
anovaRed
The ANOVA table of the reduced model (without interaction, only if interaction not significant)
varComp
A matrix with the contribution of each component to the total variation
studyVar
A matrix with the contribution to the study variation
ncat
Number of distinct categories
Note
The F test for the main effects in the ANOVA table is usually made taken the operator/appraisal interaction as the error term (repeated measures model), thereby computing F as $MS_factor/MS_interaction$, e.g. in appendix A of AIAG MSA manual, in Montgomery (2009) and by statistical software such as Minitab. However, in the example provided in page 127 of the AIAG MSA Manual, the F test is performed as $MS_factor/MS_equipment$, i.e., repeatability. Thus, since version 0.9-3 of the SixSigma package, a new argument errorTerm controls which term should be used as error Term, one of "interaction", "repeatability".
Argument alphaLim is used as upper limit to use the full model, i.e., with interaction. Above this value for the interaction effect, the ANOVA table without the interaction effect is also obtained, and the variance components are computed pooling the interaction term with the repeatibility.
Tolerance can be calculaten from usl and lsl values or specified by hand.
The type of analysis to perform can be specified with the parameter method, "crossed" or "nested". Be sure to select the correct one and to have the data prepare for such type of analysis. If you don't know wich one is for you check it before. It is really important to perform the correct one. Otherwise results have no sense.
Response from GPT
r
# Load necessary libraries
library(SixSigma)
library(dplyr)
# Read the dataset
height_data <- read.csv("height_recording.csv")
# Perform Gage R&R analysis
gage_rr_result <- ss.rr(
var = height_data$Response, # Measured variable
part = height_data$Part, # Factor for parts
appr = height_data$Operator, # Factor for appraisers (operators)
lsl = NA, # Lower specification limit (if applicable)
usl = NA, # Upper specification limit (if applicable)
sigma = 6, # Number of standard deviations
tolerance = NA, # Tolerance can be calculated from usl and lsl
data = height_data, # Data frame containing the variables
main = "Six Sigma Gage R&R Study", # Main title for the graphic output
sub = "Height Measurement Study", # Subtitle
alphaLim = 0.05, # Significance level
errorTerm = "interaction", # Error term
digits = 4, # Number of decimal digits for output
method = "crossed", # Type of analysis
print_plot = TRUE, # Print plots
signifstars = FALSE # Significance stars
)
# View the results
print(gage_rr_result)Fix bug
Run the codes and there's an error:
错误于if (!(var %in% names(data))) {: the condition has length > 1
Paste the error message to GPT-4o-Mini and then get the response:
r
# Load necessary libraries
library(SixSigma)
library(dplyr)
# Read the dataset
height_data <- read.csv("height_recording.csv")
# Ensure the column names are correct
str(height_data) # Check the structure of the data
# Perform Gage R&R analysis
gage_rr_result <- ss.rr(
var = height_data$Response, # Measured variable (ensure this is a single column)
part = height_data$Part, # Factor for parts
appr = height_data$Operator, # Factor for appraisers (operators)
lsl = NA, # Lower specification limit (if applicable)
usl = NA, # Upper specification limit (if applicable)
sigma = 6, # Number of standard deviations
tolerance = NA, # Tolerance can be calculated from usl and lsl
data = height_data, # Data frame containing the variables
main = "Six Sigma Gage R&R Study", # Main title for the graphic output
sub = "Height Measurement Study", # Subtitle
alphaLim = 0.05, # Significance level
errorTerm = "interaction", # Error term
digits = 4, # Number of decimal digits for output
method = "crossed", # Type of analysis
print_plot = TRUE, # Print plots
signifstars = FALSE # Significance stars
)
# View the results
print(gage_rr_result)GPT do not provide the correct codes at this time. I will try to fix the error by myself. By comparing the codes between MSA.R provided by TA and the response from GPT, I delete height_data$ in the var, part and appr argument, then the error is fixed. The correct codes are as follows:
r
# Load necessary libraries
library(SixSigma)
library(dplyr)
# Read the dataset
height_data <- read.csv("height_recording.csv")
# Ensure the column names are correct
str(height_data) # Check the structure of the data
# Perform Gage R&R analysis
gage_rr_result <- ss.rr(
var = Response, # Measured variable (ensure this is a single column)
part = Part, # Factor for parts
appr = Operator, # Factor for appraisers (operators)
lsl = NA, # Lower specification limit (if applicable)
usl = NA, # Upper specification limit (if applicable)
sigma = 6, # Number of standard deviations
tolerance = NA, # Tolerance can be calculated from usl and lsl
data = height_data, # Data frame containing the variables
main = "Six Sigma Gage R&R Study", # Main title for the graphic output
sub = "Height Measurement Study", # Subtitle
alphaLim = 0.05, # Significance level
errorTerm = "interaction", # Error term
digits = 4, # Number of decimal digits for output
method = "crossed", # Type of analysis
print_plot = TRUE, # Print plots
signifstars = FALSE # Significance stars
)
# View the results
print(gage_rr_result)