diff --git a/R/doeAnalysis.R b/R/doeAnalysis.R index bd47f4dd..10f30fdb 100644 --- a/R/doeAnalysis.R +++ b/R/doeAnalysis.R @@ -23,10 +23,11 @@ # avoid e-notation for small-to-moderate numbers mag <- floor(log10(absVal)) sigDigits <- max(digits, mag + 1 + digits) - trimws(formatC(val, digits = sigDigits, format = "g", drop0trailing = TRUE)) + formatted <- trimws(formatC(val, digits = sigDigits, format = "g", drop0trailing = TRUE)) } else { - trimws(formatC(val, digits = digits, format = "g", drop0trailing = TRUE)) + formatted <- trimws(formatC(val, digits = digits, format = "g", drop0trailing = TRUE)) } + sub("^-", "\u2013", formatted) }, character(1)) } @@ -1485,7 +1486,7 @@ get_levels <- function(var, num_levels, dataset) { return() } - tb <- createJaspTable(gettext("ANOVA")) + tb <- createJaspTable(gettext("Analysis of Variance")) tb$addColumnInfo(name = "terms", title = gettext("Source"), type = "string") tb$addColumnInfo(name = "adjss", title = gettext("Sum of squares"), type = "number") tb$addColumnInfo(name = "df", title = gettext("df"), type = "integer") @@ -1916,6 +1917,16 @@ get_levels <- function(var, num_levels, dataset) { plotTitle <- gettextf("%1$s of %2$s vs %3$s", plotTypeString, dep, variablePairString) plot <- createJaspPlot(title = plotTitle, width = 500, height = 500) result <- jaspResults[[dep]][["doeResult"]]$object[["regression"]] + termLabels <- attr(stats::terms(result[["object"]]), "term.labels") + missingVars <- variablePair[!sapply(variablePair, function(var) any(grepl(var, termLabels, fixed = TRUE)))] + if (length(missingVars) > 0) { + plot$setError(gettextf( + "Could not plot because the following selected variable(s) were not included in the final model: %1$s. They might have been removed during model selection.", + paste(missingVars, collapse = ", ") + )) + jaspResults[[dep]][["contourSurfacePlot"]][[plotTitle]] <- plot + next + } if (plotType == "contourPlot") { plot$plotObject <- function(){.doeContourSurfacePlotObject(result, options, dep, variablePair, type = "contour")} } else if (plotType == "surfacePlot") { diff --git a/R/msaGaugeRR.R b/R/msaGaugeRR.R index 3452c86a..72aa5c6d 100644 --- a/R/msaGaugeRR.R +++ b/R/msaGaugeRR.R @@ -250,7 +250,7 @@ msaGaugeRR <- function(jaspResults, dataset, options, ...) { # Gauge r&R ANOVA Table if (options[["anova"]]) { if (is.null(jaspResults[["gaugeANOVA"]])) { - jaspResults[["gaugeANOVA"]] <- createJaspContainer(gettext("Gauge r&R ANOVA table")) + jaspResults[["gaugeANOVA"]] <- createJaspContainer(gettext("Gauge r&R Analysis of Variance table")) jaspResults[["gaugeANOVA"]]$dependOn(c("processVariationReference", "historicalSdValue", "report")) jaspResults[["gaugeANOVA"]]$position <- 1 } @@ -334,7 +334,11 @@ msaGaugeRR <- function(jaspResults, dataset, options, ...) { if(options[["trafficLightChart"]] & is.null(jaspResults[["trafficPlot"]] )) { jaspResults[["trafficPlot"]] <- createJaspContainer(gettext("Traffic light chart")) jaspResults[["trafficPlot"]]$position <- 9 - jaspResults[["trafficPlot"]]$dependOn(c("trafficLightChart", "toleranceValue", "tolerance", "gaugeRRmethod", "processVariationReference", "historicalSdValue", "report")) + jaspResults[["trafficPlot"]]$dependOn(c("trafficLightChart", "toleranceValue", "tolerance", "gaugeRRmethod", + "processVariationReference", "historicalSdValue", "studyVarianceMultiplierType", + "studyVarianceMultiplierValue", "measurementLongFormat", "operatorLongFormat", + "partLongFormat", "measurementsWideFormat", "operatorWideFormat", + "partWideFormat", "dataFormat", "type3", "report")) trafficContainer <- jaspResults[["trafficPlot"]] valuesVec <- .gaugeANOVA(dataset = dataset, measurements = measurements, parts = parts, operators = operators, options = options, ready = ready, returnTrafficValues = TRUE, Type3 = Type3) @@ -348,11 +352,11 @@ msaGaugeRR <- function(jaspResults, dataset, options, ...) { .gaugeANOVA <- function(dataset, measurements, parts, operators, options, ready, returnPlotOnly = FALSE, returnTrafficValues = FALSE, gaugeEvaluationDfOnly = FALSE, Type3 = FALSE) { - anovaTables <- createJaspContainer(gettext("Gauge r&R study - crossed ANOVA")) + anovaTables <- createJaspContainer(gettext("Gauge r&R study - crossed Analysis of Variance")) anovaTables$dependOn(c("anova", "gaugeRRmethod", "report")) anovaTables$position <- 1 - anovaTable1 <- createJaspTable(title = ifelse(Type3, gettext("One-way ANOVA table"), gettext("Two-way ANOVA table with interaction"))) + anovaTable1 <- createJaspTable(title = ifelse(Type3, gettext("One-way Analysis of Variance table"), gettext("Two-way Analysis of Variance table with interaction"))) anovaTable1$addColumnInfo(title = gettext("Source"), name = "source", type = "string" ) anovaTable1$addColumnInfo(title = gettext("df"), name = "Df", type = "integer") anovaTable1$addColumnInfo(title = gettext("Sum of squares"), name = "Sum Sq", type = "number") @@ -569,7 +573,7 @@ msaGaugeRR <- function(jaspResults, dataset, options, ...) { } else { - anovaTable2 <- createJaspTable(title = gettext("Two-way ANOVA table without interaction")) + anovaTable2 <- createJaspTable(title = gettext("Two-way Analysis of Variance table without interaction")) anovaTable2$addColumnInfo(title = gettext("Source"), name = "source", type = "string" ) anovaTable2$addColumnInfo(title = gettext("df"), name = "Df", type = "integer") anovaTable2$addColumnInfo(title = gettext("Sum of squares"), name = "Sum Sq", type = "number") @@ -904,7 +908,7 @@ msaGaugeRR <- function(jaspResults, dataset, options, ...) { } .gaugeVarCompGraph <- function(percentContributionValues, studyVariationValues, percentToleranceValues, Type3 = FALSE) { - sources <- gettext(c('Gauge r&R', 'Repeat', 'Reprod', 'Part-to-part')) + sources <- gettext(c("Gauge r&R", "Repeatability", "Reproducibility", "Part-to-part")) if (!all(is.na(percentToleranceValues))) { references <- gettextf(c('%% Contribution', '%% Study variation', '%% Tolerance')) values <- c(percentContributionValues, studyVariationValues, percentToleranceValues) @@ -927,7 +931,8 @@ msaGaugeRR <- function(jaspResults, dataset, options, ...) { position="dodge", stat = "identity") + jaspGraphs::themeJaspRaw() + jaspGraphs::geom_rangeframe() + - ggplot2::theme(legend.position = 'right', legend.title = ggplot2::element_blank()) + + ggplot2::theme(legend.position = "right", legend.title = ggplot2::element_blank(), + plot.margin = ggplot2::margin(5.5, 30, 5.5, 5.5, "pt")) + ggplot2::xlab(NULL) + ggplot2::scale_y_continuous(name = "Percent", breaks = yBreaks, limits = range(c(yBreaks, plotframe$value))) return(p) diff --git a/R/msaGaugeRRnonrep.R b/R/msaGaugeRRnonrep.R index a29508e3..2c7b62f6 100644 --- a/R/msaGaugeRRnonrep.R +++ b/R/msaGaugeRRnonrep.R @@ -267,14 +267,19 @@ msaGaugeRRnonrep <- function(jaspResults, dataset, options, ...) { jaspResults[["trafficLightChart"]] <- .trafficplot(StudyVar = percentMSVar, ToleranceUsed = options[["tolerance"]], ToleranceVar = percentMSTolerance, options = options, ready = ready) - jaspResults[["trafficLightChart"]]$dependOn(c("trafficLightChart", "report")) + jaspResults[["trafficLightChart"]]$dependOn(c("trafficLightChart", "report", "processVariationReference", + "historicalSdValue", "tolerance", "toleranceValue", + "studyVarianceMultiplierType", "studyVarianceMultiplierValue", + "measurementLongFormat", "operatorLongFormat", "partLongFormat", + "measurementsWideFormat", "operatorWideFormat", "partWideFormat", + "dataFormat")) } } } .gaugeRRNonRep <- function(dataset, measurements, parts, operators, options, ready, plotOnly = FALSE, trafficPlotValuesOnly = FALSE, gaugeEvaluationDfOnly = FALSE) { - gaugeRRNonRepTables <- createJaspContainer(gettext("Gauge r&R study - nested ANOVA")) + gaugeRRNonRepTables <- createJaspContainer(gettext("Gauge r&R study - nested Analysis of Variance")) gaugeRRNonRepTables$position <- 1 gaugeRRNonRepTable1 <- createJaspTable(title = gettext("Gauge r&R (nested)")) diff --git a/R/msaTestRetest.R b/R/msaTestRetest.R index 06c8877c..7371f657 100644 --- a/R/msaTestRetest.R +++ b/R/msaTestRetest.R @@ -107,7 +107,13 @@ msaTestRetest <- function(jaspResults, dataset, options, ...) { if(options[["trafficLightChart"]] && is.null(jaspResults[["trafficPlot"]] )) { jaspResults[["trafficPlot"]] <- createJaspContainer(gettext("Traffic light chart")) jaspResults[["trafficPlot"]]$position <- 4 - jaspResults[["trafficPlot"]]$dependOn(c("trafficLightChart", "manualProcessSdValue", "manualProcessSd", "toleranceValue", "tolerance")) + jaspResults[["trafficPlot"]]$dependOn(c( + "trafficLightChart", "manualProcessSdValue", "manualProcessSd", + "toleranceValue", "tolerance", + "measurementLongFormat", "measurementsWideFormat", + "partLongFormat", "partWideFormat", + "operator", "dataFormat" + )) TrafficContainer <- jaspResults[["trafficPlot"]] valuesVec <- .rAndRtableRange(dataset = dataset, measurements = measurements, parts = parts, operators = operators, options = options, jaspResults, ready = ready, GRRpercent = TRUE) diff --git a/inst/help/attributesCharts.md b/inst/help/attributesCharts.md index c7e711b7..7012ff81 100644 --- a/inst/help/attributesCharts.md +++ b/inst/help/attributesCharts.md @@ -60,8 +60,6 @@ In the case that the data of the individual and moving Range chart (X-mR chart) - Duncan, A.J. (1986), *Quality control and industrial statistics*, Richard D. Irwin, Inc. - Automotive Industry Action Group, *Statistical Process Control – Reference Manual* (July 2005, 2nd Edition) -- SKF Quality Techniques, Klerx, R., Dodson, B., and Dumont, D., QT 1 – *Process capability studies* (PUB GQ/P9 10347/1 EN – December 2021) -- SKF Quality Techniques, Dodson, B., Lynch, D., Weidenbacher, M., and Klerx, R., QT 2 – *Statistical process control* (PUB GQS/P9 18343 EN – April 2019) - International Organization for Standardization, *Control charts – Part 2: Shewhart control charts*, ISO 7870-2:2023 (E) diff --git a/inst/help/doeAnalysis.md b/inst/help/doeAnalysis.md index fbb0edb6..134a443c 100644 --- a/inst/help/doeAnalysis.md +++ b/inst/help/doeAnalysis.md @@ -18,27 +18,27 @@ A response surface design is an experimental framework used to optimize and unde ### Assignment box - Response: The measured outcome variable of the process under investigation. -- Discrete predictors: The columns corresponding to the discrete predictors in the design. -- Continuous predictors: The columns corresponding to the continuous predictors in the design. -- Covariates: The columns corresponding to the covariates in the design. The difference between a covariate and a continuous predictor is that the covariate will not be analysed for interaction effects and excluded from effect plots. -- Blocks: The columns corresponding to the blocks in the design. The difference between a block variable and a discrete predictor is that the blocks will not be analysed for interaction effects and excluded from effect plots. +- Discrete factors: The columns corresponding to the discrete factors in the design. +- Continuous factors: The columns corresponding to the continuous factors in the design. +- Covariates: The columns corresponding to the covariates in the design. The difference between a covariate and a continuous factor is that the covariate will not be analysed for interaction effects and excluded from effect plots. +- Blocks: The columns corresponding to the blocks in the design. The difference between a block variable and a discrete factor is that the blocks will not be analysed for interaction effects and excluded from effect plots. ## Analysis options ------- -### Predictor levels -For the ordering of contrasts and to identify the alpha points in a response surface design, the low and high levels of all predictors need to be specified. There are two options to handle this. +### Factor levels +For the ordering of contrasts and to identify the alpha points in a response surface design, the low and high levels of all factors need to be specified. There are two options to handle this. -- Automatically detect low/high: Attempts to automatically detect the low and high levels by taking the minimum and maximum for continuous predictors, and by ordering discrete predictors alphabetically. For response surface designs, this might recognize alpha values as low/high levels. In this case, manual specification is needed. +- Automatically detect low/high: Attempts to automatically detect the low and high levels by taking the minimum and maximum for continuous factors, and by ordering discrete factors alphabetically. For response surface designs, this might recognize alpha values as low/high levels. In this case, manual specification is needed. -- Manually specify low/high: Shows all predictors in the analysis to allow for manual specification of the low and high levels. +- Manually specify low/high: Shows all factors in the analysis to allow for manual specification of the low and high levels. ### Other analysis options -- Use alias names: Check to assign alias names to predictors. Useful when predictors have long names. +- Use alias names: Check to assign alias names to factors. Useful when factors have long names. - Show regression equation: Check to display the regression coefficients as an equation predicting the response. -- Display result in coded units: Check to display the results in coded units. This means, all predictor levels are standardized between -1 and 1. -- Show optimal response: Check to display the predictor levels yielding the optimal response. +- Display result in coded units: Check to display the results in coded units. This means, all factor levels are standardized between -1 and 1. +- Show optimal response: Check to display the factor levels yielding the optimal response. ## Model options ------- diff --git a/inst/help/doeResponseSurfaceMethodology.md b/inst/help/doeResponseSurfaceMethodology.md index 08169dd8..b57403c9 100644 --- a/inst/help/doeResponseSurfaceMethodology.md +++ b/inst/help/doeResponseSurfaceMethodology.md @@ -100,4 +100,4 @@ The Box-Behnken set-up consists of twelve "edge" points (shown as red dots) all ------- - Dodson, L., Dodson, M., and Klerx, R. (2010), *A study of the hierarchical ordering principle and the effect heredity principle in factorial experimental designs*, 10th Annual Transaction of ENBIS (12 16 September 2010, Antwerp). - Box, G.E.P., and Wilson, K.B. (1951), On the experimental attainment of optimum conditions. *Journal of the Royal Statistical Society 13*(1), 1-45. -- Dodson, B., Weidenbacher, M., Lynch D., and Klerx, R., QT 9 - Design and analysis of experiments. *SKF Quality Techniques*. (PUB GQS/P9 16083 EN - August 2015). +- Automotive Industry Action Group (July 2005), *Statistical process control (SPC) – Reference manual*, AIAG. diff --git a/inst/help/msaAttribute.md b/inst/help/msaAttribute.md index b0d167bd..4d231f0c 100644 --- a/inst/help/msaAttribute.md +++ b/inst/help/msaAttribute.md @@ -38,7 +38,6 @@ Data can be in the form of all observations in one column ("Single column") or a ## References ------- - Duncan, A.J. (1986), Quality control and industrial statistics, Richard D. Irwin, Inc., and Automotive Industry Action Group (July 2005), Statistical process control (SPC) – Reference manual, AIAG. -- Dodson, B., Lynch, D., Weidenbacher, M., & Klerx, R. (2009).*Statistical process control handbook*. SKF group. ## R Packages ------- - jaspGraphs diff --git a/inst/help/msaGaugeLinearity.md b/inst/help/msaGaugeLinearity.md index 18007e91..5202c0ee 100644 --- a/inst/help/msaGaugeLinearity.md +++ b/inst/help/msaGaugeLinearity.md @@ -31,7 +31,6 @@ bias (matching between measurements and reference values) of a system. ## References ------- - Duncan, A.J. (1986), Quality control and industrial statistics, Richard D. Irwin, Inc., and Automotive Industry Action Group (July 2005), Statistical process control (SPC) – Reference manual, AIAG. -- Dodson, B., Lynch, D., Weidenbacher, M., & Klerx, R. (2009).*Statistical process control handbook*. SKF group. ## R Packages ------- diff --git a/inst/help/msaGaugeRR.md b/inst/help/msaGaugeRR.md index 4e67c16f..18629bca 100644 --- a/inst/help/msaGaugeRR.md +++ b/inst/help/msaGaugeRR.md @@ -17,13 +17,13 @@ Data can be in the form of all observations in one column ("Single column") or a ### Gauge r&R Method The method used in the analysis. -- ANOVA method: analysis is based on analysis of variance (ANOVA). +- Analysis of Variance method: analysis is based on analysis of variance (ANOVA). -### Options (ANOVA method) +### Options (Analysis of Variance method) #### Analysis options - Std.Deviation reference: either a historically known standard deviation (Historical process std.Deviation) or estimated from the data (Study std.Deviation). - Tolerance: include a value for tolerance. -- r&R table ANOVA method: options for the ANOVA table. +- r&R table Analysis of Variance method: options for the Analysis of Variance table. - Alpha interaction removal: value for alpha level. - Study Var. multiplier type: multiplier based on either Std.Deviation or Percent. - Study Var. multiplier value: value for the multiplier. @@ -44,14 +44,13 @@ The method used in the analysis. ## Output ------- -- ANOVA Table: Two-way anova tables with and without interactions for the input variables, repeatability, reproducibility, and total Gauge r&R. +- Analysis of Variance table: Two-way analysis of variance tables with and without interactions for the input variables, repeatability, reproducibility, and total Gauge r&R. - Gauge r&R Variance Components: variance and contribution in percentage of the input variables, repeatability, reproducibility, and total Gauge r&R. - Gauge Evaluation: the standard deviations, study variations, and percent of study variation and tolerance for input variables, repeatability, reproducibility, and total Gauge r&R. ## References ------- - Duncan, A.J. (1986), Quality control and industrial statistics, Richard D. Irwin, Inc., and Automotive Industry Action Group (July 2005), Statistical process control (SPC) – Reference manual, AIAG. -- Dodson, B., Lynch, D., Weidenbacher, M., & Klerx, R. (2009).*Statistical process control handbook*. SKF group. ## R Packages ------- diff --git a/inst/help/msaGaugeRRnonrep.md b/inst/help/msaGaugeRRnonrep.md index 8e9bf330..a4e43216 100644 --- a/inst/help/msaGaugeRRnonrep.md +++ b/inst/help/msaGaugeRRnonrep.md @@ -37,7 +37,7 @@ Data can be in the form of all observations in one column ("Single column") or a ## Output ------- -- Gauge r&R (Nested): Nested ANOVA table for the input variables, repeatability, and total Gauge r&R. +- Gauge r&R (Nested): Nested Analysis of Variance table for the input variables, repeatability, reproducibility, and total Gauge r&R. - Gauge r&R Variance Components: variance and contribution in percentage of the input variables, repeatability, reproducibility, and total Gauge r&R. - Gauge Evaluation: the standard deviations, study variations, and percent of study variation and tolerance for input variables, repeatability, reproducibility, and total Gauge r&R. @@ -55,7 +55,6 @@ If you are looking at the %Contribution column, the corresponding standards are: ## References ------- - Duncan, A.J. (1986), Quality control and industrial statistics, Richard D. Irwin, Inc., and Automotive Industry Action Group (July 2005), Statistical process control (SPC) – Reference manual, AIAG. -- Dodson, B., Lynch, D., Weidenbacher, M., & Klerx, R. (2009).*Statistical process control handbook*. SKF group. ## R Packages ------- diff --git a/inst/help/msaTestRetest.md b/inst/help/msaTestRetest.md index a64ebb4b..6632fb27 100644 --- a/inst/help/msaTestRetest.md +++ b/inst/help/msaTestRetest.md @@ -38,7 +38,6 @@ Data can be in the form of all observations in one column ("Single column") or a ## References ------- - Duncan, A.J. (1986), Quality control and industrial statistics, Richard D. Irwin, Inc., and Automotive Industry Action Group (July 2005), Statistical process control (SPC) – Reference manual, AIAG. -- Dodson, B., Lynch, D., Weidenbacher, M., & Klerx, R. (2009).*Statistical process control handbook*. SKF group. ## R Packages ------- diff --git a/inst/help/msaType1Gauge.md b/inst/help/msaType1Gauge.md index 9f255d4e..d0f01e79 100644 --- a/inst/help/msaType1Gauge.md +++ b/inst/help/msaType1Gauge.md @@ -39,7 +39,6 @@ Type 1 Gauge is performed before a Gauge r & R study to determine the measuremen ## References ------- - Duncan, A.J. (1986), Quality control and industrial statistics, Richard D. Irwin, Inc., and Automotive Industry Action Group (July 2005), Statistical process control (SPC) – Reference manual, AIAG. -- Dodson, B., Lynch, D., Weidenbacher, M., & Klerx, R. (2009).*Statistical process control handbook*. SKF group. ## R Packages ------- diff --git a/inst/help/processCapabilityStudies.md b/inst/help/processCapabilityStudies.md index aa4d0f9b..f1d416d5 100644 --- a/inst/help/processCapabilityStudies.md +++ b/inst/help/processCapabilityStudies.md @@ -126,10 +126,8 @@ The size of the subgroups is relevant for the calculation of the process varianc ## References ------- 1. Automotive Industry Action Group, *Statistical Process Control - Reference Manual* (July 2005, 2nd Edition) -2. SKF Quality Techniques, Klerx, R., Dodson, B., and Dumont, D., QT 1 - *Process capability studies*. (PUB GQ/P9 10347/1 EN - December 2021) -3. SKF Quality Techniques, Dodson, B., Lynch, D., Weidenbacher, M., and Klerx, R. (), *QT 2 - Statistical process control*, (PUB GQS/P9 18343 EN - April 2019) -4. Yeo, I. K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87(4), 954-959. -5. Chou, Y. M., Polansky, A. M., & Mason, R. L. (1998). Transforming non-normal data to normality in statistical process control. Journal of Quality Technology, 30(2), 133-141. +2. Yeo, I. K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87(4), 954-959. +3. Chou, Y. M., Polansky, A. M., & Mason, R. L. (1998). Transforming non-normal data to normality in statistical process control. Journal of Quality Technology, 30(2), 133-141. ## R Packages diff --git a/inst/help/timeWeightedCharts.md b/inst/help/timeWeightedCharts.md index d8837193..35a03cdd 100644 --- a/inst/help/timeWeightedCharts.md +++ b/inst/help/timeWeightedCharts.md @@ -71,7 +71,7 @@ Graphical display for the tabular CUSUM. These charts are sometimes called CUSUM -------- - Page, E.S. (1954), *Continuous inspection schemes*, Biometrika, Vol. 41, No. 1-2, pp. 100-115 - Roberts, S.W. (1959), *Control chart tests based on geometric moving averages*, Technometrics, Vol. 1, No. 3, pp. 239-250 -- SKF Quality Techniques, Dodson, B., Lynch, D., Weidenbacher, M., and Klerx, R., *QT 2 – Statistical process control* (PUB GQS/P9 18343 EN – April 2019) +- Automotive Industry Action Group (July 2005), *Statistical process control (SPC) – Reference manual*, AIAG. - International Organization for Standardization, *Control charts – Part 4: Cumulative sum charts*, ISO 7870-4:2021 (E) - Montgomery D. C. (2013), *Introduction to statistical quality control (7th Ed.)*, John Wiley & Sons, Inc , Hoboken (NJ) diff --git a/inst/help/variablesChartsIndividuals.md b/inst/help/variablesChartsIndividuals.md index 8307abda..ef6f9b75 100644 --- a/inst/help/variablesChartsIndividuals.md +++ b/inst/help/variablesChartsIndividuals.md @@ -87,8 +87,6 @@ Only Test 1, Test 2, Test 3 and Test 8 will be applied to the moving range chart ------- - Duncan, A.J. (1986), *Quality control and industrial statistics*, Richard D. Irwin, Inc. - Automotive Industry Action Group, *Statistical Process Control – Reference Manual* (July 2005, 2nd Edition) -- SKF Quality Techniques, Klerx, R., Dodson, B., and Dumont, D., *QT 1 – Process capability studies*, (PUB GQ/P9 10347/1 EN – December 2021) -- SKF Quality Techniques, Dodson, B., Lynch, D., Weidenbacher, M., & and Klerx, R. (2009).), *QT 2 – Statistical process control*, (PUB GQS/P9 18343 EN – April 2019) ## R Packages diff --git a/inst/help/variablesChartsSubgroups.md b/inst/help/variablesChartsSubgroups.md index 42c38211..bbc65a4b 100644 --- a/inst/help/variablesChartsSubgroups.md +++ b/inst/help/variablesChartsSubgroups.md @@ -82,7 +82,6 @@ Select manually which tests you want to apply and modify them as desired: ## References ------- - Duncan, A.J. (1986), Quality control and industrial statistics, Richard D. Irwin, Inc., and Automotive Industry Action Group (July 2005), Statistical process control (SPC) - Reference manual, AIAG -- Dodson, B., Lynch, D., Weidenbacher, M., & Klerx, R. (2009).*Statistical process control handbook*. SKF group. ## R Packages diff --git a/inst/qml/doeFactorial.qml b/inst/qml/doeFactorial.qml index d8fb7615..1250440a 100644 --- a/inst/qml/doeFactorial.qml +++ b/inst/qml/doeFactorial.qml @@ -478,6 +478,7 @@ Form max: 8 } + /* IntegerField { name: "repetitions" @@ -486,6 +487,7 @@ Form min: 0 max: 10 } + */ } } } diff --git a/inst/qml/msaGaugeRR.qml b/inst/qml/msaGaugeRR.qml index 7ff58596..83c1bf7b 100644 --- a/inst/qml/msaGaugeRR.qml +++ b/inst/qml/msaGaugeRR.qml @@ -178,7 +178,7 @@ Form CheckBox { name: "anova" - label: qsTr("r&R ANOVA table") + label: qsTr("r&R Analysis of Variance table") checked: true DropDown @@ -251,7 +251,7 @@ Form CheckBox { name: "xBarChart" - label: qsTr("Average chart bys operator") + label: qsTr("Average chart by operator") enabled: !type3.checked } diff --git a/inst/qml/msaGaugeRRnonrep.qml b/inst/qml/msaGaugeRRnonrep.qml index 45c78414..3b4b4f26 100644 --- a/inst/qml/msaGaugeRRnonrep.qml +++ b/inst/qml/msaGaugeRRnonrep.qml @@ -149,7 +149,7 @@ Form CheckBox { name: "anova" - label: qsTr("r&R ANOVA table") + label: qsTr("r&R Analysis of Variance table") checked: true DropDown diff --git a/tests/testthat/_snaps/msaGaugeRR/1-components-of-variation.svg b/tests/testthat/_snaps/msaGaugeRR/1-components-of-variation.svg index 29270d5a..b0591079 100644 --- a/tests/testthat/_snaps/msaGaugeRR/1-components-of-variation.svg +++ b/tests/testthat/_snaps/msaGaugeRR/1-components-of-variation.svg @@ -1,5 +1,6 @@ - + + - - + + - - + + - - + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + +0 +50 +100 +150 +200 + + + + + + + + + + +Gauge r&R +Repeatability +Reproducibility +Part-to-part +Percent + + + + + + + + +% Contribution +% Study variation +% Tolerance - - -0 -50 -100 -150 -200 - - - - - - - - - - -Gauge r&R -Repeat -Reprod -Part-to-part -Percent - - - - - - - - -% Contribution -% Study variation -% Tolerance -1_components-of-variation diff --git a/tests/testthat/_snaps/msaGaugeRR/2-components-of-variation.svg b/tests/testthat/_snaps/msaGaugeRR/2-components-of-variation.svg index f3db7f31..d599319a 100644 --- a/tests/testthat/_snaps/msaGaugeRR/2-components-of-variation.svg +++ b/tests/testthat/_snaps/msaGaugeRR/2-components-of-variation.svg @@ -1,5 +1,6 @@ - + + - - + + - - + + - - + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + +0 +50 +100 +150 +200 + + + + + + + + + + +Gauge r&R +Repeatability +Reproducibility +Part-to-part +Percent + + + + + + + + +% Contribution +% Study variation +% Tolerance - - -0 -50 -100 -150 -200 - - - - - - - - - - -Gauge r&R -Repeat -Reprod -Part-to-part -Percent - - - - - - - - -% Contribution -% Study variation -% Tolerance -2_components-of-variation diff --git a/tests/testthat/_snaps/msaGaugeRR/4-components-of-variation.svg b/tests/testthat/_snaps/msaGaugeRR/4-components-of-variation.svg index bdafea19..13e57e66 100644 --- a/tests/testthat/_snaps/msaGaugeRR/4-components-of-variation.svg +++ b/tests/testthat/_snaps/msaGaugeRR/4-components-of-variation.svg @@ -1,5 +1,6 @@ - + + - - + + - - + + - - + + - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + +0 +50 +100 +150 +200 + + + + + + + + + + +Gauge r&R +Repeatability +Reproducibility +Part-to-part +Percent + + + + + + + + +% Contribution +% Study variation +% Tolerance - - -0 -50 -100 -150 -200 - - - - - - - - - -Gauge r&R -Repeat -Part-to-part -Percent - - - - - - - - -% Contribution -% Study variation -% Tolerance -4_components-of-variation diff --git a/tests/testthat/_snaps/msaGaugeRR/5-gauge-r-r-report-subplot-2.svg b/tests/testthat/_snaps/msaGaugeRR/5-gauge-r-r-report-subplot-2.svg index f0714d74..7afddb38 100644 --- a/tests/testthat/_snaps/msaGaugeRR/5-gauge-r-r-report-subplot-2.svg +++ b/tests/testthat/_snaps/msaGaugeRR/5-gauge-r-r-report-subplot-2.svg @@ -1,5 +1,6 @@ - + + - - + + - - + + - - + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + +0 +50 +100 +150 +200 + + + + + + + + + + +Gauge r&R +Repeatability +Reproducibility +Part-to-part +Percent + + + + + + + + +% Contribution +% Study variation +% Tolerance - - -0 -50 -100 -150 -200 - - - - - - - - - - -Gauge r&R -Repeat -Reprod -Part-to-part -Percent - - - - - - - - -% Contribution -% Study variation -% Tolerance -5_gauge-r-r-report-subplot-2 diff --git a/tests/testthat/_snaps/msaGaugeRR/gauge-r-r-report-subplot-2.svg b/tests/testthat/_snaps/msaGaugeRR/gauge-r-r-report-subplot-2.svg index 19e40b83..f1013eb8 100644 --- a/tests/testthat/_snaps/msaGaugeRR/gauge-r-r-report-subplot-2.svg +++ b/tests/testthat/_snaps/msaGaugeRR/gauge-r-r-report-subplot-2.svg @@ -1,5 +1,6 @@ - + + - - + + - - + + - - + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + +0 +20 +40 +60 +80 +100 +120 + + + + + + + + + + + + +Gauge r&R +Repeatability +Reproducibility +Part-to-part +Percent + + + + + + + + +% Contribution +% Study variation +% Tolerance - - -0 -20 -40 -60 -80 -100 -120 - - - - - - - - - - - - -Gauge r&R -Repeat -Reprod -Part-to-part -Percent - - - - - - - - -% Contribution -% Study variation -% Tolerance -gauge-r-r-report-subplot-2 diff --git a/tests/testthat/_snaps/msaGaugeRR/reference_plotobject/4-components-of-variation.rds b/tests/testthat/_snaps/msaGaugeRR/reference_plotobject/4-components-of-variation.rds index d6e43f4b..7abb6a45 100644 Binary files a/tests/testthat/_snaps/msaGaugeRR/reference_plotobject/4-components-of-variation.rds and b/tests/testthat/_snaps/msaGaugeRR/reference_plotobject/4-components-of-variation.rds differ diff --git a/tests/testthat/_snaps/msaGaugeRR/reference_plotobject/wf4-components-of-variation.rds b/tests/testthat/_snaps/msaGaugeRR/reference_plotobject/wf4-components-of-variation.rds index 8abe09ae..4cb89aff 100644 Binary files a/tests/testthat/_snaps/msaGaugeRR/reference_plotobject/wf4-components-of-variation.rds and b/tests/testthat/_snaps/msaGaugeRR/reference_plotobject/wf4-components-of-variation.rds differ diff --git a/tests/testthat/_snaps/msaGaugeRR/wf1-components-of-variation.svg b/tests/testthat/_snaps/msaGaugeRR/wf1-components-of-variation.svg index 950b310b..0283b7fd 100644 --- a/tests/testthat/_snaps/msaGaugeRR/wf1-components-of-variation.svg +++ b/tests/testthat/_snaps/msaGaugeRR/wf1-components-of-variation.svg @@ -1,5 +1,6 @@ - + + - - + + - - + + - - + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + +0 +20 +40 +60 +80 +100 +120 + + + + + + + + + + + + +Gauge r&R +Repeatability +Reproducibility +Part-to-part +Percent + + + + + + + + +% Contribution +% Study variation +% Tolerance - - -0 -20 -40 -60 -80 -100 -120 - - - - - - - - - - - - -Gauge r&R -Repeat -Reprod -Part-to-part -Percent - - - - - - - - -% Contribution -% Study variation -% Tolerance -WF1_components-of-variation diff --git a/tests/testthat/_snaps/msaGaugeRR/wf2-components-of-variation.svg b/tests/testthat/_snaps/msaGaugeRR/wf2-components-of-variation.svg index 9eaf7169..41b12496 100644 --- a/tests/testthat/_snaps/msaGaugeRR/wf2-components-of-variation.svg +++ b/tests/testthat/_snaps/msaGaugeRR/wf2-components-of-variation.svg @@ -1,5 +1,6 @@ - + + - - + + - - + + - - + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + +0 +50 +100 +150 +200 + + + + + + + + + + +Gauge r&R +Repeatability +Reproducibility +Part-to-part +Percent + + + + + + + + +% Contribution +% Study variation +% Tolerance - - -0 -50 -100 -150 -200 - - - - - - - - - - -Gauge r&R -Repeat -Reprod -Part-to-part -Percent - - - - - - - - -% Contribution -% Study variation -% Tolerance -WF2_components-of-variation diff --git a/tests/testthat/_snaps/msaGaugeRR/wf4-components-of-variation.svg b/tests/testthat/_snaps/msaGaugeRR/wf4-components-of-variation.svg index b9f664bc..c254930f 100644 --- a/tests/testthat/_snaps/msaGaugeRR/wf4-components-of-variation.svg +++ b/tests/testthat/_snaps/msaGaugeRR/wf4-components-of-variation.svg @@ -1,5 +1,6 @@ - + + - - + + - - + + - - + + - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + +0 +20 +40 +60 +80 +100 +120 + + + + + + + + + + + + +Gauge r&R +Repeatability +Reproducibility +Part-to-part +Percent + + + + + + + + +% Contribution +% Study variation +% Tolerance - - -0 -20 -40 -60 -80 -100 -120 - - - - - - - - - - - -Gauge r&R -Repeat -Part-to-part -Percent - - - - - - - - -% Contribution -% Study variation -% Tolerance -WF4_components-of-variation