-
Notifications
You must be signed in to change notification settings - Fork 21
Process Capability: Adjust parameter estimation precision settings #462
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Open
JTPetter
wants to merge
16
commits into
jasp-stats:master
Choose a base branch
from
JTPetter:fix4142
base: master
Could not load branches
Branch not found: {{ refName }}
Loading
Could not load tags
Nothing to show
Loading
Are you sure you want to change the base?
Some commits from the old base branch may be removed from the timeline,
and old review comments may become outdated.
Open
Changes from all commits
Commits
Show all changes
16 commits
Select commit
Hold shift + click to select a range
f0b6821
Remove mle.tools and adjust unit test pixel changes
JTPetter d4af53e
Update tseries version
JTPetter 5cbceb9
Update other relevant packages
JTPetter 715e9c6
update ragg
JTPetter 7924e37
Delete diff viewer leftover
JTPetter c8e7d29
update more packages
JTPetter 0cfceb0
Update all packages to latest version and fix issues
JTPetter eb6a7b2
remove typo
JTPetter 57a3417
update more packages
JTPetter 6f155bd
downgrade igraph because binaries not av
JTPetter 02d6b13
adjust precision settings weibull and fix tests
JTPetter 0044e53
fix typo
JTPetter 3502495
Adjust prob. table to use common dist. function
JTPetter 6601079
Adjust unit tests to decimal changes
JTPetter 842bf61
Remove other precision settings where applicable
JTPetter 12faef8
New unit test
JTPetter File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
The table of contents is too big for display.
Diff view
Diff view
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -3,3 +3,5 @@ | |
| ^.*\.Rproj$ | ||
| ^\.Rproj\.user$ | ||
| ^\examples$ | ||
| ^\.positai$ | ||
| ^\.claude$ | ||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -64,3 +64,4 @@ renv/ | |
| _processedLockFile.lock | ||
|
|
||
| .vscode/ | ||
| .positai | ||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -28,7 +28,6 @@ Imports: | |
| jaspDescriptives, | ||
| jaspGraphs, | ||
| lubridate, | ||
| mle.tools, | ||
| psych, | ||
| qcc, | ||
| rsm, | ||
|
|
||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -59,7 +59,7 @@ processCapabilityStudies <- function(jaspResults, dataset, options) { | |
| if ((!wideFormat && options[["subgroupSizeType"]] == "manual" && | ||
| any(lapply(split(dataset[[stages]], ceiling(seq_along(dataset[[stages]])/options[["manualSubgroupSizeValue"]])), FUN = function(x)length(unique(x))) > 1)) || | ||
| (!wideFormat && options[["subgroupSizeType"]] == "groupingVariable" && | ||
| any(table(dplyr::count_(dataset, vars = c(stages, subgroupVariable))[subgroupVariable]) > 1))) { | ||
| any(table(dplyr::count(dataset, dplyr::across(dplyr::all_of(c(stages, subgroupVariable))))[[subgroupVariable]]) > 1))) { | ||
| plotNotes <- paste0(plotNotes, gettext("One or more subgroups are assigned to more than one stage, only first stage is considered.<br>")) | ||
| } | ||
| if (anyNA(dataset[[stages]])) { | ||
|
|
@@ -856,9 +856,9 @@ processCapabilityStudies <- function(jaspResults, dataset, options) { | |
| # Addition to consider that if distributions are set historically, they may fall outside the usual limits | ||
| if (distribution == "normal" && options[["historicalMean"]]) | ||
| xLimits <- range(xLimits, options[["historicalMeanValue"]] - 1.5 * sdo, options[["historicalMeanValue"]] + 1.5 * sdo) | ||
| if (distribution == "weibull" || distribution == "3ParameterWeibull" && options[["historicalScale"]]) | ||
| if ((distribution == "weibull" || distribution == "3ParameterWeibull") && options[["historicalScale"]]) | ||
| xLimits <- range(xLimits, options[["historicalScaleValue"]] - 1.5 * sdo, options[["historicalScaleValue"]] + 1.5 * sdo) | ||
| if (distribution == "lognormal" || distribution == "3ParameterLognormal" && options[["historicalLogMean"]]) | ||
| if ((distribution == "lognormal" || distribution == "3ParameterLognormal") && options[["historicalLogMean"]]) | ||
| xLimits <- range(xLimits, exp(options[["historicalLogMeanValue"]]) - 1.5 * sdo, | ||
| exp(options[["historicalLogMeanValue"]]) + 1.5 * sdo) | ||
| if (distribution == "exponential") | ||
|
|
@@ -2579,14 +2579,14 @@ processCapabilityStudies <- function(jaspResults, dataset, options) { | |
| } | ||
| table$addColumnInfo(name = "n", title = gettext("N"), type = "integer") | ||
| if (options[["nullDistribution"]] == "normal") { | ||
| table$addColumnInfo(name = "mean", title = gettextf("Mean (%1$s)", "\u03BC"), type = "number") | ||
| table$addColumnInfo(name = "sd", title = gettextf("Std. dev. (%1$s)", "\u03C3"), type = "number") | ||
| table$addColumnInfo(name = "parameterEstimate1", title = gettextf("Mean (%1$s)", "\u03BC"), type = "number") | ||
| table$addColumnInfo(name = "parameterEstimate2", title = gettextf("Std. dev. (%1$s)", "\u03C3"), type = "number") | ||
| } else if (options[["nullDistribution"]] == "lognormal") { | ||
| table$addColumnInfo(name = "mean", title = gettextf("Log mean (%1$s)", "\u03BC"), type = "number") | ||
| table$addColumnInfo(name = "sd", title = gettextf("Log std.dev (%1$s)", "\u03C3"), type = "number") | ||
| table$addColumnInfo(name = "parameterEstimate1", title = gettextf("Log mean (%1$s)", "\u03BC"), type = "number") | ||
| table$addColumnInfo(name = "parameterEstimate2", title = gettextf("Log std.dev (%1$s)", "\u03C3"), type = "number") | ||
| } else if (options[["nullDistribution"]] == "weibull") { | ||
| table$addColumnInfo(name = "mean", title = gettextf("Shape (%1$s)", "\u03B2"), type = "number") | ||
| table$addColumnInfo(name = "sd", title = gettextf("Scale (%1$s)", "\u03B8"), type = "number") | ||
| table$addColumnInfo(name = "parameterEstimate1", title = gettextf("Shape (%1$s)", "\u03B2"), type = "number") | ||
| table$addColumnInfo(name = "parameterEstimate2", title = gettextf("Scale (%1$s)", "\u03B8"), type = "number") | ||
| } | ||
| table$addColumnInfo(name = "ad", title = gettext("AD"), type = "integer") | ||
| table$addColumnInfo(name = "p", title = gettext("<i>p</i>-value"), type = "pvalue") | ||
|
|
@@ -2601,51 +2601,50 @@ processCapabilityStudies <- function(jaspResults, dataset, options) { | |
| container[["probabilityTable"]] <- table | ||
| return() | ||
| } | ||
| tableColNames <- c("mean", "sd", "n", "ad", "p") | ||
| tableColNames <- c("parameterEstimate1", "parameterEstimate2", "n", "ad", "p") | ||
| if (nStages > 1) | ||
| tableColNames <- c("stage", tableColNames) | ||
| tableDf <- data.frame(matrix(ncol = length(tableColNames), nrow = 0)) | ||
| colnames(tableDf) <- tableColNames | ||
| for (i in seq_len(nStages)) { | ||
| stage <- unique(dataset[[stages]])[i] | ||
| dataCurrentStage <- dataset[which(dataset[[stages]] == stage), ][!names(dataset) %in% stages] | ||
| values <- as.vector(na.omit(unlist(dataCurrentStage[measurements]))) # distribution fitting function complains if this is not explicitly a vector | ||
| stageValues <- as.vector(na.omit(unlist(dataCurrentStage[measurements]))) # distribution fitting function complains if this is not explicitly a vector | ||
|
|
||
| if (options[["nullDistribution"]] == "normal") { | ||
| meanx <- mean(values) | ||
| sdx <- sd(values) | ||
| test <- goftest::ad.test(x = values, "norm", mean = meanx, sd = sdx) | ||
| parameterEstimate1 <- mean(stageValues) | ||
| parameterEstimate2 <- stats::sd(stageValues) | ||
| andersonDarlingTest <- goftest::ad.test(x = stageValues, "norm", mean = parameterEstimate1, sd = parameterEstimate2) | ||
| } else if (options[["nullDistribution"]] == "lognormal") { | ||
| fit <- EnvStats::elnorm(values) | ||
| meanx <- fit$parameters[1] | ||
| sdx <- fit$parameters[2] | ||
| test <- goftest::ad.test(x = values, "plnorm", meanlog = meanx, sdlog = sdx) | ||
| fitPars <- .distributionParameters(stageValues, distribution = "lognormal") | ||
| parameterEstimate1 <- fitPars$beta | ||
| parameterEstimate2 <- fitPars$theta | ||
| andersonDarlingTest <- goftest::ad.test(x = stageValues, "plnorm", meanlog = parameterEstimate1, sdlog = parameterEstimate2) | ||
| } else if (options[["nullDistribution"]] == "weibull") { | ||
| fit <- fitdistrplus::fitdist(values, 'weibull', | ||
| control = list( | ||
| maxit = 10000, | ||
| abstol = .Machine$double.eps^0.75, | ||
| reltol = .Machine$double.eps^0.75, | ||
| ndeps = rep(1e-8, 2))) | ||
| meanx <- fit$estimate[1] | ||
| sdx <- fit$estimate[2] | ||
| test <- goftest::ad.test(x = values, "pweibull", shape = meanx, scale = sdx) | ||
| } | ||
| n <- length(values) | ||
| ad <- test$statistic | ||
| adStar <- ad*(1 + (0.75/n) + (2.25/(n^2))) | ||
| if(ad >= 0.6) { | ||
| p <- exp(1.2937 - (5.709 * adStar) + 0.0186 * (adStar^2)) | ||
| } else if(adStar < 0.6 && adStar > 0.34) { | ||
| p <- exp(0.9177 - (4.279 * adStar) - 1.38 * (adStar^2)) | ||
| } else if(adStar < 0.34 && adStar > 0.2) { | ||
| p <- 1 - exp(-8.318 + (42.796 * adStar) - 59.938 * (adStar^2)) | ||
| } else if(adStar <= 0.2) { | ||
| p <- 1 - exp(-13.436 + (101.14 * adStar) - 223.73 * (adStar^2)) #Jaentschi & Bolboaca (2018) | ||
| fitPars <- .distributionParameters(stageValues, distribution = "weibull") | ||
| parameterEstimate1 <- fitPars$beta | ||
| parameterEstimate2 <- fitPars$theta | ||
| andersonDarlingTest <- goftest::ad.test(x = stageValues, "pweibull", shape = parameterEstimate1, scale = parameterEstimate2) | ||
| } | ||
| sampleSize <- length(stageValues) | ||
| adStatistic <- andersonDarlingTest$statistic | ||
| adStatisticAdjusted <- adStatistic * (1 + (0.75 / sampleSize) + (2.25 / (sampleSize^2))) | ||
| if (adStatistic >= 0.6) { | ||
| pValue <- exp(1.2937 - (5.709 * adStatisticAdjusted) + 0.0186 * (adStatisticAdjusted^2)) | ||
| } else if (adStatisticAdjusted < 0.6 && adStatisticAdjusted > 0.34) { | ||
| pValue <- exp(0.9177 - (4.279 * adStatisticAdjusted) - 1.38 * (adStatisticAdjusted^2)) | ||
| } else if (adStatisticAdjusted < 0.34 && adStatisticAdjusted > 0.2) { | ||
|
Comment on lines
+2630
to
+2636
|
||
| pValue <- 1 - exp(-8.318 + (42.796 * adStatisticAdjusted) - 59.938 * (adStatisticAdjusted^2)) | ||
| } else if (adStatisticAdjusted <= 0.2) { | ||
| pValue <- 1 - exp(-13.436 + (101.14 * adStatisticAdjusted) - 223.73 * (adStatisticAdjusted^2)) #Jaentschi & Bolboaca (2018) | ||
| } else { | ||
| p <- test$p.value | ||
| pValue <- andersonDarlingTest$p.value | ||
| } | ||
| tableDfCurrentStage <- data.frame(mean = meanx, sd = sdx, n = n, ad = round(ad, .numDecimals), p = round(p, .numDecimals)) | ||
| tableDfCurrentStage <- data.frame(parameterEstimate1 = parameterEstimate1, | ||
| parameterEstimate2 = parameterEstimate2, | ||
| n = sampleSize, | ||
| ad = round(adStatistic, .numDecimals), | ||
| p = round(pValue, .numDecimals)) | ||
| if (i == 1 && nStages > 1) | ||
| baseLineDf <- tableDfCurrentStage | ||
| if (i > 1) { | ||
|
|
@@ -2703,6 +2702,37 @@ processCapabilityStudies <- function(jaspResults, dataset, options) { | |
| return(plot) | ||
| } | ||
|
|
||
| .qcObservedVarcov <- function(logdensity, X, parms, mle) { | ||
| mle <- unlist(mle) | ||
|
|
||
| if (!all(parms %in% names(mle))) { | ||
| if (length(mle) != length(parms)) | ||
| stop(gettext("Could not compute variance-covariance matrix: parameter vector has unexpected length."), call. = FALSE) | ||
|
|
||
| names(mle) <- parms | ||
| } | ||
|
|
||
| mle <- as.numeric(mle[parms]) | ||
| names(mle) <- parms | ||
|
|
||
| negLogLik <- function(theta) { | ||
| names(theta) <- parms | ||
| env <- list2env(c(as.list(theta), list(x = X)), parent = baseenv()) | ||
| ll <- eval(logdensity, envir = env) | ||
| -sum(ll) | ||
| } | ||
|
|
||
| hessian <- stats::optimHess(par = mle, fn = negLogLik) | ||
|
|
||
| if (anyNA(hessian) || any(!is.finite(hessian))) | ||
| stop(gettext("Could not compute variance-covariance matrix: Hessian contains invalid values."), call. = FALSE) | ||
|
|
||
| varcov <- solve(hessian) | ||
| dimnames(varcov) <- list(parms, parms) | ||
|
|
||
| list(varcov = varcov) | ||
| } | ||
|
|
||
| .qcProbabilityPlotObject <- function(options, dataset, measurements, stages) { | ||
| if (identical(stages, "")) { | ||
| nStages <- 1 | ||
|
|
@@ -2749,8 +2779,8 @@ processCapabilityStudies <- function(jaspResults, dataset, options) { | |
| # Computing according to the distribution | ||
| if (options[["nullDistribution"]] == "normal") { | ||
| lpdf <- quote(-log(sigma) - 0.5 / sigma ^ 2 * (x - mu) ^ 2) | ||
| matrix <- try(mle.tools::observed.varcov(logdensity = lpdf, X = dataCurrentStage, parms = c("mu", "sigma"), | ||
| mle = c(mean(dataCurrentStage), sd(dataCurrentStage)))) | ||
| matrix <- try(.qcObservedVarcov(logdensity = lpdf, X = dataCurrentStage, parms = c("mu", "sigma"), | ||
| mle = c(mean(dataCurrentStage), sd(dataCurrentStage)))) | ||
| # Gracefully handle confidence band computation failure | ||
| if (jaspBase::isTryError(matrix)) { | ||
| hasConfidenceBands <- FALSE | ||
|
|
@@ -2781,7 +2811,7 @@ processCapabilityStudies <- function(jaspResults, dataset, options) { | |
| meanlog <- as.numeric(fit$parameters[1]) | ||
| sdlog <- as.numeric(fit$parameters[2]) | ||
| lpdf <- quote(log(1/(sqrt(2*pi)*x*sdlog) * exp(-(log(x)- meanlog)^2/(2*sdlog^2)))) | ||
| matrix <- try(mle.tools::observed.varcov(logdensity = lpdf, X = dataCurrentStage, parms = c("meanlog", "sdlog"), mle = fit$parameters)) | ||
| matrix <- try(.qcObservedVarcov(logdensity = lpdf, X = dataCurrentStage, parms = c("meanlog", "sdlog"), mle = fit$parameters)) | ||
| # Gracefully handle confidence band computation failure | ||
| if (jaspBase::isTryError(matrix)) { | ||
| hasConfidenceBands <- FALSE | ||
|
|
@@ -2819,7 +2849,7 @@ processCapabilityStudies <- function(jaspResults, dataset, options) { | |
| shape <- as.numeric(fit_Weibull[["beta"]]) | ||
| scale <- as.numeric(fit_Weibull[["theta"]]) | ||
| lpdf <- quote(log(shape) - shape * log(scale) + shape * log(x) - (x/scale)^shape) | ||
| matrix <- try(mle.tools::observed.varcov(logdensity = lpdf, X = dataCurrentStage, parms = c("shape", "scale"), mle = c("shape" = shape, "scale" = scale))) | ||
| matrix <- try(.qcObservedVarcov(logdensity = lpdf, X = dataCurrentStage, parms = c("shape", "scale"), mle = c("shape" = shape, "scale" = scale))) | ||
| # Gracefully handle confidence band computation failure | ||
| if (jaspBase::isTryError(matrix)) { | ||
| hasConfidenceBands <- FALSE | ||
|
|
||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
In
.qcProbabilityTable(), the AD column is declared as typeinteger, but the code computes and stores non-integer Anderson–Darling statistics (rounded to.numDecimals). This will likely display incorrectly in the UI (e.g., 0.43 shown as 0). Change the column type tonumber(and optionally add a numeric format) to match the actual values.