Closed sophiahn closed 7 months ago
michaelhallquist
commented
8 months ago Thanks for this. I have a hunch as to what is causing the problem, but will be much faster to diagnose and fix if you could upload a single .out file (change the file extension to .txt for Github upload). Could I ask you to upload a file?
Michael
sophiahn
commented
8 months ago Thanks so much for your prompt response.
Here is one of the output file in txt format. Is this what you meant? anxiety-2-multigroup-cfa-gender.txt
sophiahn
commented
8 months ago Follow-up:I reverted back to an older version of R (4.2.1), and the readModels syntax works now! To be more precise, it stil generates some errors, but now I can use SummaryTable function to the list created from the readModels syntax --to create a table of fit indices.
michaelhallquist
commented
7 months ago Hi there, I took a look at this and it read correctly in the readModels function in the current Github version of the package. I need to deploy this to CRAN so that others don't bump into similar problems. In the meantime, here are instructions for getting a working version of the package for your file.
library(devtools)
install_github("michaelhallquist/MplusAutomation")
I am trying to use readModels to read multiple Mplus outputs in a specific directory. I run into an error messgae like below: This started showing only recently after I had updated my R and Rstudio.
<simpleError in length(covcoverageSubsections) == 0 || is.na(covcoverageSubsections): 'length = 2' in coercion to 'logical(1)'><simpleError in !is.null(arglist$Parameters) && !is.na(arglist$Parameters): 'length = 3' in coercion to 'logical(1)'><simpleError in if (is.null(summaries) || missing(summaries) || summaries$NCategoricalLatentVars == 1 || is.na(summaries$NCategoricalLatentVars)) { if (is.null(summaries) || missing(summaries) || is.null(summaries$Mplus.version) || as.numeric(summaries$Mplus.version) < 7.3) { modelCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$", outfiletext) ppCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS$", outfiletext) mostLikelyCounts <- getSection("^CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP$", outfiletext) } else { modelCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$::^BASED ON THE ESTIMATED MODEL$", outfiletext) ppCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$::^BASED ON ESTIMATED POSTERIOR PROBABILITIES$", outfiletext) mostLikelyCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$::^BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP$", outfiletext) } countlist[["modelEstimated"]] <- getClassCols(modelCounts) countlist[["posteriorProb"]] <- getClassCols(ppCounts) countlist[["mostLikely"]] <- getClassCols(mostLikelyCounts) mostLikelyProbs <- getSection("^Average Latent Class Probabilities for Most Likely Latent Class Membership \((Row|Column)\)$", outfiletext) if (length(mostLikelyProbs) > 1L) { mostLikelyProbs <- mostLikelyProbs[-1L] } countlist[["avgProbs.mostLikely"]] <- unlabeledMatrixExtract(mostLikelyProbs, filename) classificationProbs <- getSection("^Classification Probabilities for the Most Likely Latent Class Membership \((Column|Row)\)$", outfiletext) if (length(classificationProbs) > 1L) { classificationProbs <- classificationProbs[-1L] } countlist[["classificationProbs.mostLikely"]] <- unlabeledMatrixExtract(classificationProbs, filename) classificationLogitProbs <- getSection("^Logits for the Classification Probabilities for the Most Likely Latent Class Membership \((Column|Row)\)$", outfiletext) if (length(classificationLogitProbs) > 1L) { classificationLogitProbs <- classificationLogitProbs[-1L] } countlist[["logitProbs.mostLikely"]] <- unlabeledMatrixExtract(classificationLogitProbs, filename)} else { getClassCols_lta <- function(sectiontext) { numberLines <- grep("^\s([a-zA-Z0-9]+)?(\s+[0-9\.-]{1,}){1,}$", sectiontext, perl = TRUE) if (length(numberLines) > 0) { parsedlines <- strsplit(trimSpace(sectiontext[numberLines]), "\s+", perl = TRUE) num_values <- sapply(parsedlines, length) if (length(unique(num_values)) == 1) { counts <- data.frame(t(sapply(parsedlines, as.numeric)), stringsAsFactors = FALSE) } else { parsedlines[which(num_values != max(num_values))] <- lapply(parsedlines[which(num_values != max(num_values))], function(x) { c(rep(NA, (max(num_values) - length(x))), x) }) counts <- do.call(rbind, parsedlines) counts[, 1] <- inverse.rle(list(lengths = diff(c(which(!is.na(counts[, 1])), (nrow(counts) + 1))), values = counts[, 1][complete.cases(counts[, 1])])) counts <- data.frame(counts, stringsAsFactors = FALSE) counts[, 2:4] <- lapply(counts[, 2:4], as.numeric) } return(counts) } else { return(NULL) } } if (missing(summaries) || is.null(summaries$Mplus.version) || as.numeric(summaries$Mplus.version) < 7.3) { posteriorProb.patterns <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$::^BASED ON ESTIMATED POSTERIOR PROBABILITIES$", outfiletext) mostLikely.patterns <- getSection("^CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN$", outfiletext) mostLikelyCounts <- getSection("CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN", outfiletext) } else { posteriorProb.patterns <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS$::^BASED ON ESTIMATED POSTERIOR PROBABILITIES$", outfiletext) mostLikely.patterns <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS$::^BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN$", outfiletext) mostLikelyCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE$::^BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN$", outfiletext) } countlist[["modelEstimated"]] <- getClassCols_lta(getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE$::^BASED ON THE ESTIMATED MODEL$", outfiletext)) countlist[["posteriorProb"]] <- getClassCols_lta(getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE$::^BASED ON ESTIMATED POSTERIOR PROBABILITIES$", outfiletext)) countlist[["mostLikely"]] <- getClassCols_lta(mostLikelyCounts) countlist[which(names(countlist) %in% c("modelEstimated", "posteriorProb", "mostLikely"))] <- lapply(countlist[which(names(countlist) %in% c("modelEstimated", "posteriorProb", "mostLikely"))], setNames, c("variable", "class", "count", "proportion")) countlist[["modelEstimated.patterns"]] <- getClassCols_lta(getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS$::^BASED ON THE ESTIMATED MODEL$", outfiletext)) countlist[["posteriorProb.patterns"]] <- getClassCols_lta(posteriorProb.patterns) countlist[["mostLikely.patterns"]] <- getClassCols_lta(mostLikely.patterns) morder <- getSection("MODEL RESULTS USE THE LATENT CLASS VARIABLE ORDER", outfiletext) if (!is.null(morder)) { first_present <- which(morder != "")[1] catvar_names <- strsplit(trimSpace(morder[first_present]), "\s+")[[1]] } else { catvar_names <- unique(countlist[["mostLikely"]]$variable) } rename_sections <- which(names(countlist) %in% c("modelEstimated.patterns", "posteriorProb.patterns", "mostLikely.patterns")) if (length(rename_sections) > 0L) { for (s in rename_sections) { countlist[[s]] <- setNames(countlist[[s]], c(catvar_names, "count", "proportion")) } } avgProbs <- getSection("^Average Latent Class Probabilities for Most Likely Latent Class Pattern \((Row|Column)\)$::^by Latent Class Pattern \((Row|Column)\)$", outfiletext) if (!is.null(avgProbs)) { column_headers <- strsplit(trimws(grep("\sLatent Class\s{2,}", avgProbs, value = TRUE)), "\s+", perl = TRUE)[[1]][-1] variable_pattern_rows <- grep(paste(c("^(\s{2,}\d+){", length(column_headers), "}$"), collapse = ""), avgProbs, perl = TRUE) variable_pattern_rows <- variable_pattern_rows[!c(FALSE, diff(variable_pattern_rows) != 1)] variable_patterns <- avgProbs[variable_pattern_rows] variable_patterns <- data.frame(t(sapply(strsplit(trimws(variable_patterns), "\s+", perl = TRUE), as.numeric))) names(variable_patterns) <- c("Latent Class Pattern No.", column_headers[-1]) probs <- grep(paste(c("^\s+\d{1,}(\s{2,}[0-9\.-]+)+$"), collapse = ""), avgProbs[(variable_pattern_rows[length(variable_pattern_rows)] + 1):length(avgProbs)], perl = TRUE, value = TRUE) if (length(probs)%%nrow(variable_patterns) > 1) { for (i in 2:(length(probs)%%nrow(variable_patterns))) { probs[1:(nrow(variable_patterns) + 1)] <- paste(probs[1:(nrow(variable_patterns) + 1)], substring(probs[((i - 1) (nrow(variable_patterns) + 1) + 1):(i (nrow(variable_patterns) + 1))], first = 8)) } probs <- probs[1:nrow(variable_patterns)] } probs <- t(sapply(strsplit(trimws(probs[-1]), "\s+", perl = TRUE), as.numeric))[, -1] countlist[["avgProbs.mostLikely"]] <- probs countlist[["avgProbs.mostLikely.patterns"]] <- variable_patterns } countlist[["classificationProbs.mostLikely"]] <- NULL countlist[["logitProbs.mostLikely"]] <- NULL transitionProbs <- getSection("^LATENT TRANSITION PROBABILITIES BASED ON THE ESTIMATED MODEL$", outfiletext) if (!is.null(transitionProbs)) { section_starts <- grep("\(Columns\)$", transitionProbs) transitionProbs <- mapply(FUN = function(begin, end) { probs <- grep("^\s+\d{1,}(\s{2,}[0-9\.-]{2,}){1,}$", transitionProbs[begin:end], perl = TRUE, value = TRUE) probs <- do.call(rbind, strsplit(trimws(probs), "\s+", perl = TRUE))[, -1] cbind(paste(gsub("\s+(\w+) Classes.$", "\1", transitionProbs[begin]), ".", rep(c(1:nrow(probs)), ncol(probs)), sep = ""), paste(gsub(".+?by (\w+) Classes.$", "\1", transitionProbs[begin]), ".", as.vector(sapply(1:ncol(probs), rep, nrow(probs))), sep = ""), as.vector(probs)) }, begin = section_starts, end = c(section_starts[-1], length(transitionProbs)), SIMPLIFY = FALSE) if (length(transitionProbs) > 1) { transitionProbs <- do.call(rbind, transitionProbs) } else { transitionProbs <- transitionProbs[[1]] } transitionProbs <- data.frame(transitionProbs, stringsAsFactors = FALSE) names(transitionProbs) <- c("from", "to", "probability") transitionProbs$probability <- as.numeric(transitionProbs$probability) } countlist[["transitionProbs"]] <- transitionProbs}: missing value where TRUE/FALSE needed><simpleError in length(covcoverageSubsections) == 0 || is.na(covcoverageSubsections): 'length = 2' in coercion to 'logical(1)'><simpleError in !is.null(arglist$Parameters) && !is.na(arglist$Parameters): 'length = 3' in coercion to 'logical(1)'><simpleError in if (is.null(summaries) || missing(summaries) || summaries$NCategoricalLatentVars == 1 || is.na(summaries$NCategoricalLatentVars)) { if (is.null(summaries) || missing(summaries) || is.null(summaries$Mplus.version) || as.numeric(summaries$Mplus.version) < 7.3) { modelCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$", outfiletext) ppCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS$", outfiletext) mostLikelyCounts <- getSection("^CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP$", outfiletext) } else { modelCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$::^BASED ON THE ESTIMATED MODEL$", outfiletext) ppCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$::^BASED ON ESTIMATED POSTERIOR PROBABILITIES$", outfiletext) mostLikelyCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$::^BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP$", outfiletext) } countlist[["modelEstimated"]] <- getClassCols(modelCounts) countlist[["posteriorProb"]] <- getClassCols(ppCounts) countlist[["mostLikely"]] <- getClassCols(mostLikelyCounts) mostLikelyProbs <- getSection("^Average Latent Class Probabilities for Most Likely Latent Class Membership \((Row|Column)\)$", outfiletext) if (length(mostLikelyProbs) > 1L) { mostLikelyProbs <- mostLikelyProbs[-1L] } countlist[["avgProbs.mostLikely"]] <- unlabeledMatrixExtract(mostLikelyProbs, filename) classificationProbs <- getSection("^Classification Probabilities for the Most Likely Latent Class Membership \((Column|Row)\)$", outfiletext) if (length(classificationProbs) > 1L) { classificationProbs <- classificationProbs[-1L] } countlist[["classificationProbs.mostLikely"]] <- unlabeledMatrixExtract(classificationProbs, filename) classificationLogitProbs <- getSection("^Logits for the Classification Probabilities for the Most Likely Latent Class Membership \((Column|Row)\)$", outfiletext) if (length(classificationLogitProbs) > 1L) { classificationLogitProbs <- classificationLogitProbs[-1L] } countlist[["logitProbs.mostLikely"]] <- unlabeledMatrixExtract(classificationLogitProbs, filename)} else { getClassCols_lta <- function(sectiontext) { numberLines <- grep("^\s([a-zA-Z0-9]+)?(\s+[0-9\.-]{1,}){1,}$", sectiontext, perl = TRUE) if (length(numberLines) > 0) { parsedlines <- strsplit(trimSpace(sectiontext[numberLines]), "\s+", perl = TRUE) num_values <- sapply(parsedlines, length) if (length(unique(num_values)) == 1) { counts <- data.frame(t(sapply(parsedlines, as.numeric)), stringsAsFactors = FALSE) } else { parsedlines[which(num_values != max(num_values))] <- lapply(parsedlines[which(num_values != max(num_values))], function(x) { c(rep(NA, (max(num_values) - length(x))), x) }) counts <- do.call(rbind, parsedlines) counts[, 1] <- inverse.rle(list(lengths = diff(c(which(!is.na(counts[, 1])), (nrow(counts) + 1))), values = counts[, 1][complete.cases(counts[, 1])])) counts <- data.frame(counts, stringsAsFactors = FALSE) counts[, 2:4] <- lapply(counts[, 2:4], as.numeric) } return(counts) } else { return(NULL) } } if (missing(summaries) || is.null(summaries$Mplus.version) || as.numeric(summaries$Mplus.version) < 7.3) { posteriorProb.patterns <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES$::^BASED ON ESTIMATED POSTERIOR PROBABILITIES$", outfiletext) mostLikely.patterns <- getSection("^CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN$", outfiletext) mostLikelyCounts <- getSection("CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN", outfiletext) } else { posteriorProb.patterns <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS$::^BASED ON ESTIMATED POSTERIOR PROBABILITIES$", outfiletext) mostLikely.patterns <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS$::^BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN$", outfiletext) mostLikelyCounts <- getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE$::^BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN$", outfiletext) } countlist[["modelEstimated"]] <- getClassCols_lta(getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE$::^BASED ON THE ESTIMATED MODEL$", outfiletext)) countlist[["posteriorProb"]] <- getClassCols_lta(getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE$::^BASED ON ESTIMATED POSTERIOR PROBABILITIES$", outfiletext)) countlist[["mostLikely"]] <- getClassCols_lta(mostLikelyCounts) countlist[which(names(countlist) %in% c("modelEstimated", "posteriorProb", "mostLikely"))] <- lapply(countlist[which(names(countlist) %in% c("modelEstimated", "posteriorProb", "mostLikely"))], setNames, c("variable", "class", "count", "proportion")) countlist[["modelEstimated.patterns"]] <- getClassCols_lta(getSection("^FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS$::^BASED ON THE ESTIMATED MODEL$", outfiletext)) countlist[["posteriorProb.patterns"]] <- getClassCols_lta(posteriorProb.patterns) countlist[["mostLikely.patterns"]] <- getClassCols_lta(mostLikely.patterns) morder <- getSection("MODEL RESULTS USE THE LATENT CLASS VARIABLE ORDER", outfiletext) if (!is.null(morder)) { first_present <- which(morder != "")[1] catvar_names <- strsplit(trimSpace(morder[first_present]), "\s+")[[1]] } else { catvar_names <- unique(countlist[["mostLikely"]]$variable) } rename_sections <- which(names(countlist) %in% c("modelEstimated.patterns", "posteriorProb.patterns", "mostLikely.patterns")) if (length(rename_sections) > 0L) { for (s in rename_sections) { countlist[[s]] <- setNames(countlist[[s]], c(catvar_names, "count", "proportion")) } } avgProbs <- getSection("^Average Latent Class Probabilities for Most Likely Latent Class Pattern \((Row|Column)\)$::^by Latent Class Pattern \((Row|Column)\)$", outfiletext) if (!is.null(avgProbs)) { column_headers <- strsplit(trimws(grep("\sLatent Class\s{2,}", avgProbs, value = TRUE)), "\s+", perl = TRUE)[[1]][-1] variable_pattern_rows <- grep(paste(c("^(\s{2,}\d+){", length(column_headers), "}$"), collapse = ""), avgProbs, perl = TRUE) variable_pattern_rows <- variable_pattern_rows[!c(FALSE, diff(variable_pattern_rows) != 1)] variable_patterns <- avgProbs[variable_pattern_rows] variable_patterns <- data.frame(t(sapply(strsplit(trimws(variable_patterns), "\s+", perl = TRUE), as.numeric))) names(variable_patterns) <- c("Latent Class Pattern No.", column_headers[-1]) probs <- grep(paste(c("^\s+\d{1,}(\s{2,}[0-9\.-]+)+$"), collapse = ""), avgProbs[(variable_pattern_rows[length(variable_pattern_rows)] + 1):length(avgProbs)], perl = TRUE, value = TRUE) if (length(probs)%%nrow(variable_patterns) > 1) { for (i in 2:(length(probs)%%nrow(variable_patterns))) { probs[1:(nrow(variable_patterns) + 1)] <- paste(probs[1:(nrow(variable_patterns) + 1)], substring(probs[((i - 1) (nrow(variable_patterns) + 1) + 1):(i (nrow(variable_patterns) + 1))], first = 8)) } probs <- probs[1:nrow(variable_patterns)] } probs <- t(sapply(strsplit(trimws(probs[-1]), "\s+", perl = TRUE), as.numeric))[, -1] countlist[["avgProbs.mostLikely"]] <- probs countlist[["avgProbs.mostLikely.patterns"]] <- variable_patterns } countlist[["classificationProbs.mostLikely"]] <- NULL countlist[["logitProbs.mostLikely"]] <- NULL transitionProbs <- getSection("^LATENT TRANSITION PROBABILITIES BASED ON THE ESTIMATED MODEL$", outfiletext) if (!is.null(transitionProbs)) { section_starts <- grep("\(Columns\)$", transitionProbs) transitionProbs <- mapply(FUN = function(begin, end) { probs <- grep("^\s+\d{1,}(\s{2,}[0-9\.-]{2,}){1,}$", transitionProbs[begin:end], perl = TRUE, value = TRUE) probs <- do.call(rbind, strsplit(trimws(probs), "\s+", perl = TRUE))[, -1] cbind(paste(gsub("\s+(\w+) Classes.$", "\1", transitionProbs[begin]), ".", rep(c(1:nrow(probs)), ncol(probs)), sep = ""), paste(gsub(".+?by (\w+) Classes.$", "\1", transitionProbs[begin]), ".", as.vector(sapply(1:ncol(probs), rep, nrow(probs))), sep = ""), as.vector(probs)) }, begin = section_starts, end = c(section_starts[-1], length(transitionProbs)), SIMPLIFY = FALSE) if (length(transitionProbs) > 1) { transitionProbs <- do.call(rbind, transitionProbs) } else { transitionProbs <- transitionProbs[[1]] } transitionProbs <- data.frame(transitionProbs, stringsAsFactors = FALSE) names(transitionProbs) <- c("from", "to", "probability") transitionProbs$probability <- as.numeric(transitionProbs$probability) } countlist[["transitionProbs"]] <- transitionProbs}: missing value where TRUE/FALSE needed>