diff --git a/NAMESPACE b/NAMESPACE index e0b61f7..3e6b7b6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -34,6 +34,7 @@ S3method(summary,seminr_model) export(PLSc) export(as.reflective) export(associations) +export(boot_paths_df) export(bootstrap_model) export(browse_plot) export(check_test_plot) diff --git a/R/boot_utils.R b/R/boot_utils.R new file mode 100644 index 0000000..3a015d8 --- /dev/null +++ b/R/boot_utils.R @@ -0,0 +1,41 @@ +#' Return all path bootstraps as a long dataframe. +#' Columns of the dataframes are specified paths and rows are the estimated +#' coefficients for the paths at each bootstrap iteration. +#' +#' @param pls_boot bootstrapped PLS model +#' +#' @examples +#' data(mobi) +#' +#' mobi_mm <- constructs( +#' composite("Image", multi_items("IMAG", 1:5)), +#' composite("Expectation", multi_items("CUEX", 1:3)), +#' composite("Satisfaction", multi_items("CUSA", 1:3)) +#' ) +#' +#' mobi_sm <- relationships( +#' paths(from = c("Image", "Expectation"), to = "Satisfaction") +#' ) +#' +#' pls_model <- estimate_pls(data = mobi, +#' measurement_model = mobi_mm, +#' structural_model = mobi_sm) +#' +#' pls_boot <- bootstrap_model(seminr_model = pls_model, +#' nboot = 50, cores = 2, seed = NULL) +#' +#' boot_paths_df(pls_boot) +#' +#' @export +boot_paths_df <- function(pls_boot) { + path_names <- apply(pls_boot$smMatrix, 1, \(path) { + paste(path['source'], '->', path['target']) + }) + + boot_paths <- apply(pls_boot$smMatrix, 1, \(path) { + pls_boot$boot_paths[path['source'], path['target'], 1:pls_boot$boots] + }) + + colnames(boot_paths) <- path_names + boot_paths +} diff --git a/R/compute_safe.R b/R/compute_safe.R new file mode 100644 index 0000000..137ef39 --- /dev/null +++ b/R/compute_safe.R @@ -0,0 +1,11 @@ +#' Standardize (scale) a matrix/df and report interpretable errors +#' +#' @param x vector, data.frame, or matrix +#' @return scaled object as returned by \code{scale} function +standardize_safely <- function(x) { + # NOTE: we could return zeros for columns with zero variance: + # apply(x, 2, function(y) (y - mean(y)) / sd(y) ^ as.logical(sd(y))) + res <- scale(x, TRUE, TRUE) + if (any(is.nan(res))) stop("zero variance items cannot be scaled") + res +} diff --git a/R/estimate_bootstrap.R b/R/estimate_bootstrap.R index f32c564..6338d41 100644 --- a/R/estimate_bootstrap.R +++ b/R/estimate_bootstrap.R @@ -83,174 +83,174 @@ bootstrap_model <- function(seminr_model, nboot = 500, cores = NULL, seed = NULL stopCriterion <- seminr_model$settings$stopCriterion missing <- seminr_model$settings$missing - if (nboot > 0) { - # Initialize the cluster - suppressWarnings(ifelse(is.null(cores), cl <- parallel::makeCluster(parallel::detectCores(), setup_strategy = "sequential"), cl <- parallel::makeCluster(cores, setup_strategy = "sequential"))) - - # Function to generate random samples with replacement - getRandomIndex <- function(d) {return(sample.int(nrow(d), replace = TRUE))} - - # Check for and create random seed if NULL - if (is.null(seed)) {seed <- sample.int(100000, size = 1)} - - # Export variables and functions to cluster - parallel::clusterExport(cl=cl, varlist=c("measurement_model", - "structural_model", - "inner_weights", - "getRandomIndex", - "d", - "HTMT", - "seed", - "total_effects", - "missing_value", - "maxIt", - "stopCriterion", - "missing"), envir=environment()) - - # Calculate the expected nrow of the bootmatrix - length <- 3*nrow(seminr_model$path_coef)^2 + 2*nrow(seminr_model$outer_loadings)*ncol(seminr_model$outer_loadings) - - # Function to get PLS estimate results - getEstimateResults <- function(i, d = d, length) { - set.seed(seed + i) - # plsc-bootstrap - tryCatch({ - boot_model <- seminr::estimate_pls(data = d[getRandomIndex(d),], - measurement_model, - structural_model, - inner_weights = inner_weights) - boot_htmt <- HTMT(boot_model) - boot_total <- total_effects(boot_model$path_coef) - return(as.matrix(c(c(boot_model$path_coef), - c(boot_model$outer_loadings), - c(boot_model$outer_weights), - c(boot_htmt), - c(boot_total)))) - }, - error = function(cond) { - message("Bootstrapping encountered an ERROR: ") - message(cond) - return(rep(NA, length)) - }, - warning = function(cond) { - message("Bootstrapping encountered an ERROR: ") - message(cond) - return(rep(NA, length)) - } - ) - } - # Bootstrap the estimates - utils::capture.output(bootmatrix <- parallel::parSapply(cl, 1:nboot, getEstimateResults, d, length)) + # Initialize the cluster + suppressWarnings(ifelse(is.null(cores), cl <- parallel::makeCluster(parallel::detectCores(), setup_strategy = "sequential"), cl <- parallel::makeCluster(cores, setup_strategy = "sequential"))) + + # Function to generate random samples with replacement + getRandomIndex <- function(d) {return(sample.int(nrow(d), replace = TRUE))} + + # Check for and create random seed if NULL + if (is.null(seed)) {seed <- sample.int(100000, size = 1)} + + # Export variables and functions to cluster + parallel::clusterExport(cl=cl, varlist=c("measurement_model", + "structural_model", + "inner_weights", + "getRandomIndex", + "d", + "HTMT", + "seed", + "total_effects", + "missing_value", + "maxIt", + "stopCriterion", + "missing"), envir=environment()) + + # Calculate the expected nrow of the bootmatrix + length <- 3*nrow(seminr_model$path_coef)^2 + 2*nrow(seminr_model$outer_loadings)*ncol(seminr_model$outer_loadings) + + # Function to get PLS estimate results + getEstimateResults <- function(i, d = d, length) { + set.seed(seed + i) + # plsc-bootstrap + tryCatch({ + boot_model <- seminr::estimate_pls(data = d[getRandomIndex(d),], + measurement_model, + structural_model, + inner_weights = inner_weights) + boot_htmt <- HTMT(boot_model) + boot_total <- total_effects(boot_model$path_coef) + return(as.matrix(c(c(boot_model$path_coef), + c(boot_model$outer_loadings), + c(boot_model$outer_weights), + c(boot_htmt), + c(boot_total)))) + }, + error = function(cond) { + message("Bootstrapping encountered an ERROR: ") + message(cond) + return(rep(NA, length)) + }, + warning = function(cond) { + message("Bootstrapping encountered an ERROR: ") + message(cond) + return(rep(NA, length)) + } + ) + } - # Clean the NAs and report the NAs - bootmatrix <- bootmatrix[,!is.na(bootmatrix[1,])] - fails <- nboot - ncol(bootmatrix) - nboot <- nboot - fails - if (fails > 0) { - message(paste("Bootstrapping encountered a WARNING: ", fails, "models failed to converge in PLSc. \nThese models are excluded from the reported bootstrap statistics.")) - } + # Bootstrap the estimates + utils::capture.output(bootmatrix <- parallel::parSapply(cl, 1:nboot, getEstimateResults, d, length)) + # Clean the NAs and report the NAs + bootmatrix <- bootmatrix[,!is.na(bootmatrix[1,])] + fails <- nboot - ncol(bootmatrix) + nboot <- nboot - fails + if (fails > 0) { + message(paste("Bootstrapping encountered a WARNING: ", fails, "bootstrap iterations failed to converge (possibly due to PLSc). \nThese failed iterations are excluded from the reported bootstrap statistics.")) + } - # Collect means and sds for all estimates from bootmatrix - means <- apply(bootmatrix,1,mean) - sds <- apply(bootmatrix,1,stats::sd) - # Create the matrix and array of bootstrapped paths ---- + # Collect means and sds for all estimates from bootmatrix + means <- apply(bootmatrix,1,mean) + sds <- apply(bootmatrix,1,stats::sd) - # Collect the dimensions of the path_coef matrix - path_cols <- ncol(seminr_model$path_coef) - path_rows <- nrow(seminr_model$path_coef) + # Create the matrix and array of bootstrapped paths ---- - # Identify start and end points for path coef data - start <- 1 - end <- (path_cols*path_rows) + # Collect the dimensions of the path_coef matrix + path_cols <- ncol(seminr_model$path_coef) + path_rows <- nrow(seminr_model$path_coef) - # Create the array of bootstrap paths - boot_paths <- array(bootmatrix[start:end,1:nboot], dim = c(path_rows, path_cols, nboot), dimnames = list(rownames(seminr_model$path_coef), colnames(seminr_model$path_coef),1:nboot)) + # Identify start and end points for path coef data + start <- 1 + end <- (path_cols*path_rows) - # Create the summary matrices of means and sds for path coefs - paths_means <- matrix(means[start:end], nrow = path_rows, ncol = path_cols) - paths_sds <- matrix(sds[start:end], nrow = path_rows, ncol = path_cols) + # Create the array of bootstrap paths + boot_paths <- array(bootmatrix[start:end,1:nboot], dim = c(path_rows, path_cols, nboot), dimnames = list(rownames(seminr_model$path_coef), colnames(seminr_model$path_coef),1:nboot)) - # Create the bootstrapped paths matrix (take care to not be stranded with a single column/row vector) - paths_descriptives <- cbind(seminr_model$path_coef, paths_means, paths_sds) + # Create the summary matrices of means and sds for path coefs + paths_means <- matrix(means[start:end], nrow = path_rows, ncol = path_cols) + paths_sds <- matrix(sds[start:end], nrow = path_rows, ncol = path_cols) - # Clean the empty paths (take care to not be stranded with a single column/row vector) - filled_cols <- apply(paths_descriptives != 0, 2, any, na.rm=TRUE) - filled_rows <- apply(paths_descriptives != 0, 1, any, na.rm=TRUE) - paths_descriptives <- subset(paths_descriptives, filled_rows, filled_cols) + # Create the bootstrapped paths matrix (take care to not be stranded with a single column/row vector) + paths_descriptives <- cbind(seminr_model$path_coef, paths_means, paths_sds) - # Get the number of DVs - if (length(unique(structural_model[,"target"])) == 1) { - dependant <- unique(structural_model[,"target"]) - } else { - dependant <- colnames(paths_descriptives[, 1:length(unique(structural_model[,"target"]))]) - } + # Clean the empty paths (take care to not be stranded with a single column/row vector) + filled_cols <- apply(paths_descriptives != 0, 2, any, na.rm=TRUE) + filled_rows <- apply(paths_descriptives != 0, 1, any, na.rm=TRUE) + paths_descriptives <- subset(paths_descriptives, filled_rows, filled_cols) - # Construct the vector of column names - col_names <- c() - # Clean the column names - for (parameter in c("PLS Est.", "Boot Mean", "Boot SD")) { - for (i in 1:length(dependant)) { - col_names <- c(col_names, paste(dependant[i], parameter, sep = " ")) - } + # Get the number of DVs + if (length(unique(structural_model[,"target"])) == 1) { + dependant <- unique(structural_model[,"target"]) + } else { + dependant <- colnames(paths_descriptives[, 1:length(unique(structural_model[,"target"]))]) + } + + # Construct the vector of column names + col_names <- c() + # Clean the column names + for (parameter in c("PLS Est.", "Boot Mean", "Boot SD")) { + for (i in 1:length(dependant)) { + col_names <- c(col_names, paste(dependant[i], parameter, sep = " ")) } + } - # Assign column names - colnames(paths_descriptives) <- col_names + # Assign column names + colnames(paths_descriptives) <- col_names - # Create the matrix and array of bootstrapped loadings ---- + # Create the matrix and array of bootstrapped loadings ---- - # Collect the dimensions of the path_coef matrix - mm_cols <- ncol(seminr_model$outer_loadings) - mm_rows <- nrow(seminr_model$outer_loadings) + # Collect the dimensions of the path_coef matrix + mm_cols <- ncol(seminr_model$outer_loadings) + mm_rows <- nrow(seminr_model$outer_loadings) - # Identify start and end points for path coef data - start <- end+1 - end <- start+(mm_cols*mm_rows)-1 + # Identify start and end points for path coef data + start <- end+1 + end <- start+(mm_cols*mm_rows)-1 - # create the array of bootstrapped loadings - boot_loadings <- array(bootmatrix[start:end,1:nboot], dim = c(mm_rows, mm_cols, nboot), dimnames = list(rownames(seminr_model$outer_loadings), colnames(seminr_model$outer_loadings),1:nboot)) + # create the array of bootstrapped loadings + boot_loadings <- array(bootmatrix[start:end,1:nboot], dim = c(mm_rows, mm_cols, nboot), dimnames = list(rownames(seminr_model$outer_loadings), colnames(seminr_model$outer_loadings),1:nboot)) - # Create the summary matrices of means and sds for loadings - loadings_means <- matrix(means[start:end], nrow = mm_rows, ncol = mm_cols) - loadings_sds <- matrix(sds[start:end], nrow = mm_rows, ncol = mm_cols) + # Create the summary matrices of means and sds for loadings + loadings_means <- matrix(means[start:end], nrow = mm_rows, ncol = mm_cols) + loadings_sds <- matrix(sds[start:end], nrow = mm_rows, ncol = mm_cols) - # Subset paths matrix (take care to not be stranded with a single column/row vector) - loadings_descriptives <- cbind(seminr_model$outer_loadings, loadings_means, loadings_sds) + # Subset paths matrix (take care to not be stranded with a single column/row vector) + loadings_descriptives <- cbind(seminr_model$outer_loadings, loadings_means, loadings_sds) - # Construct the vector of column names 2 for outer model - col_names2 <- c() - # Clean the column names - for (parameter in c("PLS Est.", "Boot Mean", "Boot SD")) { - for(i in colnames(seminr_model$outer_loadings)) { - col_names2 <- c(col_names2, paste(i, parameter, sep = " ")) - } + # Construct the vector of column names 2 for outer model + col_names2 <- c() + # Clean the column names + for (parameter in c("PLS Est.", "Boot Mean", "Boot SD")) { + for(i in colnames(seminr_model$outer_loadings)) { + col_names2 <- c(col_names2, paste(i, parameter, sep = " ")) } + } - # Assign column names to loadings matrix - colnames(loadings_descriptives) <- col_names2 + # Assign column names to loadings matrix + colnames(loadings_descriptives) <- col_names2 - # Identify start and end points for path weights data - start <- end+1 - end <- start+(mm_cols*mm_rows)-1 + # Identify start and end points for path weights data + start <- end+1 + end <- start+(mm_cols*mm_rows)-1 - # create the array of bootstrapped weights - boot_weights <- array(bootmatrix[start:end,1:nboot], dim = c(mm_rows, mm_cols, nboot), dimnames = list(rownames(seminr_model$outer_loadings), colnames(seminr_model$outer_loadings),1:nboot)) + # create the array of bootstrapped weights + boot_weights <- array(bootmatrix[start:end,1:nboot], dim = c(mm_rows, mm_cols, nboot), dimnames = list(rownames(seminr_model$outer_loadings), colnames(seminr_model$outer_loadings),1:nboot)) - # Create the summary matrices of means and sds - weights_means <- matrix(means[start:end], nrow = mm_rows, ncol = mm_cols) - weights_sds <- matrix(sds[start:end], nrow = mm_rows, ncol = mm_cols) + # Create the summary matrices of means and sds + weights_means <- matrix(means[start:end], nrow = mm_rows, ncol = mm_cols) + weights_sds <- matrix(sds[start:end], nrow = mm_rows, ncol = mm_cols) - # create the weights matrix (take care to not be stranded with a single column/row vector) - weights_descriptives <- cbind(seminr_model$outer_weights, weights_means, weights_sds) + # create the weights matrix (take care to not be stranded with a single column/row vector) + weights_descriptives <- cbind(seminr_model$outer_weights, weights_means, weights_sds) - # Assign column names to weights matrix - colnames(weights_descriptives) <- col_names2 + # Assign column names to weights matrix + colnames(weights_descriptives) <- col_names2 - # Collect HTMT matrix - HTMT_matrix <- HTMT(seminr_model) + # Collect HTMT matrix + HTMT_matrix <- HTMT(seminr_model) # Identify start and end points for HTMT data start <- end+1 @@ -263,53 +263,50 @@ bootstrap_model <- function(seminr_model, nboot = 500, cores = NULL, seed = NULL htmt_means <- matrix(means[start:end], nrow = nrow(HTMT_matrix), ncol = ncol(HTMT_matrix)) htmt_sds <- matrix(sds[start:end], nrow = nrow(HTMT_matrix), ncol = ncol(HTMT_matrix)) + # create HTMT matrix (take care to not be stranded with a single column/row vector) + HTMT_descriptives <- cbind(HTMT_matrix, htmt_means, htmt_sds) - # create HTMT matrix (take care to not be stranded with a single column/row vector) - HTMT_descriptives <- cbind(HTMT_matrix, htmt_means, htmt_sds) - - # Construct the vector of column names 3 for HTMT - col_names3 <- c() - # Clean the column names - for (parameter in c("PLS Est.", "Boot Mean", "Boot SD")) { - for(i in colnames(HTMT_matrix)) { - col_names3 <- c(col_names3, paste(i, parameter, sep = " ")) - } + # Construct the vector of column names 3 for HTMT + col_names3 <- c() + # Clean the column names + for (parameter in c("PLS Est.", "Boot Mean", "Boot SD")) { + for(i in colnames(HTMT_matrix)) { + col_names3 <- c(col_names3, paste(i, parameter, sep = " ")) } - # Get boot_HTMT column names - colnames(HTMT_descriptives) <- col_names3 + } + # Get boot_HTMT column names + colnames(HTMT_descriptives) <- col_names3 - # Subset total paths matrix (take care to not be stranded with a single column/row vector) - total_matrix <- total_effects(seminr_model$path_coef) - start <- end+1 - end <- start+(path_cols*path_rows)-1 + # Subset total paths matrix (take care to not be stranded with a single column/row vector) + total_matrix <- total_effects(seminr_model$path_coef) + start <- end+1 + end <- start+(path_cols*path_rows)-1 - # Collect the array of bootstrapped total paths - boot_total_paths <- array(bootmatrix[start:end,1:nboot], dim = c(path_rows, path_cols, nboot), dimnames = list(rownames(total_matrix), colnames(total_matrix),1:nboot)) + # Collect the array of bootstrapped total paths + boot_total_paths <- array(bootmatrix[start:end,1:nboot], dim = c(path_rows, path_cols, nboot), dimnames = list(rownames(total_matrix), colnames(total_matrix),1:nboot)) - # Collect the matrices for means and sds - total_means <- matrix(means[start:end], nrow = path_rows, ncol = path_cols) - total_sds <- matrix(sds[start:end], nrow = path_rows, ncol = path_cols) + # Collect the matrices for means and sds + total_means <- matrix(means[start:end], nrow = path_rows, ncol = path_cols) + total_sds <- matrix(sds[start:end], nrow = path_rows, ncol = path_cols) - # Subset paths matrix (take care to not be stranded with a single column/row vector) - total_paths_descriptives <- cbind(total_matrix, total_means, total_sds) + # Subset paths matrix (take care to not be stranded with a single column/row vector) + total_paths_descriptives <- cbind(total_matrix, total_means, total_sds) - # Clean the empty paths (take care to not be stranded with a single column/row vector) - filled_cols <- apply(total_paths_descriptives != 0, 2, any, na.rm=TRUE) - filled_rows <- apply(total_paths_descriptives != 0, 1, any, na.rm=TRUE) - total_paths_descriptives <- subset(total_paths_descriptives, filled_rows, filled_cols) + # Clean the empty paths (take care to not be stranded with a single column/row vector) + filled_cols <- apply(total_paths_descriptives != 0, 2, any, na.rm=TRUE) + filled_rows <- apply(total_paths_descriptives != 0, 1, any, na.rm=TRUE) + total_paths_descriptives <- subset(total_paths_descriptives, filled_rows, filled_cols) - # Assign column names - colnames(total_paths_descriptives) <- col_names + # Assign column names + colnames(total_paths_descriptives) <- col_names - # Change the class of the descriptives objects - class(paths_descriptives) <- append(class(paths_descriptives), "table_output") - class(loadings_descriptives) <- append(class(loadings_descriptives), "table_output") - class(weights_descriptives) <- append(class(weights_descriptives), "table_output") - class(HTMT_descriptives) <- append(class(HTMT_descriptives), "table_output") - class(total_paths_descriptives) <- append(class(total_paths_descriptives), "table_output") - parallel::stopCluster(cl) - } + # Change the class of the descriptives objects + class(paths_descriptives) <- append(class(paths_descriptives), "table_output") + class(loadings_descriptives) <- append(class(loadings_descriptives), "table_output") + class(weights_descriptives) <- append(class(weights_descriptives), "table_output") + class(HTMT_descriptives) <- append(class(HTMT_descriptives), "table_output") + class(total_paths_descriptives) <- append(class(total_paths_descriptives), "table_output") # Add the bootstrap matrix to the seminr_model object seminr_model$boot_paths <- boot_paths @@ -331,17 +328,16 @@ bootstrap_model <- function(seminr_model, nboot = 500, cores = NULL, seed = NULL error = function(cond) { message("Bootstrapping encountered this ERROR: ") message(cond) - parallel::stopCluster(cl) - return(NULL) + seminr_model <- NULL }, warning = function(cond) { message("Bootstrapping encountered this WARNING:") message(cond) - parallel::stopCluster(cl) - return(NULL) + seminr_model <- NULL }, finally = { - # + parallel::stopCluster(cl) + return(seminr_model) } ) } diff --git a/R/estimate_factor_scores.R b/R/estimate_factor_scores.R index de9eac1..a6c3f8d 100644 --- a/R/estimate_factor_scores.R +++ b/R/estimate_factor_scores.R @@ -38,8 +38,8 @@ #' @export estimate_lavaan_ten_berge <- function (fit) { X <- lavaan::lavInspect(fit, "data") - i.means <- fit@SampleStats@mean[[1]] - i.sds <- sqrt(fit@SampleStats@var[[1]]) + i.means <- colMeans(X) + i.sds <- sqrt(diag(lavaan::lavInspect(fit, "sampstat")$cov)) Lambda_mat <- lavaan::lavInspect(fit, what = "std.lv")$lambda Phi_mat <- matrix(lavaan::lavInspect(fit, what = "cor.lv"), ncol(Lambda_mat)) calc_ten_berge_scores(X, Lambda_mat, Phi_mat, i.means, i.sds) diff --git a/R/estimate_pls_mga.R b/R/estimate_pls_mga.R index 6c2d448..ac626d6 100644 --- a/R/estimate_pls_mga.R +++ b/R/estimate_pls_mga.R @@ -50,12 +50,6 @@ estimate_pls_mga <- function(pls_model, condition, nboot = 2000, ...) { path_coef[path["source"], path["target"]] } - # Get all path estimates of a given beta metrix from a given path_coef matrix - # Typically used to apply on 3rd dimension of a 3x3 bootstrap paths array [from,to,boot] - boot_paths <- function(path_coef, beta_df) { - betas <- apply(beta_df, MARGIN=1, FUN=path_estimate, path_coef = path_coef) - } - # Allocate and Estimate Two Alternative Datasets + Models group1_data <- pls_data[condition, ] group2_data <- pls_data[!condition, ] @@ -82,11 +76,8 @@ estimate_pls_mga <- function(pls_model, condition, nboot = 2000, ...) { beta$diff <- apply(beta, MARGIN = 1, FUN=path_estimate, path_coef = beta_diff) # Get bootstrapped paths for both groups - boot1_betas <- t(apply(group1_boot$boot_paths, MARGIN=3, FUN=boot_paths, beta_df=beta)) - colnames(boot1_betas) <- path_names - - boot2_betas <- t(apply(group2_boot$boot_paths, MARGIN=3, FUN=boot_paths, beta_df=beta)) - colnames(boot2_betas) <- path_names + boot1_betas <- boot_paths_df(group1_boot) + boot2_betas <- boot_paths_df(group2_boot) # PLSc may not resolve in some bootstrap runs - limit bootstrap paths to resolved number of boots J <- min(dim(boot1_betas)[1], dim(boot2_betas)[1]) diff --git a/R/estimate_simplePLS.R b/R/estimate_simplePLS.R index 524b188..f6843c4 100644 --- a/R/estimate_simplePLS.R +++ b/R/estimate_simplePLS.R @@ -79,7 +79,8 @@ simplePLS <- function(obsData, smMatrix, mmMatrix, inner_weights = path_weightin constructs <- construct_names(smMatrix) #Extract and Normalize the measurements for the model - normData <- scale(obsData[, mmVariables], TRUE, TRUE) + # normData <- scale(obsData[, mmVariables], TRUE, TRUE) + normData <- standardize_safely(obsData[, mmVariables]) #Extract Mean and Standard Deviation of measurements for future prediction meanData <- attr(normData, "scaled:center") @@ -121,7 +122,8 @@ simplePLS <- function(obsData, smMatrix, mmMatrix, inner_weights = path_weightin construct_scores <- normData[, mmVariables]%*%outer_weights #Standardize construct Scores - construct_scores <- scale(construct_scores,TRUE,TRUE) + # construct_scores <- scale(construct_scores,TRUE,TRUE) + construct_scores <- standardize_safely(construct_scores) #Estimate inner paths using weighting scheme - factorial or path-weighting inner_paths <- inner_weights(smMatrix, construct_scores, dependant, paths_matrix) @@ -130,7 +132,8 @@ simplePLS <- function(obsData, smMatrix, mmMatrix, inner_weights = path_weightin construct_scores<-construct_scores%*%inner_paths #Standarize construct Scores - construct_scores <- scale(construct_scores, TRUE, TRUE) + #construct_scores <- scale(construct_scores, TRUE, TRUE) + construct_scores <- standardize_safely(construct_scores) #Save last outer_weights last_outer_weights <- outer_weights @@ -187,4 +190,3 @@ simplePLS <- function(obsData, smMatrix, mmMatrix, inner_weights = path_weightin class(plsModel) <- "simple_pls_model" return(plsModel) } - diff --git a/R/report_lavaan.R b/R/report_lavaan.R index 01fb507..51007ab 100644 --- a/R/report_lavaan.R +++ b/R/report_lavaan.R @@ -6,7 +6,7 @@ summarize_cb_measurement <- function(object, alpha=0.05) { model <- list( item_names = all_items(object$measurement_model), construct_names = all_construct_names(object$measurement_model), - estimation = lavaan_output@Model@estimator + estimation = lavaan::lavInspect(lavaan_output, "options")$estimator ) # Get standardized parameter estimates (won't contain R^2) @@ -29,8 +29,8 @@ summarize_cb_measurement <- function(object, alpha=0.05) { seminr = seminr_info(), engine = list( pkgname = "lavaan", - version = lavaan_output@version, - estimator = lavaan_output@Options$estimator + version = lavaan::lavInspect(lavaan_output, "version"), + estimator = lavaan::lavInspect(lavaan_output, "options")$estimator ), syntax = object$lavaan_model, call = lavaan_output@call diff --git a/man/boot_paths_df.Rd b/man/boot_paths_df.Rd new file mode 100644 index 0000000..c80b153 --- /dev/null +++ b/man/boot_paths_df.Rd @@ -0,0 +1,41 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/boot_utils.R +\name{boot_paths_df} +\alias{boot_paths_df} +\title{Return all path bootstraps as a long dataframe. +Columns of the dataframes are specified paths and rows are the estimated +coefficients for the paths at each bootstrap iteration.} +\usage{ +boot_paths_df(pls_boot) +} +\arguments{ +\item{pls_boot}{bootstrapped PLS model} +} +\description{ +Return all path bootstraps as a long dataframe. +Columns of the dataframes are specified paths and rows are the estimated +coefficients for the paths at each bootstrap iteration. +} +\examples{ +data(mobi) + +mobi_mm <- constructs( + composite("Image", multi_items("IMAG", 1:5)), + composite("Expectation", multi_items("CUEX", 1:3)), + composite("Satisfaction", multi_items("CUSA", 1:3)) +) + +mobi_sm <- relationships( + paths(from = c("Image", "Expectation"), to = "Satisfaction") +) + +pls_model <- estimate_pls(data = mobi, + measurement_model = mobi_mm, + structural_model = mobi_sm) + +pls_boot <- bootstrap_model(seminr_model = pls_model, + nboot = 50, cores = 2, seed = NULL) + +boot_paths_df(pls_boot) + +} diff --git a/man/standardize_safely.Rd b/man/standardize_safely.Rd new file mode 100644 index 0000000..8673380 --- /dev/null +++ b/man/standardize_safely.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/compute_safe.R +\name{standardize_safely} +\alias{standardize_safely} +\title{Standardize (scale) a matrix/df and report interpretable errors} +\usage{ +standardize_safely(x) +} +\arguments{ +\item{x}{vector, data.frame, or matrix} +} +\value{ +scaled object as returned by \code{scale} function +} +\description{ +Standardize (scale) a matrix/df and report interpretable errors +} diff --git a/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-factor_scores.csv b/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-factor_scores.csv index f1fd1d0..c1cdb31 100644 --- a/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-factor_scores.csv +++ b/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-factor_scores.csv @@ -1,251 +1,251 @@ "Image","Expectation","Value","Satisfaction","Image_x_Expectation" --1.65311663760844,-0.31344363595676,-2.45808469387049,-1.38942393292756,0.104365605831216 -1.63753013822352,0.45775742338512,1.83243023159266,1.4850399479121,0.345934738910032 --0.947822925106493,-0.175135525208009,0.224898655421474,-0.406940955603954,-0.204007029965296 -0.0945415052581406,-1.52084740587346,-0.86714347666662,0.730586269380107,-0.388906709067562 -0.434335057179272,0.770048867151385,-0.398869798640584,0.322310085976964,-0.162847979564333 -0.805875307864028,-0.273251033669854,1.94397761059034,0.512069997182115,-0.302271071265925 --0.881351836844197,-2.60000478038257,-0.256404664331831,-0.350803929848176,1.93176385448129 --0.00737810766433033,-2.99931711425482,-0.156477552009238,-0.451403823720454,0.0521163249420918 --0.313650839059643,-0.154597021233693,0.0065332831658741,-0.295273318325727,-0.274027748173852 --0.721761717774211,-2.04642603886283,-0.759356094358601,-1.31542465718633,1.42831485621413 --0.586375636182544,-0.452405305247811,-0.125131049124296,-0.385343763177868,0.143873815607566 -0.373070346999616,0.704362898059517,0.0253481361759513,0.0485038861441519,-0.257186359167764 -0.408908576091063,0.589866391687724,-0.53455206934767,0.208218349607361,-0.122064924544237 -1.45522819908597,1.01125394615473,0.524379712565446,0.878314995155147,1.00621383933215 --0.450306144452365,1.36769734924283,-1.26787863248018,-0.84466887368591,-1.05121430786691 --0.0876072447640088,0.467500100002468,-0.914404083754487,-0.54227735942248,-0.1058168655723 -0.293549029665814,-0.760788877198528,0.869985890646328,0.0420714528097165,-0.215106960296853 --2.09850528038388,-0.342607806869939,0.000794226575763234,-1.59469352326201,0.223940958230254 -1.04305783015469,1.3192376839753,0.394521660825692,1.05744681804961,0.803639985687271 --0.743418762639325,-0.811609300995118,0.248431401523996,-0.417297353043972,0.183526612315901 -0.660412598108308,-0.347991785014951,0.274703178120172,0.729923874814857,-0.530200151869181 --0.415105347917635,0.9593261908818,-0.928294049986689,-1.15737954790626,-0.597443220345832 --0.474241800732116,-0.183244535210648,-0.0622904250951524,-0.747596770802401,-0.233116164415581 -0.260964763585919,0.337325488978223,1.35628844238099,0.650252633160047,-0.209843031143372 --1.13344943646639,-0.505419180639499,-1.19407751120063,-2.09586548304679,0.373322413217556 -1.61634096688012,0.0212909726686243,0.568370974820772,1.41875008655622,-0.0293711139895107 -0.501407348721721,0.0298257021769877,0.155863350979446,-0.209044969436584,-0.305544436837275 --1.58978629731844,0.492695214443887,1.19712150213457,-0.332996717565797,-1.19580693320159 --0.202689733603859,-0.325690954361699,0.488550830399567,-0.0415847575988856,-0.139596889270656 -1.58477954174007,1.06966961269536,1.54710715834647,1.50212430539526,0.97385524990833 -0.0110621589958548,-0.53258059088017,1.0819952473052,0.327026146471147,-0.39205198282454 --0.875872674141562,-0.855546846989926,-1.05259667781853,-1.25984790408799,0.563564242401966 -0.546561695360829,-0.717520119492011,0.864216771095756,0.0897656445782403,-0.447721469049803 -1.89752701819962,2.13941286542403,0.678562642162269,1.82541598302801,2.33206095411415 --1.53885741703942,0.436557046108019,-3.10968889305271,-2.93028950737593,-1.18605560578108 -1.48635522567448,1.89592959361762,0.707710147294018,0.554437009520252,1.60720553324186 --0.831546945446428,0.355231307825748,-1.06371864909139,-1.45003149302256,-0.741259328726622 --0.83766373681164,-1.0606262781045,-0.81058533695349,-0.807136217828595,0.71204747704321 --0.00248921984662703,-0.841907049877694,1.00045748533096,0.448949250566045,-0.351521207698818 --0.446376409897046,0.34804032913269,0.352999095387659,-0.176282416850604,-0.289124437312442 -0.277358004535049,1.11380204385098,0.749435658742284,0.829492484062377,-0.153911721485836 -0.610046980886215,-0.115802622072614,0.321606851564394,0.703884855321245,-0.30472289923544 -0.828566683127437,-1.4788289768433,1.34204641364323,0.989472814856237,-0.996390251140038 --0.556062595664433,0.0453165747431443,-1.24024241791601,-0.114094804393026,-0.321664033312724 -1.67289835769918,1.16726300386944,1.50625901161318,1.76395316787423,1.15357133976001 -1.09569783559562,1.57605028567215,0.424677122983881,0.767411126401313,0.916954912365673 --0.166884757537172,1.13880083504372,-1.56257448411601,-0.618410572578221,-0.696591080119587 --0.181114125632897,-1.20102043657512,0.363441245706551,0.135472080636599,0.00967425483305244 --2.4702256881029,-1.05320280387935,-1.41739018609765,-2.29571131986969,2.50106435622597 --1.74212983365246,-2.85895218599765,-2.60139691010053,-2.06940067926326,5.59421153644622 -1.19307673634803,1.19241241877672,-0.20699305627263,0.951854433618074,0.68901916958916 -0.0141580093650462,0.455239317873508,-0.579013140437789,-0.269136882368153,-0.363817830666846 --0.109911990842853,0.582426052505863,0.639795584590187,0.223750596538092,-0.372983121426492 --0.385986919343532,-0.0980437730515793,0.267017759502516,-0.12318986493503,-0.602435804325513 --0.342536281459771,-1.35327389425925,0.232061499653562,-0.257280035564402,0.233962259236163 --0.434699699565327,-0.473690834389739,0.319504242898249,-0.13125512900713,-0.0738259626894632 --0.400825179685033,0.625531350039805,-0.615363285006723,-1.43502450441867,-0.872493371133668 -0.837620971437214,0.73433031801593,0.861304520783008,0.68469293315539,0.239660073668696 -0.927506660699993,-0.227949433800433,0.789309883268344,1.18808325783324,-0.406516421588705 --0.240166166528783,-1.2841909392408,-0.522020732696007,-0.199484984978598,0.0780154744429553 --1.0411581788371,-2.53271693117174,-1.48090980674062,-1.38842319526312,2.68612253071283 --0.412469104524439,-0.632303338315385,0.00672077416985213,-0.256189800599806,-0.179037812352939 -0.414701912979832,1.02784751804702,0.97899109259724,0.546308324719441,0.0686440188670508 --1.18053720970548,0.330678734356928,-0.405770208274692,-1.05164031609129,-1.05909558826418 --0.0968009165860534,0.0432192621946018,-0.144838156940617,-0.175263028206108,-0.393517639800606 --0.289652719239464,-0.688031516843187,-1.23877748418502,-0.769449445538965,0.0303806741429499 --0.0497874158680844,0.512789017426387,0.00889033127427039,0.232537209937688,-0.35225362079462 --0.983950940957415,0.397170514077019,-0.966146750254297,-2.07022218963249,-0.641861335497078 -0.067033304034909,-0.0513284047699479,0.0104903974747308,0.0890825817552725,-0.3058249969125 --0.00910771790993738,0.479199523331852,-0.292156662518444,-0.286019272412105,-0.492506959336318 -0.131436120779899,0.0450811837900239,0.115182431513655,0.228679374640856,-0.176673862681021 -0.196800884591455,0.756527434560771,-0.0779032478881387,0.260243824909476,-0.292738838157199 -0.495611087172566,-0.529612928861425,-3.17334433539135,0.703044477139772,-0.35361080283494 --1.20288425399175,-1.51736251970943,0.28353670429242,-1.06343064446783,1.76061445041855 --0.695426556921165,1.40992643340589,-0.0624576992672924,-1.1192891883454,-1.21413777554671 --1.71959589686769,0.799946154327415,-1.42972162920086,-1.17608717854309,-1.82570095891678 -0.135231400705502,1.74494579706078,0.790075593975251,0.49046650307774,-0.235968311472103 --0.721765477600898,-1.16687263300057,-1.03972666640262,-0.634488729108364,0.710112793638114 --0.100023525094771,-0.36595042756212,-0.533634429031959,-0.237295395881194,-0.143338418858985 --0.0542857714217359,-0.0148191694204197,0.338662188403945,0.125832365454457,-0.426641883956892 -0.937592306542176,0.866767694722745,0.399978657599823,0.871288658964508,0.317505503241792 --0.159882331401594,-1.64115442462673,0.227853456962177,-0.394037712579435,0.168017110174783 -0.788869268187256,0.124707486509651,0.417703574164907,1.25614494994768,-0.215423871504286 --0.815760488149183,-0.62800748337472,-0.0476563114558243,-0.818648153721641,0.269373279341273 --0.507286071259976,0.157913648904179,0.0428348944178672,-0.549695666242025,-0.536464497720653 -0.18870496580441,-1.8277772935203,0.161076385313457,0.0607643112580788,-0.440038306544884 -1.08450898690573,1.58448642019897,1.30431769165318,1.4187535784969,0.792087575425937 -0.937390329864782,0.47522907632257,1.30717178286586,0.880009285164135,0.000540057126072579 -0.122423887215111,0.0301080941150792,0.420514201239551,0.403322830119727,-0.293972739715416 -1.00739976823483,-0.0728003713083834,0.678966163814812,0.920747315384553,-0.421114032952281 -0.60847598247559,-0.356457313660838,0.593308920108266,0.434168624120511,-0.262176233308029 --0.15516808201591,-0.363412895242792,0.0128281065688134,-0.0171311958479943,-0.3299548316649 -0.978659609652877,-0.0632517040545699,0.618437336985571,1.08702090907279,-0.298364169415357 -0.253388507233389,-0.554990615158024,0.259538903320191,0.268841931496037,-0.421048446434071 --2.7560003316326,-0.016425422913998,-1.3660263348818,-2.7187059343662,-0.0116500235733519 -1.75173860933546,1.72759903027004,1.53790485847341,1.62035282031088,1.9616475814027 -0.520974646910596,0.334410638556445,0.901578953249919,0.922799333594476,-0.310834703114782 --0.563840357941899,0.218052775444952,0.0721292783495116,-0.356846560883396,-0.462872952277503 --0.945731110717942,-1.12936764300825,-0.27586609907111,-0.638936293418407,0.63520426197277 -1.59864896549025,0.64724900439353,0.196382814332795,1.33506153707909,0.301058080706597 --0.288351517016149,0.173876857865194,0.470956628358863,-0.479520053107144,-0.417375797662745 -0.334754784804884,-0.311062249200327,0.241771015668551,0.229732992837551,-0.411352760428152 --0.00207751763741621,0.233339355997984,0.554499561839669,0.232315646372328,-0.764509126883015 --1.17214793212645,-0.953094876431541,-0.544451814786467,-0.440285695165349,1.1146689504094 -0.556511831809618,-0.761002071301928,0.573322370499936,0.365209126556434,-0.492545602733514 --1.77223552272087,0.83200454397945,-0.731603560436063,-1.59087198357907,-1.74607253116935 --0.627789193210126,1.1774500669498,1.00878284219098,0.253056736049991,-1.25011466026966 -0.165444524850114,0.354101297224121,-0.55705349189918,0.392319966319336,-0.136667185493464 -0.988220045386597,-0.364469722134455,0.81732639215998,0.589237030778937,-0.482561666849208 -0.177218165088127,0.120394768078976,0.315842215031484,0.82978861976956,-0.152408501804879 --0.996836788292801,-1.25433051838905,-0.352524091851643,-0.420497522070357,1.13959018026807 -0.775506303057504,0.940188597760627,0.957495175975657,0.384378191697343,0.447604513785775 -1.08091413339697,0.736598349666272,0.865539274117694,1.29433475994001,0.256975157578083 -1.02548883909431,0.889424488112364,0.456589818664163,1.31058206840717,0.479027052664125 -1.49336509834705,0.569889794771977,0.746270849813803,1.3725009610834,0.255790269099372 --0.159995022691246,1.30251258691171,-1.37882745127821,-1.12555594557912,-0.149468915280064 -1.86693174627034,0.849420720063639,1.90097462554697,2.13554966289541,0.81060821328034 -1.41408342621126,0.999317573976669,0.659272051508478,0.776243658910661,0.974369314399656 -1.22857577791444,1.51651205323739,1.42624679681187,1.232371065258,1.05975989728174 -2.011023553017,-0.116686330515219,0.89587745470947,1.93318634126707,-0.306459439266626 -0.63124941770679,0.716180058496578,0.808884763764529,0.217092020563247,-0.0793550497932053 --0.483336917300154,-0.030289142434966,-1.52993753720273,-0.943521932057192,-0.333523199893047 -1.95315118246416,0.0221005617418407,1.8110438340212,2.00380496019865,-0.114032567672225 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+-0.31635902183182,-0.392473045236111,-1.12668225105713,-0.620466105372378,-0.179138288183613 +0.890808057147616,-1.22848414414484,0.701655427505392,0.505661529858122,-1.04250042965793 diff --git a/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-paths-coefficients.csv b/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-paths-coefficients.csv index 9d1be26..4833177 100644 --- a/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-paths-coefficients.csv +++ b/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-paths-coefficients.csv @@ -1,6 +1,6 @@ "","Satisfaction" -"R^2",0.867004748337413 -"Image",0.724633621764904 -"Expectation",0.022683957508655 -"Value",0.266527420415955 -"Image_x_Expectation",-0.041955167391547 +"R^2",0.867004736628511 +"Image",0.724633619383304 +"Expectation",0.0226839732661398 +"Value",0.26652741022581 +"Image_x_Expectation",-0.0419551600054487 diff --git a/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-quality-reliability.csv b/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-quality-reliability.csv index 7832f37..5b206e5 100644 --- a/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-quality-reliability.csv +++ b/tests/fixtures/V_3_6_0/cbsem-interaction-2stage-quality-reliability.csv @@ -1,5 +1,5 @@ "","rhoC","AVE" -"Image",0.728428596961288,0.355273071411624 +"Image",0.728428598556626,0.355273073142694 "Expectation",1,1 -"Value",0.836293354692477,0.721674405046825 -"Satisfaction",0.777839087981647,0.539811366737263 +"Value",0.836293352209213,0.721674402072776 +"Satisfaction",0.777839091786689,0.539811372194538 diff --git a/tests/fixtures/V_3_6_0/cbsem-interaction-pi-factor_scores.csv b/tests/fixtures/V_3_6_0/cbsem-interaction-pi-factor_scores.csv index 4d6e9f0..aa5d134 100644 --- a/tests/fixtures/V_3_6_0/cbsem-interaction-pi-factor_scores.csv +++ b/tests/fixtures/V_3_6_0/cbsem-interaction-pi-factor_scores.csv @@ -1,251 +1,251 @@ "Image","Expectation","Value","Satisfaction","Image_x_Expectation" 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TWO-STAGE interaction paths correctly\n", {