Develop

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: seminr
 Type: Package
 Title: Domain-Specific Language for Building PLS Structural Equation Models
-Version: 0.5.2
-Date: 2018-09-19
+Version: 0.5.3
+Date: 2018-10-18
 Authors@R: c(person("Soumya", "Ray",
               email = "soumya.ray@gmail.com", role = c("aut", "ths")),
              person("Nicholas", "Danks",

R/consistent.R

@@ -55,6 +55,8 @@ PLSc <- function(seminr_model) {
 
   # Calculate rho_A for adjustments and adjust the correlation matrix
   rho <- rho_A(seminr_model)
+  ### Coerce interactions to rhoA of 1
+  rho[grepl("\\*", rownames(rho)),] <- 1
   adjustment <- sqrt(rho %*% t(rho))
   diag(adjustment) <- 1
   adj_construct_score_cors <- stats::cor(seminr_model$construct_scores) / adjustment
@@ -102,8 +104,13 @@ PLSc <- function(seminr_model) {
 
 # Function to implement PLSc as per Dijkstra, T. K., & Henseler, J. (2015). Consistent Partial Least Squares Path Modeling, 39(X).
 model_consistent <- function(seminr_model) {
+  # if(!is.null(seminr_model$mobi_xm) && ("C" %in% seminr_model$mmMatrix[,"type"])) {
+  #   cat("Models with interactions cannot be estimated as PLS consistent and therefore no adjustment for PLS consistent has been made\n")
+  # }
   if(!is.null(seminr_model$mobi_xm) && ("C" %in% seminr_model$mmMatrix[,"type"])) {
-    cat("Models with interactions cannot be estimated as PLS consistent and therefore no adjustment for PLS consistent has been made\n")
+    cat("Models with interactions can be estimated as PLS consistent, but are subject to some bias as per Becker et al. (2018)
+        'Estimating Moderating Effects in PLS-SEM and PLSc-SEM: Interaction Term Generation*Data Treatment'\n")
+    seminr_model <- PLSc(seminr_model)
   }
   if(is.null(seminr_model$mobi_xm) && ("C" %in% seminr_model$mmMatrix[,"type"])) {
     seminr_model <- PLSc(seminr_model)

R/evaluation.R

@@ -1,6 +1,8 @@
 evaluate_model <- function(seminr_model) {
   rel <- reliability(seminr_model)
   val <- validity(seminr_model)
+  # Coerce reliability of interaction to 1
+  #rel[grepl("\\*", rownames(rel)),] <- 1
   out <- list(rel,val)
   names(out) <- c("reliability","validity")
   return(out)

R/interactions.R

@@ -90,8 +90,8 @@ interactions <- function(...) {
 interaction_ortho <- function(construct1, construct2) {
   function(data, mm) {
     interaction_name <- paste(construct1, construct2, sep="*")
-    iv1_items <- mm[mm[, "construct"] == construct1, ][, "measurement"]
-    iv2_items <- mm[mm[, "construct"] == construct2, ][, "measurement"]
+    iv1_items <- mm[mm[, "construct"] == construct1, "measurement"]
+    iv2_items <- mm[mm[, "construct"] == construct2, "measurement"]
 
     iv1_data <- as.data.frame(scale(data[iv1_items]))
     iv2_data <- as.data.frame(scale(data[iv2_items]))
@@ -100,9 +100,14 @@ interaction_ortho <- function(construct1, construct2) {
       iv2_data*col
     }
 
+    name_items <- function(item_name) {
+      sapply(iv2_items, function(item2, item1 = item_name) paste(item1, item2, sep = "*"))
+    }
+
     multiples_list <- lapply(iv1_data, mult)
     interaction_data <- do.call("cbind", multiples_list)
-    colnames(interaction_data) <- gsub("\\.", "\\*", colnames(interaction_data))
+    #colnames(interaction_data) <- gsub("\\.", "\\*", colnames(interaction_data))
+    colnames(interaction_data) <- as.vector(sapply(iv1_items, name_items))
 
     # Create formula
     frmla <- stats::as.formula(paste("interaction_data[,i]",paste(as.vector(c(iv1_items,iv2_items)), collapse ="+"), sep = " ~ "))
@@ -159,8 +164,8 @@ interaction_ortho <- function(construct1, construct2) {
 interaction_scaled <- function(construct1, construct2) {
   function(data, mm) {
     interaction_name <- paste(construct1, construct2, sep="*")
-    iv1_items <- mm[mm[, "construct"] == construct1, ][, "measurement"]
-    iv2_items <- mm[mm[, "construct"] == construct2, ][, "measurement"]
+    iv1_items <- mm[mm[, "construct"] == construct1, "measurement"]
+    iv2_items <- mm[mm[, "construct"] == construct2, "measurement"]
 
     iv1_data <- as.data.frame(scale(data[iv1_items]))
     iv2_data <- as.data.frame(scale(data[iv2_items]))
@@ -169,9 +174,14 @@ interaction_scaled <- function(construct1, construct2) {
       iv2_data*col
     }
 
+    name_items <- function(item_name) {
+      sapply(iv2_items, function(item2, item1 = item_name) paste(item1, item2, sep = "*"))
+    }
+
     multiples_list <- lapply(iv1_data, mult)
     interaction_data <- do.call("cbind", multiples_list)
-    colnames(interaction_data) <- gsub("\\.", "\\*", colnames(interaction_data))
+    #colnames(interaction_data) <- gsub("\\.", "\\*", colnames(interaction_data))
+    colnames(interaction_data) <- as.vector(sapply(iv1_items, name_items))
 
     return(list(name = interaction_name, data = interaction_data))
   }

cran-comments.md

@@ -1,21 +1,9 @@
 ## Resubmission
 This is a resubmission. In this version I have:
 
-## Attended to a CRAN error:
-We see such failures occasionally: for the record this time it is
-
- SEMinR Model succesfully bootstrapped── 1. Failure: Seminr evaluates the factor discriminant validity p_values correc
- diff[1, 2] is not strictly less than 0.1. Difference: 0.000314
-
-Seems you failed to heed the warnings in §1.6 of 'Writing R Extensions': please re-read it and correct ASAP (CRAN is shut until Sep 10) and before Sep 21 to safely retain the package on CRAN.
-
-Also try a spell checker --- it is 'successfully'.
-
--- 
-Brian D. Ripley,                  ripley@stats.ox.ac.uk
-Emeritus Professor of Applied Statistics, University of Oxford
-
-Specifically, we updated the bootstrap method to take a seed for reproducability and added a tolerance of 0.0001 for floating point calculation tests. All tests including random processes such as bootstrapping were made reproducible.
+## Attended to a user-reported error:
+Single-item interactions were generating a bug in the code. 
+Issue #84 on https://github.com/sem-in-r/seminr/issues/84
 
 ## Test environments
 * macOS Sierra 10.12.6 (on travis-ci), R 3.5.0

inst/doc/SEMinR.Rmd

@@ -348,7 +348,7 @@ mobi_pls <- estimate_pls(data = mobi,
 
 Dijkstra and Henseler (2015) offer an adjustment to generate consistent weight and path estimates of common factors estimated using PLSPM. SEMinR automatically adjusts for consistent estimates of coefficients for common-factors defined using `reflective()`.
 
-Note: At this point, SEMinR does not adjust for PLSc on models with interactions involving common-factors. Thus in models including common-factors and interactions, the coefficient of common-factors will be subject to bias.
+Note: SEMinR does adjust for PLSc on models with interactions involving common-factors. Models with interactions can be estimated as PLS consistent, but are subject to some bias as per Becker et al. (2018). In small sample sizes, bootstrapping such a PLSc model with interactions can cause errors.
 
 ## Bootstrapping the model for significance
 
@@ -415,6 +415,7 @@ summary(boot_mobi_pls)
 
 ## References
 
+* Becker et al. (2018). Estimating Moderating Effects in PLS-SEM and PLSc-SEM: Interaction Term Generation*Data Treatment
 * Dijkstra, T. K., & Henseler, J. (2015). Consistent Partial Least Squares Path Modeling, MIS Quarterly Vol. 39(X).
 * Dillon, W. R, and M. Goldstein. 1987. Multivariate Analysis: Methods, and Applications. Biometrical Journal 29 (6): 750–756.
 * Fornell, C. and D. F. Larcker (February 1981). Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, pp. 39-5)

inst/doc/SEMinR.html

@@ -288,7 +288,7 @@ <h3>Describe all model interactions with <code>interactions()</code></h3>
 #&gt;     intxns_list &lt;- lapply(all_intxns, create_interaction)
 #&gt;     return(intxns_list)
 #&gt;   }
-#&gt; &lt;environment: 0x1149fa470&gt;</code></pre>
+#&gt; &lt;environment: 0x10e7240d0&gt;</code></pre>
 <p>Not that these functions themselves return functions that are not resolved until passed as a parameter to the <code>estimate_pls()</code> function for model estimation.</p>
 </div>
 <div id="describe-an-interaction-with-interaction_ortho-or-interaction_scaled" class="section level3">
@@ -399,7 +399,7 @@ <h2>PLS SEM Model Estimation</h2>
 <div id="consistent-pls-plsc-for-common-factors" class="section level3">
 <h3>Consistent PLS (PLSc) for common-factors</h3>
 <p>Dijkstra and Henseler (2015) offer an adjustment to generate consistent weight and path estimates of common factors estimated using PLSPM. SEMinR automatically adjusts for consistent estimates of coefficients for common-factors defined using <code>reflective()</code>.</p>
-<p>Note: At this point, SEMinR does not adjust for PLSc on models with interactions involving common-factors. Thus in models including common-factors and interactions, the coefficient of common-factors will be subject to bias.</p>
+<p>Note: SEMinR does adjust for PLSc on models with interactions involving common-factors. Models with interactions can be estimated as PLS consistent, but are subject to some bias as per Becker et al. (2018). In small sample sizes, bootstrapping such a PLSc model with interactions can cause errors.</p>
 </div>
 </div>
 <div id="bootstrapping-the-model-for-significance" class="section level2">
@@ -420,7 +420,7 @@ <h2>Bootstrapping the model for significance</h2>
                                  nboot = 1000,
                                  cores = 2)
 #&gt; Bootstrapping model using seminr...
-#&gt; SEMinR Model succesfully bootstrapped</code></pre>
+#&gt; SEMinR Model successfully bootstrapped</code></pre>
 <p>Notably, bootstrapping can also be meaningfully applied to models containing interaction terms and readjusts the interaction term (Henseler and Chin 2010) for every sub-sample. This leads to slightly increased processing times, but provides accurate estimations.</p>
 </div>
 <div id="reporting-the-pls-sem-model" class="section level2">
@@ -480,19 +480,19 @@ <h3>Reporting the bootstrapped <code>boot_seminr_model</code></h3>
 #&gt; 
 #&gt; Structural Path t-values:
 #&gt;                   Satisfaction
-#&gt; Image                    8.031
-#&gt; Expectation              2.847
-#&gt; Value                    4.496
-#&gt; Image*Expectation        0.936
-#&gt; Image*Value              0.050
+#&gt; Image                    7.954
+#&gt; Expectation              2.754
+#&gt; Value                    4.590
+#&gt; Image*Expectation        0.997
+#&gt; Image*Value              0.032
 #&gt; 
 #&gt; Structural Path p-values:
 #&gt;                   Satisfaction
 #&gt; Image                    0.000
-#&gt; Expectation              0.005
+#&gt; Expectation              0.006
 #&gt; Value                    0.000
-#&gt; Image*Expectation        0.349
-#&gt; Image*Value              0.960</code></pre>
+#&gt; Image*Expectation        0.319
+#&gt; Image*Value              0.974</code></pre>
 <ol start="2" style="list-style-type: decimal">
 <li><code>boot_model_summary &lt;- summary(boot_seminr_model)</code> returns an object of class <code>summary.boot_seminr_model</code> which contains the following accessible objects:
 <ul>
@@ -506,6 +506,7 @@ <h3>Reporting the bootstrapped <code>boot_seminr_model</code></h3>
 <div id="references" class="section level2">
 <h2>References</h2>
 <ul>
+<li>Becker et al. (2018). Estimating Moderating Effects in PLS-SEM and PLSc-SEM: Interaction Term Generation*Data Treatment</li>
 <li>Dijkstra, T. K., &amp; Henseler, J. (2015). Consistent Partial Least Squares Path Modeling, MIS Quarterly Vol. 39(X).</li>
 <li>Dillon, W. R, and M. Goldstein. 1987. Multivariate Analysis: Methods, and Applications. Biometrical Journal 29 (6): 750–756.</li>
 <li>Fornell, C. and D. F. Larcker (February 1981). Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, pp. 39-5)</li>

tests/fixtures/plsc-interaction-paths.csv

@@ -0,0 +1,7 @@
+"","Satisfaction"
+"R^2",0.889
+"AdjR^2",0.888
+"Image",0.737
+"Expectation",0.042
+"Value",0.247
+"Image*Expectation",-0.137

tests/fixtures/plsc-interaction-reliability.csv

@@ -0,0 +1,6 @@
+"","rhoC","AVE","rhoA"
+"Image",NA,0.354977797487875,0.745311533816804
+"Expectation",NA,1,1
+"Value",NA,0.720468175631135,0.857495343869267
+"Image*Expectation",0.779196115356173,0.440631377299172,1.6920045542643
+"Satisfaction",NA,0.541895003066141,0.785065531561592

tests/testthat/test-consistent-interactions.R

@@ -0,0 +1,53 @@
+context("SEMinR correctly estimates PLSc for a simple model with interaction\n")
+
+# Test cases
+## Simple case
+# Creating our measurement model
+mobi_mm <- constructs(
+  reflective("Image",        multi_items("IMAG", 1:5)),
+  reflective("Expectation",  single_item("CUEX3")),
+  reflective("Value",        multi_items("PERV", 1:2)),
+  reflective("Satisfaction", multi_items("CUSA", 1:3))
+)
+
+# Interaction constructs must be created after the measurement model is defined.
+# We are using the orthogonalization method as per Henseler & Chin (2010)
+mobi_xm <- interactions(
+  interaction_ortho("Image", "Expectation")
+)
+
+# Structural model
+#  note: interactions should be the names of its main constructs joined by a '*' in between.
+mobi_sm <- relationships(
+  paths(to = "Satisfaction",
+        from = c("Image", "Expectation", "Value",
+                 "Image*Expectation")
+  )
+)
+
+# Load data, assemble model, and estimate using simplePLS
+mobi_pls <- estimate_pls(data = mobi,
+                         measurement_model = mobi_mm,
+                         interactions = mobi_xm,
+                         structural_model = mobi_sm)
+# Load outputs
+sum <- summary(mobi_pls)
+
+## Output originally created using following lines
+# write.csv(sum$paths, file = "tests/fixtures/plsc-interaction-paths.csv")
+# write.csv(sum$reliability, file = "tests/fixtures/plsc-interaction-reliability.csv")
+
+# Load controls
+paths_control <- as.matrix(read.csv("../fixtures/plsc-interaction-paths.csv", row.names = 1))
+reliability_control <- as.matrix(read.csv("../fixtures/plsc-interaction-reliability.csv", row.names = 1))
+
+# Testing
+
+test_that("Seminr estimates paths and R2 correctly\n", {
+  expect_equal(sum$paths, paths_control, tolerance = 0.00001)
+})
+
+test_that("Seminr estimates rhoA, AVE, rhoC (reliability) correctly\n", {
+  expect_equal(sum$reliability, reliability_control, tolerance = 0.00001)
+})
+

vignettes/SEMinR.Rmd

@@ -348,7 +348,7 @@ mobi_pls <- estimate_pls(data = mobi,
 
 Dijkstra and Henseler (2015) offer an adjustment to generate consistent weight and path estimates of common factors estimated using PLSPM. SEMinR automatically adjusts for consistent estimates of coefficients for common-factors defined using `reflective()`.
 
-Note: At this point, SEMinR does not adjust for PLSc on models with interactions involving common-factors. Thus in models including common-factors and interactions, the coefficient of common-factors will be subject to bias.
+Note: SEMinR does adjust for PLSc on models with interactions involving common-factors. Models with interactions can be estimated as PLS consistent, but are subject to some bias as per Becker et al. (2018). In small sample sizes, bootstrapping such a PLSc model with interactions can cause errors.
 
 ## Bootstrapping the model for significance
 
@@ -415,6 +415,7 @@ summary(boot_mobi_pls)
 
 ## References
 
+* Becker et al. (2018). Estimating Moderating Effects in PLS-SEM and PLSc-SEM: Interaction Term Generation*Data Treatment
 * Dijkstra, T. K., & Henseler, J. (2015). Consistent Partial Least Squares Path Modeling, MIS Quarterly Vol. 39(X).
 * Dillon, W. R, and M. Goldstein. 1987. Multivariate Analysis: Methods, and Applications. Biometrical Journal 29 (6): 750–756.
 * Fornell, C. and D. F. Larcker (February 1981). Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, pp. 39-5)