Minor fixes and renv dependencies

Author: perakakis

.Rprofile

@@ -0,0 +1 @@
+source("renv/activate.R")

Code/02_extract.R

@@ -41,16 +41,15 @@ for (i in seq(1, length(PIDs))) {
   basins <- earlywarnings::livpotential_ews(y3)
 
   # Identify local minima (valleys) in the potential function
-  lm <- photobiology::get_valleys(
-    basins$grid.points, basins$pot, 
-    ignore_threshold = -0.2, 
+  valley_idx <- photobiology::find_valleys(
+    basins$pot,
     strict = TRUE, 
     span = 3
   )
 
-  # Extract indices of local minima (valleys)
-  basinsind <- which(basins$grid.points %in% lm$x)
-  basinlocs <- lm$x  # Coordinates of basin locations
+  # Extract indices and coordinates of local minima (valleys)
+  basinsind <- which(valley_idx)
+  basinlocs <- basins$grid.points[valley_idx]
 
   # Check if exactly one local minimum exists in the range [-5, 5]
   values_in_range <- sum(basinlocs >= -5 & basinlocs <= 5)

Code/07_lasso.R

@@ -41,9 +41,10 @@ for (i in 1:length(rows)) {
   lres[i, res$DV_lasso$predictor] <- res$DV_lasso$coefficient  # Store coefficients in result table
 }
 
-# Handle 'Type' column separately
-lres$Type <- lres$Type1
-lres <- lres[, -ncol(lres)]  # Remove the last column (Type1 placeholder)
+# Handle 'Type' dummy columns: model.matrix creates Type0 and Type1
+if ("Type1" %in% colnames(lres)) lres$Type <- lres$Type1
+if ("Type0" %in% colnames(lres)) lres$Type0 <- NULL
+if ("Type1" %in% colnames(lres)) lres$Type1 <- NULL
 
 ## Study 2 ####
 # Subset the data for Study 2 and keep the first row per participant
@@ -72,9 +73,10 @@ for (i in 1:length(rows)) {
   lres2[i, res$DV_lasso$predictor] <- res$DV_lasso$coefficient  # Store coefficients
 }
 
-# Handle 'Type' column separately for Study 2
-lres2$Type <- lres2$Type1
-lres2 <- lres2[, -ncol(lres2)]  # Remove the last column (Type1 placeholder)
+# Handle 'Type' dummy columns for Study 2
+if ("Type1" %in% colnames(lres2)) lres2$Type <- lres2$Type1
+if ("Type0" %in% colnames(lres2)) lres2$Type0 <- NULL
+if ("Type1" %in% colnames(lres2)) lres2$Type1 <- NULL
 
 # Transpose and round results for both studies
 lres1 <- t(round(lres, 2))  # Transpose and round results for Study 1
@@ -85,14 +87,6 @@ lres <- cbind(lres1, lres2)
 rownames(lres)[2:3] <- c("P2N-ASR", "N2P-ASR")  # Rename rows for clarity
 rownames(lres)[8:11] <- c("mP2N-ASM", "sdP2N-ASM", "mN2P-ASM", "sdN2P-ASM")
 
-# Create and save LASSO table (if needed)
-source("./code/table_lasso.R")
-# Uncomment below to save results as a table:
-# gtsave(tbl, "./tables/table_lasso.html")
-# gtsave(tbl, file = "./tables/table_lasso.pdf")
-# gtsave(tbl, file = "./tables/table_lasso.png")
-# gtsave(tbl, file = "./tables/table_lasso.docx")
-
 # Visualization: Create a data frame for plotting
 data <- data.frame(t(lres))  # Convert results to a data frame
 colnames(data)[2:3] <- c("P2N-ASR", "N2P-ASR")  # Rename columns for clarity

Code/08_stepwiseRegression.R

@@ -69,14 +69,6 @@ rownames(lres)[5] <- "P2N-ASR"  # Rename row for clarity
 # Optional: Save the results to a CSV file
 # write.csv(lres, file = "./tables/stepwiseData.csv")
 
-# Create and save Stepwise regression table (optional)
-source("./code/table_stepwise.R")
-# Uncomment the lines below to save the table in different formats:
-# gtsave(tbl, "./tables/table_stepwise.html")
-# gtsave(tbl, file = "./tables/table_stepwise.pdf")
-# gtsave(tbl, file = "./tables/table_stepwise.png")
-# gtsave(tbl, file = "./tables/table_stepwise.docx")
-
 # Visualization: Create a data frame for plotting
 data <- data.frame(t(lres))  # Convert results to a data frame
 colnames(data)[5] <- "P2N-ASR"  # Rename column for clarity

README.md

@@ -1,5 +1,36 @@
 ## Code and Data for the article "Bistability and Affect Shift Dynamics in the Prediction of Psychological Well-Being"
-CITATION
-Goicoechea, C., Dakos, V., Sanabria, D., Heshmati, S., Westhoff, M., Banos, O., Pomares, H., Hofmann, S. G., & Perakakis, P. (2024). Bistability and affect shift dynamics in the prediction of psychological well-being. Emotion. Advance online publication. https://dx.doi.org/10.1037/emo0001454
+
+### Citation
+
+Goicoechea, C., Dakos, V., Sanabria, D., Heshmati, S., Westhoff, M., Banos, O., Pomares, H., Hofmann, S. G., & Perakakis, P. (2024). Bistability and affect shift dynamics in the prediction of psychological well-being. Emotion, 25(4), 982–996. https://dx.doi.org/10.1037/emo0001454
 
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15001651.svg)](https://doi.org/10.5281/zenodo.15001651)
+
+### Repository Structure
+
+```
+Data/               # EMA and survey data for Spanish and German samples
+Code/
+  01_preprocess.R   # Data cleaning and preparation
+  02_extract.R      # Bistability metrics extraction (basins, affect shifts, residence times)
+  05_PCA.R          # Principal Component Analysis
+  06_corrplot.R     # Correlation plots
+  07_lasso.R        # LASSO regression
+  08_stepwiseRegression.R
+  09_relativeImportance.R
+  auxFunctions.R    # Helper functions (LASSO, regression, nested models)
+  predictor_colors.R
+```
+
+### Reproducibility
+
+This project uses [`renv`](https://rstudio.github.io/renv/) to manage R package dependencies. To reproduce the analysis environment:
+
+```r
+# install.packages("renv")  # if not already installed
+renv::restore()
+```
+
+This will install all required packages at the exact versions recorded in `renv.lock` (R 4.5.2).
+
+Scripts are intended to be run from the project root directory.