Bug fixes

README.md

@@ -4,58 +4,56 @@ The `NetworkCausalTree` package introduces a machine learning method that uses t
 
 ## Getting Started
 
-Installing the latest developing version: 
+Installing the latest developing version:
 
-```r
+``` r
 library(devtools)
 install_github("fbargaglistoffi/NetworkCausalTree", ref="master")
 ```
 
-Import:
+Import + set seed:
 
-```r
+``` r
 library("NetworkCausalTree")
+set.seed(123)
 ```
 
 ## Examples
 
 ### Example 1
 
-Data generated using Erdos Renyi networks. 
+Data generated using Erdos Renyi networks.
 
-```r
+``` r
 ## Examples
-dataset <- data_generator_direct_indirect(N = 4000, 
-                                          M = 4,
-                                          k = 80, 
-                                          p = rep(0.2,4000), 
-                                          het = TRUE, 
-                                          h = 2, 
-                                          method_networks = "er", 
-                                          param_er = 0.1)
+dataset_direct <- data_generator_direct(N = 4000,
+                                        M = 4,
+                                        k = 80,
+                                        p = rep(0.2,4000),
+                                        het = TRUE,
+                                        h = 2,
+                                        method_networks = "er",
+                                        param_er = 0.1)
 ```
 
-Singular splitting based on the main treatment effect only 
+Singular splitting based on the main treatment effect only
 
-```r
-
-
-result <- NetworkCausalTree(X = dataset[["X"]],
-                            Y = dataset[["Y"]],
-                            W = dataset[["W"]], 
-                            A = dataset[["A"]],
-                            K = dataset[["K"]],
-                            p = dataset[["p"]], 
+``` r
+result <- NetworkCausalTree(X = dataset_direct[["X"]],
+                            Y = dataset_direct[["Y"]],
+                            W = dataset_direct[["W"]], 
+                            A = dataset_direct[["A"]],
+                            K = dataset_direct[["K"]],
+                            p = dataset_direct[["p"]], 
                             effect_weights = c(1,0,0,0),
                             ratio_disc = 0.5,
-                            depth = 3,
+                            depth = 2,
                             minsize = 5, 
                             method = "singular",
                             output = "estimation")
 
-
 title <- expression(paste("CAUSAL TREE TARGETED TO ",tau,"(1,0;0,0)"),sep="")
-cov_names <- colnames(dataset[["X"]])
+cov_names <- colnames(dataset_direct[["X"]])
 
 plot_NCT(NCT = result, 
          cov_names = cov_names,
@@ -64,55 +62,50 @@ plot_NCT(NCT = result,
 
 ### Example 2
 
-Data generated using Barabasi - Albert networks. 
-
-```r
-
-
-dataset <- data_generator_direct_indirect(N = 4000,
-                                          M = 4,
-                                          k = 80,
-                                          p = rep(0.2,4000),
-                                          het = TRUE,
-                                          h = 3,
-                                          method_networks = "sf")
+Data generated using Barabasi - Albert networks.
 
+``` r
+dataset_direct_indirect <- data_generator_direct_indirect(N = 4000,
+                                                          M = 4,
+                                                          k = 80,
+                                                          p = rep(0.2,4000),
+                                                          het = TRUE,
+                                                          h = 3,
+                                                          method_networks = "sf")
 ```
 
 Composite splitting (NCT based on all the four effects)
 
-```r
-
-result <- NetworkCausalTree(X = dataset[["X"]],
-                            Y = dataset[["Y"]],
-                            W = dataset[["W"]],
-                            A = dataset[["A"]],
-                            K = dataset[["K"]],
-                            p = dataset[["p"]],
+``` r
+result <- NetworkCausalTree(X = dataset_direct_indirect[["X"]],
+                            Y = dataset_direct_indirect[["Y"]],
+                            W = dataset_direct_indirect[["W"]],
+                            A = dataset_direct_indirect[["A"]],
+                            K = dataset_direct_indirect[["K"]],
+                            p = dataset_direct_indirect[["p"]],
                             effect_weights =   c(0.25, 0.25, 0.25, 0.25),
                             ratio_disc = 0.5,
                             depth = 2,
                             minsize = 5,
                             method = "composite",
-                            output = "detection")
+                            output = "estimation")
 
-title <- expression("CAUSAL TREE TARGETED TO ALL THE EFFECTS")
-cov_names <- colnames(dataset[["X"]])
+title <- expression(paste("CAUSAL TREE TARGETED TO ",tau,"(0.25,0.25;0.25,0.25)"),sep="")
+cov_names <- colnames(dataset_direct_indirect[["X"]])
 
 plot_NCT(NCT = result, 
          cov_names = cov_names,
          title = title,
-         output = "detection")
+         effect_color_nodes = "0100")
 ```
 
 ## Code of Conduct
 
 Please note that the CRE project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/2/1/code_of_conduct). By contributing to this project, you agree to abide by its terms. More information about the opening issues and contributing (i.e., git branching model) can be found [here](https://nsaph-software.github.io/CRE/articles/Contribution.html).
 
-
 ## Cite
 
-```bibtex
+``` bibtex
 @article{bargagli2025heterogeneous,
   title={Heterogeneous treatment and spillover effects under clustered network interference},
   author={Bargagli-Stoffi, Falco J. and Tort{\'u}, Costanza and Forastiere, Laura},