I want to create a new variable with a certain value when two other variables: CountryOfOrigin and CountryOfBirth are identical. I use Variable-Conditional Compute-Multiple. IF PAIS_NACIM == PAIS_PROC_DEST THEN "País de procedencia" I always get the following error n argument: `LugarNacimiento = ifelse(PAIS_NACIM == PAIS_PROC_DEST, "País de procedencia", NA)`. Caused by error in `Ops.factor()`: ! level sets of factors are different Thank you for any comment.

I wish to run a "cost-sensitive" randomForest algorithm incorporating a "cost matrix" that reflects a custom loss function, with the option of minimizing the expected misclassification cost. I have read that R’s randomForest package doesn’t directly support multi-class or even binary cost-sensitive learning, but I have read that there are workarounds to incorporate cost sensitivity into randomForest such as: (1) Modifying the sample weights during training based on the cost matrix. (2) Adjusting the predicted probabilities to minimize expected cost based on the cost matrix. For example, after obtaining class probabilities, apply decision thresholds that minimize expected misclassification cost. (3) Custom Splitting Criteria: Modifying the underlying decision tree base learner to use a cost-sensitive criterion rather than standard impurity measures (Gini or entropy). (4) Using Weighted Subsampling: Performing bootstrap sampling with weights reflecting the cost matrix, effectively biasing the training process towards reducing high-cost errors. I have also collected a sample R code (attached text file). However, since I don't code, I am unsure if it works. Can you please provide an easy set of technical steps that need to be followed in Bluesky Statistics? Although, a google page provides some info as in the attached image, it is not perfectly clear. Looking forward to hearing from you. Best regards, Parag J Dutta

Hi Bluesky Statistics Team, I am encountering the follow error for missing values analysis where my Missing Value Count per Variable does not Match Row Numbers. Please see following: In analyzing the Missing Values for a dataset, the "Missing Value (NAs) Count Per Variable" highlights accurately the 8 NAs from 2 different variables but the corresponding table from "Row Numbers for Variables that have NAs" from the same output for this function returns the WRONG variables but right row numbers. Why is this the case? See attached photo for reference. "Row Numbers for Variables that have NAs" should display FVC: 67, 68 and CHOLEST: 31, 39, 47, 69, 72 ,78 instead (have verified these were missing in the original data set) The same sort of error repeats for other data sets Appreciate the help, thank you!