adding the part where output for excluding grid-cells is written into results

src/model_fitting/abundance_model.R

@@ -388,8 +388,7 @@ results_res <- foreach(i = 1:nrow(all_model_terms),
                                               paste("SE", model_terms, sep = "_"),
                                               "theta", "SE.theta", "AIC", "R2"
                            )} else {
-      result <- as.data.frame(matrix(NA, ncol = 3 *
-                                       length(model_terms) + 6,
+      result <- as.data.frame(matrix(NA, ncol = 3 * length(model_terms) + 6,
                                                           nrow = 1))
       names(result) <- c("model", paste("coeff", model_terms, sep = "_"),
                                               paste("P",model_terms,sep = "_"),
@@ -446,17 +445,28 @@ results_res <- foreach(i = 1:nrow(all_model_terms),
         result[ , "R2"] <- summary(comparison_lm)$r.squared
     # if we are excluding years, this is the test of predicted data vs observed data
     # for this year (with which the model wasn't fitted)
-  if (!is.na(exclude_year)){
-  predictors_excluded_year_pred <- predictors_excluded_year
-  predictors_excluded_year_pred$offset_term <- 0
-  prediction_transect_excluded_year <-  predict.glm(res,
-                               newdata = predictors_excluded_year,
-			       type = "response")
-  cross_lm = lm(log(predictors_excluded_year$ou_dens+ 1) ~
-                     log(prediction_transect_excluded_year + 1))
-
-  result[ , "R2_cross"] <- summary(cross_lm)$r.squared
-    }
+        if (!is.na(exclude_year)){
+          predictors_excluded_year_pred <- predictors_excluded_year
+          predictors_excluded_year_pred$offset_term <- 0
+          prediction_transect_excluded_year <-  predict.glm(res,
+                                                            newdata = predictors_excluded_year,
+                                                            type = "response")
+          cross_lm_year = lm(log(predictors_excluded_year$ou_dens+ 1) ~
+                               log(prediction_transect_excluded_year + 1))
+
+          result[ , "R2_cross"] <- summary(cross_lm_year)$r.squared
+        }
+        if (!is.na(exclude_grid)){
+          predictors_excluded_grid_pred <- predictors_excluded_grid
+          predictors_excluded_grid_pred$offset_term <- 0
+          prediction_transect_excluded_grid <-  predict.glm(res,
+                                                            newdata = predictors_excluded_grid,
+                                                            type = "response")
+          cross_lm_grid = lm(log(predictors_excluded_grid$ou_dens+ 1) ~
+                               log(prediction_transect_excluded_grid + 1))
+
+          result[ , "R2_cross"] <- summary(cross_lm_grid)$r.squared
+        }
 
   return(result)
 }