Meta-analysis is a systematic method for synthesizing quantitative results of different empirical studies regarding the effect of an independent variable (or determinant, or intervention, or treatment) on a defined outcome (or dependent variable). Mainly developed in medical and psychological research as a tool for synthesizing empirical information about the outcomes of a treatment, meta-analysis is now increasingly used in the social sciences as a tool for hypothesis testing. However, the assumptions underlying meta-analytic hypothesis testing in the social sciences will usually not be met under real-life conditions. This is the reason why meta-analysis is increasingly conducted with a different aim, based on more realistic assumptions. That aim is to explore the dispersion of effect sizes.

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#Distance between the stations: 105114.58 basel_row <- select_validalo_aggregate[select_validalo_aggregate$Site == "BASEL", ] rovaniemi_row <- select_validalo_aggregate[select_validalo_aggregate$Site == "ROVANIEMI", ] svurban_row <- select_validalo_aggregate_sv[select_validalo_aggregate_sv$Site == "Sv. Urban", ] #random_integer1 = 26 min_distance = 49 random_integer <- sample(1:10000, 1) random_integer1 = 6097 set.seed(random_integer1) # Calculate the number of rows for the remaining 10% sampling desired_sample_size <- round(0.1 * nrow(select_validalo_aggregate)) -3 # Subtract 3 for Basel, Rovaniemi and Sv. Urban # Randomly select the remaining rows remaining_rows <- select_validalo_aggregate[!(select_validalo_aggregate$Site %in% c("BASEL", "ROVANIEMI","Sv. Urban")), ] random_indices <- sample(nrow(remaining_rows), size = desired_sample_size, replace = FALSE) random_selection <- remaining_rows[random_indices, ] # Combine the selected rows with Basel and Rovaniemi rows final_selection <- rbind(basel_row, rovaniemi_row,svurban_row, random_selection) final_selection_sp = final_selection coordinates(final_selection_sp) <- ~Longitude + Latitude proj4string(final_selection_sp) <- CRS("+init=epsg:4326") # Assuming final_selection_sp is your SpatialPointsDataFrame # Extract the coordinates from the SpatialPointsDataFrame coords <- coordinates(final_selection_sp) # Calculate the distance matrix in meters distance_matrix <- distm(coords, fun = distVincentyEllipsoid) # If you want to find the minimum non-zero distance min_distance <- min(distance_matrix[distance_matrix > 0]) Stations = select_validalo_aggregate_sp Validation = final_selection_sp mapview(Stations, alpha = 0.1) + mapview(Validation,zcol = "Group")

Analytics I - 2024 II