src/testing_pred.R
In environmentalinformatics-marburg/inkili: Find out which plots are included in lidar coverage
setwd("/media/aziegler/Volume/data_div/") ###alz: wenn sich diese Zeile nicht ausführen lässt: Volume mounten
# mod_date <- "16_03_17__100_g_"
# load(paste0("gpm_models_rf_", mod_date, ".rda"))
# models <- get(paste0("models_", mod_date))
load("best_mods_16_03_17.rda")
models <- best_mods
mod_date = "best_mods"
library(gpm)
library(xlsx)
###testing###
# l <- lapply(seq(11), function(x){
# temp <- lapply(seq(100), function(y){
# models_mit_sa[[x]][[y]]$model$results$Rsquared[models_mit_sa[[x]][[y]]$model$bestSubset] -
# models_no_sa[[x]][[y]]$model$results$Rsquared[models_mit_sa[[x]][[y]]$model$bestSubset]
# })
# do.call("rbind", temp)
# })
var_imp <- compVarImp(models, scale = TRUE)
var_imp_plot <- plotVarImp(var_imp)
var_imp_heat <- plotVarImpHeatmap(var_imp, xlab = "Species", ylab = "Band")
###resort variable importance values for further calculation
imp_resp <-lapply(seq(var_imp[[1]]$mean), function(i){
imp_j <- lapply((var_imp), function(j){
sngl_entry <- j$mean[[i]]
})
imp_j <- do.call("cbind", imp_j)
})
imp_resp <- do.call("rbind", imp_resp)
imp_resp <- cbind(imp_resp, rowMeans(imp_resp))
###label variale importance df
#colnames
col_nm <- lapply(seq(var_imp), function (spec){
levels(var_imp[[spec]]$RESPONSE)
})
col_nm <- do.call("rbind", col_nm)
colnames(imp_resp) <- c(col_nm, "var_imp_mean")
#rownames
row_nm <- levels(var_imp[[1]]$VARIABLE)
rownames(imp_resp) <- row_nm
imp_resp_df <- as.data.frame(imp_resp)
var_imp_srt <- imp_resp_df[with(imp_resp_df, order(imp_resp_df$var_imp_mean, decreasing = T)), ]
#write.csv(var_imp_srt, file = paste0("var_imp_srt_", mod_date, ".csv"))
#####R2 tests for the models
tests <- compRegrTests(models, per_model = TRUE, per_selector = TRUE,
sub_selectors = c(1,3), details = TRUE)
#tests_pairs <- aggregate(tests, by = list(tests$pairs), FUN = function(x){x[1]})
tests_agg <- aggregate(tests, by=list(tests$model_response, tests$model_selector), FUN="mean")
# #create a list where all landuse types are rearranged in less groups
# agg_lnduse <- as.data.frame(unique(tests$model_selector))
# colnames(agg_lnduse) <- c("plot_lnduse")
# agg_lnduse$plot_lnduse <- as.character(agg_lnduse$plot_lnduse)
# agg_lnduse$lnduse_sum <- agg_lnduse$plot_lnduse
# agg_lnduse$lnduse_sum[agg_lnduse$plot_lnduse == "flm" |
# agg_lnduse$plot_lnduse == "foc" ] <- "forest"
# agg_lnduse$lnduse_sum[agg_lnduse$plot_lnduse == "fod" ] <- "forest_disturbed"
tests$model_selector <- as.character(tests$model_selector)
tests$model_select_sum <- tests$model_selector
tests$model_select_sum[tests$model_select_sum == "flm" |
tests$model_select_sum == "foc"] <- "forest"
tests$model_select_sum[tests$model_select_sum == "fod"] <- "forest_disturbed"
tests_agg_sum <- aggregate(tests, by=list(tests$model_response, tests$model_select_sum), FUN="mean")
write.csv(tests_agg_sum, file = paste0("tests_agg_sum_", mod_date, ".csv"), row.names = F)
tests_agg_land <- aggregate(tests_agg_sum, by=list(tests_agg_sum$Group.2), FUN = "mean")
tests_agg_spec <- aggregate(tests_agg_sum, by=list(tests_agg_sum$Group.1), FUN = "mean")
#tests sorted after highest R2
tests_srt <- tests_agg[with(tests_agg, order(tests_agg$r_squared, decreasing = T)), ]
tests_srt_sum <- tests_agg_sum[with(tests_agg_sum, order(tests_agg_sum$r_squared, decreasing = T)), ]
tests_srt_land <- tests_agg_land[with(tests_agg_land, order(tests_agg_land$r_squared, decreasing = T)), ]
tests_srt_spec <- tests_agg_spec[with(tests_agg_spec, order(tests_agg_spec$r_squared, decreasing = T)), ]
####prepare and clean for write out############################################
#tests_srt
colnames(tests_srt)[1:2] <- c("spec", "land")
tests_srt <- tests_srt[,c(((which(colnames(tests_srt) == "spec")):
(which(colnames(tests_srt) == "land"))),
((which(colnames(tests_srt) == "testing_response")):
(which(colnames(tests_srt) == "residuals"))))]
#tests_srt_sum
colnames(tests_srt_sum)[1:2] <- c("spec", "land_sum")
tests_srt_sum <- tests_srt_sum[,c(((which(colnames(tests_srt_sum) == "spec")):
(which(colnames(tests_srt_sum) == "land_sum"))),
((which(colnames(tests_srt_sum) == "testing_response")):
(which(colnames(tests_srt_sum) == "residuals"))))]
all_r2 <- mean(tests_srt_sum$r_squared)
max_r2 <- max(tests_srt_sum$r_squared)
min_r2 <- min(tests_srt_sum$r_squared)
#tests_srt_land
colnames(tests_srt_land)[1] <- c("land_sum")
tests_srt_land <- tests_srt_land[,c((which(colnames(tests_srt_land) == "land_sum")),
((which(colnames(tests_srt_land) == "testing_response")):
(which(colnames(tests_srt_land) == "residuals"))))]
#tests_srt_spec
colnames(tests_srt_spec)[1] <- c("spec")
tests_srt_spec <- tests_srt_spec[,c((which(colnames(tests_srt_spec) == "spec")),
((which(colnames(tests_srt_spec) == "testing_response")):
(which(colnames(tests_srt_spec) == "residuals"))))]
#write.csv(tests_srt_spec[, c(1,5)], file = paste0("spec_r2_", mod_date, ".csv"), row.names =F)
# write.csv(tests_srt, file = "tests_srt_16_02_10.csv", row.names = F)
# write.csv(tests_srt_sum, file = "tests_srt_sum_16_02_10.csv", row.names = F)
# write.csv(tests_srt_land, file = "tests_srt_land_16_02_10.csv", row.names = F)
# write.csv(tests_srt_spec, file = "tests_srt_spec_16_02_10.csv", row.names = F)
###############################################################################
###different stuff from testing phase###
###############################################################################
###write out as excel workbook
###von: https://statmethods.wordpress.com/2014/06/19/quickly-export-multiple-r-objects-to-an-excel-workbook/
# #Funktion
save.xlsx <- function (file, ...)
{
require(xlsx, quietly = TRUE)
objects <- list(...)
fargs <- as.list(match.call(expand.dots = TRUE))
objnames <- as.character(fargs)[-c(1, 2)]
nobjects <- length(objects)
for (i in 1:nobjects) {
if (i == 1)
write.xlsx(objects[[i]], file, sheetName = objnames[i])
else write.xlsx(objects[[i]], file, sheetName = objnames[i],
append = TRUE)
}
print(paste("Workbook", file, "has", nobjects, "worksheets."))
}
# ##Aufruf
#save.xlsx(paste0("stats_modell_", mod_date ,".xlsx"), tests_srt_sum, tests_srt_land,
#tests_srt_spec, all_r2, max_r2, min_r2, var_imp_srt)
save.xlsx("best_mods_16_03_17.xlsx", tests_srt_sum, tests_srt_land,
tests_srt_spec, all_r2, max_r2, min_r2, var_imp_srt)
# #########################################################################################################
# #
# # #plot single plots
# # sngl_nm_rspns <- "Acari"
# # sngl_reg <- lm(tests$testing_response[which(tests$model_response == sngl_nm_rspns)] ~
# # tests$testing_predicted[which(tests$model_response == sngl_nm_rspns)], main = sngl_nm_rspns)
# # plot(tests$testing_response[which(tests$model_response == sngl_nm_rspns)]
# # ~ tests$testing_predicted[which(tests$model_response == sngl_nm_rspns)],
# # main = sngl_nm_rspns)
# # abline(sngl_reg)
# #
# #plot number of pairs to r²
# pair_r2_reg <- lm(tests$r_squared ~ tests$pairs)
# plot(tests$r_squared ~ tests$pairs)
# abline(pair_r2_reg)
# summary(pair_r2_reg)
# #
# pair_agg_r2_reg <- lm(tests_agg$r_squared ~ tests_agg$pairs)
# plot(tests_agg$r_squared ~ tests_agg$pairs)
# abline(pair_agg_r2_reg)
# summary(pair_agg_r2_reg)
# #
# pair_agg_sum_r2_reg <- lm(tests_agg_sum$r_squared ~ tests_agg_sum$pairs)
# plot(tests_agg_sum$r_squared ~ tests_agg_sum$pairs)
# abline(pair_agg_sum_r2_reg)
# summary(pair_agg_sum_r2_reg)
# #
# # #plot histogram of pairs
# hist(tests$pairs, breaks=50) # breaks = 5 als Abbildung
# qntl_pairs <- quantile(tests$pairs, probs = seq(0, 1, 0.20))
# #clip df to only variables with more than 100 pairs
# tests_clp <- tests_agg[which(tests_agg$pairs>= 30),]
# #
# # # check regression again
# pair_r2_reg_clp <- lm(tests_clp$r_squared ~ tests_clp$pairs)
# plot(tests_clp$r_squared ~ tests_clp$pairs)
# abline(pair_r2_reg_clp)
# summary(pair_r2_reg_clp)
#
#
# #######################################################################
# ###r² for evey single pair
# performance <- lapply(models, function(x){
# act_performance <- lapply(x, function(y){
# act <- data.frame(plotID = y$testing$SELECTOR,
# INDEPENDENT = y$testing$INDEPENDENT,
# RESPONSE = y$testing$RESPONSE,
# PREDICTED = y$testing$PREDICTED)
# })
# do.call("rbind", act_performance)
# })
# performance <- do.call("rbind", performance)
#
# ########################testing point density against r²##################
#
# grnd_ldr_25 <- read.table(paste0("16_03_17__25_grnd_ldr.csv"),
# header = TRUE, sep = ",", dec = ".")
# dns_25 <- grnd_ldr_25[,c(1,25)]
# dns_25 <- dns_25[!duplicated(dns_25),]
# #dns_25$plt_rough <- substring(dns_25$plotID, 1, 3)
# dns <- aggregate(dns_25, by = list(dns_25$plotID), FUN = mean)
# dns$plotID <- dns$Group.1
#
# performance <- merge(performance, dns_25, by = "plotID")
# ggplot(performance, aes(x = pnt_dnst, y = PREDICTED/RESPONSE)) +
# geom_point() +
# scale_y_log10()
#
#
# #####testing mean scan angle against R²#####################
# grnd_ldr_100 <- read.table(paste0("16_03_23_100_anglgrnd_ldr.csv"),
# header = TRUE, sep = ",", dec = ".")
#
# ang_100 <- grnd_ldr_100[, c(1,8)]
# ang_100$plt_rough <- substring(ang_100$plotID, 1, 3)
# ang <- aggregate(ang_100, by = list(ang_100$plt_rough), FUN = mean)
# ang$plotID <- ang$Group.1
#
# performance <- merge(performance, ang_100, by = "plotID")
# ggplot(performance, aes(x = av_angl, y = PREDICTED/RESPONSE)) +
# geom_point() +
# scale_y_log10()
################################################
grnd_ldr_25 <- read.table(paste0("16_03_17__25_grnd_ldr.csv"),
header = TRUE, sep = ",", dec = ".")
grnd_ldr_50 <- read.table(paste0("16_03_17__50_grnd_ldr.csv"),
header = TRUE, sep = ",", dec = ".")
grnd_ldr_100 <- read.table(paste0("16_03_17__100_grnd_ldr.csv"),
header = TRUE, sep = ",", dec = ".")
####density range ###
max_25 <- max(grnd_ldr_25$pnt_dnst)
max_50 <- max(grnd_ldr_50$pnt_dnst)
max_100 <- max(grnd_ldr_100$pnt_dnst)
min_25 <- min(grnd_ldr_25$pnt_dnst)
min_50 <- min(grnd_ldr_50$pnt_dnst)
min_100 <- min(grnd_ldr_100$pnt_dnst)
mean_25 <- mean(grnd_ldr_25$pnt_dnst)
mean_50 <- mean(grnd_ldr_50$pnt_dnst)
mean_100 <- mean(grnd_ldr_100$pnt_dnst)
###max return number###
max_rtrn_25 <- max(grnd_ldr_25$max_rtrn)
max_rtrn_50 <- max(grnd_ldr_50$max_rtrn)
max_rtrn_100 <- max(grnd_ldr_100$max_rtrn)
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setwd("/media/aziegler/Volume/data_div/") ###alz: wenn sich diese Zeile nicht ausführen lässt: Volume mounten
# mod_date <- "16_03_17__100_g_"
# load(paste0("gpm_models_rf_", mod_date, ".rda"))
# models <- get(paste0("models_", mod_date))
load("best_mods_16_03_17.rda")
models <- best_mods
mod_date = "best_mods"
library(gpm)
library(xlsx)
###testing###
# l <- lapply(seq(11), function(x){
# temp <- lapply(seq(100), function(y){
# models_mit_sa[[x]][[y]]$model$results$Rsquared[models_mit_sa[[x]][[y]]$model$bestSubset] -
# models_no_sa[[x]][[y]]$model$results$Rsquared[models_mit_sa[[x]][[y]]$model$bestSubset]
# })
# do.call("rbind", temp)
# })
var_imp <- compVarImp(models, scale = TRUE)
var_imp_plot <- plotVarImp(var_imp)
var_imp_heat <- plotVarImpHeatmap(var_imp, xlab = "Species", ylab = "Band")
###resort variable importance values for further calculation
imp_resp <-lapply(seq(var_imp[[1]]$mean), function(i){
imp_j <- lapply((var_imp), function(j){
sngl_entry <- j$mean[[i]]
})
imp_j <- do.call("cbind", imp_j)
})
imp_resp <- do.call("rbind", imp_resp)
imp_resp <- cbind(imp_resp, rowMeans(imp_resp))
###label variale importance df
#colnames
col_nm <- lapply(seq(var_imp), function (spec){
levels(var_imp[[spec]]$RESPONSE)
})
col_nm <- do.call("rbind", col_nm)
colnames(imp_resp) <- c(col_nm, "var_imp_mean")
#rownames
row_nm <- levels(var_imp[[1]]$VARIABLE)
rownames(imp_resp) <- row_nm
imp_resp_df <- as.data.frame(imp_resp)
var_imp_srt <- imp_resp_df[with(imp_resp_df, order(imp_resp_df$var_imp_mean, decreasing = T)), ]
#write.csv(var_imp_srt, file = paste0("var_imp_srt_", mod_date, ".csv"))
#####R2 tests for the models
tests <- compRegrTests(models, per_model = TRUE, per_selector = TRUE,
sub_selectors = c(1,3), details = TRUE)
#tests_pairs <- aggregate(tests, by = list(tests$pairs), FUN = function(x){x[1]})
tests_agg <- aggregate(tests, by=list(tests$model_response, tests$model_selector), FUN="mean")
# #create a list where all landuse types are rearranged in less groups
# agg_lnduse <- as.data.frame(unique(tests$model_selector))
# colnames(agg_lnduse) <- c("plot_lnduse")
# agg_lnduse$plot_lnduse <- as.character(agg_lnduse$plot_lnduse)
# agg_lnduse$lnduse_sum <- agg_lnduse$plot_lnduse
# agg_lnduse$lnduse_sum[agg_lnduse$plot_lnduse == "flm" |
# agg_lnduse$plot_lnduse == "foc" ] <- "forest"
# agg_lnduse$lnduse_sum[agg_lnduse$plot_lnduse == "fod" ] <- "forest_disturbed"
tests$model_selector <- as.character(tests$model_selector)
tests$model_select_sum <- tests$model_selector
tests$model_select_sum[tests$model_select_sum == "flm" |
tests$model_select_sum == "foc"] <- "forest"
tests$model_select_sum[tests$model_select_sum == "fod"] <- "forest_disturbed"
tests_agg_sum <- aggregate(tests, by=list(tests$model_response, tests$model_select_sum), FUN="mean")
write.csv(tests_agg_sum, file = paste0("tests_agg_sum_", mod_date, ".csv"), row.names = F)
tests_agg_land <- aggregate(tests_agg_sum, by=list(tests_agg_sum$Group.2), FUN = "mean")
tests_agg_spec <- aggregate(tests_agg_sum, by=list(tests_agg_sum$Group.1), FUN = "mean")
#tests sorted after highest R2
tests_srt <- tests_agg[with(tests_agg, order(tests_agg$r_squared, decreasing = T)), ]
tests_srt_sum <- tests_agg_sum[with(tests_agg_sum, order(tests_agg_sum$r_squared, decreasing = T)), ]
tests_srt_land <- tests_agg_land[with(tests_agg_land, order(tests_agg_land$r_squared, decreasing = T)), ]
tests_srt_spec <- tests_agg_spec[with(tests_agg_spec, order(tests_agg_spec$r_squared, decreasing = T)), ]
####prepare and clean for write out############################################
#tests_srt
colnames(tests_srt)[1:2] <- c("spec", "land")
tests_srt <- tests_srt[,c(((which(colnames(tests_srt) == "spec")):
(which(colnames(tests_srt) == "land"))),
((which(colnames(tests_srt) == "testing_response")):
(which(colnames(tests_srt) == "residuals"))))]
#tests_srt_sum
colnames(tests_srt_sum)[1:2] <- c("spec", "land_sum")
tests_srt_sum <- tests_srt_sum[,c(((which(colnames(tests_srt_sum) == "spec")):
(which(colnames(tests_srt_sum) == "land_sum"))),
((which(colnames(tests_srt_sum) == "testing_response")):
(which(colnames(tests_srt_sum) == "residuals"))))]
all_r2 <- mean(tests_srt_sum$r_squared)
max_r2 <- max(tests_srt_sum$r_squared)
min_r2 <- min(tests_srt_sum$r_squared)
#tests_srt_land
colnames(tests_srt_land)[1] <- c("land_sum")
tests_srt_land <- tests_srt_land[,c((which(colnames(tests_srt_land) == "land_sum")),
((which(colnames(tests_srt_land) == "testing_response")):
(which(colnames(tests_srt_land) == "residuals"))))]
#tests_srt_spec
colnames(tests_srt_spec)[1] <- c("spec")
tests_srt_spec <- tests_srt_spec[,c((which(colnames(tests_srt_spec) == "spec")),
((which(colnames(tests_srt_spec) == "testing_response")):
(which(colnames(tests_srt_spec) == "residuals"))))]
#write.csv(tests_srt_spec[, c(1,5)], file = paste0("spec_r2_", mod_date, ".csv"), row.names =F)
# write.csv(tests_srt, file = "tests_srt_16_02_10.csv", row.names = F)
# write.csv(tests_srt_sum, file = "tests_srt_sum_16_02_10.csv", row.names = F)
# write.csv(tests_srt_land, file = "tests_srt_land_16_02_10.csv", row.names = F)
# write.csv(tests_srt_spec, file = "tests_srt_spec_16_02_10.csv", row.names = F)
###############################################################################
###different stuff from testing phase###
###############################################################################
###write out as excel workbook
###von: https://statmethods.wordpress.com/2014/06/19/quickly-export-multiple-r-objects-to-an-excel-workbook/
# #Funktion
save.xlsx <- function (file, ...)
{
require(xlsx, quietly = TRUE)
objects <- list(...)
fargs <- as.list(match.call(expand.dots = TRUE))
objnames <- as.character(fargs)[-c(1, 2)]
nobjects <- length(objects)
for (i in 1:nobjects) {
if (i == 1)
write.xlsx(objects[[i]], file, sheetName = objnames[i])
else write.xlsx(objects[[i]], file, sheetName = objnames[i],
append = TRUE)
}
print(paste("Workbook", file, "has", nobjects, "worksheets."))
}
# ##Aufruf
#save.xlsx(paste0("stats_modell_", mod_date ,".xlsx"), tests_srt_sum, tests_srt_land,
#tests_srt_spec, all_r2, max_r2, min_r2, var_imp_srt)
save.xlsx("best_mods_16_03_17.xlsx", tests_srt_sum, tests_srt_land,
tests_srt_spec, all_r2, max_r2, min_r2, var_imp_srt)
# #########################################################################################################
# #
# # #plot single plots
# # sngl_nm_rspns <- "Acari"
# # sngl_reg <- lm(tests$testing_response[which(tests$model_response == sngl_nm_rspns)] ~
# # tests$testing_predicted[which(tests$model_response == sngl_nm_rspns)], main = sngl_nm_rspns)
# # plot(tests$testing_response[which(tests$model_response == sngl_nm_rspns)]
# # ~ tests$testing_predicted[which(tests$model_response == sngl_nm_rspns)],
# # main = sngl_nm_rspns)
# # abline(sngl_reg)
# #
# #plot number of pairs to r²
# pair_r2_reg <- lm(tests$r_squared ~ tests$pairs)
# plot(tests$r_squared ~ tests$pairs)
# abline(pair_r2_reg)
# summary(pair_r2_reg)
# #
# pair_agg_r2_reg <- lm(tests_agg$r_squared ~ tests_agg$pairs)
# plot(tests_agg$r_squared ~ tests_agg$pairs)
# abline(pair_agg_r2_reg)
# summary(pair_agg_r2_reg)
# #
# pair_agg_sum_r2_reg <- lm(tests_agg_sum$r_squared ~ tests_agg_sum$pairs)
# plot(tests_agg_sum$r_squared ~ tests_agg_sum$pairs)
# abline(pair_agg_sum_r2_reg)
# summary(pair_agg_sum_r2_reg)
# #
# # #plot histogram of pairs
# hist(tests$pairs, breaks=50) # breaks = 5 als Abbildung
# qntl_pairs <- quantile(tests$pairs, probs = seq(0, 1, 0.20))
# #clip df to only variables with more than 100 pairs
# tests_clp <- tests_agg[which(tests_agg$pairs>= 30),]
# #
# # # check regression again
# pair_r2_reg_clp <- lm(tests_clp$r_squared ~ tests_clp$pairs)
# plot(tests_clp$r_squared ~ tests_clp$pairs)
# abline(pair_r2_reg_clp)
# summary(pair_r2_reg_clp)
#
#
# #######################################################################
# ###r² for evey single pair
# performance <- lapply(models, function(x){
# act_performance <- lapply(x, function(y){
# act <- data.frame(plotID = y$testing$SELECTOR,
# INDEPENDENT = y$testing$INDEPENDENT,
# RESPONSE = y$testing$RESPONSE,
# PREDICTED = y$testing$PREDICTED)
# })
# do.call("rbind", act_performance)
# })
# performance <- do.call("rbind", performance)
#
# ########################testing point density against r²##################
#
# grnd_ldr_25 <- read.table(paste0("16_03_17__25_grnd_ldr.csv"),
# header = TRUE, sep = ",", dec = ".")
# dns_25 <- grnd_ldr_25[,c(1,25)]
# dns_25 <- dns_25[!duplicated(dns_25),]
# #dns_25$plt_rough <- substring(dns_25$plotID, 1, 3)
# dns <- aggregate(dns_25, by = list(dns_25$plotID), FUN = mean)
# dns$plotID <- dns$Group.1
#
# performance <- merge(performance, dns_25, by = "plotID")
# ggplot(performance, aes(x = pnt_dnst, y = PREDICTED/RESPONSE)) +
# geom_point() +
# scale_y_log10()
#
#
# #####testing mean scan angle against R²#####################
# grnd_ldr_100 <- read.table(paste0("16_03_23_100_anglgrnd_ldr.csv"),
# header = TRUE, sep = ",", dec = ".")
#
# ang_100 <- grnd_ldr_100[, c(1,8)]
# ang_100$plt_rough <- substring(ang_100$plotID, 1, 3)
# ang <- aggregate(ang_100, by = list(ang_100$plt_rough), FUN = mean)
# ang$plotID <- ang$Group.1
#
# performance <- merge(performance, ang_100, by = "plotID")
# ggplot(performance, aes(x = av_angl, y = PREDICTED/RESPONSE)) +
# geom_point() +
# scale_y_log10()
################################################
grnd_ldr_25 <- read.table(paste0("16_03_17__25_grnd_ldr.csv"),
header = TRUE, sep = ",", dec = ".")
grnd_ldr_50 <- read.table(paste0("16_03_17__50_grnd_ldr.csv"),
header = TRUE, sep = ",", dec = ".")
grnd_ldr_100 <- read.table(paste0("16_03_17__100_grnd_ldr.csv"),
header = TRUE, sep = ",", dec = ".")
####density range ###
max_25 <- max(grnd_ldr_25$pnt_dnst)
max_50 <- max(grnd_ldr_50$pnt_dnst)
max_100 <- max(grnd_ldr_100$pnt_dnst)
min_25 <- min(grnd_ldr_25$pnt_dnst)
min_50 <- min(grnd_ldr_50$pnt_dnst)
min_100 <- min(grnd_ldr_100$pnt_dnst)
mean_25 <- mean(grnd_ldr_25$pnt_dnst)
mean_50 <- mean(grnd_ldr_50$pnt_dnst)
mean_100 <- mean(grnd_ldr_100$pnt_dnst)
###max return number###
max_rtrn_25 <- max(grnd_ldr_25$max_rtrn)
max_rtrn_50 <- max(grnd_ldr_50$max_rtrn)
max_rtrn_100 <- max(grnd_ldr_100$max_rtrn)
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