From a37bca88f2b903a788b71dbc288056a90bfe7ffc Mon Sep 17 00:00:00 2001 From: "anouk.glad" <anouk.glad@irstea.fr> Date: Tue, 18 Dec 2018 17:48:25 +0100 Subject: [PATCH] Revision --- Data/Regular_transect.dbf | Bin 811 -> 625 bytes Data/Regular_transect.shp | Bin 2036 -> 1508 bytes Data/Regular_transect.shx | Bin 276 -> 228 bytes Data/transect_distance.tif | Bin 22765 -> 25574 bytes R_code.Rmd | 223 ++++++++++++++++++++++++++++++++----- 5 files changed, 195 insertions(+), 28 deletions(-) diff --git a/Data/Regular_transect.dbf b/Data/Regular_transect.dbf index 85a249f0f8f55f985ea0fb4b25f4c4feace8ac74..96a8f3fef828e4741aefe82c77d3907df1f34d63 100644 GIT binary patch delta 14 VcmZ3@_K}5^Ii6igU?Xb|6964O17iRH literal 811 zcmaLTy$%6U6h+~aC?pcCLSsLIbN%<zXqZqaCknL*Ucj3pVnt-fEpEP<eXG@Jy2C+4 z++*~-f6gIdUGZ=8QjgC!>fLr<zaQDXI^IrAdpTd*uzO#e{3{h^Cry~0G-Y<ujM+(Z zW+yF}owQ_j(u&zB$Di3rNi`=Wt(=s!a#GUDNl7awC9Ry4v~p6?%1KEpCl#tWsZh;H Ig=*h=0KHshd;kCd diff --git a/Data/Regular_transect.shp 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library(gtable) library(gridExtra) library(RStoolbox) +library(DSsim) +library(cvAUC) ##### linux paths ###### data_path<-"/media/anouk/DATAPART1/These/Data/DATAGTJPROPRE/virtual_sp/code_v_sp/Spatial_sampling_bias/Data/" @@ -616,7 +621,7 @@ plot(region, plot.units = "m") design <- make.design(transect.type = "line", design.details = c("Parallel", "Systematic"), region.obj = region, - spacing = 300) + spacing = 400) transects <- generate.transects(design, region = region) plot(region, plot.units = "m") @@ -664,7 +669,7 @@ writeOGR(transect4, dsn = '.', layer = "Regular_transect", driver = "ESRI Shapef transect<-shapefile(paste0(data_path, 'Regular_transect.shp')) #Use raster of environmental variable in order set the right extent - a<-raster(extent(new_raster3)) #choose a raster to set up extent + a<-raster(extent(new_env_raster)) #choose a raster to set up extent res(a)<-c(25,25) #Create a raster representing trails (same extent and resolution as environmental variables rasters) transectR <- rasterize(transect, a) @@ -4568,19 +4573,26 @@ row_spec(4:6, color = "black", background = "#fff") *Open environmental data* ```{r echo=TRUE, eval=TRUE, tidy=TRUE, message=FALSE} -new_env_raster <- stack( - paste0(data_path, "JURAraster_H.nb10_20relative_density.tif"), - paste0(data_path, "JURAraster_H.nb20_30relative_density.tif"), - paste0(data_path, "JURAraster_H.simpson.tif"), - +new_env_raster <- stack(paste0(data_path, "JURAraster_MV_1_8ha_Mean_H.nb10_20relative_density.tif"), + + paste0(data_path, "JURAraster_MV_1_8ha_Mean_H.nb2_5ratio.tif"), + + paste0(data_path, "JURAraster_MV_1_8ha_Mean_H.simpson.tif"), + + paste0(data_path, "JURAraster_MV_1_8ha_Mean_H.p25.tif"), + + paste0(data_path, "JURAraster_MV_1_8ha_sd_H.nb20_30relative_density.tif"), + + paste0(data_path, "JURAraster_MV_1_8ha_sd_H.nb2_5ratio.tif"), + quick=TRUE ) -names(new_env_raster) <- c("Canopy1020", "Canopy2030", "Simpson") +names(new_env_raster) <- c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") -#crs(new_env_raster)<-"+proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +vunits=m +no_defs" +crs(new_env_raster)<-"+proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +vunits=m +no_defs" new_env_raster<-normImage(new_env_raster) @@ -4592,7 +4604,7 @@ new_env_raster_tot <- overlay(new_env_raster, rpoly, fun = function(x, y) { x[is.na(y[])] <- NA return(x) }) -names(new_env_raster_tot) <-c( "Canopy1020", "Canopy2030", "Simpson" ) +names(new_env_raster_tot) <-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") predictor_tot<-rasterToPoints(new_env_raster_tot) predictor_tot<-na.omit(predictor_tot) @@ -4650,7 +4662,7 @@ new_env_raster_S <- overlay(new_env_raster, rpoly_S, fun = function(x, y) { x[is.na(y[])] <- NA return(x) }) -names(new_env_raster_S) <-c( "Canopy1020", "Canopy2030", "Simpson" ) +names(new_env_raster_S) <-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") # Non Sytematic @@ -4663,7 +4675,7 @@ new_env_raster_R <- overlay(new_env_raster, rpoly_R, fun = function(x, y) { return(x) }) -names(new_env_raster_R) <-c( "Canopy1020", "Canopy2030", "Simpson") +names(new_env_raster_R) <-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") ``` *Select data (systemtic/Conventional design and Female/male)* ```{r echo=TRUE, eval=TRUE, tidy=TRUE, message=FALSE} @@ -4745,6 +4757,92 @@ pres_pointsGDT<- data.frame(x=OBSGDTshp$coords.x1, y=OBSGDTshp$coords.x2, presen ``` +```{r echo=TRUE, eval=TRUE, tidy=TRUE, message=FALSE} +#fonction for fold and AUC +iid <- function(data, V, predictors_vector){ + +.cvFolds <- function(Y, V){ #Create CV folds (stratify by outcome) +Y0 <- split(sample(which(Y==0)), rep(1:V, length=length(which(Y==0)))) +Y1 <- split(sample(which(Y==1)), rep(1:V, length=length(which(Y==1)))) + +folds <- vector("list", length=V) + +for (v in seq(V)) {folds[[v]] <- c(Y0[[v]], Y1[[v]])} + +return(folds) +} + + +.doFit <- function(v, folds, data){ #Train/test glm for each fold +fit <- maxnet(as.vector(data[-folds[[v]],"presence"]), data[-folds[[v]], predictors_vector], f = FORMULA, regmult = 1) +pred <- predict(fit, newdata=data[folds[[v]], predictors_vector], type=c("exponential"), clamp=FALSE) +return(pred) +} + +set.seed(158) +folds <- .cvFolds(Y=data$presence, V=V) #Create folds + +predictions <- unlist(sapply(seq(V), .doFit, folds=folds, data=data)) #CV train/predict + +predictions[unlist(folds)] <- predictions #Re-order pred va + +# Get CV AUC and confidence interval +out <- ci.cvAUC(predictions=predictions, labels=data$presence, folds=folds, confidence=0.95) + +#Get models for output +# list_fit<-list() +# +# list_fit <- lapply(folds, function(x) maxnet(as.vector(data[-x,"presence"]), data[-x, predictors_vector], f = FORMULA, regmult = 1) ) +# +# OUT<-list(out, list_fit) +# return(OUT) + +return(out) +} + + +iid2 <- function(data, V, predictors_vector){ + +.cvFolds <- function(Y, V){ #Create CV folds (stratify by outcome) +Y0 <- split(sample(which(Y==0)), rep(1:V, length=length(which(Y==0)))) +Y1 <- split(sample(which(Y==1)), rep(1:V, length=length(which(Y==1)))) + +folds <- vector("list", length=V) + +for (v in seq(V)) {folds[[v]] <- c(Y0[[v]], Y1[[v]])} + +return(folds) +} + + +.doFit <- function(v, folds, data){ #Train/test glm for each fold +fit <- maxnet(as.vector(data[-folds[[v]],"presence"]), data[-folds[[v]], predictors_vector], f = FORMULA, regmult = 1) +data[, 10][data[, 10] != 0] <- 0 +pred <- predict(fit, newdata=data[folds[[v]], predictors_vector], type=c("exponential"), clamp=FALSE) +return(pred) +} + +set.seed(158) +folds <- .cvFolds(Y=data$presence, V=V) #Create folds + +predictions <- unlist(sapply(seq(V), .doFit, folds=folds, data=data)) #CV train/predict + +predictions[unlist(folds)] <- predictions #Re-order pred va + +# Get CV AUC and confidence interval +out <- ci.cvAUC(predictions=predictions, labels=data$presence, folds=folds, confidence=0.95) + +#Get models for output +# list_fit<-list() +# +# list_fit <- lapply(folds, function(x) maxnet(as.vector(data[-x,"presence"]), data[-x, predictors_vector], f = FORMULA, regmult = 1) ) +# +# OUT<-list(out, list_fit) +# return(OUT) + +return(out) +} +``` ```{r echo=TRUE, eval=TRUE, tidy=TRUE, message=FALSE} @@ -4773,11 +4871,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd")] +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") + +out_F_1 <- iid(data=pres_bgGDT, V=10, var_names) + #use it to predict results predictor<-rasterToPoints(new_env_raster_S) predictor<-na.omit(predictor) @@ -4832,11 +4935,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd")] +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") + +out_F_2 <- iid(data=pres_bgGDT, V=10, var_names) + #use it to predict results predictor<-rasterToPoints(new_env_raster_S) predictor<-na.omit(predictor) @@ -4924,10 +5032,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson", "SystTracks_distance5_5")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd", "SystTracks_distance5_5")] + +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd", "SystTracks_distance5_5") + +out_F_3 <- iid2(data=pres_bgGDT, V=10, var_names) + #use it to predict results #Set all values to 0 raster_tracks_syst_1<-raster_tracks_syst @@ -4943,7 +5057,7 @@ predictor$predicted<-predict(model_maxnet, predictor,type=c("exponential"), clam maps_predicted_F_3<-rasterFromXYZ(predictor[, c("x","y","predicted")], res=c(25,25)) -colnames(predictor_tot2) <- c("x", "y", "SystTracks_distance5_5", "Canopy1020", "Canopy2030", "Simpson") +colnames(predictor_tot2) <- c("x", "y", "SystTracks_distance5_5", "Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") predictor_tot2$predicted_tot2<-predict(model_maxnet, predictor_tot2,type=c("exponential"), clamp=FALSE) maps_predicted_F_3b<-rasterFromXYZ(predictor_tot2[, c("x","y","predicted_tot2")], res=c(25,25)) @@ -4993,10 +5107,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd")] + +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") + +out_F_4 <- iid(data=pres_bgGDT, V=10, var_names) + #use it to predict results predictor<-rasterToPoints(new_env_raster_R) predictor<-na.omit(predictor) @@ -5047,11 +5167,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd")] +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") + +out_F_5 <- iid(data=pres_bgGDT, V=10, var_names) + #use it to predict results predictor<-rasterToPoints(new_env_raster_R) predictor<-na.omit(predictor) @@ -5135,10 +5260,17 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson", "BiaisTracks_distance5_5")] + +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd", "BiaisTracks_distance5_5")] + +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd", "BiaisTracks_distance5_5") + +out_F_6 <- iid2(data=pres_bgGDT, V=10, var_names) + #use it to predict results #Set all values to 1 raster_tracks_bias_1<-raster_tracks_bias @@ -5152,7 +5284,7 @@ predictor<-as.data.frame(predictor) predictor$predicted<-predict(model_maxnet, predictor,type=c("exponential"), clamp=FALSE) -colnames(predictor_tot2) <- c("x", "y", "BiaisTracks_distance5_5", "Canopy1020", "Canopy2030", "Simpson") +colnames(predictor_tot2) <- c("x", "y", "BiaisTracks_distance5_5", "Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") predictor_tot2$predicted_tot2<-predict(model_maxnet, predictor_tot2,type=c("exponential"), clamp=FALSE) @@ -5211,10 +5343,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd")] + +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") + +out_M_1 <- iid(data=pres_bgGDT, V=10, var_names) + #use it to predict results predictor<-rasterToPoints(new_env_raster_S) predictor<-na.omit(predictor) @@ -5261,11 +5399,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd")] +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") + +out_M_2 <- iid(data=pres_bgGDT, V=10, var_names) + #use it to predict results predictor<-rasterToPoints(new_env_raster_S) predictor<-na.omit(predictor) @@ -5312,10 +5455,18 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson", "SystTracks_distance5_5")] +model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) + +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd", "SystTracks_distance5_5")] + +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd", "SystTracks_distance5_5") + +out_F_3 <- iid2(data=pres_bgGDT, V=10, var_names) + predictor<-rasterToPoints(new_env_rastersyst3) predictor<-na.omit(predictor) predictor<-as.data.frame(predictor) @@ -5323,7 +5474,7 @@ predictor<-as.data.frame(predictor) predictor$predicted<-predict(model_maxnet, predictor,type=c("exponential"), clamp=FALSE) #predictor_tot2<-predictor_tot2[, c(-7,-8)] -colnames(predictor_tot2) <- c("x", "y", "SystTracks_distance5_5", "Canopy1020", "Canopy2030", "Simpson") +colnames(predictor_tot2) <- c("x", "y", "SystTracks_distance5_5", "Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") predictor_tot2$predicted_tot2<-predict(model_maxnet, predictor_tot2,type=c("exponential"), clamp=FALSE) @@ -5375,11 +5526,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd")] +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") + +out_M_4 <- iid(data=pres_bgGDT, V=10, var_names) + #use it to predict results predictor<-rasterToPoints(new_env_raster_R) predictor<-na.omit(predictor) @@ -5427,11 +5583,16 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd")] +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") + +out_M_5 <- iid(data=pres_bgGDT, V=10, var_names) + #use it to predict results predictor<-rasterToPoints(new_env_raster_R) predictor<-na.omit(predictor) @@ -5476,17 +5637,23 @@ pres_bgGDT <- na.omit(pres_bgGDT) Pres_data<-as.vector(pres_bgGDT$presence) -predictor_maxnet<-pres_bgGDT[, c( "Canopy1020", "Canopy2030", "Simpson", "BiaisTracks_distance5_5")] +predictor_maxnet<-pres_bgGDT[, c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd", "BiaisTracks_distance5_5")] + +FORMULA<-maxnet.formula(Pres_data, predictor_maxnet, classes = "q") model_maxnet<-maxnet(Pres_data, predictor_maxnet, f = maxnet.formula(Pres_data, predictor_maxnet, classes = "q"), regmult = 1) +var_names<-c("Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd", "BiaisTracks_distance5_5") + +out_F_6 <- iid2(data=pres_bgGDT, V=10, var_names) + predictor<-rasterToPoints(new_env_rasterbias3) predictor<-na.omit(predictor) predictor<-as.data.frame(predictor) predictor$predicted<-predict(model_maxnet, predictor,type=c("exponential"), clamp=FALSE) -colnames(predictor_tot2) <- c("x", "y", "BiaisTracks_distance5_5", "Canopy1020", "Canopy2030", "Simpson") +colnames(predictor_tot2) <- c("x", "y", "BiaisTracks_distance5_5", "Canopy1020","penetrationratio0205","Simpson", "Q25", "Canopy2030sd","penetrationratio0205sd") predictor_tot2$predicted_tot2<-predict(model_maxnet, predictor_tot2,type=c("exponential"), clamp=FALSE) -- GitLab