.local(x, p, ...) 中的错误：超过一半的存在点具有 NA 预测值

如何解决.local(x, p, ...) 中的错误：超过一半的存在点具有 NA 预测值

所以我正在制作生态分布图，但一直收到错误代码。我对 R 还很陌生，所以任何建议都会有所帮助

这是我正在使用的代码

setwd("~/gfds/Salsola Files")
#loading in the needed library
library(dismo)
library(maptools)
library(raster)
library(sp)
library(rgeos)
library(caret)

#---misc variables manually defined-----------------------------------------------------------------

e <- extent(-180,-60,20,70) #Set extent of area (long_min,long_max,lat_min,lat_max)
n_abs <- 1000 

datafile <- "Salsola Tragus Data.csv"

data <- read.csv(datafile,sep = ",",quote = "\"",dec = ".",fill = TRUE,comment.char = "")

data <- data[,c("Scientific.Names","Latitude","Longitude")]

salsola.coords<-data[,-1]

data.sdf<-data              #spacial data frame for salsola data

coordinates(data.sdf) <- ~Longitude+Latitude

projection(data.sdf) <- CRS('+proj=longlat +datum=WGS84')

#---Load worldclim data
#Load bioclim stack--------------------------------------------------------------------------------------------

worldclim <-stack(getData('worldclim',var='bio',res=10))
#resolution 10 looks high enough for us
#Crop worldclim to area of interest 

worldclim.extent <-stack(crop(worldclim,e))   #crop worldclim to desired extent "e"

#---Plot points on a map to confirm outliers have been successfully removed-------------------------------------------------------------------
data(wrld_simpl)

#Plot worldwide distribution to catch outliers
windows() #open in new window

plot(wrld_simpl,xlim=c(-180,180),ylim=c(-80,80),axes=TRUE,col="light yellow")#plot map

box() #add box around plot 

points(salsola.coords$Longitude,salsola.coords$Latitude,col='orange',pch=20,cex=0.75) #plot salsola points

points(salsola.coords$Longitude,col='red',cex=0.75) #add border

#---pseudo absence simulation------------------------------------------------------------------------------------------------------------------
#Define a mask from worldclim data
mask_land <- raster(worldclim.extent@layers[[1]])

# Create circles with a radius of 100 km
x <- circles(data.sdf,d=100000,lonlat=TRUE)
pol <- polygons(x)

# sample randomly from all circles-----------------------------------------------------------------------------------------------------------------------
samp1 <- spsample(pol,1000,type='random',iter=25)
# Warning in proj4string(obj): CRS object has comment,which is lost in output
# get unique cells
cells <- cellFromXY(mask_land,samp1)
length(cells)
cells <- unique(cells)
length(cells)
xy <- xyFromCell(mask_land,cells)

#Plot the rando data we've sampled above on top of the circles.----------------------------------------------------------------------------------
#Our points don't look like they're in such a nice grid,because we're zoomed 
#out much further than the demo and don't have enough points in the plot. 
#I've upped the number of points to 1000,but you might increase later after debug.

dev.new()

plot(pol,axes=TRUE)

points(xy,cex=0.1,col='blue')

#---Choosing a subset of abiotic rasters-------------------------------------------------------------------------------------------------------------
#Correlate abiotic layers to each other
WCcorr <- layerStats(worldclim.extent,'pearson',na.rm=TRUE)

WCcorr <- WCcorr$`pearson correlation coefficient`
#write.csv(WCcorr,"correlationBioclim.csv") 

#Removing highly correlated layers---------------------------------------------------------------------------------------------------------------------
#c <- data.matrix(read.csv("correlationBioclim.csv",header = TRUE,row.names = 1,"))
WCcorr <- abs(WCcorr)

# Find rows that should be removed
kill_list <- findCorrelation(WCcorr,cutoff = 0.7,names = TRUE,exact = TRUE)

kill_list<-sort(kill_list)
# keep: bio2,bio3,bio5,bio8,bio9,bio12,bio14,and bio18 
#Define Subset layers in worldclim

worldclim.subset<-dropLayer(worldclim.extent,kill_list) 

worldclim.subset.names<-names(worldclim.subset)

print(worldclim.subset.names)

#---Construct dataframe of presences and absences-------------------------------------------------------------------------------------------------------

presvals <- extract(worldclim.subset,salsola.coords)

# setting random seed to always create the same
# random set of points for this example

set.seed(0)
stvals <- extract(worldclim.subset,randomPoints(worldclim.subset,n_abs)) #simulated absence values

pb <- c(rep(1,nrow(presvals)),rep(0,nrow(stvals)))#presence & absence binary array

sdmdata <- data.frame(cbind(pb,rbind(presvals,stvals)))#dataframe of presence and absence data

#Plot using "pairs" function--------------------------------------------------------------------------------------------------------------------------------
pairs(sdmdata[,2:6],cex=0.1)

#Save outputs to be used in next section
saveRDS(sdmdata,"s_traguasdm.Rds")
saveRDS(presvals,"s_tragus_pvals.Rds")

#---Model Fitting
#GLM: Linear Models

m2 = glm(pb ~ .,data=sdmdata)

#MaxEnt model
#get maxent() #Run this prior to attempting to load dismo

#Create training data for maxent------------------------------------------------------------------------------------------------------------------------------
group<-kfold(salsola.coords,5)

pres_test <- salsola.coords[group==1,]

pres_train <-salsola.coords[group !=1,]

#Run MaxEnt: I am not totally sure how "factors" works,------------------------------------------------------------------------------------------------------
#but it tells MaxEnt which layers we want to consider "categorical". I've set
#this series of model creation steps up to try each of our chosen variables,#as well as one which doesn't specify a factor (max).

#FYI,MaxEnt has a "removeDuplicates" Boolean option which will remove points
#which fall in the same grid cell. That might be really useful if your abiotic
#rasters have varying resolution.
max <- maxent(worldclim.subset,salsola.coords)#####THIS SHOULD BE PRES_TRAIN#### 

我得到的错误：

Error in .local(x,p,...) : 
  more than half of the presence points have NA predictor values

任何帮助将不胜感激

解决方法

消息“超过一半的存在点具有 NA 预测值”表明您的许多点不与栅格数据重叠，或者如果重叠，则单元格在那里是 NA。

我认为这样做的原因是您使用了您定义为的 salsola.coords

data <- data[,c("Scientific.Names","Latitude","Longitude")]
salsola.coords<-data[,-1]

而你可能应该这样做

salsola.coords <- data[,c("Longitude","Latitude")]

也就是说，按正确的顺序有经度和纬度