利用R中的lulcc软件包建立了土地利用变化预测模型,并用glm进行了预测。当我执行glm.pred (最后一行)时,出现了一个错误:“预测”包含NA消息。我尝试过使用na.omit函数,但是仍然存在错误,我不知道NA值在哪里。对我的问题有什么建议或解决办法吗?
这是代码
setwd("D:\\Penelitian_Tesis\\lulccLMG package")
memory.size() ### Checking your memory size
memory.limit() ## Checking the set limit
memory.limit(size=500000) ### expanding your memory
lu_lmg_2007 = raster("lu_lmg2007.tif")
lu_lmg_2013 = raster("lu_lmg_2013.tif")
lu_lmg_2019 = raster("lu_lmg_2019.tif")
ef_001 = raster("ef_001.tif")
ef_002 = raster("ef_002.tif")
ef_003 = raster("ef_003.tif")
# create raster stack
Lmg <- stack(lu_lmg_2007,lu_lmg_2013,lu_lmg_2019)
save(Lmg,file="Lmg.Rda")
na.omit("Lmg")
Expvar <- stack(ef_001,ef_002,ef_003)
save(Expvar,file="Expvar.Rda")
na.omit("Expvar")
##############
library(rgdal)
library(raster)
load(file="Lmg.Rda")
load(file="Expvar.Rda")
library(lulcc)
## observed maps
obs <- ObsLulcRasterStack(x=Lmg,
pattern="lu",
categories=c(0,1,2,3,4,5,6,7),
labels=c("HU","LAD","LN","LA","TE","SW","SB","TA"),
t=c(0,6,12))
obs
na.omit(obs)
plot(obs)
crossTabulate(obs, times=c(0,12))
## explanatory variables
ef <- ExpVarRasterList(x=Expvar, pattern="ef")
ef
na.omit(ef)
part <- partition(x=obs[[1]], size=0.1, spatial=TRUE)
na.omit("part")
train.data <- getPredictiveModelInputData(obs=obs, ef=ef, cells=part[["train"]],t=0)
na.omit(train.data)
forms <- list(HU ~ ef_001+ef_002+ef_003,
LAD ~ ef_002+ef_003,
LN ~ ef_002+ef_003,
LA ~ ef_002+ef_003,
TE ~ ef_002+ef_003,
SW ~ ef_002+ef_003,
SB ~ ef_001+ef_002+ef_003,
TA ~ ef_001+ef_002+ef_003)
glm.models <- glmModels(formula=forms, family=binomial(link="logit"), data=train.data, obs=obs)
na.omit(glm.models)
## test ability of models to predict allocation of forest, built and other
## land uses in testing partition
test.data <- getPredictiveModelInputData(obs=obs, ef=ef, cells=part[["test"]])
na.omit(test.data)
glm.pred <- PredictionList(models=glm.models, newdata=test.data)发布于 2021-08-07 04:29:52
我也遇到了同样的问题。问题是您的train.data或test.data可能包含NA。
解决方案是将na.omit(train.data)更改为train.data.no.na <- na.omit(train.data),并使用新创建的变量来输入模型。
https://stackoverflow.com/questions/66203252
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