英文:
How to use CAST package for a shapefile (polygons) in R?
问题
以下是翻译好的部分：
My goal: 我的目标是运行一个套索回归模型，以进行数据变量选择（数据以sf多边形格式存在）。
My data: 如上所述，我的数据是一个sf对象，具体而言，是一个包含多边形的shapefile。
我已经尝试使用ffs或train，但都没有成功。
以下是一个可重现的示例，其中包含一个多多边形shapefile。
请忘记以“74”结尾的变量与以“79”结尾的变量之间可能存在的时间关系。
library(sf)
library(CAST)
#加载数据
nc <- st_read(system.file("shape/nc.shp", package="sf"))
#训练和测试数据
set.seed(100)
ind <- sample(2, nrow(nc), replace=TRUE, prob = c(0.7, 0.3))
train <- nc[ind==1,]
test <- nc[ind==2,]
predictors <- c("SID74","BIR79","BIR74")
response <- "NWBIR79"
## 第一选项 ##
#==============#
#前向特征选择
set.seed(10)
ffs(train[,predictors], train$NWBIR79, method = "lasso")
[1] "model using SID74,BIR79 will be trained now..."
出现问题：所有的RMSE度量值都缺失：
RMSE Rsquared MAE
Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA
NA's :3 NA's :3 NA's :3
Error: Stopping
此外：有26个警告（使用warnings()查看它们）
## 第二选项 ##
#==============#
#没有前向特征选择的模型
set.seed(100)
model <- train(train[,predictors], train$NWBIR79, method="lasso", trControl=trainControl(method = "cv"),importance=TRUE)
出现问题：所有的RMSE度量值都缺失：
RMSE Rsquared MAE
Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA
NA's :3 NA's :3 NA's :3
Error: Stopping
此外：有11个警告（使用warnings()查看它们）
英文:
any help with the following is really appreciated!!
My goal: I need to run a lasso model for variable selection for my data (which is in sf polygon format).
My data: As said above, is a sf object. Specifically, is a shapefile with polygons.
I have tried using either ffs or train. But none of them work.
Here is a reproducible example, with a multipolygon shapefile.
Please forget about the possible time relationship between the variables that end in "74" and the ones that end in "79".
library(sf)
library(CAST)
#Loading data
nc <- st_read(system.file("shape/nc.shp", package="sf"))
#Training and test data
set.seed(100)
ind <- sample(2,nrow(nc),replace=T,prob = c(0.7,0.3))
train <- nc[ind==1,]
test <- nc[ind==2,]
predictors <- c("SID74","BIR79","BIR74")
response <- "NWBIR79"
## 1st option ##
#==============#
#ffs Forward feature selection
set.seed(10)
ffs(train[,predictors], train$NWBIR79,method = "lasso")
[1] "model using SID74,BIR79 will be trained now..."
Something is wrong; all the RMSE metric values are missing:
RMSE Rsquared MAE
Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA
NA's :3 NA's :3 NA's :3
Error: Stopping
In addition: There were 26 warnings (use warnings() to see them)
## 2nd option ##
#==============#
#model without ffs
set.seed(100)
model <- train(train[,predictors], train$NWBIR79, method="lasso", trControl=trainControl(method = "cv"),importance=T)
Something is wrong; all the RMSE metric values are missing:
RMSE Rsquared MAE
Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA
NA's :3 NA's :3 NA's :3
Error: Stopping
In addition: There were 11 warnings (use warnings() to see them)
答案1
得分: 1
-
当前错误的两个原因：
-
几何形状目前是预测变量的一部分。请删除几何形状：
st_drop_geometry(train[,predictors]) -
“importance”不是套索方法的参数
model <- train(st_drop_geometry(train[,predictors]), train$NWBIR79, method="lasso", trControl=trainControl(method = "cv"))
这应该与CAST::ffs一样工作。
英文:
Two things that lead to the error:
-
The geometries are currently part of the predictors. Drop geometries:
st_drop_geometry(train[,predictors]) -
"importance" is not a parameter of the lasso method
model <- train(st_drop_geometry(train[,predictors]), train$NWBIR79, method="lasso", trControl=trainControl(method = "cv"))
This should work with CAST::ffs in the same way.
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