R:将线性模型与`lm`拟合时的对比误差 [英] R: Error in contrasts when fitting linear models with `lm`
问题描述
我发现定义时形成对比的错误R 中的线性模型并遵循了那里的建议，但是我的因子变量都没有一个值，并且仍然遇到相同的问题.
这是我正在使用的数据集: https://www.dropbox.com/s/em7xphbeaxykgla/train.csv?dl=0 .
这是我要运行的代码:
simplelm <- lm(log_SalePrice ~ ., data = train)
#Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
# contrasts can be applied only to factors with 2 or more levels
出了什么问题?
感谢您提供数据集(我希望该链接永远有效，以便每个人都可以访问).我将其读入数据框train.
使用如何调试对比度"提供的debug_contr_error，debug_contr_error2和NA_preproc辅助功能.到具有2个或更多水平的因子"错误?，我们可以轻松地分析问题.
info <- debug_contr_error2(log_SalePrice ~ ., train)
## the data frame that is actually used by `lm`
dat <- info$mf
## number of cases in your dataset
nrow(train)
#[1] 1460
## number of complete cases used by `lm`
nrow(dat)
#[1] 1112
## number of levels for all factor variables in `dat`
info$nlevels
# MSZoning Street Alley LotShape LandContour
# 4 2 3 4 4
# Utilities LotConfig LandSlope Neighborhood Condition1
# 1 5 3 25 9
# Condition2 BldgType HouseStyle RoofStyle RoofMatl
# 6 5 8 5 7
# Exterior1st Exterior2nd MasVnrType ExterQual ExterCond
# 14 16 4 4 4
# Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1
# 6 5 5 5 7
# BsmtFinType2 Heating HeatingQC CentralAir Electrical
# 7 5 5 2 5
# KitchenQual Functional FireplaceQu GarageType GarageFinish
# 4 6 6 6 3
# GarageQual GarageCond PavedDrive PoolQC Fence
# 5 5 3 4 5
# MiscFeature SaleType SaleCondition MiscVal_bool MoYrSold
# 4 9 6 2 55
如您所见，Utilities是有问题的变量，因为它只有1级.
由于train中有许多字符/因子变量，所以我想知道您是否为它们提供NA.如果将NA添加为有效级别，则可能会得到更完整的案例.
new_train <- NA_preproc(train)
new_info <- debug_contr_error2(log_SalePrice ~ ., new_train)
new_dat <- new_info$mf
nrow(new_dat)
#[1] 1121
new_info$nlevels
# MSZoning Street Alley LotShape LandContour
# 5 2 3 4 4
# Utilities LotConfig LandSlope Neighborhood Condition1
# 1 5 3 25 9
# Condition2 BldgType HouseStyle RoofStyle RoofMatl
# 6 5 8 5 7
# Exterior1st Exterior2nd MasVnrType ExterQual ExterCond
# 14 16 4 4 4
# Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1
# 6 5 5 5 7
# BsmtFinType2 Heating HeatingQC CentralAir Electrical
# 7 5 5 2 6
# KitchenQual Functional FireplaceQu GarageType GarageFinish
# 4 6 6 6 3
# GarageQual GarageCond PavedDrive PoolQC Fence
# 5 5 3 4 5
# MiscFeature SaleType SaleCondition MiscVal_bool MoYrSold
# 4 9 6 2 55
我们确实可以获得更完整的案例，但是Utilities仍然只有一个级别.这意味着大多数不完整的情况实际上是由您的数值变量中的NA引起的，我们无法采取任何措施(除非您有统计上有效的方法来估算那些缺失值).
由于您只有一个单级因子变量，因此使用与如何在对比度可以达到"的情况下执行GLM相同的方法仅适用于具有2个或更多水平的因子"?.
new_dat$Utilities <- 1
simplelm <- lm(log_SalePrice ~ 0 + ., data = new_dat)
模型现在成功运行.但是，它是排名不足.您可能想采取一些措施来解决该问题，但可以将其保留为好.
b <- coef(simplelm)
length(b)
#[1] 301
sum(is.na(b))
#[1] 9
simplelm$rank
#[1] 292
I've found Error in contrasts when defining a linear model in R and have followed the suggestions there, but none of my factor variables take on only one value and I am still experiencing the same issue.
This is the dataset I'm using: https://www.dropbox.com/s/em7xphbeaxykgla/train.csv?dl=0.
This is the code I'm trying to run:
simplelm <- lm(log_SalePrice ~ ., data = train)
#Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
# contrasts can be applied only to factors with 2 or more levels
What is the issue?
Thanks for providing your dataset (I hope that link will forever be valid so that everyone can access). I read it into a data frame train.
Using the debug_contr_error, debug_contr_error2 and NA_preproc helper functions provided by How to debug "contrasts can be applied only to factors with 2 or more levels" error?, we can easily analyze the problem.
info <- debug_contr_error2(log_SalePrice ~ ., train)
## the data frame that is actually used by `lm`
dat <- info$mf
## number of cases in your dataset
nrow(train)
#[1] 1460
## number of complete cases used by `lm`
nrow(dat)
#[1] 1112
## number of levels for all factor variables in `dat`
info$nlevels
# MSZoning Street Alley LotShape LandContour
# 4 2 3 4 4
# Utilities LotConfig LandSlope Neighborhood Condition1
# 1 5 3 25 9
# Condition2 BldgType HouseStyle RoofStyle RoofMatl
# 6 5 8 5 7
# Exterior1st Exterior2nd MasVnrType ExterQual ExterCond
# 14 16 4 4 4
# Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1
# 6 5 5 5 7
# BsmtFinType2 Heating HeatingQC CentralAir Electrical
# 7 5 5 2 5
# KitchenQual Functional FireplaceQu GarageType GarageFinish
# 4 6 6 6 3
# GarageQual GarageCond PavedDrive PoolQC Fence
# 5 5 3 4 5
# MiscFeature SaleType SaleCondition MiscVal_bool MoYrSold
# 4 9 6 2 55
As you can see, Utilities is the offending variable here as it has only 1 level.
Since you have many character / factor variables in train, I wonder whether you have NA for them. If we add NA as a valid level, we could possibly get more complete cases.
new_train <- NA_preproc(train)
new_info <- debug_contr_error2(log_SalePrice ~ ., new_train)
new_dat <- new_info$mf
nrow(new_dat)
#[1] 1121
new_info$nlevels
# MSZoning Street Alley LotShape LandContour
# 5 2 3 4 4
# Utilities LotConfig LandSlope Neighborhood Condition1
# 1 5 3 25 9
# Condition2 BldgType HouseStyle RoofStyle RoofMatl
# 6 5 8 5 7
# Exterior1st Exterior2nd MasVnrType ExterQual ExterCond
# 14 16 4 4 4
# Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1
# 6 5 5 5 7
# BsmtFinType2 Heating HeatingQC CentralAir Electrical
# 7 5 5 2 6
# KitchenQual Functional FireplaceQu GarageType GarageFinish
# 4 6 6 6 3
# GarageQual GarageCond PavedDrive PoolQC Fence
# 5 5 3 4 5
# MiscFeature SaleType SaleCondition MiscVal_bool MoYrSold
# 4 9 6 2 55
We do get more complete cases, but Utilities still has one level. This means that most incomplete cases are actually caused by NA in your numerical variables, which we can do nothing (unless you have a statistically valid way to impute those missing values).
As you only have one single-level factor variable, the same method as given in How to do a GLM when "contrasts can be applied only to factors with 2 or more levels"? will work.
new_dat$Utilities <- 1
simplelm <- lm(log_SalePrice ~ 0 + ., data = new_dat)
The model now runs successfully. However, it is rank-deficient. You probably want to do something to address it, but leaving it as it is is fine.
b <- coef(simplelm)
length(b)
#[1] 301
sum(is.na(b))
#[1] 9
simplelm$rank
#[1] 292
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