[英]how does R handle NA values vs deleted values with regressions
假設我有一張表,我刪除了所有不適用的值,然后進行了回歸。 如果我在同一張表上運行完全相同的回歸,但這次不是刪除不適用的值,而是將它們變成 NA 值,回歸是否仍會給我相同的系數?
回歸將在進行分析之前忽略任何 NA 值(即刪除在任何預測變量或結果變量中包含缺失NA
的任何行)。 您可以通過比較兩個模型的自由度和其他統計數據來檢查這一點。
這是一個玩具示例:
head(mtcars)
# check the data set size (all non-missings)
dim(mtcars) # has 32 rows
# Introduce some missings
set.seed(5)
mtcars[sample(1:nrow(mtcars), 5), sample(1:ncol(mtcars), 5)] <- NA
head(mtcars)
# Create an alternative where all missings are omitted
mtcars_NA_omit <- na.omit(mtcars)
# Check the data set size again
dim(mtcars_NA_omit) # Now only has 27 rows
# Now compare some simple linear regressions
summary(lm(mpg ~ cyl + hp + am + gear, data = mtcars))
summary(lm(mpg ~ cyl + hp + am + gear, data = mtcars_NA_omit))
比較這兩個摘要,您可以看到它們是相同的,除了第一個 model 的一個例外,有一條警告消息指出 5 個 csaes 由於缺失而被丟棄,這正是我們在mtcars_NA_omit
示例中手動執行的操作。
# First, original model
Call:
lm(formula = mpg ~ cyl + hp + am + gear, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-5.0835 -1.7594 -0.2023 1.4313 5.6948
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.64284 7.02359 4.220 0.000352 ***
cyl -1.04494 0.83565 -1.250 0.224275
hp -0.03913 0.01918 -2.040 0.053525 .
am 4.02895 1.90342 2.117 0.045832 *
gear 0.31413 1.48881 0.211 0.834833
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.947 on 22 degrees of freedom
(5 observations deleted due to missingness)
Multiple R-squared: 0.7998, Adjusted R-squared: 0.7635
F-statistic: 21.98 on 4 and 22 DF, p-value: 2.023e-07
# Second model where we dropped missings manually
Call:
lm(formula = mpg ~ cyl + hp + am + gear, data = mtcars_NA_omit)
Residuals:
Min 1Q Median 3Q Max
-5.0835 -1.7594 -0.2023 1.4313 5.6948
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.64284 7.02359 4.220 0.000352 ***
cyl -1.04494 0.83565 -1.250 0.224275
hp -0.03913 0.01918 -2.040 0.053525 .
am 4.02895 1.90342 2.117 0.045832 *
gear 0.31413 1.48881 0.211 0.834833
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.947 on 22 degrees of freedom
Multiple R-squared: 0.7998, Adjusted R-squared: 0.7635
F-statistic: 21.98 on 4 and 22 DF, p-value: 2.023e-07
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