r - 在 R 中使用线性回归填充 NA

Question

我有一个包含一个时间列和 2 个变量的数据。(示例如下)

df <- structure(list(time = c(15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 
                              25, 26), var1 = c(20.4, 31.5, NA, 53.7, 64.8, NA, NA, NA, NA, 
                              120.3, NA, 142.5), var2 = c(30.6, 47.25, 63.9, 80.55, 97.2, 113.85, 
                              130.5, 147.15, 163.8, 180.45, 197.1, 213.75)), .Names = c("time", 
                              "var1", "var2"), row.names = c(NA, -12L), class = c("tbl_df", 
                              "tbl", "data.frame"))

var1 的 NA 很少，我想用 var1 和 var2 中剩余值之间的线性回归来填充 NA。

请帮忙!!如果您需要更多信息，请告诉我

Answer 1

这是一个使用 lm 预测 R 值的示例。

library(dplyr)

# Construct linear model based on non-NA pairs
df2 <- df %>% filter(!is.na(var1))

fit <- lm(var1 ~ var2, data = df2)

# See the result
summary(fit)

# Call:
#   lm(formula = var1 ~ var2, data = df2)
# 
# Residuals:
#   1          2          3          4          5          6 
# 8.627e-15 -2.388e-15  1.546e-16 -9.658e-15 -2.322e-15  5.587e-15 
# 
# Coefficients:
#   Estimate Std. Error   t value Pr(>|t|)    
# (Intercept) 2.321e-14  5.619e-15 4.130e+00   0.0145 *  
#   var2        6.667e-01  4.411e-17 1.511e+16   <2e-16 ***
#   ---
#   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 7.246e-15 on 4 degrees of freedom
# Multiple R-squared:      1,   Adjusted R-squared:      1 
# F-statistic: 2.284e+32 on 1 and 4 DF,  p-value: < 2.2e-16
# 
# Warning message:
#   In summary.lm(fit) : essentially perfect fit: summary may be unreliable

# Use fit to predict the value
df3 <- df %>% 
  mutate(pred = predict(fit, .)) %>%
  # Replace NA with pred in var1
  mutate(var1 = ifelse(is.na(var1), pred, var1))

# See the result
df3 %>% as.data.frame()

#    time  var1   var2  pred
# 1    15  20.4  30.60  20.4
# 2    16  31.5  47.25  31.5
# 3    17  42.6  63.90  42.6
# 4    18  53.7  80.55  53.7
# 5    19  64.8  97.20  64.8
# 6    20  75.9 113.85  75.9
# 7    21  87.0 130.50  87.0
# 8    22  98.1 147.15  98.1
# 9    23 109.2 163.80 109.2
# 10   24 120.3 180.45 120.3
# 11   25 131.4 197.10 131.4
# 12   26 142.5 213.75 142.5