如何从多元回归模型中提取置信区间?
How to extract confidence intervals from multiple regression models?
我正在提取两个不同组的回归结果,如下例所示。在 temp
data.frame 中,我得到了估计值、std.error、统计数据和 p 值。但是,我没有得到置信区间。有没有简单的方法来提取它们?
df <- tibble(
a = rnorm(1000),
b = rnorm(1000),
c = rnorm(1000),
d = rnorm(1000),
group = rbinom(n=1000, size=1, prob=0.5)
)
df$group = as.factor(df$group)
temp <- df %>%
group_by(group) %>%
do(model1 = tidy(lm(a ~ b + c + d, data = .))) %>%
gather(model_name, model, -group) %>%
unnest()
您正在整理 lm 对象。如果您选中 help page,则可以选择包含置信区间 conf.int=TRUE
:
temp <- df %>%
group_by(group) %>%
do(model1 = tidy(lm(a ~ b + c + d, data = . ), conf.int=TRUE)) %>%
gather(model_name, model, -group) %>%
unnest()
# A tibble: 8 x 9
group model_name term estimate std.error statistic p.value conf.low conf.high
<fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 model1 (Int… 0.0616 0.0423 1.46 0.146 -0.0215 0.145
2 0 model1 b 0.00178 0.0421 0.0424 0.966 -0.0808 0.0844
3 0 model1 c -0.00339 0.0431 -0.0787 0.937 -0.0881 0.0813
4 0 model1 d -0.0537 0.0445 -1.21 0.228 -0.141 0.0337
5 1 model1 (Int… -0.0185 0.0454 -0.408 0.683 -0.108 0.0707
6 1 model1 b 0.00128 0.0435 0.0295 0.976 -0.0842 0.0868
7 1 model1 c -0.0972 0.0430 -2.26 0.0244 -0.182 -0.0126
8 1 model1 d 0.0734 0.0457 1.60 0.109 -0.0165 0.163
如果你的dplyr
版本高于1.0.0,你可以使用:
df %>%
group_by(group) %>%
summarise(tidy(lm(a ~ b + c + d), conf.int = TRUE), .groups = "drop")
#> # A tibble: 8 x 8
#> group term estimate std.error statistic p.value conf.low conf.high
#> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 (Intercept) 0.0734 0.0468 1.57 0.117 -0.0185 0.165
#> 2 0 b -0.101 0.0461 -2.19 0.0292 -0.191 -0.0102
#> 3 0 c 0.0337 0.0464 0.726 0.468 -0.0575 0.125
#> 4 0 d -0.101 0.0454 -2.23 0.0265 -0.190 -0.0118
#> 5 1 (Intercept) -0.0559 0.0468 -1.20 0.232 -0.148 0.0360
#> 6 1 b -0.0701 0.0474 -1.48 0.140 -0.163 0.0230
#> 7 1 c 0.0319 0.0477 0.668 0.504 -0.0619 0.126
#> 8 1 d -0.0728 0.0466 -1.56 0.119 -0.164 0.0188
我正在提取两个不同组的回归结果,如下例所示。在 temp
data.frame 中,我得到了估计值、std.error、统计数据和 p 值。但是,我没有得到置信区间。有没有简单的方法来提取它们?
df <- tibble(
a = rnorm(1000),
b = rnorm(1000),
c = rnorm(1000),
d = rnorm(1000),
group = rbinom(n=1000, size=1, prob=0.5)
)
df$group = as.factor(df$group)
temp <- df %>%
group_by(group) %>%
do(model1 = tidy(lm(a ~ b + c + d, data = .))) %>%
gather(model_name, model, -group) %>%
unnest()
您正在整理 lm 对象。如果您选中 help page,则可以选择包含置信区间 conf.int=TRUE
:
temp <- df %>%
group_by(group) %>%
do(model1 = tidy(lm(a ~ b + c + d, data = . ), conf.int=TRUE)) %>%
gather(model_name, model, -group) %>%
unnest()
# A tibble: 8 x 9
group model_name term estimate std.error statistic p.value conf.low conf.high
<fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 model1 (Int… 0.0616 0.0423 1.46 0.146 -0.0215 0.145
2 0 model1 b 0.00178 0.0421 0.0424 0.966 -0.0808 0.0844
3 0 model1 c -0.00339 0.0431 -0.0787 0.937 -0.0881 0.0813
4 0 model1 d -0.0537 0.0445 -1.21 0.228 -0.141 0.0337
5 1 model1 (Int… -0.0185 0.0454 -0.408 0.683 -0.108 0.0707
6 1 model1 b 0.00128 0.0435 0.0295 0.976 -0.0842 0.0868
7 1 model1 c -0.0972 0.0430 -2.26 0.0244 -0.182 -0.0126
8 1 model1 d 0.0734 0.0457 1.60 0.109 -0.0165 0.163
如果你的dplyr
版本高于1.0.0,你可以使用:
df %>%
group_by(group) %>%
summarise(tidy(lm(a ~ b + c + d), conf.int = TRUE), .groups = "drop")
#> # A tibble: 8 x 8
#> group term estimate std.error statistic p.value conf.low conf.high
#> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 (Intercept) 0.0734 0.0468 1.57 0.117 -0.0185 0.165
#> 2 0 b -0.101 0.0461 -2.19 0.0292 -0.191 -0.0102
#> 3 0 c 0.0337 0.0464 0.726 0.468 -0.0575 0.125
#> 4 0 d -0.101 0.0454 -2.23 0.0265 -0.190 -0.0118
#> 5 1 (Intercept) -0.0559 0.0468 -1.20 0.232 -0.148 0.0360
#> 6 1 b -0.0701 0.0474 -1.48 0.140 -0.163 0.0230
#> 7 1 c 0.0319 0.0477 0.668 0.504 -0.0619 0.126
#> 8 1 d -0.0728 0.0466 -1.56 0.119 -0.164 0.0188