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rregressionconfidence-intervalbroom
Jack
Jack

Reputation: 857

How to extract confidence intervals from multiple regression models?

I am extracting the regression results for two different groups as shown in this example below. In the temp data.frame i get the estimate, std.error, statistic and p-value. However, i don't get the confidence intervals. Is there a simple way to extract them as well?

 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()

Upvotes: 4

Views: 1168

Answers (2)

StupidWolf
StupidWolf

Reputation: 46908

You are doing tidy on a lm object. If you check the help page, there is an option to include the confidence interval, 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

Upvotes: 5

Edo
Edo

Reputation: 7818

If your version of dplyr is higher than 1.0.0, you can use:

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

Upvotes: 2

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