95% CI 的重复回归估计

如何解决95% CI 的重复回归估计

如果以下是我的数据，我如何才能获得截距和 beta 估计值的 95% 上下置信区间？

data_long <- structure(list(ID = c("00721-2-C","00724-1-C","00733-1-C","00735-2-C","00738-1-C","00721-2-C","00738-1-C"),study_group = structure(c(2L,1L,2L,1L),.Label = c("Intervention group","Control group"),class = "factor"),timepoint = structure(c(1L,3L,4L,.Label = c("Baseline","6 months","24 months","48 months"),variable = structure(c(1L,3L),.Label = c("Energy_kcal","Protein_g","MUFA_g"),value = c(1268.406744,997.5694419165,1402.338548,2228.254514,1969.496113,1429.53627,1847.53429,1390.102819,1484.28248,2005.2722320005,58.485134107,35.8161015647956,74.273355041,59.7441761,60.049667397,57.924105381,73.246959056,51.814332349,56.547926964,60.8630037867644,9.683201376,11.0929851920735,12.213572797,24.875988218,25.29578509,20.701295571,26.720893066,18.564045616,18.833261276,30.9237008583813)),row.names = c(1L,5L,17L,22L,23L,24L,25L,28L,29L,3608L,3611L,3612L,3624L,3629L,3630L,3631L,3632L,3635L,3636L,25250L,25253L,25254L,25266L,25271L,25272L,25273L,25274L,25277L,25278L
),class = "data.frame")

我重复线性回归的代码是：

library(dplyr)
library(broom)
library(tidyr)

data_long %>% 
  group_by(variable) %>% 
  do({
    my_mdl <- lm(value ~ timepoint,.)  
    my_tidy <- tidy(my_mdl)
    my_tidy_wide <- pivot_wider(my_tidy,names_from = "term",values_from = everything())
    my_glance <- glance(my_mdl)
    bind_cols(my_tidy_wide,my_glance)
  }) %>% 
  select(variable,starts_with("estimate"),starts_with("std.error"),starts_with("p.value"),AIC,adj.r.squared)


# A tibble: 5 x 16
# Groups:   variable [5]
  variable `estimate_(Inte~ estimate_timepo~ estimate_timepo~ estimate_timepo~ `std.error_(Int~ std.error_timep~ std.error_timep~ std.error_timep~ `p.value_(Inter~ p.value_timepoi~
  <fct>               <dbl>            <dbl>            <dbl>            <dbl>            <dbl>            <dbl>            <dbl>            <dbl>            <dbl>            <dbl>
1 Energy_~           1624.            45.9            -198.            346.              168.             215.             241.             343.          0.0000713            0.838
2 Protein~             59.1           -1.49              1.76            0.929             5.18             6.61             7.42            10.6         0.0000270            0.829
3 MUFA_g               20.8           -0.139            -4.26            4.50              3.25             4.15             4.65             6.62        0.000685             0.974
4 Sugar_g              82.3           -1.29            -18.5            46.2              10.6             13.5             15.2             21.6         0.000241             0.927
5 Sweets_g             34.7           -2.32            -23.0            52.8              11.7             14.9             16.7             23.8         0.0249               0.882
# ... with 5 more variables: p.value_timepoint2 <dbl>,p.value_timepoint3 <dbl>,p.value <dbl>,AIC <dbl>,adj.r.squared <dbl>

我是否也可以将解决方案用于混合效果模型或代码如何更改？

谢谢！

解决方法

这有效：

data_long %>% 
  group_by(variable) %>% 
  do({
    my_mdl <- lm(value ~ timepoint,.)  
    my_tidy <- tidy(my_mdl,conf.int = TRUE)
    my_tidy_wide <- pivot_wider(my_tidy,names_from = "term",values_from = everything())
    my_glance <- glance(my_mdl)
    bind_cols(my_tidy_wide,my_glance)
  }) %>% 
  select(variable,starts_with("estimate"),starts_with("std.error"),starts_with("p.value"),starts_with("conf.low"),starts_with("conf.high"),AIC,BIC,adj.r.squared,df.residual)