将fit.mult.impute()结果传入rms::contrast()遇方法适配错误求助

之前按照建议，用Hmisc包的fit.mult.impute()函数处理mice包生成的多重插补数据集，目的是将其转换为可传入rms包contrast()函数的格式，以执行多重比较的置信区间同步校正。已经解决了fit.mult.impute()的运行问题（关键是移除fitter = 参数中lm的引号），但现在遇到新问题：rms::contrast()无法识别fit.mult.impute()生成的对象。

示例代码 #

library(tidyverse)
library(Hmisc)
library(mice)
library(rms)

set.seed(1234)

m <- mtcars 

# 随机将值替换为NA
m %>%
  mutate(across(.cols = everything(),
                .fns = ~ifelse(test = runif(length(.x))>0.1,
                               yes = .x,
                               no = NA))) -> m

# 创建几个因子变量
m %>%
  mutate(across(.cols = c(gear, vs),
                .fns = ~factor(.x))) -> m

使用mice执行多重插补：

mice(data = m,
     maxit = 30,
     seed = 1234) -> ini

传入fit.mult.impute()：

fit.mult.impute(formula = mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + gear + carb,
                data = ini,
                xtrans = ini,
                n.impute = 5,
                fitter = lm) -> fmi

# 输出结果
# Coefficients:
# (Intercept)       cyl         disp           hp         drat           wt         qsec  
#    12.00549  -0.29764      0.02037     -0.02233      1.53538     -4.80789      0.95003  
#     vs1           am        gear4        gear5         carb  
# 0.40091      2.19273     -0.57076     -0.65143      0.34139 

调用contrast()时：

rms::contrast(fit = fmi,
              conf.type = "simultaneous")

错误信息 #

Error in UseMethod("contrast") : 
  no applicable method for 'contrast' applied to an object of class "c('fit.mult.impute', 'lm')"

提问 #

请问是否有解决该问题的方法，或针对多重插补数据集执行相同操作的替代方案？

会话信息 #

R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22621)

Matrix products: default

locale:
[1] LC_COLLATE=English_Australia.utf8  LC_CTYPE=English_Australia.utf8   
[3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C                       
[5] LC_TIME=English_Australia.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] rms_6.3-0       SparseM_1.81    Hmisc_4.7-1     Formula_1.2-4   survival_3.4-0  lattice_0.20-45
 [7] miselect_0.9.0  naniar_1.0.0    magrittr_2.0.3  haven_2.5.1     mice_3.14.0     lubridate_1.9.2
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.2     purrr_1.0.1     readr_2.1.4     tidyr_1.3.0    
[19] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] viridis_0.6.2       viridisLite_0.4.2   splines_4.2.2       latticeExtra_0.6-30
 [5] norm_1.0-10.0       pillar_1.9.0        backports_1.4.1     quantreg_5.94      
 [9] glue_1.6.2          visdat_0.6.0        digest_0.6.29       RColorBrewer_1.1-3 
[13] checkmate_2.1.0     sandwich_3.0-2      colorspace_2.0-3    htmltools_0.5.3    
[17] Matrix_1.4-1        pkgconfig_2.0.3     broom_1.0.4         mvtnorm_1.1-3      
[21] scales_1.3.0        jpeg_0.1-9          tzdb_0.3.0          MatrixModels_0.5-0 
[25] timechange_0.2.0    htmlTable_2.4.1     generics_0.1.3      farver_2.1.1       
[29] ellipsis_0.3.2      TH.data_1.1-1       withr_3.0.1         nnet_7.3-17        
[33] cli_3.6.1           deldir_1.0-6        polspline_1.1.20    fansi_1.0.3        
[37] nlme_3.1-159        MASS_7.3-58.1       foreign_0.8-82      tools_4.2.2        
[41] data.table_1.14.2   hms_1.1.2           multcomp_1.4-20     lifecycle_1.0.4    
[45] interp_1.1-3        munsell_0.5.1       cluster_2.1.4       compiler_4.2.2     
[49] rlang_1.1.0         grid_4.2.2          rstudioapi_0.14     htmlwidgets_1.5.4  
[53] base64enc_0.1-3     labeling_0.4.3      codetools_0.2-18    gtable_0.3.5       
[57] R6_2.5.1            zoo_1.8-10          gridExtra_2.3       knitr_1.40         
[61] fastmap_1.1.0       utf8_1.2.2          stringi_1.7.8       Rcpp_1.0.9         
[65] vctrs_0.6.2         rpart_4.1.16        png_0.1-7           tidyselect_1.2.1   
[69] xfun_0.32 

内容的提问来源于stack exchange，提问作者llewmills