Minimal Reproducible Example

1 Quarto Reprex (via GitHub Actions on Linux)

The following reprex is from Quarto (via GitHub Actions on Linux):

Code
# Load libraries
library("mice")

Attaching package: 'mice'
The following object is masked from 'package:stats':

    filter
The following objects are masked from 'package:base':

    cbind, rbind
Code
# Impute data
imp <- mice::mice(
  nhanes,
  maxit = 2,
  m = 2)

 iter imp variable
  1   1  bmi  hyp  chl
  1   2  bmi  hyp  chl
  2   1  bmi  hyp  chl
  2   2  bmi  hyp  chl
Code
# Fit regression model to multiply imputed data
fit <- with(
  data = imp,
  exp = lm(bmi ~ hyp + chl))

# Pool results across models
fit_pooled <- mice::pool(fit)

# Summarize pooled results
fit_pooled
Warning in `[<-.data.frame`(`*tmp*`, is_list, value = list(`3` = "<df[,11] [0 ×
11]>", : replacement element 1 has 1 row to replace 0 rows
Warning in `[<-.data.frame`(`*tmp*`, is_list, value = list(`3` = "<df[,11] [0 ×
11]>", : replacement element 2 has 1 row to replace 0 rows
Error:
! arguments imply differing number of rows: 1, 0
Code
summary(fit_pooled)
Code
# Session Info
sessionInfo()
R version 4.5.2 (2025-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

time zone: UTC
tzcode source: system (glibc)

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

other attached packages:
[1] mice_3.19.0

loaded via a namespace (and not attached):
 [1] generics_0.1.4     tidyr_1.3.2        shape_1.4.6.1      lattice_0.22-7    
 [5] lme4_1.1-38        digest_0.6.39      magrittr_2.0.4     mitml_0.4-5       
 [9] evaluate_1.0.5     grid_4.5.2         iterators_1.0.14   fastmap_1.2.0     
[13] foreach_1.5.2      jomo_2.7-6         jsonlite_2.0.0     glmnet_4.1-10     
[17] Matrix_1.7-4       nnet_7.3-20        backports_1.5.0    survival_3.8-3    
[21] purrr_1.2.0        codetools_0.2-20   reformulas_0.4.3.1 Rdpack_2.6.4      
[25] cli_3.6.5          rlang_1.1.6        rbibutils_2.4      splines_4.5.2     
[29] yaml_2.3.12        pan_1.9            tools_4.5.2        nloptr_2.2.1      
[33] minqa_1.2.8        dplyr_1.1.4        boot_1.3-32        broom_1.0.11      
[37] rpart_4.1.24       vctrs_0.6.5        R6_2.6.1           lifecycle_1.0.5   
[41] MASS_7.3-65        pkgconfig_2.0.3    pillar_1.11.1      glue_1.8.0        
[45] Rcpp_1.1.0         xfun_0.55          tibble_3.3.0       tidyselect_1.2.1  
[49] knitr_1.51         htmltools_0.5.9    nlme_3.1-168       rmarkdown_2.30    
[53] compiler_4.5.2    

2 Reprex on Linux

The following reprex on Linux (via GitHub Actions) is from the output of reprex.R and is included via {{< include _includes/reprex.md >}}:

# Load libraries
library("mice")
#> 
#> Attaching package: 'mice'
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following objects are masked from 'package:base':
#> 
#>     cbind, rbind

# Impute data
imp <- mice::mice(
  nhanes,
  maxit = 2,
  m = 2)
#> 
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl

# Fit regression model to multiply imputed data
fit <- with(
  data = imp,
  exp = lm(bmi ~ hyp + chl))

# Pool results across models
fit_pooled <- mice::pool(fit)

# Summarize pooled results
fit_pooled
#> Class: mipo    m = 2 
#>          term m    estimate         ubar            b            t dfcom
#> 1 (Intercept) 2 22.28519109 1.616971e+01 1.061756e+00 1.776235e+01    22
#> 2         hyp 2 -1.79588048 3.477708e+00 1.263331e+00 5.372705e+00    22
#> 3         chl 2  0.03554326 4.581421e-04 1.633595e-06 4.605925e-04    22
#>          df         riv      lambda        fmi
#> 1 16.048013 0.098494915 0.089663515 0.18524687
#> 2  4.981767 0.544898164 0.352708144 0.51490077
#> 3 20.120856 0.005348541 0.005320086 0.09136187

summary(fit_pooled)
#>          term    estimate  std.error  statistic        df      p.value
#> 1 (Intercept) 22.28519109 4.21453988  5.2876925 16.048013 7.288532e-05
#> 2         hyp -1.79588048 2.31790955 -0.7747845  4.981767 4.736174e-01
#> 3         chl  0.03554326 0.02146142  1.6561468 20.120856 1.132054e-01

# Session Info
sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] mice_3.19.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] generics_0.1.4     tidyr_1.3.2        shape_1.4.6.1      lattice_0.22-7    
#>  [5] lme4_1.1-38        digest_0.6.39      magrittr_2.0.4     mitml_0.4-5       
#>  [9] evaluate_1.0.5     grid_4.5.2         iterators_1.0.14   fastmap_1.2.0     
#> [13] foreach_1.5.2      jomo_2.7-6         glmnet_4.1-10      Matrix_1.7-4      
#> [17] nnet_7.3-20        backports_1.5.0    survival_3.8-3     purrr_1.2.0       
#> [21] codetools_0.2-20   reformulas_0.4.3.1 Rdpack_2.6.4       cli_3.6.5         
#> [25] rlang_1.1.6        rbibutils_2.4      splines_4.5.2      reprex_2.1.1      
#> [29] withr_3.0.2        yaml_2.3.12        pan_1.9            tools_4.5.2       
#> [33] nloptr_2.2.1       minqa_1.2.8        dplyr_1.1.4        boot_1.3-32       
#> [37] broom_1.0.11       vctrs_0.6.5        R6_2.6.1           rpart_4.1.24      
#> [41] lifecycle_1.0.5    fs_1.6.6           MASS_7.3-65        pkgconfig_2.0.3   
#> [45] pillar_1.11.1      glue_1.8.0         Rcpp_1.1.0         xfun_0.55         
#> [49] tibble_3.3.0       tidyselect_1.2.1   knitr_1.51         htmltools_0.5.9   
#> [53] nlme_3.1-168       rmarkdown_2.30     compiler_4.5.2

Created on 2026-01-08 with reprex v2.1.1

3 Reprex on PC

The following reprex is from running the code locally on my PC:

# Load libraries
library("mice")
#> 
#> Attaching package: 'mice'
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following objects are masked from 'package:base':
#> 
#>     cbind, rbind

# Impute data
imp <- mice::mice(
  nhanes,
  maxit = 2,
  m = 2)
#> 
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl

# Fit regression model to multiply imputed data
fit <- with(
  data = imp,
  exp = lm(bmi ~ hyp + chl))

# Pool results across models
fit_pooled <- mice::pool(fit)

# Summarize pooled results
fit_pooled
#> Class: mipo    m = 2 
#>          term m    estimate         ubar            b            t dfcom
#> 1 (Intercept) 2 18.88333656 1.701025e+01 6.5351430303 2.681296e+01    22
#> 2         hyp 2 -1.57916147 5.488783e+00 0.4312712039 6.135690e+00    22
#> 3         chl 2  0.05146973 5.190739e-04 0.0000684852 6.218017e-04    22
#>          df       riv    lambda       fmi
#> 1  4.727236 0.5762829 0.3655961 0.5297956
#> 2 15.072400 0.1178598 0.1054334 0.2044315
#> 3 11.563450 0.1979059 0.1652099 0.2798517

summary(fit_pooled)
#>          term    estimate  std.error  statistic        df    p.value
#> 1 (Intercept) 18.88333656 5.17812328  3.6467530  4.727236 0.01632623
#> 2         hyp -1.57916147 2.47703245 -0.6375215 15.072400 0.53335079
#> 3         chl  0.05146973 0.02493595  2.0640772 11.563450 0.06219456

# Session Info
sessionInfo()
#> R version 4.5.2 (2025-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26100)
#> 
#> Matrix products: default
#>   LAPACK version 3.12.1
#> 
#> locale:
#> [1] LC_COLLATE=English_United States.utf8 
#> [2] LC_CTYPE=English_United States.utf8   
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C                          
#> [5] LC_TIME=English_United States.utf8    
#> 
#> time zone: America/Chicago
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] mice_3.19.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] generics_0.1.4     tidyr_1.3.2        shape_1.4.6.1      lattice_0.22-7    
#>  [5] lme4_1.1-38        digest_0.6.39      magrittr_2.0.4     mitml_0.4-5       
#>  [9] evaluate_1.0.5     grid_4.5.2         iterators_1.0.14   fastmap_1.2.0     
#> [13] foreach_1.5.2      jomo_2.7-6         glmnet_4.1-10      Matrix_1.7-4      
#> [17] nnet_7.3-20        backports_1.5.0    survival_3.8-3     purrr_1.2.0       
#> [21] codetools_0.2-20   reformulas_0.4.3.1 Rdpack_2.6.4       cli_3.6.5         
#> [25] rlang_1.1.6        rbibutils_2.4      splines_4.5.2      reprex_2.1.1      
#> [29] withr_3.0.2        yaml_2.3.12        otel_0.2.0         pan_1.9           
#> [33] tools_4.5.2        nloptr_2.2.1       minqa_1.2.8        dplyr_1.1.4       
#> [37] boot_1.3-32        broom_1.0.11       rpart_4.1.24       vctrs_0.6.5       
#> [41] R6_2.6.1           lifecycle_1.0.5    fs_1.6.6           MASS_7.3-65       
#> [45] pkgconfig_2.0.3    pillar_1.11.1      glue_1.8.0         Rcpp_1.1.0        
#> [49] xfun_0.55          tibble_3.3.0       tidyselect_1.2.1   rstudioapi_0.17.1 
#> [53] knitr_1.51         htmltools_0.5.9    nlme_3.1-168       rmarkdown_2.30    
#> [57] compiler_4.5.2

Created on 2026-01-08 with reprex v2.1.1