The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

mice and miceafter for Regression modelling

Martijn W Heymans

2022-10-02

Installing the miceafter and mice packages

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("mwheymans/miceafter")

You can install mice with:

install.packages("mice")

Examples

mice and miceafter for pooling logistic regression models

lbp_orig is a dataset that is part of the miceafter package with missing values. So we first impute them with the mice function. Than we use the mids2milist function to turn a mids object, as a result of using mice, into a milist object with multiply imputed datasets. Than we use the with function to apply repeated logistic regression analyses. With the pool_glm function we obtain the results for the pooled model.


  library(mice)
  library(miceafter)
  
  imp <- mice(lbp_orig, m=5, maxit=5, printFlag = FALSE) 
 
  dat_imp <- mids2milist(imp)
  
  ra <- with(dat_imp, expr = glm(Chronic ~ factor(Carrying) + Gender + Smoking + 
                      Function + JobControl + JobDemands + SocialSupport, 
                      family = binomial))
  
  poolm <- pool_glm(ra, method="D1")
  poolm$pmodel
#>                term    estimate  std.error   statistic        df     p.value
#> 1       (Intercept) -2.40338191 2.74423975 -0.87579152  84.20120 0.383634957
#> 2            Gender -0.28670287 0.44435492 -0.64521143 141.73082 0.519833276
#> 3           Smoking  0.04202166 0.36469966  0.11522264 147.58067 0.908425278
#> 4          Function -0.05363049 0.04956821 -1.08195346 107.14308 0.281702325
#> 5        JobControl -0.00173824 0.02061271 -0.08432858 129.67391 0.932925243
#> 6        JobDemands  0.01498679 0.04353545  0.34424352  56.23643 0.731947115
#> 7     SocialSupport  0.05310413 0.05999722  0.88510971 131.15846 0.377717252
#> 8 factor(Carrying)2  1.34848097 0.55234977  2.44135336  55.53348 0.017845620
#> 9 factor(Carrying)3  2.12170752 0.63137426  3.36045934  31.10486 0.002072278
#>           OR    lower.EXP upper.EXP
#> 1 0.09041167 0.0003857145 21.192541
#> 2 0.75073476 0.3118843294  1.807089
#> 3 1.04291707 0.5072829086  2.144121
#> 4 0.94778225 0.8590808969  1.045642
#> 5 0.99826327 0.9583722748  1.039815
#> 6 1.01509966 0.9303289078  1.107595
#> 7 1.05453944 0.9365209770  1.187430
#> 8 3.85157044 1.2735219585 11.648480
#> 9 8.34537526 2.3029413419 30.241885
  poolm$pmultiparm
#>                  p-values D1 F-statistic
#> Gender           0.518810621 0.416297788
#> Smoking          0.908268699 0.013276257
#> Function         0.279775979 1.170623285
#> JobControl       0.932805433 0.007111309
#> JobDemands       0.731342832 0.118503602
#> SocialSupport    0.376212395 0.783419204
#> factor(Carrying) 0.002402598 6.548057457

Back to Examples

mice and miceafter for pooling linear regression models

The lbp_orig is a dataset that is part of the miceafter package with missing values. So we first impute them with the mice function. Than we use the mids2milist function to turn a mids object, as a result of using mice, into a milist object with multiply imputed datasets. Than we use the with function to apply repeated linear regression analyses. With the pool_glm function we obtain the results for the pooled model.


  library(mice)
  library(miceafter)
  
  imp <- mice(lbp_orig, m=5, maxit=5, printFlag = FALSE) 
 
  dat_imp <- mids2milist(imp)
  
  ra <- with(dat_imp, expr = glm(Pain ~ factor(Carrying) + Gender + Smoking + 
                      Function + JobControl + JobDemands + SocialSupport))
  
  poolm <- pool_glm(ra, method="D1")
  poolm$pmodel
#>                term     estimate  std.error   statistic        df      p.value
#> 1       (Intercept)  5.890240724 2.23539512  2.63498863 128.45105 0.0094493646
#> 2            Gender -0.390855578 0.39833904 -0.98121335 107.45765 0.3286915488
#> 3           Smoking -0.175561523 0.31782610 -0.55238234 121.15417 0.5817040589
#> 4          Function -0.044340247 0.04278381 -1.03637903  88.25400 0.3028567022
#> 5        JobControl -0.030759130 0.01776239 -1.73170044 112.07495 0.0860784235
#> 6        JobDemands  0.030146930 0.03415546  0.88263875 112.08333 0.3793205875
#> 7     SocialSupport  0.002649424 0.05530205  0.04790825  59.62119 0.9619494899
#> 8 factor(Carrying)2  0.614705092 0.42825620  1.43536764  72.28901 0.1554952062
#> 9 factor(Carrying)3  1.735801455 0.44486453  3.90186524 110.28285 0.0001645841
#>         2.5 %       97.5 %
#> 1  1.46727777 10.313203676
#> 2 -1.18047780  0.398766644
#> 3 -0.80477403  0.453650982
#> 4 -0.12936067  0.040680181
#> 5 -0.06595276  0.004434502
#> 6 -0.03752718  0.097821042
#> 7 -0.10798560  0.113284450
#> 8 -0.23894973  1.468359916
#> 9  0.85420951  2.617393395
  poolm$pmultiparm
#>                  p-values D1 F-statistic
#> Gender           0.326939540   0.9627796
#> Smoking          0.580814131   0.3051262
#> Function         0.300938676   1.0740815
#> JobControl       0.083815260   2.9987864
#> JobDemands       0.377767222   0.7790512
#> SocialSupport    0.961870518   0.0022952
#> factor(Carrying) 0.001186615   7.2100032

Back to Examples

mice and miceafter for selecting logistic regression models

We follow the same procedure as the first example but also apply model selection here.


  library(mice)
  library(miceafter)
  
  imp <- mice(lbp_orig, m=5, maxit=5, printFlag = FALSE) 
 
  dat_imp <- mids2milist(imp)
  
  ra <- with(dat_imp, expr = glm(Chronic ~ factor(Carrying) + Gender + Smoking + 
                      Function + JobControl + JobDemands + SocialSupport, 
                      family = binomial))
  
  poolm <- pool_glm(ra, method="D1", p.crit = 0.15, direction = "BW")
#> Removed at Step 1 is - JobControl
#> Removed at Step 2 is - Smoking
#> Removed at Step 3 is - JobDemands
#> Removed at Step 4 is - Gender
#> Removed at Step 5 is - SocialSupport
#> Removed at Step 6 is - Function
#> 
#> Selection correctly terminated, 
#> No more variables removed from the model
  poolm$pmodel
#>                term  estimate std.error statistic       df      p.value
#> 1       (Intercept) -1.653325 0.4191626 -3.944353 69.55580 1.885794e-04
#> 2 factor(Carrying)2  1.474823 0.5130543  2.874595 73.06998 5.295555e-03
#> 3 factor(Carrying)3  2.347491 0.5427445  4.325223 48.89222 7.486964e-05
#>           OR  lower.EXP  upper.EXP
#> 1  0.1914124 0.08295868  0.4416499
#> 2  4.3702631 1.57196111 12.1499185
#> 3 10.4592952 3.51396246 31.1320502
  poolm$pmultiparm
#>                   p-values D1 F-statistic
#> factor(Carrying) 6.656759e-05    10.34468

Back to Examples

mice and miceafter for selecting linear regression models

We follow the same procedure as the second example but also apply model selection here.


  library(mice)
  library(miceafter)
  
  imp <- mice(lbp_orig, m=5, maxit=5, printFlag = FALSE) 
 
  dat_imp <- mids2milist(imp)
  
  ra <- with(dat_imp, expr = glm(Pain ~ factor(Carrying) + Gender + Smoking + 
                      Function + JobControl + JobDemands + SocialSupport))
  
  poolm <- pool_glm(ra, method="D1", p.crit = 0.15, direction = "BW")
#> Removed at Step 1 is - SocialSupport
#> Removed at Step 2 is - Smoking
#> Removed at Step 3 is - JobDemands
#> Removed at Step 4 is - Function
#> Removed at Step 5 is - Gender
#> 
#> Selection correctly terminated, 
#> No more variables removed from the model
  poolm$pmodel
#>                term    estimate  std.error statistic        df      p.value
#> 1       (Intercept)  6.01797705 1.03680867  5.804327  94.92544 8.517292e-08
#> 2        JobControl -0.03139866 0.01698247 -1.848887 120.12525 6.693288e-02
#> 3 factor(Carrying)2  0.75033713 0.41757890  1.796875  53.99690 7.794927e-02
#> 4 factor(Carrying)3  2.00235646 0.40304875  4.968026  74.18297 4.210313e-06
#>         2.5 %      97.5 %
#> 1  3.95963075 8.076323364
#> 2 -0.06502241 0.002225087
#> 3 -0.08685926 1.587533529
#> 4  1.19929722 2.805415710
  poolm$pmultiparm
#>                   p-values D1 F-statistic
#> JobControl       6.485343e-02    3.418382
#> factor(Carrying) 2.386445e-05   12.211908

Back to Examples

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.