The mice function is one of the most used functions to
apply multiple imputation. This page shows how functions in the
psfmi package can be easily used in combination with
mice. In this way multivariable models can easily be
developed in combination with mice.
You can install the released version of psfmi with:
And the development version from GitHub with:
You can install the released version of mice with:
library(psfmi)
library(mice)
#>
#> Attaching package: 'mice'
#> The following object is masked from 'package:stats':
#>
#> filter
#> The following objects are masked from 'package:base':
#>
#> cbind, rbind
imp <- mice(lbp_orig, m=5, maxit=5)
#>
#> iter imp variable
#> 1 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 1 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 1 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 1 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 1 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
data_comp <- complete(imp, action = "long", include = FALSE)
library(psfmi)
pool_lr <- psfmi_lr(data=data_comp, nimp=5, impvar=".imp",
formula=Chronic ~ Gender + Smoking + Function +
JobControl + JobDemands + SocialSupport, method="D1")
pool_lr$RR_model
#> $`Step 1 - no variables removed -`
#> term estimate std.error statistic df p.value
#> 1 (Intercept) 0.088437723 2.39728532 0.03689078 140.2898 0.970624534
#> 2 Gender -0.355692675 0.41302929 -0.86118027 148.7212 0.390525074
#> 3 Smoking 0.070911360 0.34038968 0.20832406 146.1281 0.835266034
#> 4 Function -0.135190580 0.04304434 -3.14072824 136.8765 0.002065484
#> 5 JobControl 0.008217615 0.01956697 0.41997377 147.7936 0.675114526
#> 6 JobDemands -0.001920148 0.03765469 -0.05099361 124.4584 0.959412367
#> 7 SocialSupport 0.039309546 0.05662603 0.69419571 131.5462 0.488783633
#> OR lower.EXP upper.EXP
#> 1 1.0924662 0.009551675 124.9500735
#> 2 0.7006879 0.309791275 1.5848205
#> 3 1.0734861 0.547818425 2.1035662
#> 4 0.8735494 0.802271267 0.9511603
#> 5 1.0082515 0.970009356 1.0480013
#> 6 0.9980817 0.926402358 1.0753071
#> 7 1.0400924 0.929874256 1.1633747Back to Examples
library(psfmi)
library(mice)
imp <- mice(lbp_orig, m=5, maxit=5)
#>
#> iter imp variable
#> 1 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 1 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 1 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 1 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 1 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 2 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 3 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 4 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 1 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 2 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 3 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 4 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
#> 5 5 Carrying Pain Tampascale Function Radiation Age Satisfaction JobControl JobDemands SocialSupport
data_comp <- complete(imp, action = "long", include = FALSE)
library(psfmi)
pool_lr <- psfmi_lr(data=data_comp, nimp=5, impvar=".imp",
formula=Chronic ~ Gender + Smoking + Function +
JobControl + JobDemands + SocialSupport,
p.crit = 0.157, method="D1", direction = "FW")
#> Entered at Step 1 is - Function
#>
#> Selection correctly terminated,
#> No new variables entered the model
pool_lr$RR_model_final
#> $`Final model`
#> term estimate std.error statistic df p.value OR
#> 1 (Intercept) 1.222286 0.46971652 2.602178 136.8128 0.010283681 3.3949402
#> 2 Function -0.139503 0.04188283 -3.330792 133.3454 0.001120439 0.8697904
#> lower.EXP upper.EXP
#> 1 1.3410364 8.5945611
#> 2 0.8006402 0.9449131Back to Examples