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")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$pmodel
#> term estimate std.error statistic df p.value
#> 1 (Intercept) -2.260292931 2.60675417 -0.86709094 131.17333 0.3874755733
#> 2 Gender -0.251458399 0.45622031 -0.55117756 123.72743 0.5825059289
#> 3 Smoking 0.092463918 0.36571987 0.25282717 146.12564 0.8007570877
#> 4 Function -0.067385062 0.04894641 -1.37671110 118.82274 0.1711900839
#> 5 JobControl 0.001945212 0.02067257 0.09409629 123.76195 0.9251848357
#> 6 JobDemands -0.001787363 0.04119629 -0.04338651 73.23443 0.9655115098
#> 7 SocialSupport 0.067720088 0.05906083 1.14661589 146.73732 0.2534066780
#> 8 factor(Carrying)2 1.320033423 0.55685121 2.37053165 53.16840 0.0214201916
#> 9 factor(Carrying)3 2.062913771 0.58999052 3.49652019 52.48606 0.0009679294
#> OR lower.EXP upper.EXP
#> 1 0.1043199 0.0006009427 18.109291
#> 2 0.7776658 0.3152258882 1.918510
#> 3 1.0968736 0.5324215277 2.259735
#> 4 0.9348352 0.8484829208 1.029976
#> 5 1.0019471 0.9617772901 1.043795
#> 6 0.9982142 0.9195351373 1.083625
#> 7 1.0700657 0.9521814836 1.202545
#> 8 3.7435465 1.2253345525 11.436991
#> 9 7.8688645 2.4091454282 25.701657
poolm$pmultiparm
#> p-values D1 F-statistic
#> Gender 0.581622395 0.303796706
#> Smoking 0.800402969 0.063921577
#> Function 0.168960812 1.895333441
#> JobControl 0.925049540 0.008854112
#> JobDemands 0.965442760 0.001882389
#> SocialSupport 0.251543772 1.314728006
#> factor(Carrying) 0.002281544 6.467414181Back to Examples
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$pmodel
#> term estimate std.error statistic df p.value
#> 1 (Intercept) 5.312432063 2.35343514 2.25730974 99.01936 0.0261851098
#> 2 Gender -0.305142321 0.42349524 -0.72053306 78.61191 0.4733340119
#> 3 Smoking -0.155036838 0.32899402 -0.47124516 107.24133 0.6384229209
#> 4 Function -0.056641302 0.04357635 -1.29981738 84.31016 0.1972061839
#> 5 JobControl -0.023216627 0.02073903 -1.11946546 33.37654 0.2709333685
#> 6 JobDemands 0.035812621 0.03393475 1.05533766 120.57064 0.2933810253
#> 7 SocialSupport 0.001992314 0.05241231 0.03801233 128.24564 0.9697369609
#> 8 factor(Carrying)2 0.646981227 0.44018691 1.46978752 70.64756 0.1460588187
#> 9 factor(Carrying)3 1.719404307 0.45865246 3.74881741 98.39179 0.0002999823
#> 2.5 % 97.5 %
#> 1 0.64271746 9.98214666
#> 2 -1.14815322 0.53786857
#> 3 -0.80721233 0.49713865
#> 4 -0.14329300 0.03001040
#> 5 -0.06539242 0.01895917
#> 6 -0.03137259 0.10299783
#> 7 -0.10171250 0.10569712
#> 8 -0.23080243 1.52476488
#> 9 0.80926873 2.62953988
poolm$pmultiparm
#> p-values D1 F-statistic
#> Gender 0.472016303 0.519167896
#> Smoking 0.637663081 0.222071997
#> Function 0.194894308 1.689525218
#> JobControl 0.268422594 1.253202906
#> JobDemands 0.291542849 1.113737573
#> SocialSupport 0.969682958 0.001444937
#> factor(Carrying) 0.001209598 7.021696406Back to Examples
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 - JobDemands
#> Removed at Step 2 is - JobControl
#> Removed at Step 3 is - Smoking
#> 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$pmodel
#> term estimate std.error statistic df p.value
#> 1 (Intercept) -1.641978 0.4235486 -3.876717 64.96485 2.492862e-04
#> 2 factor(Carrying)2 1.473997 0.5234692 2.815823 59.30149 6.595524e-03
#> 3 factor(Carrying)3 2.310557 0.5400553 4.278370 52.47278 8.005775e-05
#> OR lower.EXP upper.EXP
#> 1 0.1935967 0.08308659 0.4510917
#> 2 4.3666529 1.53211445 12.4453221
#> 3 10.0800344 3.41128404 29.7855859
poolm$pmultiparm
#> p-values D1 F-statistic
#> factor(Carrying) 0.0001027386 9.900436Back to Examples
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 - Gender
#> Removed at Step 4 is - JobControl
#> Removed at Step 5 is - JobDemands
#>
#> Selection correctly terminated,
#> No more variables removed from the model
poolm$pmodel
#> term estimate std.error statistic df p.value
#> 1 (Intercept) 5.11202356 0.59979204 8.522993 147.94717 1.643130e-14
#> 2 Function -0.07162454 0.04236532 -1.690641 88.95577 9.440666e-02
#> 3 factor(Carrying)2 0.65368674 0.41643596 1.569717 87.50921 1.200899e-01
#> 4 factor(Carrying)3 1.67783504 0.45042607 3.724995 109.48190 3.106190e-04
#> 2.5 % 97.5 %
#> 1 3.9267575 6.29728959
#> 2 -0.1558041 0.01255502
#> 3 -0.1739569 1.48133040
#> 4 0.7851493 2.57052077
poolm$pmultiparm
#> p-values D1 F-statistic
#> Function 0.092086236 2.858267
#> factor(Carrying) 0.001327855 6.867307Back to Examples