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$pmodel
#> term estimate std.error statistic df p.value
#> 1 (Intercept) -2.40338191 2.74423975 -0.87579152 84.20120 0.383634957
#> 2 Gender -0.28670287 0.44435492 -0.64521143 141.73082 0.519833276
#> 3 Smoking 0.04202166 0.36469966 0.11522264 147.58067 0.908425278
#> 4 Function -0.05363049 0.04956821 -1.08195346 107.14308 0.281702325
#> 5 JobControl -0.00173824 0.02061271 -0.08432858 129.67391 0.932925243
#> 6 JobDemands 0.01498679 0.04353545 0.34424352 56.23643 0.731947115
#> 7 SocialSupport 0.05310413 0.05999722 0.88510971 131.15846 0.377717252
#> 8 factor(Carrying)2 1.34848097 0.55234977 2.44135336 55.53348 0.017845620
#> 9 factor(Carrying)3 2.12170752 0.63137426 3.36045934 31.10486 0.002072278
#> OR lower.EXP upper.EXP
#> 1 0.09041167 0.0003857145 21.192541
#> 2 0.75073476 0.3118843294 1.807089
#> 3 1.04291707 0.5072829086 2.144121
#> 4 0.94778225 0.8590808969 1.045642
#> 5 0.99826327 0.9583722748 1.039815
#> 6 1.01509966 0.9303289078 1.107595
#> 7 1.05453944 0.9365209770 1.187430
#> 8 3.85157044 1.2735219585 11.648480
#> 9 8.34537526 2.3029413419 30.241885
poolm$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 6.548057457Back 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$pmodel
#> term estimate std.error statistic df p.value
#> 1 (Intercept) 5.890240724 2.23539512 2.63498863 128.45105 0.0094493646
#> 2 Gender -0.390855578 0.39833904 -0.98121335 107.45765 0.3286915488
#> 3 Smoking -0.175561523 0.31782610 -0.55238234 121.15417 0.5817040589
#> 4 Function -0.044340247 0.04278381 -1.03637903 88.25400 0.3028567022
#> 5 JobControl -0.030759130 0.01776239 -1.73170044 112.07495 0.0860784235
#> 6 JobDemands 0.030146930 0.03415546 0.88263875 112.08333 0.3793205875
#> 7 SocialSupport 0.002649424 0.05530205 0.04790825 59.62119 0.9619494899
#> 8 factor(Carrying)2 0.614705092 0.42825620 1.43536764 72.28901 0.1554952062
#> 9 factor(Carrying)3 1.735801455 0.44486453 3.90186524 110.28285 0.0001645841
#> 2.5 % 97.5 %
#> 1 1.46727777 10.313203676
#> 2 -1.18047780 0.398766644
#> 3 -0.80477403 0.453650982
#> 4 -0.12936067 0.040680181
#> 5 -0.06595276 0.004434502
#> 6 -0.03752718 0.097821042
#> 7 -0.10798560 0.113284450
#> 8 -0.23894973 1.468359916
#> 9 0.85420951 2.617393395
poolm$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 7.2100032Back 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 - 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 10.34468Back 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 - 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 12.211908Back to Examples