mice: Multivariate Imputation by Chained Equations
多重補完法
- CRAN: http://cran.r-project.org/web/packages/mice/index.html
- URL: http://www.stefvanbuuren.nl , http://www.multiple-imputation.com
> library(mice)
> data("boys")
> data("fdd")
> data("fdgs")
> data("nhanes")
バージョン: 2.25
| . |
|---|
| appendbreak Appends specified break to the data |
| as.mids Converts an multiply imputed dataset (long |
| format) into a 'mids' object |
| as.mira Create a 'mira' object from repeated analyses |
| boys Growth of Dutch boys |
| bwplot.mids Box-and-whisker plot of observed and imputed |
| data |
| cbind.mids Columnwise combination of a 'mids' object. |
| cc Complete cases |
| cci Complete case indicator |
| ccn Complete cases n |
| complete Creates imputed data sets from a 'mids' object |
| densityplot.mids Density plot of observed and imputed data |
| extractBS Extract broken stick estimates from a 'lmer' |
| object |
| fdd SE Fireworks disaster data |
| fdgs Fifth Dutch growth study 2009 |
| fico Fraction of incomplete cases among cases with |
| observed |
| flux Influx and outflux of multivariate missing data |
| patterns |
| fluxplot Fluxplot of the missing data pattern |
| getfit Extracts fit objects from 'mira' object |
| glm.mids Generalized linear model for 'mids' object |
| ibind Combine imputations fitted to the same data |
| ic Incomplete cases |
| ici Incomplete case indicator |
| icn Incomplete cases n |
| is.mids Check for 'mids' object |
| is.mipo Check for 'mipo' object |
| is.mira Check for 'mira' object |
| leiden85 Leiden 85+ study |
| lm.mids Linear regression for 'mids' object |
| mammalsleep Mammal sleep data |
| md.pairs Missing data pattern by variable pairs |
| md.pattern Missing data pattern |
| mdc Graphical parameter for missing data plots. |
| mice Multivariate Imputation by Chained Equations |
| (MICE) |
| mice.impute.2l.norm Imputation by a two-level normal model |
| mice.impute.2l.pan Imputation by a two-level normal model using |
| 'pan' |
| mice.impute.2lonly.mean |
| Imputation of the mean within the class |
| mice.impute.2lonly.norm |
| Imputation at level 2 by Bayesian linear |
| regression |
| mice.impute.2lonly.pmm |
| Imputation at level 2 by predictive mean |
| matching |
| mice.impute.cart Imputation by classification and regression |
| trees |
| mice.impute.fastpmm Imputation by fast predictive mean matching |
| mice.impute.lda Imputation by linear discriminant analysis |
| mice.impute.logreg Imputation by logistic regression |
| mice.impute.logreg.boot |
| Imputation by logistic regression using the |
| bootstrap |
| mice.impute.mean Imputation by the mean |
| mice.impute.norm Imputation by Bayesian linear regression |
| mice.impute.norm.boot Imputation by linear regression, bootstrap |
| method |
| mice.impute.norm.nob Imputation by linear regression (non Bayesian) |
| mice.impute.norm.predict |
| Imputation by linear regression, prediction |
| method |
| mice.impute.passive Passive imputation |
| mice.impute.pmm Imputation by predictive mean matching |
| mice.impute.polr Imputation by polytomous regression - ordered |
| mice.impute.polyreg Imputation by polytomous regression - unordered |
| mice.impute.quadratic Imputation of quadratric terms |
| mice.impute.rf Imputation by random forests |
| mice.impute.ri Imputation by the random indicator method for |
| nonignorable data |
| mice.impute.sample Imputation by simple random sampling |
| mice.mids Multivariate Imputation by Chained Equations |
| (Iteration Step) |
| mice.theme Set the theme for the plotting Trellis |
| functions |
| mids-class Multiply imputed data set ('mids') |
| mids2mplus Export 'mids' object to Mplus |
| mids2spss Export 'mids' object to SPSS |
| mipo-class Multiply imputed pooled analysis ('mipo') |
| mira-class Multiply imputed repeated analyses ('mira') |
| nelsonaalen Cumulative hazard rate or Nelson-Aalen |
| estimator |
| nhanes NHANES example - all variables numerical |
| nhanes2 NHANES example - mixed numerical and discrete |
| variables |
| norm.draw Draws values of beta and sigma by Bayesian |
| linear regression |
| pattern Datasets with various missing data patterns |
| plot.mids Plot the trace lines of the MICE algorithm |
| pool Multiple imputation pooling |
| pool.compare Compare two nested models fitted to imputed |
| data |
| pool.r.squared Pooling: R squared |
| pool.scalar Multiple imputation pooling: univariate version |
| popmis Hox pupil popularity data with missing |
| popularity scores |
| pops Project on preterm and small for gestational |
| age infants (POPS) |
| potthoffroy Potthoff-Roy data |
| print.mids Print a 'mids' object |
| quickpred Quick selection of predictors from the data |
| rbind.mids Rowwise combination of a 'mids' object. |
| selfreport Self-reported and measured BMI |
| squeeze Squeeze the imputed values to be within |
| specified boundaries. |
| stripplot.mids Stripplot of observed and imputed data |
| summary.mira Summary of a 'mira' object |
| supports.transparent Supports semi-transparent foreground colors? |
| tbc Terneuzen birth cohort |
| version Echoes the package version number |
| walking Walking disability data |
| windspeed Subset of Irish wind speed data |
| with.mids Evaluate an expression in multiple imputed |
| datasets |
| xyplot.mids Scatterplot of observed and imputed data |
| 関数名 | 概略 |
|---|---|
appendbreak |
Appends specified break to the data |
as.mids |
Converts an multiply imputed dataset (long format) into a 'mids' object |
as.mira |
Create a 'mira' object from repeated analyses |
boys |
Growth of Dutch boys |
bwplot.mids |
Box-and-whisker plot of observed and imputed data |
cbind.mids |
Columnwise combination of a 'mids' object. |
cc |
Complete cases |
cci |
Complete case indicator |
ccn |
Complete cases n |
complete |
Creates imputed data sets from a 'mids' object |
densityplot.mids |
Density plot of observed and imputed data |
extractBS |
Extract broken stick estimates from a 'lmer' object |
fdd |
SE Fireworks disaster data |
fdgs |
Fifth Dutch growth study 2009 |
fico |
Fraction of incomplete cases among cases with observed |
flux |
Influx and outflux of multivariate missing data patterns |
fluxplot |
Fluxplot of the missing data pattern |
getfit |
Extracts fit objects from 'mira' object |
glm.mids |
Generalized linear model for 'mids' object |
ibind |
Combine imputations fitted to the same data |
ic |
Incomplete cases |
ici |
Incomplete case indicator |
icn |
Incomplete cases n |
is.mids |
Check for 'mids' object |
is.mipo |
Check for 'mipo' object |
is.mira |
Check for 'mira' object |
leiden85 |
Leiden 85+ study |
lm.mids |
Linear regression for 'mids' object |
mammalsleep |
Mammal sleep data |
md.pairs |
Missing data pattern by variable pairs |
md.pattern |
Missing data pattern |
mdc |
Graphical parameter for missing data plots. |
mice |
Multivariate Imputation by Chained Equations (MICE) |
mice.impute.2l.norm |
Imputation by a two-level normal model |
mice.impute.2l.pan |
Imputation by a two-level normal model using 'pan' |
mice.impute.2lonly.mean |
Imputation of the mean within the class |
mice.impute.2lonly.norm |
Imputation at level 2 by Bayesian linear regression |
mice.impute.2lonly.pmm |
Imputation at level 2 by predictive mean matching |
mice.impute.cart |
Imputation by classification and regression trees |
mice.impute.fastpmm |
Imputation by fast predictive mean matching |
mice.impute.lda |
Imputation by linear discriminant analysis |
mice.impute.logreg |
Imputation by logistic regression |
mice.impute.logreg.boot |
Imputation by logistic regression using the bootstrap |
mice.impute.mean |
Imputation by the mean |
mice.impute.norm |
Imputation by Bayesian linear regression |
mice.impute.norm.boot |
Imputation by linear regression, bootstrap method |
mice.impute.norm.nob |
Imputation by linear regression (non Bayesian) |
mice.impute.norm.predict |
Imputation by linear regression, prediction method |
mice.impute.passive |
Passive imputation |
mice.impute.pmm |
Imputation by predictive mean matching |
mice.impute.polr |
Imputation by polytomous regression - ordered |
mice.impute.polyreg |
Imputation by polytomous regression - unordered |
mice.impute.quadratic |
Imputation of quadratric terms |
mice.impute.rf |
Imputation by random forests |
mice.impute.ri |
Imputation by the random indicator method for nonignorable data |
mice.impute.sample |
Imputation by simple random sampling |
mice.mids |
Multivariate Imputation by Chained Equations (Iteration Step) |
mice.theme |
Set the theme for the plotting Trellis functions |
mids-class |
Multiply imputed data set ('mids') |
mids2mplus |
Export 'mids' object to Mplus |
mids2spss |
Export 'mids' object to SPSS |
mipo-class |
Multiply imputed pooled analysis ('mipo') |
mira-class |
Multiply imputed repeated analyses ('mira') |
nelsonaalen |
Cumulative hazard rate or Nelson-Aalen estimator |
nhanes |
NHANES example - all variables numerical |
nhanes2 |
NHANES example - mixed numerical and discrete variables |
norm.draw |
Draws values of beta and sigma by Bayesian linear regression |
pattern |
Datasets with various missing data patterns |
plot.mids |
Plot the trace lines of the MICE algorithm |
pool |
Multiple imputation pooling |
pool.compare |
Compare two nested models fitted to imputed data |
pool.r.squared |
Pooling: R squared |
pool.scalar |
Multiple imputation pooling: univariate version |
popmis |
Hox pupil popularity data with missing popularity scores |
pops |
Project on preterm and small for gestational age infants (POPS) |
potthoffroy |
Potthoff-Roy data |
print.mids |
Print a 'mids' object |
quickpred |
Quick selection of predictors from the data |
rbind.mids |
Rowwise combination of a 'mids' object. |
selfreport |
Self-reported and measured BMI |
squeeze |
Squeeze the imputed values to be within specified boundaries. |
stripplot.mids |
Stripplot of observed and imputed data |
summary.mira |
Summary of a 'mira' object |
supports.transparent |
Supports semi-transparent foreground colors? |
tbc |
Terneuzen birth cohort |
version |
Echoes the package version number |
walking |
Walking disability data |
windspeed |
Subset of Irish wind speed data |
with.mids |
Evaluate an expression in multiple imputed datasets |
xyplot.mids |
Scatterplot of observed and imputed data |
boys
オランダにおける児童の成長データセット（欠損値を含む）
> data("boys")
> boys %>% {
+ print(class(.))
+ dplyr::glimpse(.)
+ }
[1] "data.frame"
Observations: 748
Variables: 9
$ age <dbl> 0.035, 0.038, 0.057, 0.060, 0.062, 0.068, 0.068, 0.071, 0....
$ hgt <dbl> 50.1, 53.5, 50.0, 54.5, 57.5, 55.5, 52.5, 53.0, 55.1, 54.5...
$ wgt <dbl> 3.650, 3.370, 3.140, 4.270, 5.030, 4.655, 3.810, 3.890, 3....
$ bmi <dbl> 14.54, 11.77, 12.56, 14.37, 15.21, 15.11, 13.82, 13.84, 12...
$ hc <dbl> 33.7, 35.0, 35.2, 36.7, 37.3, 37.0, 34.9, 35.8, 36.8, 38.0...
$ gen <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ phb <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ tv <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ reg <fctr> south, south, south, south, south, south, south, west, we...
cc
欠損データの除去
> nhanes$bmi
[1] NA 22.7 NA NA 20.4 NA 22.5 30.1 22.0 NA NA NA 21.7 28.7
[15] 29.6 NA 27.2 26.3 35.3 25.5 NA 33.2 27.5 24.9 27.4
> nhanes$bmi %>% cc()
[1] 22.7 20.4 22.5 30.1 22.0 21.7 28.7 29.6 27.2 26.3 35.3 25.5 33.2 27.5
[15] 24.9 27.4
> cc(nhanes[, 2, drop = FALSE], drop = FALSE)
bmi
2 22.7
5 20.4
7 22.5
8 30.1
9 22.0
13 21.7
14 28.7
15 29.6
17 27.2
18 26.3
19 35.3
20 25.5
22 33.2
23 27.5
24 24.9
25 27.4
cci
欠損値の確認
> nhanes %>% cci()
1 2 3 4 5 6 7 8 9 10 11 12
FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
13 14 15 16 17 18 19 20 21 22 23 24
TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE
25
TRUE
> nhanes$bmi %>% cci()
[1] FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE
[12] FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
[23] TRUE TRUE TRUE
ccn
欠損値の確認。欠損箇所がいくつあるか。
> ccn(nhanes)
[1] 13
complete
> mice(nhanes) %>% mice::complete()
iter imp variable
1 1 bmi hyp chl
1 2 bmi hyp chl
1 3 bmi hyp chl
1 4 bmi hyp chl
1 5 bmi hyp chl
2 1 bmi hyp chl
2 2 bmi hyp chl
2 3 bmi hyp chl
2 4 bmi hyp chl
2 5 bmi hyp chl
3 1 bmi hyp chl
3 2 bmi hyp chl
3 3 bmi hyp chl
3 4 bmi hyp chl
3 5 bmi hyp chl
4 1 bmi hyp chl
4 2 bmi hyp chl
4 3 bmi hyp chl
4 4 bmi hyp chl
4 5 bmi hyp chl
5 1 bmi hyp chl
5 2 bmi hyp chl
5 3 bmi hyp chl
5 4 bmi hyp chl
5 5 bmi hyp chl
age bmi hyp chl
1 1 22.0 1 131
2 2 22.7 1 187
3 1 22.0 1 187
4 3 24.9 1 218
5 1 20.4 1 113
6 3 22.5 2 184
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 27.5 1 204
11 1 30.1 2 187
12 2 22.5 1 187
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 229
16 1 27.4 1 131
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 229
21 1 33.2 2 199
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 184
25 2 27.4 1 186
fdd
> fdd %>% {
+ print(class(.))
+ dplyr::glimpse(.)
+ }
[1] "data.frame"
Observations: 52
Variables: 65
$ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18,...
$ trt <fctr> E, C, E, C, E, C, C, C, C, C, E, C, E, C, C, E, E, E, ...
$ pp <fctr> Y, N, N, Y, Y, Y, Y, N, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, ...
$ trtp <dbl> 3, 0, NA, 4, 4, 4, 0, 0, 7, 4, 4, 4, 4, 3, 4, 4, 5, 3, ...
$ sex <fctr> F, F, M, F, M, F, M, F, M, M, F, F, F, F, F, M, F, M, ...
$ etn <fctr> OT, NL, NL, OT, OT, NL, OT, OT, NL, NL, NL, OT, OT, OT...
$ age <dbl> 6, 8, 4, 4, 14, 10, 11, 6, 17, 18, 10, 17, 9, 14, 14, 8...
$ trauma <dbl> 4, 4, 2, 4, 3, 1, NA, 4, 3, 2, 2, 3, 3, 1, 3, 1, 1, 2, ...
$ prop1 <dbl> 30.96774, 58.00000, 26.66667, 36.00000, 30.00000, 14.00...
$ prop2 <dbl> 35.00000, NA, 30.00000, 17.00000, 15.00000, 3.00000, 36...
$ prop3 <dbl> 46.00000, NA, 23.46667, 11.00000, 13.00000, 4.00000, 40...
$ crop1 <dbl> NA, 45, NA, NA, 9, 7, 26, NA, 22, 18, NA, 24, 28, 38, 3...
$ crop2 <dbl> NA, NA, NA, NA, 7, 1, 22, NA, 10, 4, NA, 5, 6, 14, 18, ...
$ crop3 <dbl> NA, NA, NA, NA, 2, 2, 27, NA, 19, 10, NA, 2, 2, 25, 13,...
$ masc1 <dbl> NA, NA, NA, NA, 57, 23, 54, NA, 34, 35, NA, 34, 51, 71,...
$ masc2 <dbl> NA, NA, NA, NA, 20, 6, 46, NA, 31, 13, NA, 13, 25, 26, ...
$ masc3 <dbl> NA, NA, NA, NA, 6, 11, 37, NA, 18, 7, NA, 9, 24, 55, 33...
$ cbcl1 <dbl> NA, NA, NA, 74, 96, 38, 79, 42, 28, 36, 44, 16, NA, NA,...
$ cbcl3 <dbl> NA, NA, NA, 32, 39, 11, 94, 48, NA, 43, 28, 0, NA, NA, ...
$ prs1 <dbl> 21, 29, NA, 21, 23, 17, 24, 15, 19, 22, 20, 21, 25, 21,...
$ prs2 <dbl> 25, NA, 15, 16, 10, 3, 19, 12, 13, 8, 7, 9, 3, 11, 9, 1...
$ prs3 <dbl> 21, NA, 21, 9, NA, 3, 18, 21, NA, NA, 16, NA, 0, NA, NA...
$ ypa1 <dbl> 14.000000, NA, 3.000000, 13.000000, 8.000000, 8.000000,...
$ ypb1 <dbl> 4.000000, NA, 0.750000, 4.000000, 8.000000, 9.000000, N...
$ ypc1 <dbl> 18.000000, NA, 9.000000, 16.000000, 11.000000, 15.00000...
$ yp1 <dbl> 36, NA, 13, 33, 27, 32, NA, 24, 48, 45, 26, 37, 25, 39,...
$ ypa2 <dbl> 13.000000, NA, 4.000000, 9.000000, 5.000000, 3.000000, ...
$ ypb2 <dbl> 8.00000, NA, 3.00000, 7.00000, 3.00000, 3.00000, 15.533...
$ ypc2 <dbl> 14, NA, 12, 11, 8, 9, 13, 6, 10, 13, 8, 3, 11, 9, 9, 15...
$ yp2 <dbl> 35, NA, 19, 27, 16, 15, 39, 13, 23, 33, 17, 7, 27, 21, ...
$ ypa3 <dbl> 13.000000, NA, 0.000000, 5.000000, 1.000000, 2.000000, ...
$ ypb3 <dbl> 10.000000, NA, 0.750000, 5.000000, 3.000000, 2.000000, ...
$ ypc3 <dbl> 15, NA, 12, 10, 7, 9, 16, 9, NA, 11, 9, 3, 1, 12, 7, 15...
$ yp3 <dbl> 38, NA, 13, 20, 11, 13, 39, 35, NA, 36, 14, 3, 1, 34, 1...
$ yca1 <dbl> NA, 13, NA, NA, 8, 0, 9, NA, 11, 4, NA, 12, 10, 20, 7, ...
$ ycb1 <dbl> NA, 19, NA, NA, 8, 2, 18, NA, 10, 11, NA, 10, 17, 12, 1...
$ ycc1 <dbl> NA, 13, NA, NA, 10, 6, 14, NA, 14, 13, NA, 11, 16, 18, ...
$ yc1 <dbl> NA, 45, NA, NA, 26, 8, 41, NA, 35, 28, NA, 33, 43, 50, ...
$ yca2 <dbl> NA, NA, NA, NA, 0, 0, 7, NA, 7, 2, NA, 2, NA, 0, 7, 2, ...
$ ycb2 <dbl> NA, NA, NA, NA, 3.000, 0.000, 11.000, NA, 6.000, 6.000,...
$ ycc2 <dbl> NA, NA, NA, NA, 3, 1, 8, NA, 14, 7, NA, 5, NA, 4, 7, 9,...
$ yc2 <dbl> NA, NA, NA, NA, 6, 1, 26, NA, 27, 15, NA, 8, NA, 8, 21,...
$ yca3 <dbl> NA, NA, NA, NA, 0, 0, 9, NA, 1, 1, NA, 1, 1, 7, 2, 3, 1...
$ ycb3 <dbl> NA, NA, NA, NA, 0, 0, 10, NA, 6, 7, NA, 3, 4, 12, 5, 3,...
$ ycc3 <dbl> NA, NA, NA, NA, 4, 2, 12, NA, 7, 5, NA, 5, 2, 16, 3, 10...
$ yc3 <dbl> NA, NA, NA, NA, 4, 2, 31, NA, 14, 13, NA, 9, 7, 35, 10,...
$ ypf1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0...
$ ypf2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ ypf3 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ ypp1 <dbl> 1, 0, 0, 0, 1, 0, NA, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, ...
$ ypp2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ ypp3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ ycf1 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1...
$ ycf2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ ycf3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ ycp1 <dbl> NA, 1, NA, NA, 1, 0, 1, NA, 1, 0, NA, 1, 1, 1, 1, 1, 1,...
$ ycp2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ ycp3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ cbin1 <dbl> NA, NA, NA, 16, 24, 14, 24, 16, 13, 14, 22, 7, NA, NA, ...
$ cbin3 <dbl> NA, NA, NA, 7, 6, 0, 28, 19, NA, 19, 14, 0, NA, NA, 7, ...
$ cbex1 <dbl> NA, NA, NA, 31, 36, 7, 21, 8, 3, 2, 13, 1, NA, NA, 6, 1...
$ cbex3 <dbl> NA, NA, NA, 16, 20, 1, 33, 11, NA, 5, 7, 0, NA, NA, 2, ...
$ bir1 <dbl> NA, 19, NA, NA, 17, 3, 14, NA, 6, 19, NA, 11, 19, 25, 2...
$ bir2 <dbl> NA, NA, NA, NA, 13, 1, 15, NA, 4, 10, NA, 3, 12, 5, 13,...
$ bir3 <dbl> NA, NA, NA, NA, 2, 2, 17, NA, 2, 19, NA, 4, 3, 21, 5, 1...
fdgs
> fdgs %>% {
+ print(class(.))
+ dplyr::glimpse(.)
+ }
[1] "data.frame"
Observations: 10,030
Variables: 8
$ id <dbl> 100001, 100003, 100004, 100005, 100006, 100018, 100027, ...
$ reg <fctr> West, West, West, West, West, East, West, West, City, N...
$ age <dbl> 13.095140, 13.817933, 13.971253, 13.982204, 13.522245, 1...
$ sex <fctr> boy, boy, boy, girl, girl, boy, boy, boy, boy, boy, boy...
$ hgt <dbl> 175.5, 148.4, 159.9, 159.7, 160.3, 157.8, 175.3, 184.0, ...
$ wgt <dbl> 75.0, 40.0, 42.0, 46.5, 47.8, 39.7, 66.7, 80.7, 35.5, 55...
$ hgt.z <dbl> 1.751, -2.292, -1.000, -0.743, -0.414, 2.025, 0.879, 2.1...
$ wgt.z <dbl> 2.410, -1.494, -1.315, -0.783, -0.355, 0.823, 1.291, 2.4...
md.pairs
変数の組み合わせによる欠損パターンの表示
> md.pairs(nhanes)
$rr
age bmi hyp chl
age 25 16 17 15
bmi 16 16 16 13
hyp 17 16 17 14
chl 15 13 14 15
$rm
age bmi hyp chl
age 0 9 8 10
bmi 0 0 0 3
hyp 0 1 0 3
chl 0 2 1 0
$mr
age bmi hyp chl
age 0 0 0 0
bmi 9 0 1 2
hyp 8 0 0 1
chl 10 3 3 0
$mm
age bmi hyp chl
age 0 0 0 0
bmi 0 9 8 7
hyp 0 8 8 7
chl 0 7 7 10
md.pattern
欠損パターンの表示
> md.pattern(nhanes)
age hyp bmi chl
13 1 1 1 1 0
1 1 1 0 1 1
3 1 1 1 0 1
1 1 0 0 1 2
7 1 0 0 0 3
0 8 9 10 27
mice
多重補間代入
Arguments
- data
- m
- method
- predictorMatrix
- visitSequence
- form
- post
- defaultMethod
- maxit
- diagnostics
- printFlag
- seed
- imputationMethod
- defaultImputationMethod
- data.init
- ...
> nhanes %>% mice()
iter imp variable
1 1 bmi hyp chl
1 2 bmi hyp chl
1 3 bmi hyp chl
1 4 bmi hyp chl
1 5 bmi hyp chl
2 1 bmi hyp chl
2 2 bmi hyp chl
2 3 bmi hyp chl
2 4 bmi hyp chl
2 5 bmi hyp chl
3 1 bmi hyp chl
3 2 bmi hyp chl
3 3 bmi hyp chl
3 4 bmi hyp chl
3 5 bmi hyp chl
4 1 bmi hyp chl
4 2 bmi hyp chl
4 3 bmi hyp chl
4 4 bmi hyp chl
4 5 bmi hyp chl
5 1 bmi hyp chl
5 2 bmi hyp chl
5 3 bmi hyp chl
5 4 bmi hyp chl
5 5 bmi hyp chl
Multiply imputed data set
Call:
mice(data = .)
Number of multiple imputations: 5
Missing cells per column:
age bmi hyp chl
0 9 8 10
Imputation methods:
age bmi hyp chl
"" "pmm" "pmm" "pmm"
VisitSequence:
bmi hyp chl
2 3 4
PredictorMatrix:
age bmi hyp chl
age 0 0 0 0
bmi 1 0 1 1
hyp 1 1 0 1
chl 1 1 1 0
Random generator seed value: NA
> nhanes %>% mice() %>% class()
iter imp variable
1 1 bmi hyp chl
1 2 bmi hyp chl
1 3 bmi hyp chl
1 4 bmi hyp chl
1 5 bmi hyp chl
2 1 bmi hyp chl
2 2 bmi hyp chl
2 3 bmi hyp chl
2 4 bmi hyp chl
2 5 bmi hyp chl
3 1 bmi hyp chl
3 2 bmi hyp chl
3 3 bmi hyp chl
3 4 bmi hyp chl
3 5 bmi hyp chl
4 1 bmi hyp chl
4 2 bmi hyp chl
4 3 bmi hyp chl
4 4 bmi hyp chl
4 5 bmi hyp chl
5 1 bmi hyp chl
5 2 bmi hyp chl
5 3 bmi hyp chl
5 4 bmi hyp chl
5 5 bmi hyp chl
[1] "mids"
nhanes
欠損値を多く含んだ模擬データセット
> data("nhanes")
> nhanes %>% {
+ print(class(.))
+ dplyr::as_data_frame(.) %>% head(.)
+ }
[1] "data.frame"
# A tibble: 6 × 4
age bmi hyp chl
<dbl> <dbl> <dbl> <dbl>
1 1 NA NA NA
2 2 22.7 1 187
3 1 NA 1 187
4 3 NA NA NA
5 1 20.4 1 113
6 3 NA NA 184
pool
> imp <- mice(nhanes)
iter imp variable
1 1 bmi hyp chl
1 2 bmi hyp chl
1 3 bmi hyp chl
1 4 bmi hyp chl
1 5 bmi hyp chl
2 1 bmi hyp chl
2 2 bmi hyp chl
2 3 bmi hyp chl
2 4 bmi hyp chl
2 5 bmi hyp chl
3 1 bmi hyp chl
3 2 bmi hyp chl
3 3 bmi hyp chl
3 4 bmi hyp chl
3 5 bmi hyp chl
4 1 bmi hyp chl
4 2 bmi hyp chl
4 3 bmi hyp chl
4 4 bmi hyp chl
4 5 bmi hyp chl
5 1 bmi hyp chl
5 2 bmi hyp chl
5 3 bmi hyp chl
5 4 bmi hyp chl
5 5 bmi hyp chl
> fit <- with(data=imp,exp=lm(bmi~hyp+chl))
> pool(fit)
Call: pool(object = fit)
Pooled coefficients:
(Intercept) hyp chl
22.42887115 -1.11753665 0.02849406
Fraction of information about the coefficients missing due to nonresponse:
(Intercept) hyp chl
0.2251261 0.3821697 0.3744282