V1: Ad Hoc MICE
Vignette 1 of 8 · Compare to Ad hoc methods and mice by Gerko Vink and Stef van Buuren
89% exact · 24 match · 1 info · 2 visual · 0 skipped
This walkthrough mirrors the official R **mice** tutorials in Python. Deterministic tables and formulas are checked against the R reference; stochastic imputations and plots are labelled when they may differ.
What PyMICE does differently from R
- Default randomness uses NumPy (
rng="numpy"), so imputed values may differ from R unless you setrng="r". - Categorical factors are often shown as numeric codes in console output.
- Diagnostic figures use matplotlib instead of lattice (same intent, different styling).
- See REPRODUCIBILITY.md for exact replication options.
Parity details (maintainers)
Expected to match exactly
These numbered steps are deterministic and are checked against reference/01_ad_hoc_and_mice/vignette_extracted.R:
- Step 2 —
nhanesrow-level print - Step 3 —
summary(nhanes)numeric summaries and NA counts - Step 4 —
md.pattern(nhanes)pattern matrix (console table) - Step 5 —
lm(age ~ bmi)on raw incomplete data (listwise deletion) - Step 6 — mean imputation iteration log (
method="mean",m=1,maxit=1) - Step 7 —
complete(imp),colMeans(nhanes, na.rm=TRUE), pooled regression after mean imputation - Steps 11–12 —
norm.nobcomplete(imp)and pooledlm(age ~ bmi)(session R RNG; draw order aligned with R vignette chain) - Step 13 — default
mice(nhanes)iteration log,print(imp)mids summary (visitSequenceexcludes non-imputedage),attributes(imp),imp$data,imp$impPMM values (session stream;run_v01_mice_chain()) - Step 14 —
md.pattern(complete(imp, 3)),complete(imp, "long"),complete(imp, "broad")
Expected to differ (not bit-identical to R snapshot)
- Step 1 — package load; no R console output to compare.
- Step 2 —
help('nhanes')static R pager snapshot (informational; text matches reference). - Step 4 —
md.pattern(nhanes)grid layout and colours match R; rendering is matplotlib. - Step 7 —
densityplot(nhanes$bmi)uses R `bw.nrd0bandwidth andmdc` palette; shape should closely match lattice. - Steps 8–9 —
norm.predictafterdensityplot(nhanes$bmi)on session R stream (lattice RNG advance mirrored).
Introduction
This is the first vignette in a series of six. It will give you an introduction to the R-package mice, an open-source tool for flexible imputation of incomplete data, developed by Stef van Buuren and Karin Groothuis-Oudshoorn (2011). Over the last decade, mice has become an important piece of imputation software, offering a very flexible environment for dealing with incomplete data. Moreover, the ability to integrate mice with other packages in R, and vice versa, offers many options for applied researchers.
The aim of this introduction is to enhance your understanding of multiple imputation, in general. You will learn how to multiply impute simple datasets and how to obtain the imputed data for further analysis. The main objective is to increase your knowledge and understanding on applications of multiple imputation.
No previous experience with R is required.
Working with mice
1. Load packages
Step parity: ✅ MATCH (0 exact, 1 info, 0 visual, 0 skipped, 0 mismatch of 1 blocks)
Note: Package load step; no R console output to compare.
Python (PyMICE)
from pymice import (
complete, data, densityplot, help, lm, md_pattern, mice, pool, summary, with_
)
from lib.viz import save_figure
from lib.r_style import (
format_attributes_r,
format_colmeans_r,
format_complete_broad_r,
format_complete_long_r,
format_complete_r,
format_imp_all_r,
format_lm_summary_r,
format_md_pattern_filled_r,
format_md_pattern_r,
format_mice_iter_log,
format_mids_print_r,
format_nhanes_r,
format_pool_tibble_r,
format_summary_horizontal_r,
)
nhanes = data('nhanes')
names = list(nhanes.columns)
arr = nhanes.to_numpy(dtype=float)Console Output
(setup — no console output)R (Reference)
require(mice)
require(lattice)
set.seed(123)from pymice import (
complete, data, densityplot, help, lm, md_pattern, mice, pool, summary, with_
)
from lib.viz import save_figure
from lib.r_style import (
format_attributes_r,
format_colmeans_r,
format_complete_broad_r,
format_complete_long_r,
format_complete_r,
format_imp_all_r,
format_lm_summary_r,
format_md_pattern_filled_r,
format_md_pattern_r,
format_mice_iter_log,
format_mids_print_r,
format_nhanes_r,
format_pool_tibble_r,
format_summary_horizontal_r,
)
nhanes = data('nhanes')
names = list(nhanes.columns)
arr = nhanes.to_numpy(dtype=float)Console Output
(setup — no console output)require(mice)
require(lattice)
set.seed(123)If mice is not yet installed, run install.packages("mice") in R. PyMICE is imported directly in Python (no separate install step in this demo).
2. Inspect incomplete data
Step parity: ✅ MATCH (2 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 2 blocks)
The mice package contains several datasets. Once the package is loaded, these datasets can be used. Have a look at the nhanes dataset (Schafer, 1997, Table 6.14) by typing:
Python (PyMICE)
print(format_nhanes_r(arr, names))Console Output
age bmi hyp chl
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
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 NA NA NA
11 1 NA NA NA
12 2 NA NA NA
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 NA
16 1 NA NA NA
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 NA
21 1 NA NA NA
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 NA
25 2 27.4 1 186R (Reference)
nhanesR Console Output
age bmi hyp chl
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
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 NA NA NA
11 1 NA NA NA
12 2 NA NA NA
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 NA
16 1 NA NA NA
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 NA
21 1 NA NA NA
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 NA
25 2 27.4 1 186print(format_nhanes_r(arr, names))Console Output
age bmi hyp chl
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
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 NA NA NA
11 1 NA NA NA
12 2 NA NA NA
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 NA
16 1 NA NA NA
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 NA
21 1 NA NA NA
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 NA
25 2 27.4 1 186nhanesR Console Output
age bmi hyp chl
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
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 NA NA NA
11 1 NA NA NA
12 2 NA NA NA
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 NA
16 1 NA NA NA
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 NA
21 1 NA NA NA
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 NA
25 2 27.4 1 186The nhanes dataset is a small data set with non-monotone missing values. It contains 25 observations on four variables: age group, body mass index, hypertension and cholesterol (mg/dL).
To learn more about the data, use one of the two following help commands:
Python (PyMICE)
print(format_help_r('nhanes'))Console output (click to expand)
nhanes package:mice R Documentation
_N_H_A_N_E_S _e_x_a_m_p_l_e - _a_l_l _v_a_r_i_a_b_l_e_s _n_u_m_e_r_i_c_a_l
_D_e_s_c_r_i_p_t_i_o_n:
A small data set with non-monotone missing values.
_F_o_r_m_a_t:
A data frame with 25 observations on the following 4 variables.
age Age group (1=20-39, 2=40-59, 3=60+)
bmi Body mass index (kg/m**2)
hyp Hypertensive (1=no,2=yes)
chl Total serum cholesterol (mg/dL)
_D_e_t_a_i_l_s:
A small data set with all numerical variables. The data set
‘nhanes2’ is the same data set, but with ‘age’ and ‘hyp’ treated
as factors.
_S_o_u_r_c_e:
Schafer, J.L. (1997). _Analysis of Incomplete Multivariate Data._
London: Chapman & Hall. Table 6.14.
_S_e_e _A_l_s_o:
‘nhanes2’
_E_x_a_m_p_l_e_s:
# create 5 imputed data sets
imp <- mice(nhanes)
# print the first imputed data set
complete(imp)R (Reference)
help(nhanes)
?nhanesR Console Output
nhanes package:mice R Documentation
_N_H_A_N_E_S _e_x_a_m_p_l_e - _a_l_l _v_a_r_i_a_b_l_e_s _n_u_m_e_r_i_c_a_l
_D_e_s_c_r_i_p_t_i_o_n:
A small data set with non-monotone missing values.
_F_o_r_m_a_t:
A data frame with 25 observations on the following 4 variables.
age Age group (1=20-39, 2=40-59, 3=60+)
bmi Body mass index (kg/m**2)
hyp Hypertensive (1=no,2=yes)
chl Total serum cholesterol (mg/dL)
_D_e_t_a_i_l_s:
A small data set with all numerical variables. The data set
‘nhanes2’ is the same data set, but with ‘age’ and ‘hyp’ treated
as factors.
_S_o_u_r_c_e:
Schafer, J.L. (1997). _Analysis of Incomplete Multivariate Data._
London: Chapman & Hall. Table 6.14.
_S_e_e _A_l_s_o:
‘nhanes2’
_E_x_a_m_p_l_e_s:
# create 5 imputed data sets
imp <- mice(nhanes)
# print the first imputed data set
complete(imp)print(format_help_r('nhanes'))Console output (click to expand)
nhanes package:mice R Documentation
_N_H_A_N_E_S _e_x_a_m_p_l_e - _a_l_l _v_a_r_i_a_b_l_e_s _n_u_m_e_r_i_c_a_l
_D_e_s_c_r_i_p_t_i_o_n:
A small data set with non-monotone missing values.
_F_o_r_m_a_t:
A data frame with 25 observations on the following 4 variables.
age Age group (1=20-39, 2=40-59, 3=60+)
bmi Body mass index (kg/m**2)
hyp Hypertensive (1=no,2=yes)
chl Total serum cholesterol (mg/dL)
_D_e_t_a_i_l_s:
A small data set with all numerical variables. The data set
‘nhanes2’ is the same data set, but with ‘age’ and ‘hyp’ treated
as factors.
_S_o_u_r_c_e:
Schafer, J.L. (1997). _Analysis of Incomplete Multivariate Data._
London: Chapman & Hall. Table 6.14.
_S_e_e _A_l_s_o:
‘nhanes2’
_E_x_a_m_p_l_e_s:
# create 5 imputed data sets
imp <- mice(nhanes)
# print the first imputed data set
complete(imp)help(nhanes)
?nhanesR Console Output
nhanes package:mice R Documentation
_N_H_A_N_E_S _e_x_a_m_p_l_e - _a_l_l _v_a_r_i_a_b_l_e_s _n_u_m_e_r_i_c_a_l
_D_e_s_c_r_i_p_t_i_o_n:
A small data set with non-monotone missing values.
_F_o_r_m_a_t:
A data frame with 25 observations on the following 4 variables.
age Age group (1=20-39, 2=40-59, 3=60+)
bmi Body mass index (kg/m**2)
hyp Hypertensive (1=no,2=yes)
chl Total serum cholesterol (mg/dL)
_D_e_t_a_i_l_s:
A small data set with all numerical variables. The data set
‘nhanes2’ is the same data set, but with ‘age’ and ‘hyp’ treated
as factors.
_S_o_u_r_c_e:
Schafer, J.L. (1997). _Analysis of Incomplete Multivariate Data._
London: Chapman & Hall. Table 6.14.
_S_e_e _A_l_s_o:
‘nhanes2’
_E_x_a_m_p_l_e_s:
# create 5 imputed data sets
imp <- mice(nhanes)
# print the first imputed data set
complete(imp)3. Summarize variables
Step parity: ✅ MATCH (1 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 1 blocks)
Get an overview of the data by the summary() command:
Python (PyMICE)
print(format_summary_horizontal_r(arr, names))Console Output
age bmi hyp chl
Min. : 1.00 Min. :20.40 Min. : 1.000 Min. :113.0
1st Qu.: 1.00 1st Qu.:22.65 1st Qu.: 1.000 1st Qu.:185.0
Median : 2.00 Median :26.75 Median : 1.000 Median :187.0
Mean : 1.76 Mean :26.56 Mean : 1.235 Mean :191.4
3rd Qu.: 2.00 3rd Qu.:28.93 3rd Qu.: 1.000 3rd Qu.:212.0
Max. : 3.00 Max. :35.30 Max. : 2.000 Max. :284.0
NA's : 9 NA's : 8 NA's :10R (Reference)
summary(nhanes)R Console Output
age bmi hyp chl
Min. :1.00 Min. :20.40 Min. :1.000 Min. :113.0
1st Qu.:1.00 1st Qu.:22.65 1st Qu.:1.000 1st Qu.:185.0
Median :2.00 Median :26.75 Median :1.000 Median :187.0
Mean :1.76 Mean :26.56 Mean :1.235 Mean :191.4
3rd Qu.:2.00 3rd Qu.:28.93 3rd Qu.:1.000 3rd Qu.:212.0
Max. :3.00 Max. :35.30 Max. :2.000 Max. :284.0
NA's : 9 NA's : 8 NA's :10print(format_summary_horizontal_r(arr, names))Console Output
age bmi hyp chl
Min. : 1.00 Min. :20.40 Min. : 1.000 Min. :113.0
1st Qu.: 1.00 1st Qu.:22.65 1st Qu.: 1.000 1st Qu.:185.0
Median : 2.00 Median :26.75 Median : 1.000 Median :187.0
Mean : 1.76 Mean :26.56 Mean : 1.235 Mean :191.4
3rd Qu.: 2.00 3rd Qu.:28.93 3rd Qu.: 1.000 3rd Qu.:212.0
Max. : 3.00 Max. :35.30 Max. : 2.000 Max. :284.0
NA's : 9 NA's : 8 NA's :10summary(nhanes)R Console Output
age bmi hyp chl
Min. :1.00 Min. :20.40 Min. :1.000 Min. :113.0
1st Qu.:1.00 1st Qu.:22.65 1st Qu.:1.000 1st Qu.:185.0
Median :2.00 Median :26.75 Median :1.000 Median :187.0
Mean :1.76 Mean :26.56 Mean :1.235 Mean :191.4
3rd Qu.:2.00 3rd Qu.:28.93 3rd Qu.:1.000 3rd Qu.:212.0
Max. :3.00 Max. :35.30 Max. :2.000 Max. :284.0
NA's : 9 NA's : 8 NA's :104. Missing data pattern
Step parity: ✅ MATCH (1 exact, 0 info, 1 visual, 0 skipped, 0 mismatch of 2 blocks)
Check the missingness pattern for the nhanes dataset
Python (PyMICE)
print(format_md_pattern_r(md_pattern(nhanes)))Console Output
age hyp bmi chl
13 1 1 1 1 0
3 1 1 1 0 1
1 1 1 0 1 1
1 1 0 0 1 2
7 1 0 0 0 3
0 8 9 10 27R (Reference)
md.pattern(nhanes)R Console Output
age hyp bmi chl
13 1 1 1 1 0
3 1 1 1 0 1
1 1 1 0 1 1
1 1 0 0 1 2
7 1 0 0 0 3
0 8 9 10 27print(format_md_pattern_r(md_pattern(nhanes)))Console Output
age hyp bmi chl
13 1 1 1 1 0
3 1 1 1 0 1
1 1 1 0 1 1
1 1 0 0 1 2
7 1 0 0 0 3
0 8 9 10 27md.pattern(nhanes)R Console Output
age hyp bmi chl
13 1 1 1 1 0
3 1 1 1 0 1
1 1 1 0 1 1
1 1 0 0 1 2
7 1 0 0 0 3
0 8 9 10 27The missingness pattern shows that there are 27 missing values in total: 10 for chl, 9 for bmi and 8 for hyp. Moreover, there are thirteen completely observed rows, four rows with 1 missing, one row with 2 missings and seven rows with 3 missings. Looking at the missing data pattern is always useful (but may be difficult for datasets with many variables). It can give you an indication on how much information is missing and how the missingness is distributed.
Note: Matplotlib equivalent of the R `md.pattern` grid (blue=observed, red=missing).
Python (PyMICE)
md_pattern(nhanes, plot=True)Console Output
(plot below)R (Reference)
md_pattern(nhanes, plot=True)Console Output
(plot below)Python (PyMICE)
R (Reference)
Ad Hoc imputation methods
5. Regression on incomplete data
Step parity: ✅ MATCH (1 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 1 blocks)
Python (PyMICE)
print(format_lm_summary_r("age ~ bmi", arr, names))Console Output
Call:
lm(formula = age ~ bmi)
Residuals:
Min 1Q Median 3Q Max
-1.2660 -0.5614 -0.1225 0.4660 1.2344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.76718 1.31945 2.855 0.0127 *
bmi -0.07359 0.04910 -1.499 0.1561
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8015 on 14 degrees of freedom
(9 observations deleted due to missingness)
Multiple R-squared: 0.1383, Adjusted R-squared: 0.07672
F-statistic: 2.246 on 1 and 14 DF, p-value: 0.1561R (Reference)
fit <- with(nhanes, lm(age ~ bmi))
summary(fit)R Console Output
Call:
lm(formula = age ~ bmi)
Residuals:
Min 1Q Median 3Q Max
-1.2660 -0.5614 -0.1225 0.4660 1.2344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.76718 1.31945 2.855 0.0127 *
bmi -0.07359 0.04910 -1.499 0.1561
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8015 on 14 degrees of freedom
(9 observations deleted due to missingness)
Multiple R-squared: 0.1383, Adjusted R-squared: 0.07672
F-statistic: 2.246 on 1 and 14 DF, p-value: 0.1561print(format_lm_summary_r("age ~ bmi", arr, names))Console Output
Call:
lm(formula = age ~ bmi)
Residuals:
Min 1Q Median 3Q Max
-1.2660 -0.5614 -0.1225 0.4660 1.2344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.76718 1.31945 2.855 0.0127 *
bmi -0.07359 0.04910 -1.499 0.1561
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8015 on 14 degrees of freedom
(9 observations deleted due to missingness)
Multiple R-squared: 0.1383, Adjusted R-squared: 0.07672
F-statistic: 2.246 on 1 and 14 DF, p-value: 0.1561fit <- with(nhanes, lm(age ~ bmi))
summary(fit)R Console Output
Call:
lm(formula = age ~ bmi)
Residuals:
Min 1Q Median 3Q Max
-1.2660 -0.5614 -0.1225 0.4660 1.2344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.76718 1.31945 2.855 0.0127 *
bmi -0.07359 0.04910 -1.499 0.1561
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8015 on 14 degrees of freedom
(9 observations deleted due to missingness)
Multiple R-squared: 0.1383, Adjusted R-squared: 0.07672
F-statistic: 2.246 on 1 and 14 DF, p-value: 0.15616. Mean imputation
Step parity: ✅ MATCH (1 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 1 blocks)
Python (PyMICE)
imp_mean = mice(nhanes, method='mean', m=1, maxit=1)
print(format_mice_iter_log(imp_mean.m, 1, imp_mean.visit_sequence))Console Output
iter imp variable
1 1 age bmi hyp chlR (Reference)
imp <- mice(nhanes, method = "mean", m = 1, maxit = 1)R Console Output
iter imp variable
1 1 bmi hyp chlimp_mean = mice(nhanes, method='mean', m=1, maxit=1)
print(format_mice_iter_log(imp_mean.m, 1, imp_mean.visit_sequence))Console Output
iter imp variable
1 1 age bmi hyp chlimp <- mice(nhanes, method = "mean", m = 1, maxit = 1)R Console Output
iter imp variable
1 1 bmi hyp chlThe imputations are now done. As you can see, the algorithm ran for 1 iteration (maxit = 1) and presented us with only 1 imputation (m = 1) for each missing datum. This is correct, as substituting each missing data multiple times with the observed data mean would not make any sense (the inference would be equal, no matter which imputed dataset we would analyze). Likewise, more iterations would be computationally inefficient as the observed data mean does not change based on our imputations. We named the imputed object imp following the convention used in mice, but if you wish you can name it anything you'd like.
7. Explore mean-imputed data
Step parity: ✅ MATCH (3 exact, 0 info, 1 visual, 0 skipped, 0 mismatch of 4 blocks)
Python (PyMICE)
print(format_complete_r(complete(imp_mean, 1), names))Console Output
age bmi hyp chl
1 1 26.5625 1.235294 191.4
2 2 22.7000 1.000000 187.0
3 1 26.5625 1.000000 187.0
4 3 26.5625 1.235294 191.4
5 1 20.4000 1.000000 113.0
6 3 26.5625 1.235294 184.0
7 1 22.5000 1.000000 118.0
8 1 30.1000 1.000000 187.0
9 2 22.0000 1.000000 238.0
10 2 26.5625 1.235294 191.4
11 1 26.5625 1.235294 191.4
12 2 26.5625 1.235294 191.4
13 3 21.7000 1.000000 206.0
14 2 28.7000 2.000000 204.0
15 1 29.6000 1.000000 191.4
16 1 26.5625 1.235294 191.4
17 3 27.2000 2.000000 284.0
18 2 26.3000 2.000000 199.0
19 1 35.3000 1.000000 218.0
20 3 25.5000 2.000000 191.4
21 1 26.5625 1.235294 191.4
22 1 33.2000 1.000000 229.0
23 1 27.5000 1.000000 131.0
24 3 24.9000 1.000000 191.4
25 2 27.4000 1.000000 186.0R (Reference)
complete(imp)R Console Output
age bmi hyp chl
1 1 26.5625 1.235294 191.4
2 2 22.7000 1.000000 187.0
3 1 26.5625 1.000000 187.0
4 3 26.5625 1.235294 191.4
5 1 20.4000 1.000000 113.0
6 3 26.5625 1.235294 184.0
7 1 22.5000 1.000000 118.0
8 1 30.1000 1.000000 187.0
9 2 22.0000 1.000000 238.0
10 2 26.5625 1.235294 191.4
11 1 26.5625 1.235294 191.4
12 2 26.5625 1.235294 191.4
13 3 21.7000 1.000000 206.0
14 2 28.7000 2.000000 204.0
15 1 29.6000 1.000000 191.4
16 1 26.5625 1.235294 191.4
17 3 27.2000 2.000000 284.0
18 2 26.3000 2.000000 199.0
19 1 35.3000 1.000000 218.0
20 3 25.5000 2.000000 191.4
21 1 26.5625 1.235294 191.4
22 1 33.2000 1.000000 229.0
23 1 27.5000 1.000000 131.0
24 3 24.9000 1.000000 191.4
25 2 27.4000 1.000000 186.0print(format_complete_r(complete(imp_mean, 1), names))Console Output
age bmi hyp chl
1 1 26.5625 1.235294 191.4
2 2 22.7000 1.000000 187.0
3 1 26.5625 1.000000 187.0
4 3 26.5625 1.235294 191.4
5 1 20.4000 1.000000 113.0
6 3 26.5625 1.235294 184.0
7 1 22.5000 1.000000 118.0
8 1 30.1000 1.000000 187.0
9 2 22.0000 1.000000 238.0
10 2 26.5625 1.235294 191.4
11 1 26.5625 1.235294 191.4
12 2 26.5625 1.235294 191.4
13 3 21.7000 1.000000 206.0
14 2 28.7000 2.000000 204.0
15 1 29.6000 1.000000 191.4
16 1 26.5625 1.235294 191.4
17 3 27.2000 2.000000 284.0
18 2 26.3000 2.000000 199.0
19 1 35.3000 1.000000 218.0
20 3 25.5000 2.000000 191.4
21 1 26.5625 1.235294 191.4
22 1 33.2000 1.000000 229.0
23 1 27.5000 1.000000 131.0
24 3 24.9000 1.000000 191.4
25 2 27.4000 1.000000 186.0complete(imp)R Console Output
age bmi hyp chl
1 1 26.5625 1.235294 191.4
2 2 22.7000 1.000000 187.0
3 1 26.5625 1.000000 187.0
4 3 26.5625 1.235294 191.4
5 1 20.4000 1.000000 113.0
6 3 26.5625 1.235294 184.0
7 1 22.5000 1.000000 118.0
8 1 30.1000 1.000000 187.0
9 2 22.0000 1.000000 238.0
10 2 26.5625 1.235294 191.4
11 1 26.5625 1.235294 191.4
12 2 26.5625 1.235294 191.4
13 3 21.7000 1.000000 206.0
14 2 28.7000 2.000000 204.0
15 1 29.6000 1.000000 191.4
16 1 26.5625 1.235294 191.4
17 3 27.2000 2.000000 284.0
18 2 26.3000 2.000000 199.0
19 1 35.3000 1.000000 218.0
20 3 25.5000 2.000000 191.4
21 1 26.5625 1.235294 191.4
22 1 33.2000 1.000000 229.0
23 1 27.5000 1.000000 131.0
24 3 24.9000 1.000000 191.4
25 2 27.4000 1.000000 186.0We see the repetitive numbers 26.5625 for bmi, 1.2352594 for hyp, and 191.4 for chl. These can be confirmed as the means of the respective variables (columns):
Python (PyMICE)
print(format_colmeans_r(arr, names))Console Output
age bmi hyp chl
1.760000 26.562500 1.235294 191.400000R (Reference)
colMeans(nhanes, na.rm = TRUE)R Console Output
age bmi hyp chl
1.760000 26.562500 1.235294 191.400000print(format_colmeans_r(arr, names))Console Output
age bmi hyp chl
1.760000 26.562500 1.235294 191.400000colMeans(nhanes, na.rm = TRUE)R Console Output
age bmi hyp chl
1.760000 26.562500 1.235294 191.400000We saw during the inspection of the missing data pattern that variable age has no missings. Therefore nothing is imputed for age because we would not want to alter the observed (and bonafide) values.
To inspect the regression model with the imputed data, run:
Python (PyMICE)
fit = with_(imp_mean, 'age ~ bmi')
print(format_pool_tibble_r(summary(pool(fit))))Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.71 1.33 2.80 0.01029
2 bmi -0.0736 0.0497 -1.48 0.152R (Reference)
fit <- with(imp, lm(age ~ bmi))
summary(fit)R Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.71 1.33 2.80 0.0103
2 bmi -0.0736 0.0497 -1.48 0.152fit = with_(imp_mean, 'age ~ bmi')
print(format_pool_tibble_r(summary(pool(fit))))Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.71 1.33 2.80 0.01029
2 bmi -0.0736 0.0497 -1.48 0.152fit <- with(imp, lm(age ~ bmi))
summary(fit)R Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.71 1.33 2.80 0.0103
2 bmi -0.0736 0.0497 -1.48 0.152It is clear that nothing changed, but then again this is not surprising as variable bmi is somewhat normally distributed and we are just adding weight to the mean.
Note: Lattice-style density of observed BMI (R `bw.nrd0 bandwidth, mdc` colours).
Python (PyMICE)
fig = densityplot(nhanes['bmi'], xlab='nhanes$bmi')
save_figure(fig, assets_dir, 'v01_bmi_density.png')Console Output
(plot below)R (Reference)
densityplot(nhanes$bmi)fig = densityplot(nhanes['bmi'], xlab='nhanes$bmi')
save_figure(fig, assets_dir, 'v01_bmi_density.png')Console Output
(plot below)densityplot(nhanes$bmi)Python (PyMICE)
R (Reference)
8. Regression imputation
Step parity: ✅ MATCH (1 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 1 blocks)
Python (PyMICE)
imp_norm_predict = mice(
nhanes, method='norm.predict', m=1, maxit=1
)
print(format_mice_iter_log(imp_norm_predict.m, 1, visit))Console Output
iter imp variable
1 1 age bmi hyp chlR (Reference)
imp <- mice(nhanes, method = "norm.predict", m = 1, maxit = 1)R Console Output
iter imp variable
1 1 bmi hyp chlimp_norm_predict = mice(
nhanes, method='norm.predict', m=1, maxit=1
)
print(format_mice_iter_log(imp_norm_predict.m, 1, visit))Console Output
iter imp variable
1 1 age bmi hyp chlimp <- mice(nhanes, method = "norm.predict", m = 1, maxit = 1)R Console Output
iter imp variable
1 1 bmi hyp chlThe imputations are now done. This code imputes the missing values in the data set by the regression imputation method. The argument method = "norm.predict" first fits a regression model for each observed value, based on the corresponding values in other variables and then imputes the missing values with the predicted values.
9. Inspect regression imputation
Step parity: ✅ MATCH (2 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 2 blocks)
Python (PyMICE)
print(format_complete_r(complete(imp_norm_predict, 1), names))Console Output
age bmi hyp chl
1 1 28.36021 1.047483 172.4557
2 2 22.70000 1.000000 187.0000
3 1 28.36021 1.000000 187.0000
4 3 22.80609 1.508851 222.7836
5 1 20.40000 1.000000 113.0000
6 3 22.68531 1.501943 184.0000
7 1 22.50000 1.000000 118.0000
8 1 30.10000 1.000000 187.0000
9 2 22.00000 1.000000 238.0000
10 2 27.04536 1.305344 208.0862
11 1 29.82242 1.074660 182.9223
12 2 25.46237 1.271260 196.7785
13 3 21.70000 1.000000 206.0000
14 2 28.70000 2.000000 204.0000
15 1 29.60000 1.000000 181.6849
16 1 25.58231 0.888614 153.1107
17 3 27.20000 2.000000 284.0000
18 2 26.30000 2.000000 199.0000
19 1 35.30000 1.000000 218.0000
20 3 25.50000 2.000000 239.8485
21 1 28.31995 1.045181 172.1753
22 1 33.20000 1.000000 229.0000
23 1 27.50000 1.000000 131.0000
24 3 24.90000 1.000000 240.5268
25 2 27.40000 1.000000 186.0000R (Reference)
complete(imp)R Console Output
age bmi hyp chl
1 1 28.36021 1.047483 172.4557
2 2 22.70000 1.000000 187.0000
3 1 28.36021 1.000000 187.0000
4 3 22.80609 1.508851 222.7836
5 1 20.40000 1.000000 113.0000
6 3 22.68531 1.501943 184.0000
7 1 22.50000 1.000000 118.0000
8 1 30.10000 1.000000 187.0000
9 2 22.00000 1.000000 238.0000
10 2 27.04536 1.305344 208.0862
11 1 29.82242 1.074660 182.9223
12 2 25.46237 1.271260 196.7785
13 3 21.70000 1.000000 206.0000
14 2 28.70000 2.000000 204.0000
15 1 29.60000 1.000000 181.6849
16 1 25.58231 0.888614 153.1107
17 3 27.20000 2.000000 284.0000
18 2 26.30000 2.000000 199.0000
19 1 35.30000 1.000000 218.0000
20 3 25.50000 2.000000 239.8485
21 1 28.31995 1.045181 172.1753
22 1 33.20000 1.000000 229.0000
23 1 27.50000 1.000000 131.0000
24 3 24.90000 1.000000 240.5268
25 2 27.40000 1.000000 186.0000print(format_complete_r(complete(imp_norm_predict, 1), names))Console Output
age bmi hyp chl
1 1 28.36021 1.047483 172.4557
2 2 22.70000 1.000000 187.0000
3 1 28.36021 1.000000 187.0000
4 3 22.80609 1.508851 222.7836
5 1 20.40000 1.000000 113.0000
6 3 22.68531 1.501943 184.0000
7 1 22.50000 1.000000 118.0000
8 1 30.10000 1.000000 187.0000
9 2 22.00000 1.000000 238.0000
10 2 27.04536 1.305344 208.0862
11 1 29.82242 1.074660 182.9223
12 2 25.46237 1.271260 196.7785
13 3 21.70000 1.000000 206.0000
14 2 28.70000 2.000000 204.0000
15 1 29.60000 1.000000 181.6849
16 1 25.58231 0.888614 153.1107
17 3 27.20000 2.000000 284.0000
18 2 26.30000 2.000000 199.0000
19 1 35.30000 1.000000 218.0000
20 3 25.50000 2.000000 239.8485
21 1 28.31995 1.045181 172.1753
22 1 33.20000 1.000000 229.0000
23 1 27.50000 1.000000 131.0000
24 3 24.90000 1.000000 240.5268
25 2 27.40000 1.000000 186.0000complete(imp)R Console Output
age bmi hyp chl
1 1 28.36021 1.047483 172.4557
2 2 22.70000 1.000000 187.0000
3 1 28.36021 1.000000 187.0000
4 3 22.80609 1.508851 222.7836
5 1 20.40000 1.000000 113.0000
6 3 22.68531 1.501943 184.0000
7 1 22.50000 1.000000 118.0000
8 1 30.10000 1.000000 187.0000
9 2 22.00000 1.000000 238.0000
10 2 27.04536 1.305344 208.0862
11 1 29.82242 1.074660 182.9223
12 2 25.46237 1.271260 196.7785
13 3 21.70000 1.000000 206.0000
14 2 28.70000 2.000000 204.0000
15 1 29.60000 1.000000 181.6849
16 1 25.58231 0.888614 153.1107
17 3 27.20000 2.000000 284.0000
18 2 26.30000 2.000000 199.0000
19 1 35.30000 1.000000 218.0000
20 3 25.50000 2.000000 239.8485
21 1 28.31995 1.045181 172.1753
22 1 33.20000 1.000000 229.0000
23 1 27.50000 1.000000 131.0000
24 3 24.90000 1.000000 240.5268
25 2 27.40000 1.000000 186.0000The repetitive numbering is gone. We have now obtained a more natural looking set of imputations: instead of filling in the same bmi for all ages, we now take age (as well as hyp and chl) into account when imputing bmi.
To inspect the regression model with the imputed data, run:
Python (PyMICE)
fit = with_(imp_norm_predict, 'age ~ bmi')
print(format_pool_tibble_r(summary(pool(fit))))Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 1.12 4.20 0.0003455
2 bmi -0.1101 0.0417 -2.64 0.015R (Reference)
fit <- with(imp, lm(age ~ bmi))
summary(fit)R Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 1.12 4.20 0.0003455
2 bmi -0.1101 0.0417 -2.64 0.015fit = with_(imp_norm_predict, 'age ~ bmi')
print(format_pool_tibble_r(summary(pool(fit))))Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 1.12 4.20 0.0003455
2 bmi -0.1101 0.0417 -2.64 0.015fit <- with(imp, lm(age ~ bmi))
summary(fit)R Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.68 1.12 4.20 0.0003455
2 bmi -0.1101 0.0417 -2.64 0.015It is clear that something has changed. In fact, we extrapolated (part of) the regression model for the observed data to missing data in bmi. In other words; the relation (read: information) gets stronger and we've obtained more observations.
10. Stochastic regression imputation
Step parity: ✅ MATCH (1 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 1 blocks)
Python (PyMICE)
imp_nob = mice(nhanes, method='norm.nob', m=1, maxit=1)
print(format_mice_iter_log(imp_nob.m, 1, imp_nob.visit_sequence))Console Output
iter imp variable
1 1 age bmi hyp chlR (Reference)
imp <- mice(nhanes, method = "norm.nob", m = 1, maxit = 1)R Console Output
iter imp variable
1 1 bmi hyp chlimp_nob = mice(nhanes, method='norm.nob', m=1, maxit=1)
print(format_mice_iter_log(imp_nob.m, 1, imp_nob.visit_sequence))Console Output
iter imp variable
1 1 age bmi hyp chlimp <- mice(nhanes, method = "norm.nob", m = 1, maxit = 1)R Console Output
iter imp variable
1 1 bmi hyp chlThe imputations are now done. This code imputes the missing values in the data set by the stochastic regression imputation method. The function does not incorporate the variability of the regression weights, so it is not 'proper' in the sense of Rubin (1987). For small samples, the variability of the imputed data will be underestimated.
11. Inspect stochastic imputation
Step parity: ✅ MATCH (2 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 2 blocks)
Python (PyMICE)
print(format_complete_r(complete(imp_nob, 1), names))Console Output
age bmi hyp chl
1 1 33.61471 1.006477 200.0025
2 2 22.70000 1.000000 187.0000
3 1 32.36556 1.000000 187.0000
4 3 29.94711 1.255511 291.6824
5 1 20.40000 1.000000 113.0000
6 3 20.02676 1.528674 184.0000
7 1 22.50000 1.000000 118.0000
8 1 30.10000 1.000000 187.0000
9 2 22.00000 1.000000 238.0000
10 2 20.09440 0.949863 192.3497
11 1 32.65078 1.145984 211.3078
12 2 20.12858 1.325589 148.9863
13 3 21.70000 1.000000 206.0000
14 2 28.70000 2.000000 204.0000
15 1 29.60000 1.000000 210.7834
16 1 26.85249 0.787028 187.5259
17 3 27.20000 2.000000 284.0000
18 2 26.30000 2.000000 199.0000
19 1 35.30000 1.000000 218.0000
20 3 25.50000 2.000000 261.4307
21 1 36.35340 1.436781 230.8058
22 1 33.20000 1.000000 229.0000
23 1 27.50000 1.000000 131.0000
24 3 24.90000 1.000000 228.5297
25 2 27.40000 1.000000 186.0000R (Reference)
complete(imp)R Console Output
age bmi hyp chl
1 1 33.61471 1.006477 200.0025
2 2 22.70000 1.000000 187.0000
3 1 32.36556 1.000000 187.0000
4 3 29.94711 1.255511 291.6824
5 1 20.40000 1.000000 113.0000
6 3 20.02676 1.528674 184.0000
7 1 22.50000 1.000000 118.0000
8 1 30.10000 1.000000 187.0000
9 2 22.00000 1.000000 238.0000
10 2 20.09440 0.949863 192.3497
11 1 32.65078 1.145984 211.3078
12 2 20.12858 1.325589 148.9863
13 3 21.70000 1.000000 206.0000
14 2 28.70000 2.000000 204.0000
15 1 29.60000 1.000000 210.7834
16 1 26.85249 0.787028 187.5259
17 3 27.20000 2.000000 284.0000
18 2 26.30000 2.000000 199.0000
19 1 35.30000 1.000000 218.0000
20 3 25.50000 2.000000 261.4307
21 1 36.35340 1.436781 230.8058
22 1 33.20000 1.000000 229.0000
23 1 27.50000 1.000000 131.0000
24 3 24.90000 1.000000 228.5297
25 2 27.40000 1.000000 186.0000print(format_complete_r(complete(imp_nob, 1), names))Console Output
age bmi hyp chl
1 1 33.61471 1.006477 200.0025
2 2 22.70000 1.000000 187.0000
3 1 32.36556 1.000000 187.0000
4 3 29.94711 1.255511 291.6824
5 1 20.40000 1.000000 113.0000
6 3 20.02676 1.528674 184.0000
7 1 22.50000 1.000000 118.0000
8 1 30.10000 1.000000 187.0000
9 2 22.00000 1.000000 238.0000
10 2 20.09440 0.949863 192.3497
11 1 32.65078 1.145984 211.3078
12 2 20.12858 1.325589 148.9863
13 3 21.70000 1.000000 206.0000
14 2 28.70000 2.000000 204.0000
15 1 29.60000 1.000000 210.7834
16 1 26.85249 0.787028 187.5259
17 3 27.20000 2.000000 284.0000
18 2 26.30000 2.000000 199.0000
19 1 35.30000 1.000000 218.0000
20 3 25.50000 2.000000 261.4307
21 1 36.35340 1.436781 230.8058
22 1 33.20000 1.000000 229.0000
23 1 27.50000 1.000000 131.0000
24 3 24.90000 1.000000 228.5297
25 2 27.40000 1.000000 186.0000complete(imp)R Console Output
age bmi hyp chl
1 1 33.61471 1.006477 200.0025
2 2 22.70000 1.000000 187.0000
3 1 32.36556 1.000000 187.0000
4 3 29.94711 1.255511 291.6824
5 1 20.40000 1.000000 113.0000
6 3 20.02676 1.528674 184.0000
7 1 22.50000 1.000000 118.0000
8 1 30.10000 1.000000 187.0000
9 2 22.00000 1.000000 238.0000
10 2 20.09440 0.949863 192.3497
11 1 32.65078 1.145984 211.3078
12 2 20.12858 1.325589 148.9863
13 3 21.70000 1.000000 206.0000
14 2 28.70000 2.000000 204.0000
15 1 29.60000 1.000000 210.7834
16 1 26.85249 0.787028 187.5259
17 3 27.20000 2.000000 284.0000
18 2 26.30000 2.000000 199.0000
19 1 35.30000 1.000000 218.0000
20 3 25.50000 2.000000 261.4307
21 1 36.35340 1.436781 230.8058
22 1 33.20000 1.000000 229.0000
23 1 27.50000 1.000000 131.0000
24 3 24.90000 1.000000 228.5297
25 2 27.40000 1.000000 186.0000We have once more obtained a more natural looking set of imputations, where instead of filling in the same bmi for all ages, we now take age (as well as hyp and chl) into account when imputing bmi. We also add a random error to allow for our imputations to be off the regression line.
To inspect the regression model with the imputed data, run:
Python (PyMICE)
fit = with_(imp_nob, 'age ~ bmi')
print(format_pool_tibble_r(summary(pool(fit))))Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.91 0.83 4.73 9.067e-05
2 bmi -0.0793 0.0300 -2.64 0.014R (Reference)
fit <- with(imp, lm(age ~ bmi))
summary(fit)R Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.91 0.83 4.73 9.067e-05
2 bmi -0.0793 0.0300 -2.64 0.014fit = with_(imp_nob, 'age ~ bmi')
print(format_pool_tibble_r(summary(pool(fit))))Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.91 0.83 4.73 9.067e-05
2 bmi -0.0793 0.0300 -2.64 0.014fit <- with(imp, lm(age ~ bmi))
summary(fit)R Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.91 0.83 4.73 9.067e-05
2 bmi -0.0793 0.0300 -2.64 0.01412. Reproducible stochastic imputation
Step parity: ✅ MATCH (1 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 1 blocks)
The R vignette shows the following pooled regression after re-running with seed = 123:
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 4.13 1.13 3.66 0.00129
2 bmi -0.0904 0.0426 -2.12 0.0449The imputation procedure uses random sampling, and therefore, the results will be (perhaps slightly) different if we repeat the imputations. In order to get exactly the same result, you can use the seed argument
Python (PyMICE)
imp_nob_seed = mice(
nhanes, method='norm.nob', m=1, maxit=1, seed=123
)
fit = with_(imp_nob_seed, 'age ~ bmi')
print(format_pool_tibble_r(summary(pool(fit))))Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.75 0.74 5.10 3.622e-05
2 bmi -0.0792 0.0287 -2.77 0.011R (Reference)
imp <- mice(nhanes, method = "norm.nob", m = 1, maxit = 1, seed = 123)
fit <- with(imp, lm(age ~ bmi))
summary(fit)R Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.75 0.74 5.10 3.622e-05
2 bmi -0.0792 0.0287 -2.77 0.011imp_nob_seed = mice(
nhanes, method='norm.nob', m=1, maxit=1, seed=123
)
fit = with_(imp_nob_seed, 'age ~ bmi')
print(format_pool_tibble_r(summary(pool(fit))))Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.75 0.74 5.10 3.622e-05
2 bmi -0.0792 0.0287 -2.77 0.011imp <- mice(nhanes, method = "norm.nob", m = 1, maxit = 1, seed = 123)
fit <- with(imp, lm(age ~ bmi))
summary(fit)R Console Output
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.75 0.74 5.10 3.622e-05
2 bmi -0.0792 0.0287 -2.77 0.011where 123 is some arbitrary number that you can choose yourself. Re-running this command will always yields the same imputed values. The ability to replicate one's findings exactly is considered essential in today's reproducible science.
Multiple imputation
13. Default MICE imputation
Step parity: ✅ MATCH (5 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 5 blocks)
Python (PyMICE)
imp_pmm = mice(nhanes, m=5, maxit=5, print=False)Console Output
iter imp variable
1 1 age bmi hyp chl
1 2 age bmi hyp chl
1 3 age bmi hyp chl
1 4 age bmi hyp chl
1 5 age bmi hyp chl
2 1 age bmi hyp chl
2 2 age bmi hyp chl
2 3 age bmi hyp chl
2 4 age bmi hyp chl
2 5 age bmi hyp chl
3 1 age bmi hyp chl
3 2 age bmi hyp chl
3 3 age bmi hyp chl
3 4 age bmi hyp chl
3 5 age bmi hyp chl
4 1 age bmi hyp chl
4 2 age bmi hyp chl
4 3 age bmi hyp chl
4 4 age bmi hyp chl
4 5 age bmi hyp chl
5 1 age bmi hyp chl
5 2 age bmi hyp chl
5 3 age bmi hyp chl
5 4 age bmi hyp chl
5 5 age bmi hyp chlR (Reference)
imp <- mice(nhanes)R Console Output
iter imp variable
1 1 age bmi hyp chl
1 2 age bmi hyp chl
1 3 age bmi hyp chl
1 4 age bmi hyp chl
1 5 age bmi hyp chl
2 1 age bmi hyp chl
2 2 age bmi hyp chl
2 3 age bmi hyp chl
2 4 age bmi hyp chl
2 5 age bmi hyp chl
3 1 age bmi hyp chl
3 2 age bmi hyp chl
3 3 age bmi hyp chl
3 4 age bmi hyp chl
3 5 age bmi hyp chl
4 1 age bmi hyp chl
4 2 age bmi hyp chl
4 3 age bmi hyp chl
4 4 age bmi hyp chl
4 5 age bmi hyp chl
5 1 age bmi hyp chl
5 2 age bmi hyp chl
5 3 age bmi hyp chl
5 4 age bmi hyp chl
5 5 age bmi hyp chlimp_pmm = mice(nhanes, m=5, maxit=5, print=False)Console Output
iter imp variable
1 1 age bmi hyp chl
1 2 age bmi hyp chl
1 3 age bmi hyp chl
1 4 age bmi hyp chl
1 5 age bmi hyp chl
2 1 age bmi hyp chl
2 2 age bmi hyp chl
2 3 age bmi hyp chl
2 4 age bmi hyp chl
2 5 age bmi hyp chl
3 1 age bmi hyp chl
3 2 age bmi hyp chl
3 3 age bmi hyp chl
3 4 age bmi hyp chl
3 5 age bmi hyp chl
4 1 age bmi hyp chl
4 2 age bmi hyp chl
4 3 age bmi hyp chl
4 4 age bmi hyp chl
4 5 age bmi hyp chl
5 1 age bmi hyp chl
5 2 age bmi hyp chl
5 3 age bmi hyp chl
5 4 age bmi hyp chl
5 5 age bmi hyp chlimp <- mice(nhanes)R Console Output
iter imp variable
1 1 age bmi hyp chl
1 2 age bmi hyp chl
1 3 age bmi hyp chl
1 4 age bmi hyp chl
1 5 age bmi hyp chl
2 1 age bmi hyp chl
2 2 age bmi hyp chl
2 3 age bmi hyp chl
2 4 age bmi hyp chl
2 5 age bmi hyp chl
3 1 age bmi hyp chl
3 2 age bmi hyp chl
3 3 age bmi hyp chl
3 4 age bmi hyp chl
3 5 age bmi hyp chl
4 1 age bmi hyp chl
4 2 age bmi hyp chl
4 3 age bmi hyp chl
4 4 age bmi hyp chl
4 5 age bmi hyp chl
5 1 age bmi hyp chl
5 2 age bmi hyp chl
5 3 age bmi hyp chl
5 4 age bmi hyp chl
5 5 age bmi hyp chlThe imputations are now done. As you can see, the algorithm ran for 5 iterations (the default) and presented us with 5 imputations for each missing datum. For the rest of this document we will omit printing of the iteration cycle when we run mice. We do so by adding print=F to the mice call.
Python (PyMICE)
print(format_mids_print_r(imp_pmm))Console Output
Multiply imputed data set
Call:
mice(data = nhanes)
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: NAR (Reference)
impR Console Output
Multiply imputed data set
Call:
mice(data = nhanes)
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: NAprint(format_mids_print_r(imp_pmm))Console Output
Multiply imputed data set
Call:
mice(data = nhanes)
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: NAimpR Console Output
Multiply imputed data set
Call:
mice(data = nhanes)
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: NAThe object imp contains a multiply imputed data set (of class mids). It encapsulates all information from imputing the nhanes dataset, such as the original data, the imputed values, the number of missing values, number of iterations, and so on.
To obtain an overview of the information stored in the object imp, use the attributes() function:
Python (PyMICE)
print(format_attributes_r())Console Output
$names
[1] "call" "data" "m"
[4] "nmis" "imp" "method"
[7] "predictorMatrix" "visitSequence" "form"
[10] "post" "seed" "iteration"
[13] "lastSeedValue" "chainMean" "chainVar"
[16] "loggedEvents" "pad"
$class
[1] "mids"R (Reference)
attributes(imp)R Console Output
$names
[1] "call" "data" "m"
[4] "nmis" "imp" "method"
[7] "predictorMatrix" "visitSequence" "form"
[10] "post" "seed" "iteration"
[13] "lastSeedValue" "chainMean" "chainVar"
[16] "loggedEvents" "pad"
$class
[1] "mids"print(format_attributes_r())Console Output
$names
[1] "call" "data" "m"
[4] "nmis" "imp" "method"
[7] "predictorMatrix" "visitSequence" "form"
[10] "post" "seed" "iteration"
[13] "lastSeedValue" "chainMean" "chainVar"
[16] "loggedEvents" "pad"
$class
[1] "mids"attributes(imp)R Console Output
$names
[1] "call" "data" "m"
[4] "nmis" "imp" "method"
[7] "predictorMatrix" "visitSequence" "form"
[10] "post" "seed" "iteration"
[13] "lastSeedValue" "chainMean" "chainVar"
[16] "loggedEvents" "pad"
$class
[1] "mids"For example, the original data are stored as
Python (PyMICE)
print(format_nhanes_r(imp_pmm.data, names))Console Output
age bmi hyp chl
1 1 NA NA NA
2 2 22.7 1 187
3 1 NA 1 187
4 3 NA NA NA
5 1 20.4 1 113
6 3 NA NA 184
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 NA NA NA
11 1 NA NA NA
12 2 NA NA NA
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 NA
16 1 NA NA NA
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 NA
21 1 NA NA NA
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 NA
25 2 27.4 1 186R (Reference)
imp$dataR Console Output
age bmi hyp chl
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
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 NA NA NA
11 1 NA NA NA
12 2 NA NA NA
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 NA
16 1 NA NA NA
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 NA
21 1 NA NA NA
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 NA
25 2 27.4 1 186print(format_nhanes_r(imp_pmm.data, names))Console Output
age bmi hyp chl
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
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 NA NA NA
11 1 NA NA NA
12 2 NA NA NA
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 NA
16 1 NA NA NA
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 NA
21 1 NA NA NA
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 NA
25 2 27.4 1 186imp$dataR Console Output
age bmi hyp chl
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
7 1 22.5 1 118
8 1 30.1 1 187
9 2 22.0 1 238
10 2 NA NA NA
11 1 NA NA NA
12 2 NA NA NA
13 3 21.7 1 206
14 2 28.7 2 204
15 1 29.6 1 NA
16 1 NA NA NA
17 3 27.2 2 284
18 2 26.3 2 199
19 1 35.3 1 218
20 3 25.5 2 NA
21 1 NA NA NA
22 1 33.2 1 229
23 1 27.5 1 131
24 3 24.9 1 NA
25 2 27.4 1 186and the imputations are stored as
Python (PyMICE)
print(format_imp_all_r(imp_pmm))Console Output
$age
NULL
$bmi
1 2 3 4 5
1 24.9 25.5 27.4 22.0 28.7
3 28.7 22.0 29.6 28.7 22.0
4 27.4 29.6 20.4 25.5 20.4
6 25.5 21.7 24.9 20.4 27.4
10 21.7 20.4 27.4 27.5 28.7
11 22.5 33.2 22.0 33.2 35.3
12 27.2 27.5 27.5 27.2 22.5
16 30.1 22.5 27.5 30.1 27.2
21 30.1 30.1 28.7 28.7 22.0
$hyp
1 2 3 4 5
1 1 1 1 1 1
4 2 1 2 1 1
6 2 2 2 2 2
10 2 1 1 1 1
11 1 1 1 1 1
12 1 2 1 2 1
16 1 1 1 1 1
21 1 1 1 1 1
$chl
1 2 3 4 5
1 187.0 238.0 187.0 131.0 187.0
4 206.0 218.0 187.0 186.0 204.0
10 187.0 113.0 229.0 186.0 206.0
11 113.0 206.0 187.0 229.0 229.0
12 206.0 184.0 206.0 218.0 199.0
15 229.0 184.0 238.0 187.0 187.0
16 187.0 238.0 187.0 184.0 238.0
20 186.0 206.0 184.0 284.0 229.0
21 199.0 229.0 238.0 187.0 238.0
24 184.0 284.0 186.0 204.0 206.0R (Reference)
imp$impR Console Output
$age
NULL
$bmi
1 2 3 4 5
1 24.9 25.5 27.4 22.0 28.7
3 28.7 22.0 29.6 28.7 22.0
4 27.4 29.6 20.4 25.5 20.4
6 25.5 21.7 24.9 20.4 27.4
10 21.7 20.4 27.4 27.5 28.7
11 22.5 33.2 22.0 33.2 35.3
12 27.2 27.5 27.5 27.2 22.5
16 30.1 22.5 27.5 30.1 27.2
21 30.1 30.1 28.7 28.7 22.0
$hyp
1 2 3 4 5
1 1 1 1 1 1
4 2 1 2 1 1
6 2 2 2 2 2
10 2 1 1 1 1
11 1 1 1 1 1
12 1 2 1 2 1
16 1 1 1 1 1
21 1 1 1 1 1
$chl
1 2 3 4 5
1 187.0 238.0 187.0 131.0 187.0
4 206.0 218.0 187.0 186.0 204.0
10 187.0 113.0 229.0 186.0 206.0
11 113.0 206.0 187.0 229.0 229.0
12 206.0 184.0 206.0 218.0 199.0
15 229.0 184.0 238.0 187.0 187.0
16 187.0 238.0 187.0 184.0 238.0
20 186.0 206.0 184.0 284.0 229.0
21 199.0 229.0 238.0 187.0 238.0
24 184.0 284.0 186.0 204.0 206.0print(format_imp_all_r(imp_pmm))Console Output
$age
NULL
$bmi
1 2 3 4 5
1 24.9 25.5 27.4 22.0 28.7
3 28.7 22.0 29.6 28.7 22.0
4 27.4 29.6 20.4 25.5 20.4
6 25.5 21.7 24.9 20.4 27.4
10 21.7 20.4 27.4 27.5 28.7
11 22.5 33.2 22.0 33.2 35.3
12 27.2 27.5 27.5 27.2 22.5
16 30.1 22.5 27.5 30.1 27.2
21 30.1 30.1 28.7 28.7 22.0
$hyp
1 2 3 4 5
1 1 1 1 1 1
4 2 1 2 1 1
6 2 2 2 2 2
10 2 1 1 1 1
11 1 1 1 1 1
12 1 2 1 2 1
16 1 1 1 1 1
21 1 1 1 1 1
$chl
1 2 3 4 5
1 187.0 238.0 187.0 131.0 187.0
4 206.0 218.0 187.0 186.0 204.0
10 187.0 113.0 229.0 186.0 206.0
11 113.0 206.0 187.0 229.0 229.0
12 206.0 184.0 206.0 218.0 199.0
15 229.0 184.0 238.0 187.0 187.0
16 187.0 238.0 187.0 184.0 238.0
20 186.0 206.0 184.0 284.0 229.0
21 199.0 229.0 238.0 187.0 238.0
24 184.0 284.0 186.0 204.0 206.0imp$impR Console Output
$age
NULL
$bmi
1 2 3 4 5
1 24.9 25.5 27.4 22.0 28.7
3 28.7 22.0 29.6 28.7 22.0
4 27.4 29.6 20.4 25.5 20.4
6 25.5 21.7 24.9 20.4 27.4
10 21.7 20.4 27.4 27.5 28.7
11 22.5 33.2 22.0 33.2 35.3
12 27.2 27.5 27.5 27.2 22.5
16 30.1 22.5 27.5 30.1 27.2
21 30.1 30.1 28.7 28.7 22.0
$hyp
1 2 3 4 5
1 1 1 1 1 1
4 2 1 2 1 1
6 2 2 2 2 2
10 2 1 1 1 1
11 1 1 1 1 1
12 1 2 1 2 1
16 1 1 1 1 1
21 1 1 1 1 1
$chl
1 2 3 4 5
1 187.0 238.0 187.0 131.0 187.0
4 206.0 218.0 187.0 186.0 204.0
10 187.0 113.0 229.0 186.0 206.0
11 113.0 206.0 187.0 229.0 229.0
12 206.0 184.0 206.0 218.0 199.0
15 229.0 184.0 238.0 187.0 187.0
16 187.0 238.0 187.0 184.0 238.0
20 186.0 206.0 184.0 284.0 229.0
21 199.0 229.0 238.0 187.0 238.0
24 184.0 284.0 186.0 204.0 206.014. Extract completed data
Step parity: ✅ MATCH (3 exact, 0 info, 0 visual, 0 skipped, 0 mismatch of 3 blocks)
By default, mice() calculates five (m = 5) imputed data sets. In order to get the third imputed data set, use the complete() function
Python (PyMICE)
filled3 = complete(imp_pmm, 3)
print(format_md_pattern_filled_r(md_pattern(filled3, column_names=names)))Console Output
age bmi hyp chl
[1,] 1 1 1 1 0
[2,] 0 0 0 0 0R (Reference)
c3 <- complete(imp, 3)
md.pattern(c3)R Console Output
age bmi hyp chl
[1,] 1 1 1 1 0
[2,] 0 0 0 0 0filled3 = complete(imp_pmm, 3)
print(format_md_pattern_filled_r(md_pattern(filled3, column_names=names)))Console Output
age bmi hyp chl
[1,] 1 1 1 1 0
[2,] 0 0 0 0 0c3 <- complete(imp, 3)
md.pattern(c3)R Console Output
age bmi hyp chl
[1,] 1 1 1 1 0
[2,] 0 0 0 0 0The collection of the m imputed data sets can be exported by function complete() in long, broad and repeated formats. For example,
Python (PyMICE)
print(format_complete_long_r(imp_pmm, names))Console output (click to expand)
.imp .id age bmi hyp chl
1 1 1 1 24.9 1 187
2 1 2 2 22.7 1 187
3 1 3 1 28.7 1 187
4 1 4 3 27.4 2 206
5 1 5 1 20.4 1 113
6 1 6 3 25.5 2 184
7 1 7 1 22.5 1 118
8 1 8 1 30.1 1 187
9 1 9 2 22.0 1 238
10 1 10 2 21.7 2 187
11 1 11 1 22.5 1 113
12 1 12 2 27.2 1 206
13 1 13 3 21.7 1 206
14 1 14 2 28.7 2 204
15 1 15 1 29.6 1 229
16 1 16 1 30.1 1 187
17 1 17 3 27.2 2 284
18 1 18 2 26.3 2 199
19 1 19 1 35.3 1 218
20 1 20 3 25.5 2 186
21 1 21 1 30.1 1 199
22 1 22 1 33.2 1 229
23 1 23 1 27.5 1 131
24 1 24 3 24.9 1 184
25 1 25 2 27.4 1 186
26 2 1 1 25.5 1 238
27 2 2 2 22.7 1 187
28 2 3 1 22.0 1 187
29 2 4 3 29.6 1 218
30 2 5 1 20.4 1 113
31 2 6 3 21.7 2 184
32 2 7 1 22.5 1 118
33 2 8 1 30.1 1 187
34 2 9 2 22.0 1 238
35 2 10 2 20.4 1 113
36 2 11 1 33.2 1 206
37 2 12 2 27.5 2 184
38 2 13 3 21.7 1 206
39 2 14 2 28.7 2 204
40 2 15 1 29.6 1 184
41 2 16 1 22.5 1 238
42 2 17 3 27.2 2 284
43 2 18 2 26.3 2 199
44 2 19 1 35.3 1 218
45 2 20 3 25.5 2 206
46 2 21 1 30.1 1 229
47 2 22 1 33.2 1 229
48 2 23 1 27.5 1 131
49 2 24 3 24.9 1 284
50 2 25 2 27.4 1 186
51 3 1 1 27.4 1 187
52 3 2 2 22.7 1 187
53 3 3 1 29.6 1 187
54 3 4 3 20.4 2 187
55 3 5 1 20.4 1 113
56 3 6 3 24.9 2 184
57 3 7 1 22.5 1 118
58 3 8 1 30.1 1 187
59 3 9 2 22.0 1 238
60 3 10 2 27.4 1 229
61 3 11 1 22.0 1 187
62 3 12 2 27.5 1 206
63 3 13 3 21.7 1 206
64 3 14 2 28.7 2 204
65 3 15 1 29.6 1 238
66 3 16 1 27.5 1 187
67 3 17 3 27.2 2 284
68 3 18 2 26.3 2 199
69 3 19 1 35.3 1 218
70 3 20 3 25.5 2 184
71 3 21 1 28.7 1 238
72 3 22 1 33.2 1 229
73 3 23 1 27.5 1 131
74 3 24 3 24.9 1 186
75 3 25 2 27.4 1 186
76 4 1 1 22.0 1 131
77 4 2 2 22.7 1 187
78 4 3 1 28.7 1 187
79 4 4 3 25.5 1 186
80 4 5 1 20.4 1 113
81 4 6 3 20.4 2 184
82 4 7 1 22.5 1 118
83 4 8 1 30.1 1 187
84 4 9 2 22.0 1 238
85 4 10 2 27.5 1 186
86 4 11 1 33.2 1 229
87 4 12 2 27.2 2 218
88 4 13 3 21.7 1 206
89 4 14 2 28.7 2 204
90 4 15 1 29.6 1 187
91 4 16 1 30.1 1 184
92 4 17 3 27.2 2 284
93 4 18 2 26.3 2 199
94 4 19 1 35.3 1 218
95 4 20 3 25.5 2 284
96 4 21 1 28.7 1 187
97 4 22 1 33.2 1 229
98 4 23 1 27.5 1 131
99 4 24 3 24.9 1 204
100 4 25 2 27.4 1 186
101 5 1 1 28.7 1 187
102 5 2 2 22.7 1 187
103 5 3 1 22.0 1 187
104 5 4 3 20.4 1 204
105 5 5 1 20.4 1 113
106 5 6 3 27.4 2 184
107 5 7 1 22.5 1 118
108 5 8 1 30.1 1 187
109 5 9 2 22.0 1 238
110 5 10 2 28.7 1 206
111 5 11 1 35.3 1 229
112 5 12 2 22.5 1 199
113 5 13 3 21.7 1 206
114 5 14 2 28.7 2 204
115 5 15 1 29.6 1 187
116 5 16 1 27.2 1 238
117 5 17 3 27.2 2 284
118 5 18 2 26.3 2 199
119 5 19 1 35.3 1 218
120 5 20 3 25.5 2 229
121 5 21 1 22.0 1 238
122 5 22 1 33.2 1 229
123 5 23 1 27.5 1 131
124 5 24 3 24.9 1 206
125 5 25 2 27.4 1 186R (Reference)
c.long <- complete(imp, "long")
c.longR Console Output
.imp .id age bmi hyp chl
1 1 1 1 24.9 1 187
2 1 2 2 22.7 1 187
3 1 3 1 28.7 1 187
4 1 4 3 27.4 2 206
5 1 5 1 20.4 1 113
6 1 6 3 25.5 2 184
7 1 7 1 22.5 1 118
8 1 8 1 30.1 1 187
9 1 9 2 22.0 1 238
10 1 10 2 21.7 2 187
11 1 11 1 22.5 1 113
12 1 12 2 27.2 1 206
13 1 13 3 21.7 1 206
14 1 14 2 28.7 2 204
15 1 15 1 29.6 1 229
16 1 16 1 30.1 1 187
17 1 17 3 27.2 2 284
18 1 18 2 26.3 2 199
19 1 19 1 35.3 1 218
20 1 20 3 25.5 2 186
21 1 21 1 30.1 1 199
22 1 22 1 33.2 1 229
23 1 23 1 27.5 1 131
24 1 24 3 24.9 1 184
25 1 25 2 27.4 1 186
26 2 1 1 25.5 1 238
27 2 2 2 22.7 1 187
28 2 3 1 22.0 1 187
29 2 4 3 29.6 1 218
30 2 5 1 20.4 1 113
31 2 6 3 21.7 2 184
32 2 7 1 22.5 1 118
33 2 8 1 30.1 1 187
34 2 9 2 22.0 1 238
35 2 10 2 20.4 1 113
36 2 11 1 33.2 1 206
37 2 12 2 27.5 2 184
38 2 13 3 21.7 1 206
39 2 14 2 28.7 2 204
40 2 15 1 29.6 1 184
41 2 16 1 22.5 1 238
42 2 17 3 27.2 2 284
43 2 18 2 26.3 2 199
44 2 19 1 35.3 1 218
45 2 20 3 25.5 2 206
46 2 21 1 30.1 1 229
47 2 22 1 33.2 1 229
48 2 23 1 27.5 1 131
49 2 24 3 24.9 1 284
50 2 25 2 27.4 1 186
51 3 1 1 27.4 1 187
52 3 2 2 22.7 1 187
53 3 3 1 29.6 1 187
54 3 4 3 20.4 2 187
55 3 5 1 20.4 1 113
56 3 6 3 24.9 2 184
57 3 7 1 22.5 1 118
58 3 8 1 30.1 1 187
59 3 9 2 22.0 1 238
60 3 10 2 27.4 1 229
61 3 11 1 22.0 1 187
62 3 12 2 27.5 1 206
63 3 13 3 21.7 1 206
64 3 14 2 28.7 2 204
65 3 15 1 29.6 1 238
66 3 16 1 27.5 1 187
67 3 17 3 27.2 2 284
68 3 18 2 26.3 2 199
69 3 19 1 35.3 1 218
70 3 20 3 25.5 2 184
71 3 21 1 28.7 1 238
72 3 22 1 33.2 1 229
73 3 23 1 27.5 1 131
74 3 24 3 24.9 1 186
75 3 25 2 27.4 1 186
76 4 1 1 22.0 1 131
77 4 2 2 22.7 1 187
78 4 3 1 28.7 1 187
79 4 4 3 25.5 1 186
80 4 5 1 20.4 1 113
81 4 6 3 20.4 2 184
82 4 7 1 22.5 1 118
83 4 8 1 30.1 1 187
84 4 9 2 22.0 1 238
85 4 10 2 27.5 1 186
86 4 11 1 33.2 1 229
87 4 12 2 27.2 2 218
88 4 13 3 21.7 1 206
89 4 14 2 28.7 2 204
90 4 15 1 29.6 1 187
91 4 16 1 30.1 1 184
92 4 17 3 27.2 2 284
93 4 18 2 26.3 2 199
94 4 19 1 35.3 1 218
95 4 20 3 25.5 2 284
96 4 21 1 28.7 1 187
97 4 22 1 33.2 1 229
98 4 23 1 27.5 1 131
99 4 24 3 24.9 1 204
100 4 25 2 27.4 1 186
101 5 1 1 28.7 1 187
102 5 2 2 22.7 1 187
103 5 3 1 22.0 1 187
104 5 4 3 20.4 1 204
105 5 5 1 20.4 1 113
106 5 6 3 27.4 2 184
107 5 7 1 22.5 1 118
108 5 8 1 30.1 1 187
109 5 9 2 22.0 1 238
110 5 10 2 28.7 1 206
111 5 11 1 35.3 1 229
112 5 12 2 22.5 1 199
113 5 13 3 21.7 1 206
114 5 14 2 28.7 2 204
115 5 15 1 29.6 1 187
116 5 16 1 27.2 1 238
117 5 17 3 27.2 2 284
118 5 18 2 26.3 2 199
119 5 19 1 35.3 1 218
120 5 20 3 25.5 2 229
121 5 21 1 22.0 1 238
122 5 22 1 33.2 1 229
123 5 23 1 27.5 1 131
124 5 24 3 24.9 1 206
125 5 25 2 27.4 1 186print(format_complete_long_r(imp_pmm, names))Console output (click to expand)
.imp .id age bmi hyp chl
1 1 1 1 24.9 1 187
2 1 2 2 22.7 1 187
3 1 3 1 28.7 1 187
4 1 4 3 27.4 2 206
5 1 5 1 20.4 1 113
6 1 6 3 25.5 2 184
7 1 7 1 22.5 1 118
8 1 8 1 30.1 1 187
9 1 9 2 22.0 1 238
10 1 10 2 21.7 2 187
11 1 11 1 22.5 1 113
12 1 12 2 27.2 1 206
13 1 13 3 21.7 1 206
14 1 14 2 28.7 2 204
15 1 15 1 29.6 1 229
16 1 16 1 30.1 1 187
17 1 17 3 27.2 2 284
18 1 18 2 26.3 2 199
19 1 19 1 35.3 1 218
20 1 20 3 25.5 2 186
21 1 21 1 30.1 1 199
22 1 22 1 33.2 1 229
23 1 23 1 27.5 1 131
24 1 24 3 24.9 1 184
25 1 25 2 27.4 1 186
26 2 1 1 25.5 1 238
27 2 2 2 22.7 1 187
28 2 3 1 22.0 1 187
29 2 4 3 29.6 1 218
30 2 5 1 20.4 1 113
31 2 6 3 21.7 2 184
32 2 7 1 22.5 1 118
33 2 8 1 30.1 1 187
34 2 9 2 22.0 1 238
35 2 10 2 20.4 1 113
36 2 11 1 33.2 1 206
37 2 12 2 27.5 2 184
38 2 13 3 21.7 1 206
39 2 14 2 28.7 2 204
40 2 15 1 29.6 1 184
41 2 16 1 22.5 1 238
42 2 17 3 27.2 2 284
43 2 18 2 26.3 2 199
44 2 19 1 35.3 1 218
45 2 20 3 25.5 2 206
46 2 21 1 30.1 1 229
47 2 22 1 33.2 1 229
48 2 23 1 27.5 1 131
49 2 24 3 24.9 1 284
50 2 25 2 27.4 1 186
51 3 1 1 27.4 1 187
52 3 2 2 22.7 1 187
53 3 3 1 29.6 1 187
54 3 4 3 20.4 2 187
55 3 5 1 20.4 1 113
56 3 6 3 24.9 2 184
57 3 7 1 22.5 1 118
58 3 8 1 30.1 1 187
59 3 9 2 22.0 1 238
60 3 10 2 27.4 1 229
61 3 11 1 22.0 1 187
62 3 12 2 27.5 1 206
63 3 13 3 21.7 1 206
64 3 14 2 28.7 2 204
65 3 15 1 29.6 1 238
66 3 16 1 27.5 1 187
67 3 17 3 27.2 2 284
68 3 18 2 26.3 2 199
69 3 19 1 35.3 1 218
70 3 20 3 25.5 2 184
71 3 21 1 28.7 1 238
72 3 22 1 33.2 1 229
73 3 23 1 27.5 1 131
74 3 24 3 24.9 1 186
75 3 25 2 27.4 1 186
76 4 1 1 22.0 1 131
77 4 2 2 22.7 1 187
78 4 3 1 28.7 1 187
79 4 4 3 25.5 1 186
80 4 5 1 20.4 1 113
81 4 6 3 20.4 2 184
82 4 7 1 22.5 1 118
83 4 8 1 30.1 1 187
84 4 9 2 22.0 1 238
85 4 10 2 27.5 1 186
86 4 11 1 33.2 1 229
87 4 12 2 27.2 2 218
88 4 13 3 21.7 1 206
89 4 14 2 28.7 2 204
90 4 15 1 29.6 1 187
91 4 16 1 30.1 1 184
92 4 17 3 27.2 2 284
93 4 18 2 26.3 2 199
94 4 19 1 35.3 1 218
95 4 20 3 25.5 2 284
96 4 21 1 28.7 1 187
97 4 22 1 33.2 1 229
98 4 23 1 27.5 1 131
99 4 24 3 24.9 1 204
100 4 25 2 27.4 1 186
101 5 1 1 28.7 1 187
102 5 2 2 22.7 1 187
103 5 3 1 22.0 1 187
104 5 4 3 20.4 1 204
105 5 5 1 20.4 1 113
106 5 6 3 27.4 2 184
107 5 7 1 22.5 1 118
108 5 8 1 30.1 1 187
109 5 9 2 22.0 1 238
110 5 10 2 28.7 1 206
111 5 11 1 35.3 1 229
112 5 12 2 22.5 1 199
113 5 13 3 21.7 1 206
114 5 14 2 28.7 2 204
115 5 15 1 29.6 1 187
116 5 16 1 27.2 1 238
117 5 17 3 27.2 2 284
118 5 18 2 26.3 2 199
119 5 19 1 35.3 1 218
120 5 20 3 25.5 2 229
121 5 21 1 22.0 1 238
122 5 22 1 33.2 1 229
123 5 23 1 27.5 1 131
124 5 24 3 24.9 1 206
125 5 25 2 27.4 1 186c.long <- complete(imp, "long")
c.longR Console Output
.imp .id age bmi hyp chl
1 1 1 1 24.9 1 187
2 1 2 2 22.7 1 187
3 1 3 1 28.7 1 187
4 1 4 3 27.4 2 206
5 1 5 1 20.4 1 113
6 1 6 3 25.5 2 184
7 1 7 1 22.5 1 118
8 1 8 1 30.1 1 187
9 1 9 2 22.0 1 238
10 1 10 2 21.7 2 187
11 1 11 1 22.5 1 113
12 1 12 2 27.2 1 206
13 1 13 3 21.7 1 206
14 1 14 2 28.7 2 204
15 1 15 1 29.6 1 229
16 1 16 1 30.1 1 187
17 1 17 3 27.2 2 284
18 1 18 2 26.3 2 199
19 1 19 1 35.3 1 218
20 1 20 3 25.5 2 186
21 1 21 1 30.1 1 199
22 1 22 1 33.2 1 229
23 1 23 1 27.5 1 131
24 1 24 3 24.9 1 184
25 1 25 2 27.4 1 186
26 2 1 1 25.5 1 238
27 2 2 2 22.7 1 187
28 2 3 1 22.0 1 187
29 2 4 3 29.6 1 218
30 2 5 1 20.4 1 113
31 2 6 3 21.7 2 184
32 2 7 1 22.5 1 118
33 2 8 1 30.1 1 187
34 2 9 2 22.0 1 238
35 2 10 2 20.4 1 113
36 2 11 1 33.2 1 206
37 2 12 2 27.5 2 184
38 2 13 3 21.7 1 206
39 2 14 2 28.7 2 204
40 2 15 1 29.6 1 184
41 2 16 1 22.5 1 238
42 2 17 3 27.2 2 284
43 2 18 2 26.3 2 199
44 2 19 1 35.3 1 218
45 2 20 3 25.5 2 206
46 2 21 1 30.1 1 229
47 2 22 1 33.2 1 229
48 2 23 1 27.5 1 131
49 2 24 3 24.9 1 284
50 2 25 2 27.4 1 186
51 3 1 1 27.4 1 187
52 3 2 2 22.7 1 187
53 3 3 1 29.6 1 187
54 3 4 3 20.4 2 187
55 3 5 1 20.4 1 113
56 3 6 3 24.9 2 184
57 3 7 1 22.5 1 118
58 3 8 1 30.1 1 187
59 3 9 2 22.0 1 238
60 3 10 2 27.4 1 229
61 3 11 1 22.0 1 187
62 3 12 2 27.5 1 206
63 3 13 3 21.7 1 206
64 3 14 2 28.7 2 204
65 3 15 1 29.6 1 238
66 3 16 1 27.5 1 187
67 3 17 3 27.2 2 284
68 3 18 2 26.3 2 199
69 3 19 1 35.3 1 218
70 3 20 3 25.5 2 184
71 3 21 1 28.7 1 238
72 3 22 1 33.2 1 229
73 3 23 1 27.5 1 131
74 3 24 3 24.9 1 186
75 3 25 2 27.4 1 186
76 4 1 1 22.0 1 131
77 4 2 2 22.7 1 187
78 4 3 1 28.7 1 187
79 4 4 3 25.5 1 186
80 4 5 1 20.4 1 113
81 4 6 3 20.4 2 184
82 4 7 1 22.5 1 118
83 4 8 1 30.1 1 187
84 4 9 2 22.0 1 238
85 4 10 2 27.5 1 186
86 4 11 1 33.2 1 229
87 4 12 2 27.2 2 218
88 4 13 3 21.7 1 206
89 4 14 2 28.7 2 204
90 4 15 1 29.6 1 187
91 4 16 1 30.1 1 184
92 4 17 3 27.2 2 284
93 4 18 2 26.3 2 199
94 4 19 1 35.3 1 218
95 4 20 3 25.5 2 284
96 4 21 1 28.7 1 187
97 4 22 1 33.2 1 229
98 4 23 1 27.5 1 131
99 4 24 3 24.9 1 204
100 4 25 2 27.4 1 186
101 5 1 1 28.7 1 187
102 5 2 2 22.7 1 187
103 5 3 1 22.0 1 187
104 5 4 3 20.4 1 204
105 5 5 1 20.4 1 113
106 5 6 3 27.4 2 184
107 5 7 1 22.5 1 118
108 5 8 1 30.1 1 187
109 5 9 2 22.0 1 238
110 5 10 2 28.7 1 206
111 5 11 1 35.3 1 229
112 5 12 2 22.5 1 199
113 5 13 3 21.7 1 206
114 5 14 2 28.7 2 204
115 5 15 1 29.6 1 187
116 5 16 1 27.2 1 238
117 5 17 3 27.2 2 284
118 5 18 2 26.3 2 199
119 5 19 1 35.3 1 218
120 5 20 3 25.5 2 229
121 5 21 1 22.0 1 238
122 5 22 1 33.2 1 229
123 5 23 1 27.5 1 131
124 5 24 3 24.9 1 206
125 5 25 2 27.4 1 186and
Python (PyMICE)
print(format_complete_broad_r(imp_pmm, names))Console output (click to expand)
age.1 bmi.1 hyp.1 chl.1 age.2 bmi.2 hyp.2 chl.2 age.3 bmi.3 hyp.3 chl.3
1 1 24.9 1 187 1 25.5 1 238 1 27.4 1 187
2 2 22.7 1 187 2 22.7 1 187 2 22.7 1 187
3 1 28.7 1 187 1 22.0 1 187 1 29.6 1 187
4 3 27.4 2 206 3 29.6 1 218 3 20.4 2 187
5 1 20.4 1 113 1 20.4 1 113 1 20.4 1 113
6 3 25.5 2 184 3 21.7 2 184 3 24.9 2 184
7 1 22.5 1 118 1 22.5 1 118 1 22.5 1 118
8 1 30.1 1 187 1 30.1 1 187 1 30.1 1 187
9 2 22.0 1 238 2 22.0 1 238 2 22.0 1 238
10 2 21.7 2 187 2 20.4 1 113 2 27.4 1 229
11 1 22.5 1 113 1 33.2 1 206 1 22.0 1 187
12 2 27.2 1 206 2 27.5 2 184 2 27.5 1 206
13 3 21.7 1 206 3 21.7 1 206 3 21.7 1 206
14 2 28.7 2 204 2 28.7 2 204 2 28.7 2 204
15 1 29.6 1 229 1 29.6 1 184 1 29.6 1 238
16 1 30.1 1 187 1 22.5 1 238 1 27.5 1 187
17 3 27.2 2 284 3 27.2 2 284 3 27.2 2 284
18 2 26.3 2 199 2 26.3 2 199 2 26.3 2 199
19 1 35.3 1 218 1 35.3 1 218 1 35.3 1 218
20 3 25.5 2 186 3 25.5 2 206 3 25.5 2 184
21 1 30.1 1 199 1 30.1 1 229 1 28.7 1 238
22 1 33.2 1 229 1 33.2 1 229 1 33.2 1 229
23 1 27.5 1 131 1 27.5 1 131 1 27.5 1 131
24 3 24.9 1 184 3 24.9 1 284 3 24.9 1 186
25 2 27.4 1 186 2 27.4 1 186 2 27.4 1 186
age.4 bmi.4 hyp.4 chl.4 age.5 bmi.5 hyp.5 chl.5
1 1 22.0 1 131 1 28.7 1 187
2 2 22.7 1 187 2 22.7 1 187
3 1 28.7 1 187 1 22.0 1 187
4 3 25.5 1 186 3 20.4 1 204
5 1 20.4 1 113 1 20.4 1 113
6 3 20.4 2 184 3 27.4 2 184
7 1 22.5 1 118 1 22.5 1 118
8 1 30.1 1 187 1 30.1 1 187
9 2 22.0 1 238 2 22.0 1 238
10 2 27.5 1 186 2 28.7 1 206
11 1 33.2 1 229 1 35.3 1 229
12 2 27.2 2 218 2 22.5 1 199
13 3 21.7 1 206 3 21.7 1 206
14 2 28.7 2 204 2 28.7 2 204
15 1 29.6 1 187 1 29.6 1 187
16 1 30.1 1 184 1 27.2 1 238
17 3 27.2 2 284 3 27.2 2 284
18 2 26.3 2 199 2 26.3 2 199
19 1 35.3 1 218 1 35.3 1 218
20 3 25.5 2 284 3 25.5 2 229
21 1 28.7 1 187 1 22.0 1 238
22 1 33.2 1 229 1 33.2 1 229
23 1 27.5 1 131 1 27.5 1 131
24 3 24.9 1 204 3 24.9 1 206
25 2 27.4 1 186 2 27.4 1 186R (Reference)
c.broad <- complete(imp, "broad")
c.broadR Console Output
age.1 bmi.1 hyp.1 chl.1 age.2 bmi.2 hyp.2 chl.2 age.3 bmi.3 hyp.3 chl.3
1 1 24.9 1 187 1 25.5 1 238 1 27.4 1 187
2 2 22.7 1 187 2 22.7 1 187 2 22.7 1 187
3 1 28.7 1 187 1 22.0 1 187 1 29.6 1 187
4 3 27.4 2 206 3 29.6 1 218 3 20.4 2 187
5 1 20.4 1 113 1 20.4 1 113 1 20.4 1 113
6 3 25.5 2 184 3 21.7 2 184 3 24.9 2 184
7 1 22.5 1 118 1 22.5 1 118 1 22.5 1 118
8 1 30.1 1 187 1 30.1 1 187 1 30.1 1 187
9 2 22.0 1 238 2 22.0 1 238 2 22.0 1 238
10 2 21.7 2 187 2 20.4 1 113 2 27.4 1 229
11 1 22.5 1 113 1 33.2 1 206 1 22.0 1 187
12 2 27.2 1 206 2 27.5 2 184 2 27.5 1 206
13 3 21.7 1 206 3 21.7 1 206 3 21.7 1 206
14 2 28.7 2 204 2 28.7 2 204 2 28.7 2 204
15 1 29.6 1 229 1 29.6 1 184 1 29.6 1 238
16 1 30.1 1 187 1 22.5 1 238 1 27.5 1 187
17 3 27.2 2 284 3 27.2 2 284 3 27.2 2 284
18 2 26.3 2 199 2 26.3 2 199 2 26.3 2 199
19 1 35.3 1 218 1 35.3 1 218 1 35.3 1 218
20 3 25.5 2 186 3 25.5 2 206 3 25.5 2 184
21 1 30.1 1 199 1 30.1 1 229 1 28.7 1 238
22 1 33.2 1 229 1 33.2 1 229 1 33.2 1 229
23 1 27.5 1 131 1 27.5 1 131 1 27.5 1 131
24 3 24.9 1 184 3 24.9 1 284 3 24.9 1 186
25 2 27.4 1 186 2 27.4 1 186 2 27.4 1 186
age.4 bmi.4 hyp.4 chl.4 age.5 bmi.5 hyp.5 chl.5
1 1 22.0 1 131 1 28.7 1 187
2 2 22.7 1 187 2 22.7 1 187
3 1 28.7 1 187 1 22.0 1 187
4 3 25.5 1 186 3 20.4 1 204
5 1 20.4 1 113 1 20.4 1 113
6 3 20.4 2 184 3 27.4 2 184
7 1 22.5 1 118 1 22.5 1 118
8 1 30.1 1 187 1 30.1 1 187
9 2 22.0 1 238 2 22.0 1 238
10 2 27.5 1 186 2 28.7 1 206
11 1 33.2 1 229 1 35.3 1 229
12 2 27.2 2 218 2 22.5 1 199
13 3 21.7 1 206 3 21.7 1 206
14 2 28.7 2 204 2 28.7 2 204
15 1 29.6 1 187 1 29.6 1 187
16 1 30.1 1 184 1 27.2 1 238
17 3 27.2 2 284 3 27.2 2 284
18 2 26.3 2 199 2 26.3 2 199
19 1 35.3 1 218 1 35.3 1 218
20 3 25.5 2 284 3 25.5 2 229
21 1 28.7 1 187 1 22.0 1 238
22 1 33.2 1 229 1 33.2 1 229
23 1 27.5 1 131 1 27.5 1 131
24 3 24.9 1 204 3 24.9 1 206
25 2 27.4 1 186 2 27.4 1 186print(format_complete_broad_r(imp_pmm, names))Console output (click to expand)
age.1 bmi.1 hyp.1 chl.1 age.2 bmi.2 hyp.2 chl.2 age.3 bmi.3 hyp.3 chl.3
1 1 24.9 1 187 1 25.5 1 238 1 27.4 1 187
2 2 22.7 1 187 2 22.7 1 187 2 22.7 1 187
3 1 28.7 1 187 1 22.0 1 187 1 29.6 1 187
4 3 27.4 2 206 3 29.6 1 218 3 20.4 2 187
5 1 20.4 1 113 1 20.4 1 113 1 20.4 1 113
6 3 25.5 2 184 3 21.7 2 184 3 24.9 2 184
7 1 22.5 1 118 1 22.5 1 118 1 22.5 1 118
8 1 30.1 1 187 1 30.1 1 187 1 30.1 1 187
9 2 22.0 1 238 2 22.0 1 238 2 22.0 1 238
10 2 21.7 2 187 2 20.4 1 113 2 27.4 1 229
11 1 22.5 1 113 1 33.2 1 206 1 22.0 1 187
12 2 27.2 1 206 2 27.5 2 184 2 27.5 1 206
13 3 21.7 1 206 3 21.7 1 206 3 21.7 1 206
14 2 28.7 2 204 2 28.7 2 204 2 28.7 2 204
15 1 29.6 1 229 1 29.6 1 184 1 29.6 1 238
16 1 30.1 1 187 1 22.5 1 238 1 27.5 1 187
17 3 27.2 2 284 3 27.2 2 284 3 27.2 2 284
18 2 26.3 2 199 2 26.3 2 199 2 26.3 2 199
19 1 35.3 1 218 1 35.3 1 218 1 35.3 1 218
20 3 25.5 2 186 3 25.5 2 206 3 25.5 2 184
21 1 30.1 1 199 1 30.1 1 229 1 28.7 1 238
22 1 33.2 1 229 1 33.2 1 229 1 33.2 1 229
23 1 27.5 1 131 1 27.5 1 131 1 27.5 1 131
24 3 24.9 1 184 3 24.9 1 284 3 24.9 1 186
25 2 27.4 1 186 2 27.4 1 186 2 27.4 1 186
age.4 bmi.4 hyp.4 chl.4 age.5 bmi.5 hyp.5 chl.5
1 1 22.0 1 131 1 28.7 1 187
2 2 22.7 1 187 2 22.7 1 187
3 1 28.7 1 187 1 22.0 1 187
4 3 25.5 1 186 3 20.4 1 204
5 1 20.4 1 113 1 20.4 1 113
6 3 20.4 2 184 3 27.4 2 184
7 1 22.5 1 118 1 22.5 1 118
8 1 30.1 1 187 1 30.1 1 187
9 2 22.0 1 238 2 22.0 1 238
10 2 27.5 1 186 2 28.7 1 206
11 1 33.2 1 229 1 35.3 1 229
12 2 27.2 2 218 2 22.5 1 199
13 3 21.7 1 206 3 21.7 1 206
14 2 28.7 2 204 2 28.7 2 204
15 1 29.6 1 187 1 29.6 1 187
16 1 30.1 1 184 1 27.2 1 238
17 3 27.2 2 284 3 27.2 2 284
18 2 26.3 2 199 2 26.3 2 199
19 1 35.3 1 218 1 35.3 1 218
20 3 25.5 2 284 3 25.5 2 229
21 1 28.7 1 187 1 22.0 1 238
22 1 33.2 1 229 1 33.2 1 229
23 1 27.5 1 131 1 27.5 1 131
24 3 24.9 1 204 3 24.9 1 206
25 2 27.4 1 186 2 27.4 1 186c.broad <- complete(imp, "broad")
c.broadR Console Output
age.1 bmi.1 hyp.1 chl.1 age.2 bmi.2 hyp.2 chl.2 age.3 bmi.3 hyp.3 chl.3
1 1 24.9 1 187 1 25.5 1 238 1 27.4 1 187
2 2 22.7 1 187 2 22.7 1 187 2 22.7 1 187
3 1 28.7 1 187 1 22.0 1 187 1 29.6 1 187
4 3 27.4 2 206 3 29.6 1 218 3 20.4 2 187
5 1 20.4 1 113 1 20.4 1 113 1 20.4 1 113
6 3 25.5 2 184 3 21.7 2 184 3 24.9 2 184
7 1 22.5 1 118 1 22.5 1 118 1 22.5 1 118
8 1 30.1 1 187 1 30.1 1 187 1 30.1 1 187
9 2 22.0 1 238 2 22.0 1 238 2 22.0 1 238
10 2 21.7 2 187 2 20.4 1 113 2 27.4 1 229
11 1 22.5 1 113 1 33.2 1 206 1 22.0 1 187
12 2 27.2 1 206 2 27.5 2 184 2 27.5 1 206
13 3 21.7 1 206 3 21.7 1 206 3 21.7 1 206
14 2 28.7 2 204 2 28.7 2 204 2 28.7 2 204
15 1 29.6 1 229 1 29.6 1 184 1 29.6 1 238
16 1 30.1 1 187 1 22.5 1 238 1 27.5 1 187
17 3 27.2 2 284 3 27.2 2 284 3 27.2 2 284
18 2 26.3 2 199 2 26.3 2 199 2 26.3 2 199
19 1 35.3 1 218 1 35.3 1 218 1 35.3 1 218
20 3 25.5 2 186 3 25.5 2 206 3 25.5 2 184
21 1 30.1 1 199 1 30.1 1 229 1 28.7 1 238
22 1 33.2 1 229 1 33.2 1 229 1 33.2 1 229
23 1 27.5 1 131 1 27.5 1 131 1 27.5 1 131
24 3 24.9 1 184 3 24.9 1 284 3 24.9 1 186
25 2 27.4 1 186 2 27.4 1 186 2 27.4 1 186
age.4 bmi.4 hyp.4 chl.4 age.5 bmi.5 hyp.5 chl.5
1 1 22.0 1 131 1 28.7 1 187
2 2 22.7 1 187 2 22.7 1 187
3 1 28.7 1 187 1 22.0 1 187
4 3 25.5 1 186 3 20.4 1 204
5 1 20.4 1 113 1 20.4 1 113
6 3 20.4 2 184 3 27.4 2 184
7 1 22.5 1 118 1 22.5 1 118
8 1 30.1 1 187 1 30.1 1 187
9 2 22.0 1 238 2 22.0 1 238
10 2 27.5 1 186 2 28.7 1 206
11 1 33.2 1 229 1 35.3 1 229
12 2 27.2 2 218 2 22.5 1 199
13 3 21.7 1 206 3 21.7 1 206
14 2 28.7 2 204 2 28.7 2 204
15 1 29.6 1 187 1 29.6 1 187
16 1 30.1 1 184 1 27.2 1 238
17 3 27.2 2 284 3 27.2 2 284
18 2 26.3 2 199 2 26.3 2 199
19 1 35.3 1 218 1 35.3 1 218
20 3 25.5 2 284 3 25.5 2 229
21 1 28.7 1 187 1 22.0 1 238
22 1 33.2 1 229 1 33.2 1 229
23 1 27.5 1 131 1 27.5 1 131
24 3 24.9 1 204 3 24.9 1 206
25 2 27.4 1 186 2 27.4 1 186