V3: Missingness Models

import numpy as np
from pymice import mice, complete, with_mids, pool, help, md_pattern, summary_pool
from pymice.diagnostics.plots import plot_histogram, plot_mids
from lib.data import load_boys_full_matrix, load_mammalsleep_full
from lib.viz import save_figure
from lib.r_style import (
    format_bool_vector_r,
    format_dataframe_r,
    format_md_pattern_r,
    format_pool_mipo_r,
    format_pool_v03_summary_r,
    format_summary_r,
    format_tv_means_tibble_r
)

(setup — no console output)

require(mice)
require(lattice)
set.seed(123)

print(format_help_r('boys', max_lines=32))

boys                   package:mice                    R Documentation

_G_r_o_w_t_h _o_f _D_u_t_c_h _b_o_y_s

_D_e_s_c_r_i_p_t_i_o_n:

     Height, weight, head circumference and puberty of 748 Dutch boys.

_F_o_r_m_a_t:

     A data frame with 748 rows on the following 9 variables:

     age Decimal age (0-21 years)

     hgt Height (cm)

     wgt Weight (kg)

     bmi Body mass index

     hc Head circumference (cm)

     gen Genital Tanner stage (G1-G5)

     phb Pubic hair (Tanner P1-P6)

     tv Testicular volume (ml)

     reg Region (north, east, west, south, city)

_D_e_t_a_i_l_s:


... (72 more lines — full R help page omitted)

help(boys)
?boys

boys                   package:mice                    R Documentation

_G_r_o_w_t_h _o_f _D_u_t_c_h _b_o_y_s

_D_e_s_c_r_i_p_t_i_o_n:

     Height, weight, head circumference and puberty of 748 Dutch boys.

_F_o_r_m_a_t:

     A data frame with 748 rows on the following 9 variables:

     age Decimal age (0-21 years)

     hgt Height (cm)

     wgt Weight (kg)

     bmi Body mass index

     hc Head circumference (cm)

     gen Genital Tanner stage (G1-G5)

     phb Pubic hair (Tanner P1-P6)

     tv Testicular volume (ml)

     reg Region (north, east, west, south, city)

_D_e_t_a_i_l_s:

     Random sample of 10\ Dutch growth references 1997. Variables ‘gen’
     and ‘phb’ are ordered factors. ‘reg’ is a factor.

_S_o_u_r_c_e:

     Fredriks, A.M,, van Buuren, S., Burgmeijer, R.J., Meulmeester JF,
     Beuker, R.J., Brugman, E., Roede, M.J., Verloove-Vanhorick, S.P.,
     Wit, J.M. (2000) Continuing positive secular growth change in The
     Netherlands 1955-1997.  _Pediatric Research_, *47*, 316-323.

     Fredriks, A.M., van Buuren, S., Wit, J.M., Verloove-Vanhorick,
     S.P. (2000). Body index measurements in 1996-7 compared with 1980.
     _Archives of Disease in Childhood_, *82*, 107-112.

_E_x_a_m_p_l_e_s:

     # create two imputed data sets
     imp <- mice(boys, m = 1, maxit = 2)
     z <- complete(imp, 1)
     
     # create imputations for age <8yrs
     plot(z$age, z$gen,
       col = mdc(1:2)[1 + is.na(boys$gen)],
       xlab = "Age (years)", ylab = "Tanner Stage Genital"
     )
     
     # figure to show that the default imputation method does not impute BMI
     # consistently
     plot(z$bmi, z$wgt / (z$hgt / 100)^2,
       col = mdc(1:2)[1 + is.na(boys$bmi)],
       xlab = "Imputed BMI", ylab = "Calculated BMI"
     )
     
     # also, BMI distributions are somewhat different
     oldpar <- par(mfrow = c(1, 2))
     MASS::truehist(z$bmi[!is.na(boys$bmi)],
       h = 1, xlim = c(10, 30), ymax = 0.25,
       col = mdc(1), xlab = "BMI observed"
     )
     MASS::truehist(z$bmi[is.na(boys$bmi)],
       h = 1, xlim = c(10, 30), ymax = 0.25,
       col = mdc(2), xlab = "BMI imputed"
     )
     par(oldpar)
     
     # repair the inconsistency problem by passive imputation
     meth <- imp$meth
     meth["bmi"] <- "~I(wgt/(hgt/100)^2)"
     pred <- imp$predictorMatrix
     pred["hgt", "bmi"] <- 0
     pred["wgt", "bmi"] <- 0
     imp2 <- mice(boys, m = 1, maxit = 2, meth = meth, pred = pred)
     z2 <- complete(imp2, 1)
     
     # show that new imputations are consistent
     plot(z2$bmi, z2$wgt / (z2$hgt / 100)^2,
       col = mdc(1:2)[1 + is.na(boys$bmi)],
       ylab = "Calculated BMI"
     )
     
     # and compare distributions
     oldpar <- par(mfrow = c(1, 2))
     MASS::truehist(z2$bmi[!is.na(boys$bmi)],
       h = 1, xlim = c(10, 30), ymax = 0.25, col = mdc(1),
       xlab = "BMI observed"
     )
     MASS::truehist(z2$bmi[is.na(boys$bmi)],
       h = 1, xlim = c(10, 30), ymax = 0.25, col = mdc(2),
       xlab = "BMI imputed"
     )
     par(oldpar)

print(format_boys_head_r())

     age  hgt   wgt   bmi   hc  gen  phb tv   reg
3  0.035 50.1 3.650 14.54 33.7 <NA> <NA> NA south
4  0.038 53.5 3.370 11.77 35.0 <NA> <NA> NA south
18 0.057 50.0 3.140 12.56 35.2 <NA> <NA> NA south
23 0.060 54.5 4.270 14.37 36.7 <NA> <NA> NA south
28 0.062 57.5 5.030 15.21 37.3 <NA> <NA> NA south
36 0.068 55.5 4.655 15.11 37.0 <NA> <NA> NA south

head(boys)

     age  hgt   wgt   bmi   hc  gen  phb tv   reg
3  0.035 50.1 3.650 14.54 33.7 <NA> <NA> NA south
4  0.038 53.5 3.370 11.77 35.0 <NA> <NA> NA south
18 0.057 50.0 3.140 12.56 35.2 <NA> <NA> NA south
23 0.060 54.5 4.270 14.37 36.7 <NA> <NA> NA south
28 0.062 57.5 5.030 15.21 37.3 <NA> <NA> NA south
36 0.068 55.5 4.655 15.11 37.0 <NA> <NA> NA south

print(f'[1] {boys.shape[0]}')

[1] 748

nrow(boys)

[1] 748

print(format_summary_boys_r(boys, boy_names))

      age              hgt              wgt              bmi
 Min.   : 0.035   Min.   :  50.00   Min.   :   3.14   Min.   :11.77
 1st Qu.: 1.581   1st Qu.:  84.88   1st Qu.:  11.70   1st Qu.:15.90
 Median : 10.505   Median : 147.30   Median :  34.65   Median :17.45
 Mean   : 9.159   Mean   : 132.15   Mean   :  37.15   Mean   :18.07
 3rd Qu.: 15.267   3rd Qu.: 175.22   3rd Qu.:  59.58   3rd Qu.:19.53
 Max.   : 21.177   Max.   : 198.00   Max.   : 117.40   Max.   :31.74
                  NA's   :20       NA's   : 4        NA's   :21
       hc          gen        phb            tv           reg
 Min.   :33.70   G1  :  56   P1  :  63   Min.   :  1.00   north:  81
 1st Qu.:48.12   G2  :  50   P2  :  40   1st Qu.:  4.00   east :161
 Median :53.00   G3  :  22   P3  :  19   Median :12.00   west :239
 Mean   :51.51   G4  :  42   P4  :  32   Mean   :11.89   south:191
 3rd Qu.:56.00   G5  :  75   P5  :  50   3rd Qu.: 20.00   city : 73
 Max.   :65.00   NA's:503   P6  :  41   Max.   :25.00   NA's :   3
 NA's   :46                 NA's:503   NA's   :522

summary(boys)

      age              hgt              wgt              bmi
 Min.   : 0.035   Min.   : 50.00   Min.   :  3.14   Min.   :11.77
 1st Qu.: 1.581   1st Qu.: 84.88   1st Qu.: 11.70   1st Qu.:15.90
 Median :10.505   Median :147.30   Median : 34.65   Median :17.45
 Mean   : 9.159   Mean   :132.15   Mean   : 37.15   Mean   :18.07
 3rd Qu.:15.267   3rd Qu.:175.22   3rd Qu.: 59.58   3rd Qu.:19.53
 Max.   :21.177   Max.   :198.00   Max.   :117.40   Max.   :31.74
                  NA's   :20       NA's   :4        NA's   :21
       hc          gen        phb            tv           reg
 Min.   :33.70   G1  : 56   P1  : 63   Min.   : 1.00   north: 81
 1st Qu.:48.12   G2  : 50   P2  : 40   1st Qu.: 4.00   east :161
 Median :53.00   G3  : 22   P3  : 19   Median :12.00   west :239
 Mean   :51.51   G4  : 42   P4  : 32   Mean   :11.89   south:191
 3rd Qu.:56.00   G5  : 75   P5  : 50   3rd Qu.:20.00   city : 73
 Max.   :65.00   NA's:503   P6  : 41   Max.   :25.00   NA's :  3
 NA's   :46                 NA's:503   NA's   :522

print(format_md_pattern_r(md_pattern(boys, boy_names)))

    age reg wgt hgt bmi  hc gen phb  tv     
223   1   1   1   1   1   1   1   1   1  0
 19   1   1   1   1   1   1   1   1   0  1
  1   1   1   1   1   1   1   1   0   1  1
  1   1   1   1   1   1   1   0   1   0  2
437   1   1   1   1   1   1   0   0   0  3
 43   1   1   1   1   1   0   0   0   0  4
 16   1   1   1   0   0   1   0   0   0  5
  1   1   1   1   0   0   0   0   0   0  6
  1   1   1   0   1   0   1   0   0   0  5
  1   1   1   0   0   0   1   1   1   1  3
  1   1   1   0   0   0   0   1   1   1  4
  1   1   1   0   0   0   0   0   0   0  7
  3   1   0   1   1   1   1   0   0   0  4
      0   3   4   20   21   46   503   503   522  1622

md.pattern(boys)

    age reg wgt hgt bmi hc gen phb  tv
223   1   1   1   1   1  1   1   1   1    0
19    1   1   1   1   1  1   1   1   0    1
1     1   1   1   1   1  1   1   0   1    1
1     1   1   1   1   1  1   0   1   0    2
437   1   1   1   1   1  1   0   0   0    3
43    1   1   1   1   1  0   0   0   0    4
16    1   1   1   0   0  1   0   0   0    5
1     1   1   1   0   0  0   0   0   0    6
1     1   1   0   1   0  1   0   0   0    5
1     1   1   0   0   0  1   1   1   1    3
1     1   1   0   0   0  0   1   1   1    4
1     1   1   0   0   0  0   0   0   0    7
3     1   0   1   1   1  1   0   0   0    4
      0   3   4  20  21 46 503 503 522 1622

mpat = md_pattern(boys, boy_names)

(plot below)

mpat <- md.pattern(boys)

gen_col = boy_names.index("gen")
print(f'[1] {int(np.sum(mpat.matrix[:-1, gen_col] == 0))}')

[1] 8

sum(mpat[, "gen"] == 0)

[1] 8

print(format_bool_vector_r(np.isnan(boys[:, gen_col])))

[1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [12]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [23]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [34]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [45]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [56]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [67]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [78]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [89]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [100]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [111]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [122]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [133]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [144]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [155]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [166]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [177]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [188]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [199]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [210]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [221]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [232]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [243]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [254]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [265]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [276]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [287]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [298]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [309]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
  [320]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
  [331] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [342] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
  [353] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
  [364] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
  [375]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
  [386] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
  [397] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
  [408] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
  [419] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
  [430] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
  [441] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
  [452]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
  [463] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
  [474] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
  [485] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
  [496]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
  [507] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
  [518]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
  [529] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
  [540]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
  [551]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
  [562]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
  [573] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
  [584]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
  [595] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
  [606] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
  [617] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
  [628]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
  [639] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
  [650]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
  [661]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE
  [672]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
  [683]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
  [694] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
  [705]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
  [716]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
  [727] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
  [738] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

R <- is.na(boys$gen)
R

  [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [12]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [23]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [34]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [45]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [56]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [67]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [78]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [89]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[100]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[111]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[122]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[133]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[144]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[155]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[166]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[177]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[188]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[199]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[210]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[221]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[232]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[243]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[254]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[265]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[276]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[287]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[298]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[309]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
[320]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
[331] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[342] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
[353] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE
[364] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
[375]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
[386] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[397] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
[408] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
[419] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
[430] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
[441] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
[452]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
[463] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
[474] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
[485] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
[496]  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
[507] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE
[518]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
[529] FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
[540]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
[551]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE
[562]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
[573] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
[584]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
[595] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
[606] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
[617] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
[628]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
[639] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
[650]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
[661]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE
[672]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
[683]  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
[694] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
[705]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
[716]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
[727] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
[738] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

plot_histogram(boys, boy_names, 'gen')

(plot below)

histogram(boys$gen)

plot_histogram(boys, boy_names, 'age', condition=np.isnan(boys[:, gen_col]))

(plot below)

histogram(~age|R, data=boys)

imp1 = mice(boys, column_names=boy_names, m=5, maxit=5, print_flag=False)

(imputation complete — no printed output)

imp1 <- mice(boys, print=FALSE)

print(format_summary_boys_r(boys, boy_names))

      age              hgt              wgt              bmi
 Min.   : 0.035   Min.   :  50.00   Min.   :   3.14   Min.   :11.77
 1st Qu.: 1.581   1st Qu.:  84.88   1st Qu.:  11.70   1st Qu.:15.90
 Median : 10.505   Median : 147.30   Median :  34.65   Median :17.45
 Mean   : 9.159   Mean   : 132.15   Mean   :  37.15   Mean   :18.07
 3rd Qu.: 15.267   3rd Qu.: 175.22   3rd Qu.:  59.58   3rd Qu.:19.53
 Max.   : 21.177   Max.   : 198.00   Max.   : 117.40   Max.   :31.74
                  NA's   :20       NA's   : 4        NA's   :21
       hc          gen        phb            tv           reg
 Min.   :33.70   G1  :  56   P1  :  63   Min.   :  1.00   north:  81
 1st Qu.:48.12   G2  :  50   P2  :  40   1st Qu.:  4.00   east :161
 Median :53.00   G3  :  22   P3  :  19   Median :12.00   west :239
 Mean   :51.51   G4  :  42   P4  :  32   Mean   :11.89   south:191
 3rd Qu.:56.00   G5  :  75   P5  :  50   3rd Qu.: 20.00   city : 73
 Max.   :65.00   NA's:503   P6  :  41   Max.   :25.00   NA's :   3
 NA's   :46                 NA's:503   NA's   :522

summary(boys)

      age              hgt              wgt              bmi
 Min.   : 0.035   Min.   : 50.00   Min.   :  3.14   Min.   :11.77
 1st Qu.: 1.581   1st Qu.: 84.88   1st Qu.: 11.70   1st Qu.:15.90
 Median :10.505   Median :147.30   Median : 34.65   Median :17.45
 Mean   : 9.159   Mean   :132.15   Mean   : 37.15   Mean   :18.07
 3rd Qu.:15.267   3rd Qu.:175.22   3rd Qu.: 59.58   3rd Qu.:19.53
 Max.   :21.177   Max.   :198.00   Max.   :117.40   Max.   :31.74
                  NA's   :20       NA's   :4        NA's   :21
       hc          gen        phb            tv           reg
 Min.   :33.70   G1  : 56   P1  : 63   Min.   : 1.00   north: 81
 1st Qu.:48.12   G2  : 50   P2  : 40   1st Qu.: 4.00   east :161
 Median :53.00   G3  : 22   P3  : 19   Median :12.00   west :239
 Mean   :51.51   G4  : 42   P4  : 32   Mean   :11.89   south:191
 3rd Qu.:56.00   G5  : 75   P5  : 50   3rd Qu.:20.00   city : 73
 Max.   :65.00   NA's:503   P6  : 41   Max.   :25.00   NA's :  3
 NA's   :46                 NA's:503   NA's   :522

filled = complete(imp1, 1)
print(format_summary_boys_r(filled, boy_names, compact_factors=True))

age              hgt              wgt              bmi
 Min.   : 0.035   Min.   :  50.00   Min.   :   3.14   Min.   :11.77
 1st Qu.: 1.581   1st Qu.:  82.90   1st Qu.:  11.70   1st Qu.:15.89
 Median : 10.505   Median : 145.75   Median :  34.55   Median :17.40
 Mean   : 9.159   Mean   : 131.05   Mean   :  37.15   Mean   :18.04
 3rd Qu.: 15.267   3rd Qu.: 175.00   3rd Qu.:  59.58   3rd Qu.:19.45
 Max.   : 21.177   Max.   : 198.00   Max.   : 117.40   Max.   :31.74
       hc        gen      phb            tv            reg
 Min.   :33.70   G1:384   P1:408   Min.   :  1.000   north: 81
 1st Qu.:48.45   G2: 87   P2: 56   1st Qu.:  2.000   east :162
 Median :53.20   G3: 36   P3: 34   Median :  4.000   west :240
 Mean   :51.63   G4: 87   P4: 60   Mean   :   8.389   south:191
 3rd Qu.:56.00   G5:154   P5:106   3rd Qu.: 15.000   city : 74
 Max.   :65.00             P6: 84   Max.   : 25.000

summary(complete(imp1))

age              hgt              wgt              bmi
 Min.   : 0.035   Min.   :  50.00   Min.   :   3.14   Min.   :11.77
 1st Qu.: 1.581   1st Qu.:  82.90   1st Qu.:  11.70   1st Qu.:15.89
 Median : 10.505   Median : 145.75   Median :  34.55   Median :17.40
 Mean   : 9.159   Mean   : 131.05   Mean   :  37.15   Mean   :18.04
 3rd Qu.: 15.267   3rd Qu.: 175.00   3rd Qu.:  59.58   3rd Qu.:19.45
 Max.   : 21.177   Max.   : 198.00   Max.   : 117.40   Max.   :31.74
       hc        gen      phb            tv            reg
 Min.   :33.70   G1:384   P1:408   Min.   :  1.000   north: 81
 1st Qu.:48.45   G2: 87   P2: 56   1st Qu.:  2.000   east :162
 Median :53.20   G3: 36   P3: 34   Median :  4.000   west :240
 Mean   :51.63   G4: 87   P4: 60   Mean   :   8.389   south:191
 3rd Qu.:56.00   G5:154   P5:106   3rd Qu.: 15.000   city : 74
 Max.   :65.00             P6: 84   Max.   : 25.000

means = [np.nanmean(complete(imp1, i)[:, tv_idx]) for i in range(1, imp1.m + 1)]
print(format_tv_means_tibble_r(means))

# A tibble: 5 x 1
      x
  <dbl>
1  8.39
2  8.50
3  8.42
4  8.48
5  8.47

summary(with(imp1, mean(tv)))

# A tibble: 5 x 1
      x
  <dbl>
1  8.39
2  8.50
3  8.42
4  8.48
5  8.47

print(format_help_r('mammalsleep', max_lines=28))

mammalsleep                package:mice                R Documentation

_M_a_m_m_a_l _s_l_e_e_p _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Dataset from Allison and Cicchetti (1976) of 62 mammal species on
     the interrelationship between sleep, ecological, and
     constitutional variables. The dataset contains missing values on
     five variables.

_F_o_r_m_a_t:

     ‘mammalsleep’ is a data frame with 62 rows and 11 columns:

     species Species of animal

     bw Body weight (kg)

     brw Brain weight (g)

     sws Slow wave ("nondreaming") sleep (hrs/day)

     ps Paradoxical ("dreaming") sleep (hrs/day)

     ts Total sleep (hrs/day) (sum of slow wave and paradoxical sleep)

     mls Maximum life span (years)

... (33 more lines — full R help page omitted)

help(mammalsleep)

mammalsleep                package:mice                R Documentation

_M_a_m_m_a_l _s_l_e_e_p _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Dataset from Allison and Cicchetti (1976) of 62 mammal species on
     the interrelationship between sleep, ecological, and
     constitutional variables. The dataset contains missing values on
     five variables.

_F_o_r_m_a_t:

     ‘mammalsleep’ is a data frame with 62 rows and 11 columns:

     species Species of animal

     bw Body weight (kg)

     brw Brain weight (g)

     sws Slow wave ("nondreaming") sleep (hrs/day)

     ps Paradoxical ("dreaming") sleep (hrs/day)

     ts Total sleep (hrs/day) (sum of slow wave and paradoxical sleep)

     mls Maximum life span (years)

     gt Gestation time (days)

     pi Predation index (1-5), 1 = least likely to be preyed upon

     sei Sleep exposure index (1-5), 1 = least exposed (e.g. animal
          sleeps in a well-protected den), 5 = most exposed

     odi Overall danger index (1-5) based on the above two indices and
          other information, 1 = least danger (from other animals), 5 =
          most danger (from other animals)

_D_e_t_a_i_l_s:

     Allison and Cicchetti (1976) investigated the interrelationship
     between sleep, ecological, and constitutional variables.  They
     assessed these variables for 39 mammalian species. The authors
     concluded that slow-wave sleep is negatively associated with a
     factor related to body size. This suggests that large amounts of
     this sleep phase are disadvantageous in large species.  Also,
     paradoxical sleep (REM sleep) was associated with a factor related
     to predatory danger, suggesting that large amounts of this sleep
     phase are disadvantageous in prey species.

_S_o_u_r_c_e:

     Allison, T., Cicchetti, D.V. (1976). Sleep in Mammals: Ecological
     and Constitutional Correlates. Science, 194(4266), 732-734.

_E_x_a_m_p_l_e_s:

     sleep <- data(mammalsleep)

print(format_mammalsleep_head_r())

                     species       bw    brw sws  ps   ts  mls  gt pi sei
1          African elephant 6654.000 5712.0   NA  NA  3.3  38.6  645  3   5
2 African giant pouched rat    1.000    6.6  6.3 2.0  8.3   4.5   42  3   1
3                Arctic Fox    3.385   44.5   NA  NA 12.5  14.0   60  1   1
4    Arctic ground squirrel    0.920    5.7   NA  NA 16.5    NA   25  5   2
5            Asian elephant 2547.000 4603.0  2.1 1.8  3.9  69.0  624  3   5
6                    Baboon   10.550  179.5  9.1 0.7  9.8  27.0  180  4   4
  odi
1   3
2   3
3   1
4   3
5   4
6   4

head(mammalsleep)

                    species       bw    brw sws  ps   ts  mls  gt pi sei
1          African elephant 6654.000 5712.0  NA  NA  3.3 38.6 645  3   5
2 African giant pouched rat    1.000    6.6 6.3 2.0  8.3  4.5  42  3   1
3                Arctic Fox    3.385   44.5  NA  NA 12.5 14.0  60  1   1
4    Arctic ground squirrel    0.920    5.7  NA  NA 16.5   NA  25  5   2
5            Asian elephant 2547.000 4603.0 2.1 1.8  3.9 69.0 624  3   5
6                    Baboon   10.550  179.5 9.1 0.7  9.8 27.0 180  4   4
  odi
1   3
2   3
3   1
4   3
5   4
6   4

print(format_summary_mammalsleep_r(ms_full, ms_names))

                      species         bw                brw
 African elephant         : 1   Min.   :   0.005   Min.   :   0.14
 African giant pouched rat: 1   1st Qu.:   0.600   1st Qu.:   4.25
 Arctic Fox               : 1   Median :   3.342   Median :  17.25
 Arctic ground squirrel   : 1   Mean   : 198.790   Mean   : 283.13
 Asian elephant           : 1   3rd Qu.:  48.203   3rd Qu.: 166.00
 Baboon                   : 1   Max.   :6654.000   Max.   :5712.00
 (Other)                  :56
      sws               ps              ts             mls
 Min.   : 2.100   Min.   :0.000   Min.   :  2.60   Min.   :   2.000
 1st Qu.: 6.250   1st Qu.:0.900   1st Qu.:  8.05   1st Qu.:   6.625
 Median : 8.350   Median :1.800   Median :10.45   Median :  15.100
 Mean   : 8.673   Mean   :1.972   Mean   :10.53   Mean   :  19.878
 3rd Qu.: 11.000   3rd Qu.:2.550   3rd Qu.: 13.20   3rd Qu.:  27.750
 Max.   : 17.900   Max.   :6.600   Max.   : 19.90   Max.   : 100.000
 NA's   :14       NA's   :12      NA's   : 4       NA's   : 4
       gt               pi             sei             odi
 Min.   :  12.00   Min.   : 1.000   Min.   : 1.000   Min.   : 1.000
 1st Qu.:  35.75   1st Qu.: 2.000   1st Qu.: 1.000   1st Qu.: 1.000
 Median :  79.00   Median : 3.000   Median : 2.000   Median : 2.000
 Mean   :  142.35   Mean   : 2.871   Mean   : 2.419   Mean   : 2.613
 3rd Qu.: 207.50   3rd Qu.: 4.000   3rd Qu.: 4.000   3rd Qu.: 4.000
 Max.   : 645.00   Max.   : 5.000   Max.   : 5.000   Max.   : 5.000
 NA's   : 4

summary(mammalsleep)

                      species         bw                brw
 African elephant         : 1   Min.   :   0.005   Min.   :   0.14
 African giant pouched rat: 1   1st Qu.:   0.600   1st Qu.:   4.25
 Arctic Fox               : 1   Median :   3.342   Median :  17.25
 Arctic ground squirrel   : 1   Mean   : 198.790   Mean   : 283.13
 Asian elephant           : 1   3rd Qu.:  48.203   3rd Qu.: 166.00
 Baboon                   : 1   Max.   :6654.000   Max.   :5712.00
 (Other)                  :56
      sws               ps              ts             mls
 Min.   : 2.100   Min.   :0.000   Min.   : 2.60   Min.   :  2.000
 1st Qu.: 6.250   1st Qu.:0.900   1st Qu.: 8.05   1st Qu.:  6.625
 Median : 8.350   Median :1.800   Median :10.45   Median : 15.100
 Mean   : 8.673   Mean   :1.972   Mean   :10.53   Mean   : 19.878
 3rd Qu.:11.000   3rd Qu.:2.550   3rd Qu.:13.20   3rd Qu.: 27.750
 Max.   :17.900   Max.   :6.600   Max.   :19.90   Max.   :100.000
 NA's   :14       NA's   :12      NA's   :4       NA's   :4
       gt               pi             sei             odi
 Min.   : 12.00   Min.   :1.000   Min.   :1.000   Min.   :1.000
 1st Qu.: 35.75   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000
 Median : 79.00   Median :3.000   Median :2.000   Median :2.000
 Mean   :142.35   Mean   :2.871   Mean   :2.419   Mean   :2.613
 3rd Qu.:207.50   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000
 Max.   :645.00   Max.   :5.000   Max.   :5.000   Max.   :5.000
 NA's   :4

# str() layout — static R reference

'data.frame':    62 obs. of  11 variables:
 $ species: Factor w/ 62 levels "African elephant",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ bw     : num  6654 1 3.38 0.92 2547 ...
 $ brw    : num  5712 6.6 44.5 5.7 4603 ...
 $ sws    : num  NA 6.3 NA NA 2.1 9.1 15.8 5.2 10.9 8.3 ...
 $ ps     : num  NA 2 NA NA 1.8 0.7 3.9 1 3.6 1.4 ...
 $ ts     : num  3.3 8.3 12.5 16.5 3.9 9.8 19.7 6.2 14.5 9.7 ...
 $ mls    : num  38.6 4.5 14 NA 69 27 19 30.4 28 50 ...
 $ gt     : num  645 42 60 25 624 180 35 392 63 230 ...
 $ pi     : int  3 3 1 5 3 4 1 4 1 1 ...
 $ sei    : int  5 1 1 2 5 4 1 5 2 1 ...
 $ odi    : int  3 3 1 3 4 4 1 4 1 1 ...

str(mammalsleep)

'data.frame':    62 obs. of  11 variables:
 $ species: Factor w/ 62 levels "African elephant",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ bw     : num  6654 1 3.38 0.92 2547 ...
 $ brw    : num  5712 6.6 44.5 5.7 4603 ...
 $ sws    : num  NA 6.3 NA NA 2.1 9.1 15.8 5.2 10.9 8.3 ...
 $ ps     : num  NA 2 NA NA 1.8 0.7 3.9 1 3.6 1.4 ...
 $ ts     : num  3.3 8.3 12.5 16.5 3.9 9.8 19.7 6.2 14.5 9.7 ...
 $ mls    : num  38.6 4.5 14 NA 69 27 19 30.4 28 50 ...
 $ gt     : num  645 42 60 25 624 180 35 392 63 230 ...
 $ pi     : int  3 3 1 5 3 4 1 4 1 1 ...
 $ sei    : int  5 1 1 2 5 4 1 5 2 1 ...
 $ odi    : int  3 3 1 3 4 4 1 4 1 1 ...

print(format_md_pattern_r(md_pattern(ms_full, ms_names)))

    species  bw brw  pi sei odi  ts mls  gt  ps sws     
 42   1   1   1   1   1   1   1   1   1   1   1  0
  9   1   1   1   1   1   1   1   1   1   0   0  2
  3   1   1   1   1   1   1   1   1   0   1   1  1
  2   1   1   1   1   1   1   1   0   1   1   1  1
  1   1   1   1   1   1   1   1   0   1   0   0  3
  1   1   1   1   1   1   1   1   0   0   1   1  2
  2   1   1   1   1   1   1   0   1   1   1   0  2
  2   1   1   1   1   1   1   0   1   1   0   0  3
      0   0   0   0   0   0   4   4   4   12   14  38

md.pattern(mammalsleep)

   species bw brw pi sei odi ts mls gt ps sws
42       1  1   1  1   1   1  1   1  1  1   1  0
9        1  1   1  1   1   1  1   1  1  0   0  2
3        1  1   1  1   1   1  1   1  0  1   1  1
2        1  1   1  1   1   1  1   0  1  1   1  1
1        1  1   1  1   1   1  1   0  1  0   0  3
1        1  1   1  1   1   1  1   0  0  1   1  2
2        1  1   1  1   1   1  0   1  1  1   0  2
2        1  1   1  1   1   1  0   1  1  0   0  3
         0  0   0  0   0   0  4   4  4 12  14 38

imp_ms = mice(ms_full, column_names=ms_names, m=5, maxit=10, print_flag=False)

Warning: Number of logged events: 27

imp <- mice(mammalsleep, maxit = 10, print=F)

Warning: Number of logged events: 27

plot_mids(imp_ms, variables=['sws', 'ps', 'ts'])

(plot below)

plot(imp)

fit1 = with_mids(imp_ms, formula='sws ~ log10(bw) + odi')

(mira object created — no printed output)

fit1 <- with(imp, lm(sws ~ log10(bw) + odi), print=F)

print(format_pool_mipo_r(pool(fit1)))

Class: mipo    m = 5 
              estimate       ubar          b         t dfcom        df
(Intercept)   11.3832377  0.59934692  0.03444354   0.6406792    59  50.60108
log10(bw)     -1.1540768  0.08460947  0.00778340   0.0939495    59  45.62378
odi           -0.8281281  0.07493542  0.00824923   0.0848345    59  43.04458
                  riv    lambda       fmi
(Intercept)    0.0689621   0.0645132   0.0994187
log10(bw)      0.1103904   0.0994159   0.1364588
odi            0.1321015   0.1166870   0.1550547

pool(fit1)

Class: mipo    m = 5 
              estimate       ubar          b         t dfcom        df
(Intercept)   11.3832377  0.59934692  0.03444354   0.6406792    59  50.60108
log10(bw)     -1.1540768  0.08460947  0.00778340   0.0939495    59  45.62378
odi           -0.8281281  0.07493542  0.00824923   0.0848345    59  43.04458
                  riv    lambda       fmi
(Intercept)    0.0689621   0.0645132   0.0994187
log10(bw)      0.1103904   0.0994159   0.1364588
odi            0.1321015   0.1166870   0.1550547

print(format_pool_v03_summary_r(summary_pool(pool(fit1))))

              estimate std.error statistic        df      p.value
(Intercept)  11.3832377 0.8004244  14.221503 50.601082 0.000000000e+00
log10(bw)    -1.1540768 0.3065119  -3.765194 45.623781 4.744272933e-04
odi          -0.8281281 0.2912636  -2.843225 43.044582 6.804305093e-03

summary(pool(fit1))

              estimate std.error statistic        df      p.value
(Intercept)  11.3832377 0.8004244  14.221503 50.601082 0.000000000e+00
log10(bw)    -1.1540768 0.3065119  -3.765194 45.623781 4.744272933e-04
odi          -0.8281281 0.2912636  -2.843225 43.044582 6.804305093e-03

impnew = mice(ms_no_sp, column_names=ms_no_names, m=5, maxit=10, print_flag=False)

Warning: Number of logged events: 15

impnew <- mice(mammalsleep[ , -1], maxit = 10, print = F)

Warning: Number of logged events: 15

fit2 = with_mids(impnew, formula='sws ~ log10(bw) + odi')
print(format_pool_mipo_r(pool(fit2)))

Class: mipo    m = 5 
              estimate       ubar          b         t dfcom        df
(Intercept)   11.3500427  0.61817617  0.04037797   0.6666297    59  49.48618
log10(bw)     -1.1079037  0.08726759  0.00546546   0.0938261    59  49.87051
odi           -0.8082814  0.07728961  0.00810531   0.0870160    59  43.77962
                  riv    lambda       fmi
(Intercept)    0.0783815   0.0726844   0.1080200
log10(bw)      0.0751545   0.0699011   0.1050852
odi            0.1258433   0.1117769   0.1497517

fit2 <- with(impnew, lm(sws ~ log10(bw) + odi))
pool(fit2)

Class: mipo    m = 5 
              estimate       ubar          b         t dfcom        df
(Intercept)   11.3500427  0.61817617  0.04037797   0.6666297    59  49.48618
log10(bw)     -1.1079037  0.08726759  0.00546546   0.0938261    59  49.87051
odi           -0.8082814  0.07728961  0.00810531   0.0870160    59  43.77962
                  riv    lambda       fmi
(Intercept)    0.0783815   0.0726844   0.1080200
log10(bw)      0.0751545   0.0699011   0.1050852
odi            0.1258433   0.1117769   0.1497517

print(format_pool_v03_summary_r(summary_pool(pool(fit2))))

              estimate std.error statistic        df      p.value
(Intercept)  11.3500427 0.8164740  13.901292 49.486180 0.000000000e+00
log10(bw)    -1.1079037 0.3063105  -3.616930 49.870508 6.945071261e-04
odi          -0.8082814 0.2949847  -2.740079 43.779621 8.854862806e-03

summary(pool(fit2))

              estimate std.error statistic        df      p.value
(Intercept)  11.3500427 0.8164740  13.901292 49.486180 0.000000000e+00
log10(bw)    -1.1079037 0.3063105  -3.616930 49.870508 6.945071261e-04
odi          -0.8082814 0.2949847  -2.740079 43.779621 8.854862806e-03

plot_mids(impnew, variables=['mls', 'gt'])

(plot below)

plot(impnew)