This imputes any NAs in the data set specified by dset
by invoking the function f_i and any optional arguments f_i_para on each column at a time (if
impute_by = "column"), or on each row at a time (if impute_by = "row"), or by passing the entire
data frame to f_i if impute_by = "df".
Usage
# S3 method for class 'coin'
Impute(
x,
dset,
f_i = NULL,
f_i_para = NULL,
impute_by = "column",
use_group = NULL,
group_level = NULL,
normalise_first = NULL,
out2 = "coin",
write_to = NULL,
disable = FALSE,
warn_on_NAs = TRUE,
...
)Arguments
- x
A coin class object
- dset
The name of the data set to apply the function to, which should be accessible in
.$Data.- f_i
An imputation function. See details.
- f_i_para
Further arguments to pass to
f_i, other thanx. See details.- impute_by
Specifies how to impute: if
"column", passes each column (indicator) separately as a numerical vector tof_i; if"row", passes each row separately; and if"df"passes the entire data set (data frame) tof_i. The function called byf_ishould be compatible with the type of data passed to it.- use_group
Optional grouping variable name to pass to imputation function if this supports group imputation.
- group_level
A level of the framework to use for grouping indicators. This is only relevant if
impute_by = "row"or"df". In that case, indicators will be split into their groups at the level specified bygroup_level, and imputation will be performed across rows of the group, rather than the whole data set. This can make more sense because indicators within a group are likely to be more similar.- normalise_first
Logical: if
TRUE, each column is normalised using a min-max operation before imputation. By default this isFALSEunlessimpute_by = "row". See details.- out2
Either
"coin"to return normalised data set back to the coin, ordfto simply return a data frame.- write_to
Optional character string for naming the data set in the coin. Data will be written to
.$Data[[write_to]]. Default iswrite_to == "Imputed".- disable
Logical: if
TRUEwill disable imputation completely and write the unaltered data set. This option is mainly useful in sensitivity and uncertainty analysis (to test the effect of turning imputation on/off).- warn_on_NAs
Logical: if
TRUEwill issue a warning if there are anyNAs detected in the data frame after imputation has been applied. SetFALSEto suppress these warnings.- ...
arguments passed to or from other methods.
Details
Clearly, the function f_i needs to be able to accept with the data class passed to it - if
impute_by is "row" or "column" this will be a numeric vector, or if "df" it will be a data
frame. Moreover, this function should return a vector or data frame identical to the vector/data frame passed to
it except for NA values, which can be replaced. The function f_i is not required to replace all NA
values.
COINr has several built-in imputation functions of the form i_*() for vectors which can be called by Impute(). See the
online documentation for more details.
When imputing row-wise, prior normalisation of the data is recommended. This is because imputation
will use e.g. the mean of the unit values over all indicators (columns). If the indicators are on
very different scales, the result will likely make no sense. If the indicators are normalised first,
more sensible results can be obtained. There are two options to pre-normalise: first is by setting
normalise_first = TRUE - this is anyway the default if impute_by = "row". In this case, you also
need to supply a vector of directions. The data will then be normalised using a min-max approach
before imputation, followed by the inverse operation to return the data to the original scales.
Another approach which gives more control is to simply run Normalise() first, and work with the
normalised data from that point onwards. In that case it is better to set normalise_first = FALSE,
since by default if impute_by = "row" it will be set to TRUE.
Checks are made on the format of the data returned by imputation functions, to ensure the
type and that non-NA values have not been inadvertently altered. This latter check is allowed
a degree of tolerance for numerical precision, controlled by the sfigs argument. This is because
if the data frame is normalised, and/or depending on the imputation function, there may be a very
small differences. By default sfigs = 9, meaning that the non-NA values pre and post-imputation
are compared to 9 significant figures.
See also documentation for Impute.data.frame() and Impute.numeric() which are called by this function.
Examples
#' # build coin
coin <- build_example_coin(up_to = "new_coin")
#> iData checked and OK.
#> iMeta checked and OK.
#> Written data set to .$Data$Raw
# impute raw data set using population groups
# output to data frame directly
Impute(coin, dset = "Raw", f_i = "i_mean_grp",
use_group = "Pop_group", out2 = "df")
#> uCode LPI Flights Ship Bord Elec Gas ConSpeed
#> 1 AUS 3.793385 36.05498 14.004198 0 0.00000000 1.14000 11.10000
#> 2 AUT 4.097985 29.01725 0.000000 35 35.36972983 0.27300 14.10000
#> 3 BEL 4.108538 31.88546 20.567121 48 26.53304667 36.10000 16.30000
#> 4 BGD 2.663902 4.27955 9.698165 16 1.33161667 0.14400 11.65000
#> 5 BGR 2.807685 9.23588 7.919366 18 11.27758417 0.31200 15.50000
#> 6 BRN 2.870492 2.01900 7.492593 2 0.00000000 6.04000 13.65455
#> 7 CHE 3.987158 51.78846 0.000000 41 68.77328717 3.05000 21.70000
#> 8 CHN 3.661104 114.20080 21.171976 32 6.43630583 9.46000 7.60000
#> 9 CYP 2.999061 8.75467 11.689495 0 0.43936433 0.02900 6.90000
#> 10 CZE 3.674309 15.30953 0.000000 35 46.75817867 2.70000 16.90000
#> 11 DEU 4.225967 174.35880 20.501262 105 110.17956283 48.70000 15.30000
#> 12 DNK 3.815794 32.77050 14.407080 3 20.10336500 0.41800 20.10000
#> 13 ESP 3.727412 170.96280 19.328835 26 24.17962333 3.29000 15.50000
#> 14 EST 3.363489 3.12946 9.171997 6 9.49907183 0.06670 11.60000
#> 15 FIN 3.920745 18.85806 10.044155 18 24.41426050 0.38000 20.50000
#> 16 FRA 3.900953 97.63489 19.536115 75 78.52593033 29.90000 10.80000
#> 17 GBR 4.069669 210.82440 20.800425 13 25.78370767 42.50000 16.90000
#> 18 GRC 3.239516 34.83849 15.679508 12 2.43086017 0.13900 7.90000
#> 19 HRV 3.160829 9.24529 12.440452 41 19.52836200 0.42200 8.60000
#> 20 HUN 3.428968 13.01196 0.000000 28 20.54324117 0.41900 14.80000
#> 21 IDN 2.984537 34.68020 12.209998 2 0.00000200 22.30000 7.20000
#> 22 IND 3.420043 25.26430 16.522118 35 1.33225167 0.75100 6.50000
#> 23 IRL 3.794886 34.17721 10.575193 12 3.39668933 2.17000 15.60000
#> 24 ITA 3.755414 110.27550 18.383778 24 49.32954433 4.22000 9.20000
#> 25 JPN 3.970464 69.00941 17.261338 0 0.00003600 34.50000 20.20000
#> 26 KAZ 2.751998 4.02303 0.000000 46 3.08950333 12.70000 14.74000
#> 27 KHM 2.800590 9.52120 9.017474 13 2.18452350 0.13500 14.74000
#> 28 KOR 3.717126 69.84827 20.427418 1 0.00003600 12.50000 28.60000
#> 29 LAO 2.067254 3.07440 0.000000 20 2.04784200 0.00841 16.68333
#> 30 LTU 3.631688 5.37919 9.234349 22 7.49402750 0.98000 14.60000
#> 31 LUX 4.219409 4.84458 0.000000 12 6.91123783 0.48200 11.60000
#> 32 LVA 3.327107 6.77976 7.850937 17 5.68129900 0.37700 16.60000
#> 33 MLT 3.069256 6.75251 17.060552 0 1.06719433 0.07190 12.80000
#> 34 MMR 2.458571 6.69500 9.182403 8 0.39678050 24.10000 14.74000
#> 35 MNG 2.506056 0.98951 0.000000 9 1.52714167 0.02670 13.65455
#> 36 MYS 3.426307 53.33988 20.551337 9 0.06733867 26.30000 8.90000
#> 37 NLD 4.187530 63.59241 20.541548 32 44.09012050 23.00000 17.40000
#> 38 NOR 3.732163 25.64994 9.287889 19 19.54137450 94.80000 23.50000
#> 39 NZL 3.388000 13.37242 11.910746 0 0.00000000 0.02260 14.70000
#> 40 PAK 2.923219 2.21146 15.319999 43 0.00301600 0.19500 11.65000
#> 41 PHL 2.856259 19.43838 11.621228 0 0.00000000 0.68300 5.50000
#> 42 POL 3.425877 33.79735 14.815517 62 26.34419950 3.68000 12.60000
#> 43 PRT 3.409367 40.46484 16.198871 11 10.12481567 0.57300 12.90000
#> 44 ROU 2.993120 18.79894 12.689838 24 8.26470100 0.37600 17.00000
#> 45 RUS 2.570864 34.06447 15.811485 122 14.18802800 19.80000 11.80000
#> 46 SGP 4.143632 67.86156 20.276872 2 0.00000200 9.43000 20.30000
#> 47 SVK 3.336895 2.07397 0.000000 25 18.81965283 4.63000 13.00000
#> 48 SVN 3.184508 1.51736 13.380177 20 21.65607267 0.44200 13.70000
#> 49 SWE 4.204593 31.74444 15.578093 18 42.47252133 1.69000 22.50000
#> 50 THA 3.255100 73.92221 14.506399 17 2.25995317 24.50000 16.00000
#> 51 VNM 2.976629 28.52418 16.548761 25 3.52419900 0.77600 9.50000
#> Cov4G Goods Services FDI PRemit ForPort Embs IGOs UNVote
#> 1 94.00 288.48930 108.66450 20.900 14.7103856 2188.31100 82 196 38.46245
#> 2 98.00 278.42640 108.12730 5.000 5.0690118 808.68320 88 227 42.63920
#> 3 99.89 597.87230 216.31460 5.710 13.3545270 1574.30800 84 248 43.00308
#> 4 65.00 56.24166 10.07891 3.010 7.4693891 8.48896 52 145 38.60601
#> 5 56.73 42.82515 12.97674 1.350 1.0395414 15.50880 67 209 42.95986
#> 6 80.00 7.23391 2.11464 0.130 0.7604628 1642.16572 41 85 38.50109
#> 7 98.00 402.86490 207.36370 12.200 9.4893412 2615.08900 82 222 42.64127
#> 8 85.00 1713.61900 657.10900 75.600 26.5510247 1541.03500 100 193 37.13661
#> 9 60.00 8.76681 15.23712 1.230 0.4772705 40.76228 43 172 42.34705
#> 10 98.70 274.13650 43.46382 3.880 4.6915576 107.71030 84 201 42.22506
#> 11 95.70 1919.19400 578.42640 47.000 28.6815953 6386.91800 100 309 42.62615
#> 12 99.99 146.67710 113.67530 9.100 2.1877554 1021.27800 77 259 42.79140
#> 13 91.30 447.12290 197.19810 17.100 16.7522764 2040.57100 89 280 43.10103
#> 14 100.00 28.24110 10.22780 0.580 0.5888337 17.41899 46 194 42.87997
#> 15 99.90 101.57500 53.80751 6.030 1.5102145 747.62680 74 269 42.74629
#> 16 80.00 849.33030 471.29310 30.900 30.2099053 6745.32800 100 329 40.42295
#> 17 97.80 778.90520 518.22560 55.500 20.6965329 8206.97800 100 285 39.40118
#> 18 83.00 56.62702 38.17887 0.883 0.7630075 185.10480 83 216 42.69301
#> 19 98.00 28.36795 17.35676 0.387 1.5624119 16.88908 62 197 42.74966
#> 20 97.30 173.61590 39.11952 3.210 4.1655816 72.52570 79 212 42.53351
#> 21 5.00 222.41860 54.06682 19.300 4.4459418 287.61530 83 175 38.10477
#> 22 4.00 288.98060 294.28220 41.800 14.6034747 270.28240 95 198 36.92131
#> 23 90.00 139.99370 338.09850 6.100 1.9605859 6908.41200 74 195 42.68568
#> 24 93.00 658.19810 202.53980 10.300 16.2027497 3264.08700 93 288 42.99491
#> 25 99.00 732.20780 351.42530 37.100 10.5489456 7449.99300 95 201 42.17231
#> 26 65.50 55.04856 16.85995 4.990 2.5553953 87.49439 62 117 38.72687
#> 27 30.00 20.75014 5.79267 4.190 0.6288657 1857.95820 42 95 38.02232
#> 28 99.00 568.99200 200.84810 26.700 8.7110922 1195.63500 86 210 42.00012
#> 29 5.00 8.95527 1.37855 1.750 0.1662127 743.46552 41 82 37.63291
#> 30 91.00 42.12889 12.35732 0.965 1.1467629 31.03612 50 190 42.71283
#> 31 96.00 30.97279 165.79100 7.990 2.8993036 10601.75000 37 190 42.98426
#> 32 90.00 28.29194 7.25574 0.444 1.0374761 26.82834 48 184 42.70066
#> 33 100.00 13.27830 20.06847 0.179 0.2330044 139.86780 28 169 42.56485
#> 34 0.00 31.70398 6.24003 6.700 0.9065301 0.04805 52 87 35.75572
#> 35 90.00 7.81803 2.72198 2.100 0.3115324 5.20507 40 107 39.05257
#> 36 71.00 278.03190 72.93042 21.900 7.3896926 248.16150 74 177 38.28493
#> 37 99.00 770.39350 346.55110 13.900 4.8949538 4697.46400 89 265 42.80321
#> 38 99.00 142.60950 83.35802 4.570 2.2852030 1632.78100 78 259 43.11114
#> 39 88.00 48.20846 26.55097 1.850 2.3361618 235.98580 44 149 42.32222
#> 40 16.00 45.28752 11.00249 6.950 3.9638222 13.38957 72 157 38.14939
#> 41 39.00 118.56180 55.21572 6.130 8.2693569 103.68870 66 158 37.68160
#> 42 100.00 363.01320 82.74544 10.100 6.2945847 218.88300 87 238 42.58490
#> 43 94.30 102.49150 43.77949 2.330 5.2309644 327.18220 72 236 43.18605
#> 44 72.00 115.31850 31.05017 4.050 4.7341943 35.20750 75 217 42.52487
#> 45 50.00 343.85040 122.55080 17.000 5.5610250 304.70170 99 232 36.12970
#> 46 100.00 414.23890 304.71690 27.000 6.0574964 1487.02000 58 119 38.74750
#> 47 75.00 140.36600 16.24510 2.320 2.7675602 70.29841 62 201 43.10103
#> 48 97.70 54.98977 11.79091 0.362 0.7759290 49.05213 43 197 43.10103
#> 49 99.99 233.30670 132.10610 8.070 4.7452582 1463.55900 78 278 42.75153
#> 50 21.00 282.80140 108.01090 17.200 7.3177969 220.11210 78 152 38.82629
#> 51 0.00 269.07660 30.53244 24.900 5.0849807 0.00189 80 138 37.53063
#> CostImpEx Tariff TBTs TIRcon RTAs Visa StMob Research Pat
#> 1 364.0 1.170000 205.0000 0 14 68 268.00 46761 229.8000
#> 2 0.0 1.600000 1144.0000 1 30 80 69.80 14432 519.1000
#> 3 0.0 1.600000 1348.0000 1 30 80 42.30 20176 655.8000
#> 4 595.0 10.530000 526.9091 0 12 45 50.40 2084 627.1600
#> 5 52.0 1.600000 1140.0000 1 30 79 28.80 1854 12.2000
#> 6 140.0 0.500000 2.0000 0 15 90 3.50 229 41.9250
#> 7 150.0 0.000000 300.0000 1 33 80 49.80 30056 1315.7000
#> 8 255.5 3.540000 1230.0000 1 17 11 445.00 95919 927.8000
#> 9 100.0 1.600000 1141.0000 1 30 79 29.40 1337 7.4000
#> 10 0.0 1.600000 1456.0000 1 30 79 47.90 9190 112.6000
#> 11 45.0 1.600000 1163.0000 1 30 82 233.00 78353 2771.7000
#> 12 0.0 1.600000 1393.0000 1 30 81 33.20 15986 306.0000
#> 13 0.0 1.600000 1212.0000 1 30 81 51.00 39777 334.7000
#> 14 0.0 1.600000 1153.0000 1 30 79 5.99 1594 14.4000
#> 15 70.0 1.600000 1215.0000 1 30 81 22.00 10303 412.5000
#> 16 0.0 1.600000 1385.0000 1 30 81 152.00 56677 1469.0000
#> 17 25.0 1.600000 1187.0000 1 30 81 326.00 96337 1313.7000
#> 18 30.0 1.600000 1140.0000 1 30 80 48.60 8020 43.4000
#> 19 0.0 1.600000 1179.0000 1 30 78 5.05 2342 15.1000
#> 20 0.0 1.600000 1173.0000 1 30 79 26.20 5180 109.5000
#> 21 303.2 2.640000 118.0000 1 17 59 31.10 2624 23.7000
#> 22 226.7 6.320000 117.0000 1 15 51 120.00 20234 437.6000
#> 23 150.0 1.600000 1143.0000 1 30 80 24.50 7451 140.6000
#> 24 0.0 1.600000 1171.0000 1 30 81 97.00 45825 513.0000
#> 25 161.0 1.350000 805.0000 0 14 84 133.00 31184 583.5000
#> 26 320.0 2.510000 19.0000 1 2 48 83.90 1026 3.6000
#> 27 220.0 9.770000 3.0000 0 15 61 4.43 378 398.4545
#> 28 38.0 7.680000 839.0000 1 46 85 83.80 20437 249.8000
#> 29 350.0 1.650000 1.0000 0 16 61 6.46 175 321.1083
#> 30 28.0 1.600000 1170.0000 1 30 79 12.10 1482 7.5000
#> 31 0.0 1.600000 1140.0000 1 30 80 12.70 1206 66.2000
#> 32 35.0 1.600000 1170.0000 1 30 79 8.79 794 1.5000
#> 33 25.0 1.600000 1140.0000 1 30 80 1.77 347 4.7000
#> 34 350.0 3.000833 2.0000 0 17 17 5.81 299 398.4545
#> 35 147.0 1.432500 7.0000 1 1 21 8.48 293 0.3000
#> 36 105.0 3.680000 236.0000 0 17 91 115.00 8080 64.2000
#> 37 0.0 1.600000 1754.0000 1 30 80 69.30 33445 641.2000
#> 38 0.0 1.020000 81.0000 1 31 81 22.60 11696 168.1000
#> 39 147.0 1.270000 108.0000 0 13 80 49.20 7731 46.5000
#> 40 992.0 9.990000 112.0000 1 11 1 30.30 7122 7.2000
#> 41 103.0 3.400000 256.0000 0 17 57 10.60 1361 11.3000
#> 42 0.0 1.600000 1147.0000 1 30 79 35.60 13008 179.3000
#> 43 0.0 1.600000 1141.0000 1 30 80 14.40 10943 46.1000
#> 44 0.0 1.600000 1230.0000 1 30 79 38.70 4173 42.6000
#> 45 244.5 3.430000 85.0000 1 2 17 135.00 16182 141.5000
#> 46 77.0 0.000000 57.0000 0 19 92 19.30 11411 270.5000
#> 47 0.0 1.600000 1187.0000 1 30 79 40.90 2741 44.2000
#> 48 0.0 1.600000 1250.0000 1 30 79 3.60 2653 30.8000
#> 49 40.0 1.600000 1364.0000 1 30 81 28.10 23514 661.5000
#> 50 140.0 4.296364 609.0000 0 17 61 30.30 5317 53.6000
#> 51 322.0 2.860000 114.0000 0 19 31 53.40 3618 627.1600
#> CultServ CultGood Tourist MigStock Lang Renew PrimEner
#> 1 2.0009800 6.80700 8.263 5.66849 19.00543746 9.18050296 123.01003
#> 2 1.5275800 8.93200 28.121 1.30365 14.19041597 34.39499175 84.92785
#> 3 2.3878700 12.69800 7.481 1.32919 10.69085182 9.20164053 113.76871
#> 4 0.0147200 22.26911 0.125 5.08139 0.17744048 34.74706901 74.74533
#> 5 0.1199100 0.88000 8.252 0.69462 6.28997761 17.65007793 151.98183
#> 6 1.1553375 0.36400 0.219 0.14041 6.24168794 0.01493504 113.44995
#> 7 1.1597600 28.96500 9.205 2.12634 13.63554873 25.29198368 53.47854
#> 8 2.8862400 74.46800 59.270 4.30982 0.73963574 12.41335257 175.31089
#> 9 0.1370000 0.52900 3.187 0.23838 12.32117982 9.94206602 77.69450
#> 10 0.3223000 8.80700 9.321 1.10637 7.72876772 14.82856195 134.44935
#> 11 4.8360300 47.16100 35.555 10.92850 15.26134955 14.20625270 86.76327
#> 12 1.9881600 4.91200 10.781 0.54405 19.26996939 33.17027717 63.75411
#> 13 1.5920175 11.75900 75.315 3.34468 5.15846746 16.25409774 79.00655
#> 14 0.1027500 0.74500 3.147 0.32117 12.21152796 27.47683347 169.38370
#> 15 0.5887800 1.85800 2.789 0.46894 15.04353335 43.23526259 159.24526
#> 16 7.0190500 41.02800 82.570 4.37339 11.50318182 13.49926232 97.46473
#> 17 9.5699600 35.13900 35.814 8.91445 19.35213020 8.71168557 72.59383
#> 18 0.4748300 1.62600 24.799 1.06705 10.59013987 17.17004755 88.19577
#> 19 0.2833100 0.73700 13.809 0.54069 11.97641492 33.12746664 94.25010
#> 20 1.5107100 2.61400 5.302 0.84562 5.22493581 15.55936205 95.79304
#> 21 0.1340500 22.26911 11.519 2.06821 0.03222059 36.87934810 88.36380
#> 22 3.2919800 7.99100 14.569 8.43759 3.77194295 36.02122257 118.26344
#> 23 0.5222000 3.42400 10.100 1.28606 19.46425530 9.08139901 56.06685
#> 24 0.5783200 26.84300 52.372 4.87039 6.40814649 16.51685058 71.12753
#> 25 2.2075400 16.18200 24.040 2.19471 0.03107851 6.29735708 92.99299
#> 26 0.0578000 0.98500 6.509 6.38381 7.20274577 1.55844291 188.00931
#> 27 0.0050100 14.11330 5.012 0.93047 4.41049248 64.92377591 133.44976
#> 28 1.7980000 14.11330 17.242 2.06535 2.13461064 2.70770280 158.23984
#> 29 0.8463708 7.01150 3.315 1.12532 0.16638354 59.31586075 99.50804
#> 30 0.0653200 1.28100 2.296 0.57490 10.50042321 28.96117925 90.93856
#> 31 8.0450000 0.63600 1.054 0.29738 21.48526056 9.03131366 73.25911
#> 32 0.0332000 0.73500 1.793 0.47182 12.00784247 38.09801717 97.98307
#> 33 2.4627200 0.36900 1.966 0.11826 16.91195826 5.35500475 55.99671
#> 34 0.0890500 14.11330 2.907 2.57501 0.01884848 61.52781165 77.95974
#> 35 0.0026600 0.04600 0.404 0.08170 2.25185464 3.42973729 161.97873
#> 36 1.1529200 7.55500 26.757 4.00744 6.66895073 5.19444259 122.65287
#> 37 2.4231700 19.15900 15.828 1.68457 18.15321190 5.88946308 94.71582
#> 38 1.6616300 2.98100 5.960 0.66093 15.09626505 57.77200160 88.23266
#> 39 0.3461500 1.21300 3.370 1.48606 16.68013129 30.78917555 132.29055
#> 40 0.0355300 1.25600 0.966 4.09286 1.71805798 46.47632492 105.86127
#> 41 0.2955500 3.18500 5.967 1.40403 10.89425114 27.45154445 72.31282
#> 42 1.4471400 11.91200 17.471 4.24078 9.10344746 11.91148821 101.58949
#> 43 0.5394600 2.61600 11.223 1.75075 6.93615852 27.15729839 78.17923
#> 44 0.1449000 2.38700 10.223 3.32536 7.05215979 23.69779403 80.93064
#> 45 1.4463300 8.37900 24.571 7.65393 5.92039782 3.30422835 192.45083
#> 46 0.9278000 14.50700 12.914 2.47645 12.57967365 0.70863530 63.77904
#> 47 0.0882000 2.40700 2.027 0.49825 8.23424298 13.40916822 107.86727
#> 48 0.2021100 1.21200 3.032 0.18037 14.10243000 20.87739614 113.88117
#> 49 0.9089600 6.01400 6.782 1.03933 18.16742472 53.24776980 112.44515
#> 50 0.0896900 6.66100 32.530 4.13663 0.15905946 22.86307013 132.60433
#> 51 2.2552382 22.26911 10.013 1.16007 0.08500231 34.99856984 130.35543
#> CO2 MatCon Forest Poverty Palma TertGrad FreePress
#> 1 15.3701378 38.381073 6.3295294 0.3000000 1.400000 29.78425 22
#> 2 6.8737132 15.763616 3.0382810 0.7000000 1.110000 13.08167 22
#> 3 8.3281599 15.884942 5.7098277 0.0000000 0.960000 31.70948 12
#> 4 0.4591420 2.577148 5.2605370 19.6000000 1.260000 14.60314 62
#> 5 5.8716159 18.704688 1.5483901 1.5000000 1.490000 24.47098 42
#> 6 22.1247012 23.331756 3.2269754 0.3363636 1.239000 25.99135 76
#> 7 4.3115630 13.520983 0.9916749 0.0000000 1.200000 36.94929 13
#> 8 7.5439076 23.647067 3.7251454 1.4000000 2.100000 3.57765 87
#> 9 5.2603520 24.426326 1.7662068 0.0000000 1.400000 25.54672 23
#> 10 9.1659784 17.115225 4.9847983 0.0000000 0.920000 19.34180 21
#> 11 8.8893704 15.015616 2.3703489 0.0000000 1.100000 24.74205 20
#> 12 5.9357125 16.651923 5.3043935 0.2000000 1.040000 30.38373 12
#> 13 5.0338245 11.998735 6.9265991 1.0000000 1.460000 29.29108 28
#> 14 14.8488192 32.980617 9.4062132 0.5000000 1.260000 37.60498 16
#> 15 8.6607212 24.605785 8.5039377 0.0000000 0.950000 22.50554 12
#> 16 4.5720882 11.827461 3.7879808 0.0000000 1.310000 17.66307 26
#> 17 6.4974405 8.495755 5.4662193 0.2000000 1.250000 29.33262 25
#> 18 6.1803373 11.624611 1.8997660 1.5000000 1.550000 22.92907 44
#> 19 3.9738049 9.791906 1.7280976 0.7000000 1.220000 18.25773 41
#> 20 4.2655750 16.923169 5.8471809 0.5000000 1.130000 20.67309 44
#> 21 1.8193633 7.190287 10.6347954 7.2000000 1.810000 8.48301 49
#> 22 1.7300004 5.337525 3.3552538 21.2000000 1.480000 9.13629 43
#> 23 7.3781178 13.938004 3.5608942 0.5000000 1.250000 26.83780 18
#> 24 5.2708668 10.904800 2.0543587 0.8100000 1.420000 14.35266 31
#> 25 9.5387061 9.381838 1.4232737 6.0545455 1.220000 29.87681 27
#> 26 14.3623897 28.212457 0.4801006 0.1000000 0.920000 22.91112 85
#> 27 0.4377600 4.882918 22.8067646 0.8100000 1.160000 22.91112 70
#> 28 11.5703454 15.904725 3.0383172 0.3000000 1.311818 24.35774 34
#> 29 0.2972009 10.606696 13.1990072 22.7000000 1.680000 23.22077 85
#> 30 4.3780901 14.803791 8.0974972 0.7000000 1.430000 33.09735 21
#> 31 17.3621214 28.154633 4.5446296 0.2000000 1.400000 34.27727 14
#> 32 3.4981929 16.002621 10.3812689 0.7000000 1.440000 27.79356 26
#> 33 5.4915248 16.261072 2.9263320 0.0000000 1.239000 13.28486 23
#> 34 0.4166004 3.300184 6.3602604 6.4000000 1.311818 22.91112 73
#> 35 7.1273263 33.493893 2.9419658 0.2000000 1.230000 23.71332 37
#> 36 8.0329916 18.856906 17.2735413 0.8100000 2.620000 16.36954 69
#> 37 9.9201381 14.304186 1.8284548 0.0000000 0.990000 28.86907 11
#> 38 9.2709451 21.150196 3.1175512 0.2000000 0.880000 26.65009 8
#> 39 7.6865758 24.861794 6.0694910 0.3363636 1.239000 26.00903 19
#> 40 0.8962641 4.432336 0.3263907 6.1000000 1.180000 8.75331 65
#> 41 1.0554568 3.972959 4.4737686 8.3000000 2.170000 16.99506 44
#> 42 7.5171516 17.682962 5.8058431 0.0000000 1.230000 23.70020 34
#> 43 4.3315540 11.090858 31.8455434 0.5000000 1.500000 18.18253 17
#> 44 3.5161537 11.469474 2.4348105 0.0000000 0.960000 13.01408 38
#> 45 11.8575278 16.362004 5.3007501 0.0000000 2.020000 2.00915 83
#> 46 10.3063319 33.369013 5.9836906 2.4000000 1.198333 28.21053 67
#> 47 5.6615813 10.910721 5.0249404 0.7000000 0.880000 18.92215 26
#> 48 6.2136868 13.770686 2.0597113 0.0000000 0.880000 18.82353 23
#> 49 4.4781822 17.027679 8.5016067 0.5000000 0.930000 22.99885 11
#> 50 4.6218600 12.304692 6.7611565 0.0000000 1.650000 13.12545 77
#> 51 1.8035657 13.594893 12.7836337 2.6000000 1.640000 14.60314 84
#> TolMin NGOs CPI FemLab WomParl PubDebt PrivDebt GDPGrow RDExp
#> 1 3.600000 311 77 0.8592661 28.6667 37.640 202.50016 0.34888036 2.202070
#> 2 4.800000 301 75 0.8872138 30.6011 86.190 141.28513 2.18677558 3.071810
#> 3 4.600000 1824 75 0.8699724 38.0000 106.050 225.72427 1.36616464 2.456680
#> 4 8.400000 16 28 0.5414029 20.2857 31.670 40.81502 6.16478747 1.196272
#> 5 5.100000 28 43 0.8927631 19.1667 26.300 116.98938 4.32022261 0.956570
#> 6 5.600000 1 62 0.8618006 9.0909 2.810 189.43663 0.02851029 1.124665
#> 7 3.600000 741 85 0.8936418 32.5000 45.660 237.77122 -0.01982295 2.966350
#> 8 7.600000 117 41 0.8359555 23.7030 42.920 193.43419 6.30396712 2.065580
#> 9 6.000000 19 57 0.8633334 17.8571 108.870 345.59967 0.00000000 0.455850
#> 10 5.100000 55 57 0.8119483 20.0000 40.310 73.21357 4.04300124 1.948650
#> 11 4.900000 986 81 0.8844910 36.9841 70.990 105.91991 1.79471020 2.877490
#> 12 4.400000 208 88 0.9270000 37.4302 45.530 223.23741 1.50309097 3.013870
#> 13 5.800000 341 57 0.8631487 39.1429 99.260 165.79129 2.85707386 1.219610
#> 14 7.536316 21 71 0.9000751 26.7327 9.740 126.58231 4.87884628 1.495020
#> 15 1.500000 118 85 0.9651910 42.0000 62.510 181.23815 2.33629470 2.904740
#> 16 7.000000 1071 70 0.8953486 25.8232 96.140 188.68200 1.42667764 2.231350
#> 17 6.400000 1582 82 0.8697419 30.0000 88.980 169.14574 1.12929130 1.703040
#> 18 5.100000 102 48 0.7784910 18.3333 176.940 124.65858 1.49779228 0.956730
#> 19 5.500000 30 49 0.8664952 19.8675 86.670 130.34435 3.99021292 0.854390
#> 20 4.500000 62 45 0.8249007 10.0503 75.330 92.51395 4.33844641 1.377620
#> 21 7.200000 32 37 0.6149829 19.8214 27.300 40.83840 3.92307260 0.084660
#> 22 8.300000 167 40 0.3455764 11.8081 69.070 55.03717 5.42924312 0.627400
#> 23 1.100000 64 74 0.8078358 22.1519 78.680 299.06965 6.49733804 1.513680
#> 24 4.800000 498 50 0.7364464 30.9524 132.710 115.32313 1.62982757 1.334880
#> 25 3.400000 252 73 0.7796105 9.2632 247.980 158.42065 1.88001976 3.283630
#> 26 7.900000 5 31 0.9010363 27.1028 21.890 52.65279 2.59552272 0.169440
#> 27 6.600000 0 21 0.8788125 20.3252 32.540 68.15849 5.19299957 0.118270
#> 28 2.600000 145 54 0.7311271 17.0000 37.890 193.24990 2.62122194 4.228160
#> 29 6.406549 1 29 1.0274716 27.5168 61.860 19.86313 5.33713473 2.092836
#> 30 4.100000 16 59 0.9398145 21.2766 42.780 63.30250 5.31639215 1.042460
#> 31 2.800000 66 82 0.8341093 28.3333 21.450 421.38527 -0.67739873 1.287730
#> 32 8.300000 18 58 0.9234106 16.0000 34.940 96.90818 5.56194830 0.625500
#> 33 3.600000 14 56 0.6323963 12.5000 63.960 190.06811 4.14990811 0.769230
#> 34 9.800000 1 30 0.8208780 10.1617 34.340 21.86631 5.40145516 1.563575
#> 35 3.500000 3 36 0.8351940 17.1053 49.245 85.61135 4.22356684 0.155010
#> 36 6.200000 113 47 0.6499864 10.3604 57.390 145.18617 4.43836861 1.298130
#> 37 4.500000 731 82 0.8748585 38.0000 65.120 231.32965 2.54478724 2.013290
#> 38 3.400000 160 85 0.9480041 39.6450 27.940 252.12094 0.99831301 1.933010
#> 39 3.300000 54 89 0.8854174 34.1667 29.950 174.09862 0.86445133 1.152360
#> 40 9.700000 25 32 0.2970086 20.5882 63.570 19.67393 3.65519604 0.245540
#> 41 8.200000 104 34 0.6490328 29.4521 34.790 56.33445 5.05974739 0.138390
#> 42 5.900000 50 60 0.8201792 28.0435 51.280 86.22427 4.53454572 1.003370
#> 43 2.200000 81 63 0.9104053 34.7826 128.980 184.73320 2.99637372 1.278850
#> 44 6.500000 30 48 0.7703541 20.6687 39.300 28.38030 7.57933150 0.487650
#> 45 8.500000 82 29 0.8649159 15.7778 16.420 70.67039 0.00000000 1.132020
#> 46 2.300000 139 84 0.7977098 23.7624 104.680 165.48938 3.52678166 2.197540
#> 47 6.600000 24 50 0.8132671 20.0000 52.910 92.63818 3.22730973 1.178450
#> 48 4.500000 31 61 0.9055214 36.6667 83.150 88.14863 4.91413516 2.211740
#> 49 1.800000 299 84 0.9490853 43.5530 43.400 207.22266 0.81838277 3.262850
#> 50 8.200000 98 37 0.8148830 4.8583 42.720 130.82868 3.64110272 0.627200
#> 51 5.700000 8 35 0.9194402 26.7206 58.250 123.81487 5.72587785 0.374040
#> NEET
#> 1 8.70000
#> 2 6.50000
#> 3 9.30000
#> 4 27.40000
#> 5 15.30000
#> 6 17.20000
#> 7 6.50000
#> 8 16.09091
#> 9 16.10000
#> 10 6.30000
#> 11 6.30000
#> 12 7.00000
#> 13 13.30000
#> 14 9.40000
#> 15 9.40000
#> 16 11.50000
#> 17 10.30000
#> 18 15.30000
#> 19 15.40000
#> 20 11.00000
#> 21 21.50000
#> 22 27.90000
#> 23 10.90000
#> 24 20.00000
#> 25 3.50000
#> 26 9.50000
#> 27 12.70000
#> 28 11.99167
#> 29 42.10000
#> 30 9.20000
#> 31 5.90000
#> 32 10.30000
#> 33 8.00000
#> 34 17.40000
#> 35 19.80000
#> 36 12.80000
#> 37 4.00000
#> 38 4.60000
#> 39 11.80000
#> 40 30.40000
#> 41 21.70000
#> 42 9.50000
#> 43 9.30000
#> 44 15.20000
#> 45 12.40000
#> 46 4.00000
#> 47 12.10000
#> 48 6.50000
#> 49 6.20000
#> 50 15.00000
#> 51 0.60000