Anmul
208_210
2026-03-02
library(psych)
library(vctrs)
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:vctrs':
##
## data_frame## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#import data
data <- read.csv("C:/Users/ASUS/Downloads/hdr_general.csv")
head(data)## X iso3 country hdicode hdi_rank_2022 gii_rank_2022 year hdi
## 1 1 AFG Afghanistan Low 182 162 1990 0.284
## 2 2 AFG Afghanistan Low 182 162 1991 0.292
## 3 3 AFG Afghanistan Low 182 162 1992 0.299
## 4 4 AFG Afghanistan Low 182 162 1993 0.307
## 5 5 AFG Afghanistan Low 182 162 1994 0.300
## 6 6 AFG Afghanistan Low 182 162 1995 0.318
## life_expectancy pop_millions hdi_f hdi_m life_expec_f life_expec_m
## 1 45.967 10.69480 NA NA 48.397 43.709
## 2 46.663 10.74517 NA NA 49.144 44.353
## 3 47.596 12.05743 NA NA 50.320 45.070
## 4 51.466 14.00376 NA NA 52.739 50.216
## 5 51.495 15.45555 NA NA 53.544 49.531
## 6 52.544 16.41891 NA NA 54.087 51.039
## expec_yr_school expec_yr_school_f expec_yr_school_m mean_yr_school
## 1 2.936460 2.117230 4.532768 0.8719620
## 2 3.228456 2.246242 4.768261 0.9152675
## 3 3.520452 2.383115 5.015989 0.9585729
## 4 3.812448 2.528328 5.276587 1.0018784
## 5 4.104445 2.682390 5.550723 1.0451839
## 6 4.396441 2.845840 5.839102 1.0884894
## mean_yr_school_f mean_yr_school_m gross_inc_percap gross_inc_percap_f
## 1 0.2016592 1.493952 3115.670 NA
## 2 0.2189443 1.578809 2817.305 NA
## 3 0.2362294 1.663665 2474.682 NA
## 4 0.2535145 1.748522 1723.020 NA
## 5 0.2707995 1.833378 1202.010 NA
## 6 0.2880846 1.918235 1477.062 NA
## gross_inc_percap_m gender_development gender_inequality
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## secondary_education_f_. secondary_education_m_. seats_in_parliament_f_.
## 1 1.107733 7.899011 NA
## 2 1.221396 8.137953 NA
## 3 1.335059 8.376896 NA
## 4 1.448722 8.615838 NA
## 5 1.562385 8.854780 NA
## 6 1.676049 9.093722 NA
## seats_in_parliament_m_. labour_participation_f_. labour_participation_m_.
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## co2_emission_tons mat_footprint_percap_tons
## 1 0.18927897 2.1809
## 2 0.17815451 2.5264
## 3 0.12292003 2.6421
## 4 0.10617863 2.3022
## 5 0.09406327 1.8948
## 6 0.08632119 2.1968str(data)## 'data.frame': 6798 obs. of 33 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ iso3 : chr "AFG" "AFG" "AFG" "AFG" ...
## $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ hdicode : chr "Low" "Low" "Low" "Low" ...
## $ hdi_rank_2022 : int 182 182 182 182 182 182 182 182 182 182 ...
## $ gii_rank_2022 : int 162 162 162 162 162 162 162 162 162 162 ...
## $ year : int 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 ...
## $ hdi : num 0.284 0.292 0.299 0.307 0.3 0.318 0.326 0.33 0.329 0.337 ...
## $ life_expectancy : num 46 46.7 47.6 51.5 51.5 ...
## $ pop_millions : num 10.7 10.7 12.1 14 15.5 ...
## $ hdi_f : num NA NA NA NA NA NA NA NA NA NA ...
## $ hdi_m : num NA NA NA NA NA NA NA NA NA NA ...
## $ life_expec_f : num 48.4 49.1 50.3 52.7 53.5 ...
## $ life_expec_m : num 43.7 44.4 45.1 50.2 49.5 ...
## $ expec_yr_school : num 2.94 3.23 3.52 3.81 4.1 ...
## $ expec_yr_school_f : num 2.12 2.25 2.38 2.53 2.68 ...
## $ expec_yr_school_m : num 4.53 4.77 5.02 5.28 5.55 ...
## $ mean_yr_school : num 0.872 0.915 0.959 1.002 1.045 ...
## $ mean_yr_school_f : num 0.202 0.219 0.236 0.254 0.271 ...
## $ mean_yr_school_m : num 1.49 1.58 1.66 1.75 1.83 ...
## $ gross_inc_percap : num 3116 2817 2475 1723 1202 ...
## $ gross_inc_percap_f : num NA NA NA NA NA NA NA NA NA NA ...
## $ gross_inc_percap_m : num NA NA NA NA NA NA NA NA NA NA ...
## $ gender_development : num NA NA NA NA NA NA NA NA NA NA ...
## $ gender_inequality : num NA NA NA NA NA NA NA NA NA NA ...
## $ secondary_education_f_. : num 1.11 1.22 1.34 1.45 1.56 ...
## $ secondary_education_m_. : num 7.9 8.14 8.38 8.62 8.85 ...
## $ seats_in_parliament_f_. : num NA NA NA NA NA NA NA NA NA NA ...
## $ seats_in_parliament_m_. : num NA NA NA NA NA NA NA NA NA NA ...
## $ labour_participation_f_. : num NA NA NA NA NA NA NA NA NA NA ...
## $ labour_participation_m_. : num NA NA NA NA NA NA NA NA NA NA ...
## $ co2_emission_tons : num 0.1893 0.1782 0.1229 0.1062 0.0941 ...
## $ mat_footprint_percap_tons: num 2.18 2.53 2.64 2.3 1.89 ...data_selected <- data[, c(
"hdi",
"life_expectancy",
"pop_millions",
"hdi_f",
"hdi_m",
"life_expec_f",
"life_expec_m",
"expec_yr_school",
"expec_yr_school_f",
"expec_yr_school_m",
"mean_yr_school",
"mean_yr_school_f",
"mean_yr_school_m",
"gross_inc_percap",
"gross_inc_percap_f",
"gross_inc_percap_m",
"gender_development",
"gender_inequality",
"secondary_education_f_.",
"secondary_education_m_.",
"seats_in_parliament_f_.",
"seats_in_parliament_m_.",
"labour_participation_f_.",
"labour_participation_m_.",
"co2_emission_tons",
"mat_footprint_percap_tons"
)]#pre-processing
colSums(is.na(data_selected))## hdi life_expectancy pop_millions
## 627 0 0
## hdi_f hdi_m life_expec_f
## 1784 1784 0
## life_expec_m expec_yr_school expec_yr_school_f
## 0 248 528
## expec_yr_school_m mean_yr_school mean_yr_school_f
## 528 544 634
## mean_yr_school_m gross_inc_percap gross_inc_percap_f
## 634 139 1506
## gross_inc_percap_m gender_development gender_inequality
## 1506 1784 2087
## secondary_education_f_. secondary_education_m_. seats_in_parliament_f_.
## 981 981 529
## seats_in_parliament_m_. labour_participation_f_. labour_participation_m_.
## 529 1488 1488
## co2_emission_tons mat_footprint_percap_tons
## 85 1454data_selected<- na.omit(data_selected)
dim(data_selected)## [1] 4148 26#asumsi
#korelasi
cor(data_selected)## hdi life_expectancy pop_millions hdi_f
## hdi 1.00000000 0.91948736 -0.05754446 0.99106436
## life_expectancy 0.91948736 1.00000000 -0.02908129 0.89561263
## pop_millions -0.05754446 -0.02908129 1.00000000 -0.06625480
## hdi_f 0.99106436 0.89561263 -0.06625480 1.00000000
## hdi_m 0.99597957 0.92483275 -0.04675895 0.97657742
## life_expec_f 0.92292312 0.99335926 -0.03517194 0.90481206
## life_expec_m 0.89959270 0.99186646 -0.01997010 0.86951785
## expec_yr_school 0.91518828 0.80403731 -0.08728305 0.91779961
## expec_yr_school_f 0.92187538 0.81495664 -0.09244302 0.93262408
## expec_yr_school_m 0.88663661 0.77283809 -0.08278937 0.87941651
## mean_yr_school 0.90337546 0.73836314 -0.08400010 0.91016272
## mean_yr_school_f 0.90628738 0.74317732 -0.09296520 0.92040756
## mean_yr_school_m 0.88271838 0.71840605 -0.07359930 0.88158661
## gross_inc_percap 0.78866189 0.70616201 -0.08914788 0.77465651
## gross_inc_percap_f 0.80886787 0.70814419 -0.08819953 0.80682243
## gross_inc_percap_m 0.78544696 0.70982637 -0.08577737 0.76508601
## gender_development 0.69205035 0.56250517 -0.08485821 0.77732378
## gender_inequality -0.87937283 -0.80591638 0.10408636 -0.89280647
## secondary_education_f_. 0.84137970 0.68188673 -0.04703002 0.85285752
## secondary_education_m_. 0.80494515 0.64551538 -0.01402896 0.80476977
## seats_in_parliament_f_. 0.28901327 0.25384684 -0.02201620 0.31365010
## seats_in_parliament_m_. -0.28901327 -0.25384684 0.02201620 -0.31365010
## labour_participation_f_. -0.06713688 -0.17861747 -0.06440056 0.02329269
## labour_participation_m_. -0.21973893 -0.13353021 0.09969434 -0.21482697
## co2_emission_tons 0.62961171 0.54088424 -0.03982968 0.61837906
## mat_footprint_percap_tons 0.69295787 0.63451490 -0.06544618 0.68385483
## hdi_m life_expec_f life_expec_m expec_yr_school
## hdi 0.99597957 0.92292312 0.8995927 0.91518828
## life_expectancy 0.92483275 0.99335926 0.9918665 0.80403731
## pop_millions -0.04675895 -0.03517194 -0.0199701 -0.08728305
## hdi_f 0.97657742 0.90481206 0.8695179 0.91779961
## hdi_m 1.00000000 0.92279066 0.9107192 0.90695471
## life_expec_f 0.92279066 1.00000000 0.9709327 0.80956330
## life_expec_m 0.91071918 0.97093272 1.0000000 0.78160027
## expec_yr_school 0.90695471 0.80956330 0.7816003 1.00000000
## expec_yr_school_f 0.90619546 0.82192660 0.7918268 0.99014800
## expec_yr_school_m 0.88781055 0.77596409 0.7526188 0.98662195
## mean_yr_school 0.89049270 0.75569821 0.7039993 0.82111253
## mean_yr_school_f 0.88654734 0.76241976 0.7073803 0.82334146
## mean_yr_school_m 0.87822264 0.73264335 0.6870370 0.80234565
## gross_inc_percap 0.79149241 0.68012585 0.7247718 0.66314471
## gross_inc_percap_f 0.80778660 0.68563356 0.7185741 0.71793819
## gross_inc_percap_m 0.79151487 0.68380438 0.7287766 0.65309646
## gender_development 0.63029571 0.60248077 0.5088927 0.68508750
## gender_inequality -0.86860536 -0.81138737 -0.7810412 -0.84649949
## secondary_education_f_. 0.82389389 0.70282862 0.6453310 0.75556337
## secondary_education_m_. 0.79902958 0.66463331 0.6105103 0.72424759
## seats_in_parliament_f_. 0.27668121 0.23894780 0.2588106 0.41031638
## seats_in_parliament_m_. -0.27668121 -0.23894780 -0.2588106 -0.41031638
## labour_participation_f_. -0.10947718 -0.17365949 -0.1872240 0.04650433
## labour_participation_m_. -0.22197093 -0.14856188 -0.1090662 -0.25146824
## co2_emission_tons 0.62655117 0.53016493 0.5499566 0.50977498
## mat_footprint_percap_tons 0.69270054 0.61386446 0.6512203 0.58771075
## expec_yr_school_f expec_yr_school_m mean_yr_school
## hdi 0.92187538 0.88663661 0.90337546
## life_expectancy 0.81495664 0.77283809 0.73836314
## pop_millions -0.09244302 -0.08278937 -0.08400010
## hdi_f 0.93262408 0.87941651 0.91016272
## hdi_m 0.90619546 0.88781055 0.89049270
## life_expec_f 0.82192660 0.77596409 0.75569821
## life_expec_m 0.79182678 0.75261880 0.70399926
## expec_yr_school 0.99014800 0.98662195 0.82111253
## expec_yr_school_f 1.00000000 0.95581583 0.82924901
## expec_yr_school_m 0.95581583 1.00000000 0.79211524
## mean_yr_school 0.82924901 0.79211524 1.00000000
## mean_yr_school_f 0.83941617 0.78540916 0.99305926
## mean_yr_school_m 0.80143666 0.78438851 0.98955591
## gross_inc_percap 0.67144895 0.64757686 0.64741686
## gross_inc_percap_f 0.71825743 0.70487720 0.70253805
## gross_inc_percap_m 0.65921800 0.63984202 0.63279004
## gender_development 0.73967621 0.60224984 0.69598454
## gender_inequality -0.84827243 -0.82344221 -0.83206178
## secondary_education_f_. 0.76802306 0.72274225 0.95229296
## secondary_education_m_. 0.72329465 0.70721970 0.93416250
## seats_in_parliament_f_. 0.41081113 0.39749732 0.28789088
## seats_in_parliament_m_. -0.41081113 -0.39749732 -0.28789088
## labour_participation_f_. 0.05250900 0.03875490 0.05737468
## labour_participation_m_. -0.23642484 -0.25308072 -0.27106570
## co2_emission_tons 0.53156911 0.48664462 0.52030236
## mat_footprint_percap_tons 0.60447039 0.56590594 0.54228505
## mean_yr_school_f mean_yr_school_m gross_inc_percap
## hdi 0.90628738 0.88271838 0.78866189
## life_expectancy 0.74317732 0.71840605 0.70616201
## pop_millions -0.09296520 -0.07359930 -0.08914788
## hdi_f 0.92040756 0.88158661 0.77465651
## hdi_m 0.88654734 0.87822264 0.79149241
## life_expec_f 0.76241976 0.73264335 0.68012585
## life_expec_m 0.70738032 0.68703700 0.72477180
## expec_yr_school 0.82334146 0.80234565 0.66314471
## expec_yr_school_f 0.83941617 0.80143666 0.67144895
## expec_yr_school_m 0.78540916 0.78438851 0.64757686
## mean_yr_school 0.99305926 0.98955591 0.64741686
## mean_yr_school_f 1.00000000 0.96647821 0.65568895
## mean_yr_school_m 0.96647821 1.00000000 0.63152190
## gross_inc_percap 0.65568895 0.63152190 1.00000000
## gross_inc_percap_f 0.70520029 0.68945771 0.95130215
## gross_inc_percap_m 0.63957458 0.61831262 0.99478791
## gender_development 0.74083330 0.63240889 0.45744703
## gender_inequality -0.82863702 -0.82044672 -0.68571797
## secondary_education_f_. 0.95518632 0.93141912 0.59890368
## secondary_education_m_. 0.91260138 0.94315760 0.55796463
## seats_in_parliament_f_. 0.29206287 0.27600419 0.21931511
## seats_in_parliament_m_. -0.29206287 -0.27600419 -0.21931511
## labour_participation_f_. 0.06881491 0.05046666 0.03654048
## labour_participation_m_. -0.24952245 -0.28922271 -0.04256320
## co2_emission_tons 0.53643542 0.50017379 0.80797912
## mat_footprint_percap_tons 0.55846299 0.52342929 0.85907100
## gross_inc_percap_f gross_inc_percap_m
## hdi 0.80886787 0.78544696
## life_expectancy 0.70814419 0.70982637
## pop_millions -0.08819953 -0.08577737
## hdi_f 0.80682243 0.76508601
## hdi_m 0.80778660 0.79151487
## life_expec_f 0.68563356 0.68380438
## life_expec_m 0.71857412 0.72877664
## expec_yr_school 0.71793819 0.65309646
## expec_yr_school_f 0.71825743 0.65921800
## expec_yr_school_m 0.70487720 0.63984202
## mean_yr_school 0.70253805 0.63279004
## mean_yr_school_f 0.70520029 0.63957458
## mean_yr_school_m 0.68945771 0.61831262
## gross_inc_percap 0.95130215 0.99478791
## gross_inc_percap_f 1.00000000 0.92578165
## gross_inc_percap_m 0.92578165 1.00000000
## gender_development 0.51047624 0.43118573
## gender_inequality -0.77417344 -0.66632244
## secondary_education_f_. 0.64837685 0.58248036
## secondary_education_m_. 0.61205724 0.54426299
## seats_in_parliament_f_. 0.33183671 0.18335943
## seats_in_parliament_m_. -0.33183671 -0.18335943
## labour_participation_f_. 0.15770513 -0.01923348
## labour_participation_m_. -0.10967281 -0.04667395
## co2_emission_tons 0.67513289 0.82008375
## mat_footprint_percap_tons 0.77510994 0.85734281
## gender_development gender_inequality
## hdi 0.69205035 -0.8793728
## life_expectancy 0.56250517 -0.8059164
## pop_millions -0.08485821 0.1040864
## hdi_f 0.77732378 -0.8928065
## hdi_m 0.63029571 -0.8686054
## life_expec_f 0.60248077 -0.8113874
## life_expec_m 0.50889266 -0.7810412
## expec_yr_school 0.68508750 -0.8464995
## expec_yr_school_f 0.73967621 -0.8482724
## expec_yr_school_m 0.60224984 -0.8234422
## mean_yr_school 0.69598454 -0.8320618
## mean_yr_school_f 0.74083330 -0.8286370
## mean_yr_school_m 0.63240889 -0.8204467
## gross_inc_percap 0.45744703 -0.6857180
## gross_inc_percap_f 0.51047624 -0.7741734
## gross_inc_percap_m 0.43118573 -0.6663224
## gender_development 1.00000000 -0.6822961
## gender_inequality -0.68229610 1.0000000
## secondary_education_f_. 0.67382935 -0.7894176
## secondary_education_m_. 0.57803083 -0.7677738
## seats_in_parliament_f_. 0.30995544 -0.4691333
## seats_in_parliament_m_. -0.30995544 0.4691333
## labour_participation_f_. 0.34525992 -0.1450750
## labour_participation_m_. -0.14534366 0.2711740
## co2_emission_tons 0.39221695 -0.4927096
## mat_footprint_percap_tons 0.43058441 -0.5763813
## secondary_education_f_. secondary_education_m_.
## hdi 0.84137970 0.80494515
## life_expectancy 0.68188673 0.64551538
## pop_millions -0.04703002 -0.01402896
## hdi_f 0.85285752 0.80476977
## hdi_m 0.82389389 0.79902958
## life_expec_f 0.70282862 0.66463331
## life_expec_m 0.64533097 0.61051028
## expec_yr_school 0.75556337 0.72424759
## expec_yr_school_f 0.76802306 0.72329465
## expec_yr_school_m 0.72274225 0.70721970
## mean_yr_school 0.95229296 0.93416250
## mean_yr_school_f 0.95518632 0.91260138
## mean_yr_school_m 0.93141912 0.94315760
## gross_inc_percap 0.59890368 0.55796463
## gross_inc_percap_f 0.64837685 0.61205724
## gross_inc_percap_m 0.58248036 0.54426299
## gender_development 0.67382935 0.57803083
## gender_inequality -0.78941755 -0.76777379
## secondary_education_f_. 1.00000000 0.97097467
## secondary_education_m_. 0.97097467 1.00000000
## seats_in_parliament_f_. 0.23163475 0.19810752
## seats_in_parliament_m_. -0.23163475 -0.19810752
## labour_participation_f_. 0.05938978 0.03849814
## labour_participation_m_. -0.28023634 -0.31777722
## co2_emission_tons 0.50824521 0.46709693
## mat_footprint_percap_tons 0.50378135 0.45864716
## seats_in_parliament_f_. seats_in_parliament_m_.
## hdi 0.28901327 -0.28901327
## life_expectancy 0.25384684 -0.25384684
## pop_millions -0.02201620 0.02201620
## hdi_f 0.31365010 -0.31365010
## hdi_m 0.27668121 -0.27668121
## life_expec_f 0.23894780 -0.23894780
## life_expec_m 0.25881062 -0.25881062
## expec_yr_school 0.41031638 -0.41031638
## expec_yr_school_f 0.41081113 -0.41081113
## expec_yr_school_m 0.39749732 -0.39749732
## mean_yr_school 0.28789088 -0.28789088
## mean_yr_school_f 0.29206287 -0.29206287
## mean_yr_school_m 0.27600419 -0.27600419
## gross_inc_percap 0.21931511 -0.21931511
## gross_inc_percap_f 0.33183671 -0.33183671
## gross_inc_percap_m 0.18335943 -0.18335943
## gender_development 0.30995544 -0.30995544
## gender_inequality -0.46913333 0.46913333
## secondary_education_f_. 0.23163475 -0.23163475
## secondary_education_m_. 0.19810752 -0.19810752
## seats_in_parliament_f_. 1.00000000 -1.00000000
## seats_in_parliament_m_. -1.00000000 1.00000000
## labour_participation_f_. 0.30858654 -0.30858654
## labour_participation_m_. -0.02539630 0.02539630
## co2_emission_tons 0.01412198 -0.01412198
## mat_footprint_percap_tons 0.10326557 -0.10326557
## labour_participation_f_. labour_participation_m_.
## hdi -0.06713688 -0.21973893
## life_expectancy -0.17861747 -0.13353021
## pop_millions -0.06440056 0.09969434
## hdi_f 0.02329269 -0.21482697
## hdi_m -0.10947718 -0.22197093
## life_expec_f -0.17365949 -0.14856188
## life_expec_m -0.18722397 -0.10906618
## expec_yr_school 0.04650433 -0.25146824
## expec_yr_school_f 0.05250900 -0.23642484
## expec_yr_school_m 0.03875490 -0.25308072
## mean_yr_school 0.05737468 -0.27106570
## mean_yr_school_f 0.06881491 -0.24952245
## mean_yr_school_m 0.05046666 -0.28922271
## gross_inc_percap 0.03654048 -0.04256320
## gross_inc_percap_f 0.15770513 -0.10967281
## gross_inc_percap_m -0.01923348 -0.04667395
## gender_development 0.34525992 -0.14534366
## gender_inequality -0.14507499 0.27117401
## secondary_education_f_. 0.05938978 -0.28023634
## secondary_education_m_. 0.03849814 -0.31777722
## seats_in_parliament_f_. 0.30858654 -0.02539630
## seats_in_parliament_m_. -0.30858654 0.02539630
## labour_participation_f_. 1.00000000 0.34379991
## labour_participation_m_. 0.34379991 1.00000000
## co2_emission_tons -0.03568691 0.03144423
## mat_footprint_percap_tons 0.02855475 0.02549166
## co2_emission_tons mat_footprint_percap_tons
## hdi 0.62961171 0.69295787
## life_expectancy 0.54088424 0.63451490
## pop_millions -0.03982968 -0.06544618
## hdi_f 0.61837906 0.68385483
## hdi_m 0.62655117 0.69270054
## life_expec_f 0.53016493 0.61386446
## life_expec_m 0.54995656 0.65122029
## expec_yr_school 0.50977498 0.58771075
## expec_yr_school_f 0.53156911 0.60447039
## expec_yr_school_m 0.48664462 0.56590594
## mean_yr_school 0.52030236 0.54228505
## mean_yr_school_f 0.53643542 0.55846299
## mean_yr_school_m 0.50017379 0.52342929
## gross_inc_percap 0.80797912 0.85907100
## gross_inc_percap_f 0.67513289 0.77510994
## gross_inc_percap_m 0.82008375 0.85734281
## gender_development 0.39221695 0.43058441
## gender_inequality -0.49270963 -0.57638127
## secondary_education_f_. 0.50824521 0.50378135
## secondary_education_m_. 0.46709693 0.45864716
## seats_in_parliament_f_. 0.01412198 0.10326557
## seats_in_parliament_m_. -0.01412198 -0.10326557
## labour_participation_f_. -0.03568691 0.02855475
## labour_participation_m_. 0.03144423 0.02549166
## co2_emission_tons 1.00000000 0.83899965
## mat_footprint_percap_tons 0.83899965 1.00000000#korelasi yang tinggi akan dihilangkan untuk menghindari redundan
# cek korelasi
cor_matrix <- cor(na.omit(data_selected))
cor_tri <- as.data.frame(as.table(cor_matrix))
high_cor <- subset(cor_tri, abs(Freq) > 0.90 & Freq < 1)
high_cor <- high_cor[order(-abs(high_cor$Freq)), ]
print(high_cor)## Var1 Var2 Freq
## 542 seats_in_parliament_m_. seats_in_parliament_f_. -1.0000000
## 567 seats_in_parliament_f_. seats_in_parliament_m_. -1.0000000
## 5 hdi_m hdi 0.9959796
## 105 hdi hdi_m 0.9959796
## 354 gross_inc_percap_m gross_inc_percap 0.9947879
## 404 gross_inc_percap gross_inc_percap_m 0.9947879
## 32 life_expec_f life_expectancy 0.9933593
## 132 life_expectancy life_expec_f 0.9933593
## 272 mean_yr_school_f mean_yr_school 0.9930593
## 297 mean_yr_school mean_yr_school_f 0.9930593
## 33 life_expec_m life_expectancy 0.9918665
## 158 life_expectancy life_expec_m 0.9918665
## 4 hdi_f hdi 0.9910644
## 79 hdi hdi_f 0.9910644
## 191 expec_yr_school_f expec_yr_school 0.9901480
## 216 expec_yr_school expec_yr_school_f 0.9901480
## 273 mean_yr_school_m mean_yr_school 0.9895559
## 323 mean_yr_school mean_yr_school_m 0.9895559
## 192 expec_yr_school_m expec_yr_school 0.9866220
## 242 expec_yr_school expec_yr_school_m 0.9866220
## 83 hdi_m hdi_f 0.9765774
## 108 hdi_f hdi_m 0.9765774
## 488 secondary_education_m_. secondary_education_f_. 0.9709747
## 513 secondary_education_f_. secondary_education_m_. 0.9709747
## 137 life_expec_m life_expec_f 0.9709327
## 162 life_expec_f life_expec_m 0.9709327
## 299 mean_yr_school_m mean_yr_school_f 0.9664782
## 324 mean_yr_school_f mean_yr_school_m 0.9664782
## 218 expec_yr_school_m expec_yr_school_f 0.9558158
## 243 expec_yr_school_f expec_yr_school_m 0.9558158
## 305 secondary_education_f_. mean_yr_school_f 0.9551863
## 480 mean_yr_school_f secondary_education_f_. 0.9551863
## 279 secondary_education_f_. mean_yr_school 0.9522930
## 479 mean_yr_school secondary_education_f_. 0.9522930
## 353 gross_inc_percap_f gross_inc_percap 0.9513021
## 378 gross_inc_percap gross_inc_percap_f 0.9513021
## 332 secondary_education_m_. mean_yr_school_m 0.9431576
## 507 mean_yr_school_m secondary_education_m_. 0.9431576
## 280 secondary_education_m_. mean_yr_school 0.9341625
## 505 mean_yr_school secondary_education_m_. 0.9341625
## 87 expec_yr_school_f hdi_f 0.9326241
## 212 hdi_f expec_yr_school_f 0.9326241
## 331 secondary_education_f_. mean_yr_school_m 0.9314191
## 481 mean_yr_school_m secondary_education_f_. 0.9314191
## 380 gross_inc_percap_m gross_inc_percap_f 0.9257817
## 405 gross_inc_percap_f gross_inc_percap_m 0.9257817
## 31 hdi_m life_expectancy 0.9248327
## 106 life_expectancy hdi_m 0.9248327
## 6 life_expec_f hdi 0.9229231
## 131 hdi life_expec_f 0.9229231
## 110 life_expec_f hdi_m 0.9227907
## 135 hdi_m life_expec_f 0.9227907
## 9 expec_yr_school_f hdi 0.9218754
## 209 hdi expec_yr_school_f 0.9218754
## 90 mean_yr_school_f hdi_f 0.9204076
## 290 hdi_f mean_yr_school_f 0.9204076
## 2 life_expectancy hdi 0.9194874
## 27 hdi life_expectancy 0.9194874
## 86 expec_yr_school hdi_f 0.9177996
## 186 hdi_f expec_yr_school 0.9177996
## 8 expec_yr_school hdi 0.9151883
## 183 hdi expec_yr_school 0.9151883
## 306 secondary_education_m_. mean_yr_school_f 0.9126014
## 506 mean_yr_school_f secondary_education_m_. 0.9126014
## 111 life_expec_m hdi_m 0.9107192
## 161 hdi_m life_expec_m 0.9107192
## 89 mean_yr_school hdi_f 0.9101627
## 264 hdi_f mean_yr_school 0.9101627
## 112 expec_yr_school hdi_m 0.9069547
## 187 hdi_m expec_yr_school 0.9069547
## 12 mean_yr_school_f hdi 0.9062874
## 287 hdi mean_yr_school_f 0.9062874
## 113 expec_yr_school_f hdi_m 0.9061955
## 213 hdi_m expec_yr_school_f 0.9061955
## 84 life_expec_f hdi_f 0.9048121
## 134 hdi_f life_expec_f 0.9048121
## 11 mean_yr_school hdi 0.9033755
## 261 hdi mean_yr_school 0.9033755#hdi_f & hdi_m (r=0.99)=hdi #life_expec_f & life_expec_m (r=0.99)=life_expectancy #expec_yr_school_f & expec_yr_school_m (r=0.98)=expec_yr_school #mean_yr_school_f & mean_yr_school_m (r=0.99)=mean_yr_school #gross_inc_percap_f & gross_inc_percap_m (r=0.99)=gross_inc_percap #seats_in_parliament_m_% korelasi -1.00(singular) dgn f= seats_in_parliament_f_% #secondary_education_m_% & secondary_education_f_% (r=0.97) = secondary_education_m_%
#jadi variabel yg dipilih adalah
data_hdr <- data_selected[, c(
"hdi",
"life_expectancy",
"pop_millions",
"expec_yr_school",
"mean_yr_school",
"gross_inc_percap",
"gender_development",
"gender_inequality",
"secondary_education_f_.",
"seats_in_parliament_f_.",
"labour_participation_f_.",
"labour_participation_m_.",
"co2_emission_tons",
"mat_footprint_percap_tons"
)]#rename variabel
colnames(data_hdr) <- paste0("X", 1:ncol(data_hdr))
head(data_hdr)## X1 X2 X3 X4 X5 X6 X7 X8 X9
## 19 0.431 59.852 26.42720 8.630830 1.742754 1838.977 0.682 0.690 2.906114
## 20 0.441 60.364 27.38531 8.888430 1.816490 1953.658 0.685 0.696 2.866621
## 21 0.449 60.851 28.18967 9.180810 1.890226 2006.789 0.684 0.707 2.827128
## 22 0.457 61.419 29.24916 9.473190 1.937043 2059.012 0.671 0.718 3.176786
## 23 0.467 61.923 30.46648 9.803687 1.983860 2174.351 0.634 0.734 3.526445
## 24 0.475 62.417 31.54121 10.134183 2.030677 2244.209 0.667 0.712 3.876103
## X10 X11 X12 X13 X14
## 19 25.87209 43.79 82.04 0.1609894 1.5415
## 20 25.92593 36.85 80.79 0.2334062 1.7339
## 21 27.63533 29.90 79.55 0.2967328 1.8507
## 22 27.63533 22.96 78.30 0.4047399 1.6795
## 23 27.63533 16.01 77.05 0.3293882 1.7636
## 24 27.63533 20.90 77.07 0.2932830 1.7198data.frame(
Nama_Baru = colnames(data_hdr),
Variabel_Asli = c(
"hdi",
"life_expectancy",
"pop_millions",
"expec_yr_school",
"mean_yr_school",
"gross_inc_percap",
"gender_development",
"gender_inequality",
"secondary_education_f_.",
"seats_in_parliament_f_.",
"labour_participation_f_.",
"labour_participation_m_.",
"co2_emission_tons",
"mat_footprint_percap_tons"
)
)## Nama_Baru Variabel_Asli
## 1 X1 hdi
## 2 X2 life_expectancy
## 3 X3 pop_millions
## 4 X4 expec_yr_school
## 5 X5 mean_yr_school
## 6 X6 gross_inc_percap
## 7 X7 gender_development
## 8 X8 gender_inequality
## 9 X9 secondary_education_f_.
## 10 X10 seats_in_parliament_f_.
## 11 X11 labour_participation_f_.
## 12 X12 labour_participation_m_.
## 13 X13 co2_emission_tons
## 14 X14 mat_footprint_percap_tons#MSA
r <- cor(data_hdr)
KMO(r)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = r)
## Overall MSA = 0.79
## MSA for each item =
## X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14
## 0.73 0.81 0.30 0.81 0.78 0.79 0.80 0.90 0.88 0.64 0.23 0.41 0.87 0.91#DELETE X12
data_hdr = data_hdr[-12]
#DELETE X11
data_hdr = data_hdr [-11]
#DELETE X3
data_hdr = data_hdr[-3]
r <- cor(data_hdr)
KMO(r)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = r)
## Overall MSA = 0.84
## MSA for each item =
## X1 X2 X4 X5 X6 X7 X8 X9 X10 X13 X14
## 0.76 0.78 0.84 0.80 0.84 0.94 0.95 0.88 0.63 0.90 0.90#Bartlett Test
bartlett.test(data_hdr)##
## Bartlett test of homogeneity of variances
##
## data: data_hdr
## Bartlett's K-squared = 638071, df = 10, p-value < 2.2e-16describe(data_hdr)## vars n mean sd median trimmed mad min max
## X1 1 4148 0.70 0.16 0.72 0.71 0.18 0.25 0.97
## X2 2 4148 70.84 8.31 72.52 71.57 7.97 39.97 84.82
## X4 3 4148 12.81 3.14 12.84 12.87 3.15 2.71 23.25
## X5 4 4148 8.28 3.20 8.49 8.40 3.80 0.98 14.26
## X6 5 4148 19393.57 19147.36 12226.80 16376.79 13553.52 450.07 109543.07
## X7 6 4148 0.93 0.08 0.96 0.94 0.05 0.38 1.06
## X8 7 4148 0.38 0.20 0.40 0.38 0.25 0.01 0.84
## X9 8 4148 54.45 29.38 56.18 54.96 38.65 0.94 100.00
## X10 9 4148 18.55 11.19 16.96 17.63 11.13 0.01 57.55
## X13 10 4148 5.11 5.85 3.48 4.07 4.17 0.02 58.75
## X14 11 4148 13.93 12.40 10.16 12.10 9.18 1.18 140.82
## range skew kurtosis se
## X1 0.72 -0.43 -0.71 0.00
## X2 44.85 -0.78 0.12 0.13
## X4 20.53 -0.15 -0.03 0.05
## X5 13.28 -0.27 -0.91 0.05
## X6 109093.01 1.47 2.13 297.30
## X7 0.67 -1.78 4.83 0.00
## X8 0.83 -0.02 -1.11 0.00
## X9 99.06 -0.11 -1.26 0.46
## X10 57.54 0.71 0.03 0.17
## X13 58.73 2.42 9.25 0.09
## X14 139.63 2.66 15.61 0.19PRINCIPLE COMPONENT ANALYSIS (PCA)
# scale data
data_scaled <- scale(data_hdr)#pca manual
# eigen value and vector
pc <- eigen(r)
print(pc$values)## [1] 7.38504608 1.37258268 0.80430973 0.46216674 0.35962025 0.16841333
## [7] 0.14779811 0.12726566 0.12040905 0.04202165 0.01036672print(pc$vectors)## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.3588106 -0.026342299 -0.099112635 -0.214520802 0.10176330
## [2,] -0.3215638 -0.002359137 -0.031035414 -0.514956212 0.41530653
## [3,] -0.3329991 -0.165394355 0.003610092 -0.152833835 0.19663972
## [4,] -0.3336045 -0.151102804 -0.280392282 0.006786841 -0.40092643
## [5,] -0.3087252 0.326400579 0.261504720 -0.036593056 -0.09526446
## [6,] -0.2719385 -0.236622963 -0.204652929 0.730205709 0.50248312
## [7,] 0.3341612 0.199592995 -0.056989337 0.129942851 -0.01104417
## [8,] -0.3174139 -0.138679560 -0.356121103 0.076676362 -0.52957950
## [9,] -0.1304642 -0.520082991 0.767538210 0.114838966 -0.19428051
## [10,] -0.2604671 0.507187393 0.144229559 0.282825914 -0.17209892
## [11,] -0.2813008 0.446979475 0.245613497 0.122124363 0.09083807
## [,6] [,7] [,8] [,9] [,10]
## [1,] -0.086794378 -0.02593679 0.14033684 -0.1202045821 0.241861586
## [2,] 0.190208867 -0.45329652 0.29698988 0.1170519972 -0.122071959
## [3,] -0.748417347 0.35100286 -0.14755550 -0.1108126824 -0.193055115
## [4,] -0.007515755 0.06502009 0.18488829 0.0050177315 0.679586801
## [5,] 0.332382271 0.26046467 0.05165906 -0.7133041609 -0.120859184
## [6,] 0.145860465 -0.04669206 0.08153677 -0.1052819805 -0.014057814
## [7,] -0.356971488 -0.03823705 0.81270268 -0.1908412654 -0.048354594
## [8,] 0.085816119 -0.03970418 0.15192718 0.1397358475 -0.638411794
## [9,] -0.029289318 -0.15187282 0.20858415 0.0597372525 -0.001900085
## [10,] -0.345855365 -0.60677585 -0.23242777 0.0007886955 0.049628596
## [11,] 0.095300650 0.44957330 0.21385006 0.6165567953 0.017718774
## [,11]
## [1,] 0.83875872
## [2,] -0.31544515
## [3,] -0.21228282
## [4,] -0.35515754
## [5,] -0.12228693
## [6,] -0.03318053
## [7,] -0.01044708
## [8,] 0.08798893
## [9,] 0.03797702
## [10,] -0.01509840
## [11,] 0.01417196#menentukan jumlah kompenen (kaiser criterion)
sum(pc$values > 1)## [1] 2#scree plot (visualisasi eigenvalue, menentukan jml komponen)
plot(pc$values, type="b",
xlab="Komponen",
ylab="Eigenvalue",
main="Scree Plot")
abline(h=1, col="red")
# hitung proporsi varians dan varians kumulatif
proporsi <- pc$values / sum(pc$values)
kumulatif <- cumsum(proporsi)hasil <- data.frame(
Eigenvalue = pc$values,
Proporsi = proporsi,
Kumulatif = kumulatif
)
print(hasil)## Eigenvalue Proporsi Kumulatif
## 1 7.38504608 0.6713678259 0.6713678
## 2 1.37258268 0.1247802434 0.7961481
## 3 0.80430973 0.0731190663 0.8692671
## 4 0.46216674 0.0420151581 0.9112823
## 5 0.35962025 0.0326927501 0.9439750
## 6 0.16841333 0.0153103023 0.9592853
## 7 0.14779811 0.0134361920 0.9727215
## 8 0.12726566 0.0115696056 0.9842911
## 9 0.12040905 0.0109462776 0.9952374
## 10 0.04202165 0.0038201499 0.9990576
## 11 0.01036672 0.0009424288 1.0000000#pca principal
#pca final
pca_final <- principal(data_hdr,
nfactors = 2,
rotate = "none")
print(pca_final)## Principal Components Analysis
## Call: principal(r = data_hdr, nfactors = 2, rotate = "none")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 h2 u2 com
## X1 0.98 -0.03 0.95 0.048 1.0
## X2 0.87 0.00 0.76 0.236 1.0
## X4 0.90 -0.19 0.86 0.144 1.1
## X5 0.91 -0.18 0.85 0.147 1.1
## X6 0.84 0.38 0.85 0.150 1.4
## X7 0.74 -0.28 0.62 0.377 1.3
## X8 -0.91 0.23 0.88 0.121 1.1
## X9 0.86 -0.16 0.77 0.230 1.1
## X10 0.35 -0.61 0.50 0.503 1.6
## X13 0.71 0.59 0.85 0.146 1.9
## X14 0.76 0.52 0.86 0.141 1.8
##
## PC1 PC2
## SS loadings 7.39 1.37
## Proportion Var 0.67 0.12
## Cumulative Var 0.67 0.80
## Proportion Explained 0.84 0.16
## Cumulative Proportion 0.84 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.05
## with the empirical chi square 925.2 with prob < 2.7e-172
##
## Fit based upon off diagonal values = 1#communalities
pca_final$communality## X1 X2 X4 X5 X6 X7 X8 X9
## 0.9517407 0.7636458 0.8564636 0.8532353 0.8501092 0.6229798 0.8793215 0.7704524
## X10 X13 X14
## 0.4969650 0.8541065 0.8586090#loading signifikan
print(pca_final$loadings, cutoff=0.5)##
## Loadings:
## PC1 PC2
## X1 0.975
## X2 0.874
## X4 0.905
## X5 0.907
## X6 0.839
## X7 0.739
## X8 -0.908
## X9 0.863
## X10 -0.609
## X13 0.708 0.594
## X14 0.764 0.524
##
## PC1 PC2
## SS loadings 7.385 1.373
## Proportion Var 0.671 0.125
## Cumulative Var 0.671 0.796FACTOR ANALYSIS (FA)
#FA Manual
# Factor Analysis
varcov = cov(data_hdr)
pc = eigen(varcov)cat("eigen value:")## eigen value:pc$values## [1] 3.666220e+08 5.710421e+02 1.204858e+02 4.213734e+01 2.689597e+01
## [6] 7.621200e+00 2.289441e+00 6.465000e-01 6.397555e-03 2.596035e-03
## [11] 3.522990e-04cat("eigen vector:")## eigen vector:pc$vectors## [,1] [,2] [,3] [,4] [,5]
## [1,] -6.482853e-06 -0.0031185768 -6.798534e-04 0.0028496067 -9.305072e-03
## [2,] -3.064323e-04 -0.1194682042 -4.433943e-02 0.2449042025 -9.346820e-01
## [3,] -1.088805e-04 -0.0613271601 -5.740094e-02 0.0761366827 -1.545178e-01
## [4,] -1.083433e-04 -0.0952098947 -1.002414e-02 0.0068567435 -3.908852e-02
## [5,] -9.999993e-01 0.0009638218 -4.690445e-05 -0.0006396754 2.785177e-05
## [6,] -1.832877e-06 -0.0016339299 -8.622701e-04 0.0016952912 -8.971017e-04
## [7,] 7.096670e-06 0.0040697488 4.576078e-03 -0.0029063516 8.132358e-03
## [8,] -9.189146e-04 -0.9833079867 8.664421e-02 -0.0408544556 1.186930e-01
## [9,] -1.282193e-04 -0.0766856125 -9.750054e-01 0.1538523522 1.153760e-01
## [10,] -2.469887e-04 -0.0053708878 1.149026e-01 0.2472518070 1.848561e-01
## [11,] -5.564255e-04 0.0082091548 1.525488e-01 0.9206982041 1.980931e-01
## [,6] [,7] [,8] [,9] [,10]
## [1,] 1.237075e-03 1.583561e-02 1.391904e-02 -1.559931e-02 -9.285393e-02
## [2,] 1.256294e-01 -1.850822e-01 -4.978535e-03 6.407598e-03 -6.571739e-04
## [3,] 1.168973e-02 9.499034e-01 -2.457618e-01 1.433323e-02 7.308440e-03
## [4,] -1.375787e-02 2.359846e-01 9.658953e-01 9.953354e-03 5.550924e-03
## [5,] -8.810334e-05 -1.695747e-06 -1.285018e-05 1.181566e-06 -1.025768e-06
## [6,] 5.048863e-04 9.299162e-03 3.253401e-03 -1.737146e-01 -9.802094e-01
## [7,] 1.456505e-03 -1.278820e-02 -5.206720e-03 9.844868e-01 -1.745873e-01
## [8,] -2.666874e-02 -5.548096e-02 -7.777145e-02 1.520261e-03 2.909908e-04
## [9,] 5.594568e-02 -5.795054e-02 8.296653e-04 3.509709e-03 -5.414406e-05
## [10,] 9.439578e-01 9.767777e-03 1.745654e-02 -2.265861e-03 1.244203e-03
## [11,] -2.983119e-01 -2.658542e-02 6.111030e-03 6.015052e-04 7.580713e-04
## [,11]
## [1,] 9.952808e-01
## [2,] -6.946960e-03
## [3,] -1.267865e-02
## [4,] -1.726204e-02
## [5,] -1.194746e-06
## [6,] -9.438357e-02
## [7,] -4.831431e-04
## [8,] 2.592719e-04
## [9,] 6.227372e-04
## [10,] -4.102614e-04
## [11,] 1.343289e-04# jml faktor berdasarkan jml pc
sp = sum(pc$values[1:2])
L1 = sqrt(pc$values[1])*pc$vectors[,1]
L2 = sqrt(pc$values[2])*pc$vectors[,2]
L = cbind(L1,L2)
cat("factor loading:")## factor loading:L## L1 L2
## [1,] -1.241296e-01 -0.07452303
## [2,] -5.867374e+00 -2.85487051
## [3,] -2.084777e+00 -1.46550375
## [4,] -2.074491e+00 -2.27518210
## [5,] -1.914736e+04 0.02303196
## [6,] -3.509479e-02 -0.03904519
## [7,] 1.358826e-01 0.09725270
## [8,] -1.759480e+01 -23.49760750
## [9,] -2.455064e+00 -1.83251682
## [10,] -4.729185e+00 -0.12834536
## [11,] -1.065409e+01 0.19616997#fa principle
fa <- principal(data_hdr, nfactors = 2, rotate = "none")
fa## Principal Components Analysis
## Call: principal(r = data_hdr, nfactors = 2, rotate = "none")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 h2 u2 com
## X1 0.98 -0.03 0.95 0.048 1.0
## X2 0.87 0.00 0.76 0.236 1.0
## X4 0.90 -0.19 0.86 0.144 1.1
## X5 0.91 -0.18 0.85 0.147 1.1
## X6 0.84 0.38 0.85 0.150 1.4
## X7 0.74 -0.28 0.62 0.377 1.3
## X8 -0.91 0.23 0.88 0.121 1.1
## X9 0.86 -0.16 0.77 0.230 1.1
## X10 0.35 -0.61 0.50 0.503 1.6
## X13 0.71 0.59 0.85 0.146 1.9
## X14 0.76 0.52 0.86 0.141 1.8
##
## PC1 PC2
## SS loadings 7.39 1.37
## Proportion Var 0.67 0.12
## Cumulative Var 0.67 0.80
## Proportion Explained 0.84 0.16
## Cumulative Proportion 0.84 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.05
## with the empirical chi square 925.2 with prob < 2.7e-172
##
## Fit based upon off diagonal values = 1# scores FA
fa$scores## PC1 PC2
## 19 -1.773811e+00 3.253630e-01
## 20 -1.741529e+00 3.175032e-01
## 21 -1.712290e+00 2.536554e-01
## 22 -1.706042e+00 2.831146e-01
## 23 -1.732447e+00 3.732838e-01
## 24 -1.646482e+00 2.450954e-01
## 25 -1.625771e+00 2.326071e-01
## 26 -1.588879e+00 1.726077e-01
## 27 -1.567678e+00 1.423643e-01
## 28 -1.542994e+00 9.437886e-02
## 29 -1.525174e+00 7.215384e-02
## 30 -1.502571e+00 7.515656e-02
## 31 -1.515590e+00 7.832468e-02
## 32 -1.493528e+00 -1.870250e-02
## 33 -1.638420e+00 2.548293e-01
## 43 -2.321441e-01 -1.929275e-01
## 44 -1.652296e-01 -2.017898e-01
## 45 -8.934134e-02 -2.647252e-01
## 46 -6.424892e-02 -2.442548e-01
## 47 -4.142340e-02 -1.585282e-01
## 48 -3.379535e-04 -1.803004e-01
## 49 7.387365e-02 -2.223166e-01
## 50 1.088989e-01 -2.724162e-01
## 51 1.477608e-01 -2.977917e-01
## 52 2.160680e-01 -3.425589e-01
## 53 3.300621e-01 -7.235402e-01
## 54 3.722433e-01 -8.631907e-01
## 55 4.326356e-01 -8.696670e-01
## 56 4.935390e-01 -9.887670e-01
## 57 5.045404e-01 -1.089174e+00
## 58 5.495913e-01 -1.075799e+00
## 59 5.492171e-01 -1.165480e+00
## 60 5.614592e-01 -1.189871e+00
## 61 5.747467e-01 -1.425845e+00
## 62 5.784590e-01 -1.451050e+00
## 63 6.054706e-01 -1.528148e+00
## 64 5.586841e-01 -1.511455e+00
## 65 5.993522e-01 -1.716091e+00
## 66 6.066504e-01 -1.685675e+00
## 77 -8.432549e-01 8.168867e-01
## 78 -7.952653e-01 7.750797e-01
## 79 -7.157442e-01 6.636768e-01
## 80 -6.720869e-01 6.499178e-01
## 81 -6.068325e-01 5.971053e-01
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## 1897 -1.873011e+00 3.528388e-01
## 1898 -1.772228e+00 2.173588e-01
## 1899 -1.691688e+00 1.506145e-01
## 1900 -1.618056e+00 9.624708e-02
## 1901 -1.566719e+00 6.481378e-02
## 1902 -1.506269e+00 -1.569451e-01
## 1903 -1.465545e+00 -1.860774e-01
## 1904 -1.426594e+00 -2.235602e-01
## 1905 -1.391114e+00 -2.286334e-01
## 1906 -1.338325e+00 -3.006827e-01
## 1907 -1.225020e+00 -8.119589e-01
## 1908 -1.176331e+00 -8.504802e-01
## 1909 -1.146867e+00 -8.674907e-01
## 1910 -1.108821e+00 -9.144624e-01
## 1911 -1.077617e+00 -9.449730e-01
## 1912 -1.051666e+00 -9.562172e-01
## 1913 -1.050987e+00 -1.041274e+00
## 1914 -1.034714e+00 -1.032853e+00
## 1948 9.159781e-01 9.173091e-02
## 1949 8.899326e-01 -1.788362e-01
## 1950 9.474709e-01 -1.779286e-01
## 1951 9.514673e-01 -2.967322e-01
## 1952 1.048768e+00 -1.566692e-01
## 1953 1.063516e+00 -2.451950e-01
## 1954 1.104065e+00 -2.492174e-01
## 1955 1.138761e+00 -2.686413e-01
## 1956 1.176790e+00 -3.188021e-01
## 1957 1.301609e+00 -2.642570e-01
## 1958 1.328830e+00 -3.078616e-01
## 1959 1.439414e+00 -6.508382e-02
## 1960 1.475095e+00 -2.102910e-02
## 1961 1.570249e+00 1.477395e-01
## 1962 1.538565e+00 1.199811e-01
## 1963 1.524499e+00 -5.603650e-02
## 1964 1.588784e+00 4.636180e-02
## 1965 1.626793e+00 -1.081622e-01
## 1966 1.620035e+00 -1.991871e-01
## 1967 1.534811e+00 -4.071559e-01
## 1968 1.592732e+00 -2.915672e-01
## 1969 1.608964e+00 -5.045247e-01
## 1970 1.588535e+00 -6.195300e-01
## 1971 1.667896e+00 -7.580375e-01
## 1972 1.667427e+00 -8.267285e-01
## 1973 1.653931e+00 -9.066394e-01
## 1974 1.713131e+00 -7.309672e-01
## 1975 1.715183e+00 -8.381916e-01
## 1976 1.755334e+00 -7.385481e-01
## 1977 1.739137e+00 -1.073117e+00
## 1978 1.724140e+00 -1.106517e+00
## 1979 1.747723e+00 -1.067632e+00
## 1980 1.755014e+00 -1.036219e+00
## 1981 4.145964e-01 6.234032e-01
## 1982 4.555278e-01 5.709795e-01
## 1983 4.952437e-01 5.408024e-01
## 1984 5.057590e-01 4.468302e-01
## 1985 5.803589e-01 4.027447e-01
## 1986 6.179659e-01 4.000443e-01
## 1987 6.549077e-01 3.979655e-01
## 1988 6.789873e-01 3.589060e-01
## 1989 7.248641e-01 4.330564e-01
## 1990 7.556185e-01 4.559543e-01
## 1991 7.497373e-01 5.168500e-01
## 1992 7.800356e-01 4.184722e-01
## 1993 8.004128e-01 3.447962e-01
## 1994 8.308891e-01 3.310002e-01
## 1995 8.957636e-01 2.625125e-01
## 1996 9.396182e-01 2.456700e-01
## 1997 9.630429e-01 2.309968e-01
## 1998 1.016597e+00 5.006929e-02
## 1999 1.017275e+00 -5.364874e-02
## 2000 9.980874e-01 -1.819670e-01
## 2001 1.015461e+00 -1.860665e-01
## 2002 1.036949e+00 -2.107810e-01
## 2003 1.062761e+00 -4.747194e-01
## 2004 1.082953e+00 -4.707072e-01
## 2005 1.097901e+00 -5.585817e-01
## 2006 1.102796e+00 -5.992130e-01
## 2007 1.121334e+00 -5.955117e-01
## 2008 1.194976e+00 -9.896266e-01
## 2009 1.214547e+00 -1.009937e+00
## 2010 1.222463e+00 -1.084871e+00
## 2011 1.192800e+00 -1.208324e+00
## 2012 1.240474e+00 -1.106648e+00
## 2013 1.253367e+00 -1.037201e+00
## 2019 -6.677830e-01 4.148964e-01
## 2020 -6.439230e-01 4.268001e-01
## 2021 -6.261837e-01 3.881776e-01
## 2022 -5.730530e-01 4.916245e-01
## 2023 -5.921367e-01 3.388802e-01
## 2024 -5.586783e-01 1.551163e-01
## 2025 -5.521443e-01 1.452034e-01
## 2026 -5.279577e-01 1.366077e-01
## 2027 -4.998019e-01 1.115073e-01
## 2028 -4.984809e-01 8.184499e-04
## 2029 -4.846192e-01 6.665739e-03
## 2030 -4.627789e-01 -9.698175e-02
## 2031 -4.331762e-01 -2.216136e-01
## 2032 -4.050306e-01 -2.190268e-01
## 2033 -3.951235e-01 -2.751297e-01
## 2034 -3.517769e-01 -3.057461e-01
## 2035 -3.534192e-01 -2.967752e-01
## 2036 -2.973591e-01 -3.296232e-01
## 2037 -2.635436e-01 -3.461722e-01
## 2038 -2.272726e-01 -2.949810e-01
## 2039 -2.074283e-01 -4.010197e-01
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## 2044 -1.485066e-01 -5.512968e-01
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## 2091 -1.786547e-02 -6.380575e-01
## 2092 1.142475e-02 -6.430971e-01
## 2093 5.319231e-02 -6.347926e-01
## 2094 1.063910e-01 -7.053167e-01
## 2095 1.437985e-01 -6.640549e-01
## 2096 1.844700e-01 -6.004414e-01
## 2097 2.361961e-01 -6.442209e-01
## 2098 1.724550e-01 -4.304232e-01
## 2099 1.810860e-01 -4.314955e-01
## 2100 2.313238e-01 -5.033301e-01
## 2101 3.025970e-01 -4.438520e-01
## 2102 4.007882e-01 -6.686922e-01
## 2103 4.139227e-01 -7.439991e-01
## 2104 4.655670e-01 -7.474706e-01
## 2105 4.938064e-01 -7.349550e-01
## 2106 5.717023e-01 -8.949812e-01
## 2107 6.205129e-01 -9.249603e-01
## 2108 6.355825e-01 -9.347973e-01
## 2109 6.589307e-01 -8.987613e-01
## 2110 6.678589e-01 -1.230775e+00
## 2111 6.632471e-01 -1.157116e+00
## 2112 6.812355e-01 -1.150585e+00
## 2113 7.875198e-01 6.777228e-01
## 2114 8.184238e-01 4.838709e-01
## 2115 8.818705e-01 4.365294e-01
## 2116 9.327682e-01 3.484400e-01
## 2117 9.775946e-01 3.256964e-01
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## 2121 1.105098e+00 2.301001e-01
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## 2123 1.188860e+00 1.205784e-02
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## 2127 1.416798e+00 -3.550960e-01
## 2128 1.381737e+00 -2.613217e-01
## 2129 1.406005e+00 -2.304642e-01
## 2130 1.429852e+00 -2.221329e-01
## 2131 1.436920e+00 -2.508485e-01
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