| title: “WJS2 WORLD COMPARISION 2022 March jan.hovden@uib.no” |
| output: html_notebook |
MFA ROLES AND INFLUENCES, WORLD
rolesinfluences<-mfadata[,c(225:242,246:270,201,1)]
rolesinfluences<-droplevels(rolesinfluences)
rolesinfluences<-rolesinfluences %>% filter(complete.cases(.))
roles<-rolesinfluences[,1:18]
influences<-rolesinfluences[,19:43]
nrow(rolesinfluences)## [1] 1158rolesinfluences<-rolesinfluences %>% filter(complete.cases(.))
nrow(rolesinfluences)## [1] 1158res<-MFA(rolesinfluences, group=c(18, 25,1,1), type=c("n", "n","s","n"), ncp=5,
name.group=c("ROLES", "INFLUENCES", "WEIGHT", "COUNTRY"), num.group.sup=c(3,4), graph=FALSE, row.w = rolesinfluences$nworldweight)
eig.val <- get_eigenvalue(res)
head(eig.val)## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 1.4265966 16.666926 16.66693
## Dim.2 0.6635307 7.752028 24.41895
## Dim.3 0.4878224 5.699228 30.11818
## Dim.4 0.4156238 4.855732 34.97391
## Dim.5 0.3658409 4.274119 39.24803
## Dim.6 0.2869441 3.352367 42.60040fviz_screeplot(res)
# Contribution to dimensions
stargazer(res$group$contrib, type="text", digits=2)##
## ========================================
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## ----------------------------------------
## ROLES 49.61 68.23 59.95 50.91 74.13
## INFLUENCES 50.39 31.77 40.05 49.09 25.87
## ----------------------------------------stargazer(res$quali.var$contrib, type="text", digits=2)##
## =====================================================
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## -----------------------------------------------------
## notDetachedObserver 0.01 1.03 2.58 0.23 18.02
## DetachedObserver 0.004 0.38 0.96 0.08 6.72
## notReportReality 0.07 3.95 6.17 0.04 7.99
## ReportReality 0.01 0.41 0.64 0.004 0.83
## notAnalysCurrAffairs 0.65 3.64 0.72 0.22 5.01
## AnalysCurrAffairs 0.21 1.16 0.23 0.07 1.59
## notMonPolLeaders 1.20 7.88 1.53 2.27 1.45
## MonPolLeaders 0.68 4.46 0.87 1.28 0.82
## notMonBusiness 0.83 4.32 1.16 3.12 0.03
## MonBusiness 0.71 3.70 0.99 2.67 0.03
## notSetPolAgenda 1.43 1.81 0.35 0.08 2.10
## SetPolAgenda 2.24 2.83 0.54 0.12 3.30
## notInflPublOpinion 2.02 0.95 1.13 1.41 1.07
## InflPublOpinion 1.96 0.92 1.10 1.37 1.03
## notAdvSocialChange 2.40 2.32 0.51 2.49 0.79
## AdvSocialChange 1.61 1.55 0.34 1.67 0.53
## notAdvGoverment 0.57 0.53 0.27 0.76 0.33
## AdvGoverment 1.84 1.70 0.88 2.43 1.07
## notSupNatDevelop 2.72 0.99 0.60 2.38 0.32
## SupNatDevelop 2.66 0.97 0.59 2.32 0.32
## notPosImagePolit 0.61 0.0002 1.82 0.30 0.26
## PosImagePolit 3.38 0.001 10.18 1.66 1.44
## notSupGovPolicy 0.76 0 1.63 0.29 0.18
## SupGovPolicy 3.92 0 8.34 1.48 0.91
## notEntertainment 0.81 0.65 2.69 3.36 3.23
## Entertainment 1.22 0.98 4.06 5.06 4.87
## notLargeAudience 1.98 0.37 1.79 3.21 2.82
## LargeAudience 1.58 0.29 1.43 2.57 2.25
## notAdvDailyLife 1.50 0.03 1.98 1.75 1.20
## AdvDailyLife 1.52 0.03 2.02 1.77 1.22
## notInfoMakePolDec 2.00 5.60 0.35 0.05 0.70
## InfoMakePolDec 1.02 2.87 0.18 0.03 0.36
## notMotivPolPart 1.48 2.84 0.19 0.13 0.42
## MotivPolPart 1.72 3.29 0.22 0.15 0.49
## notLetPeopleExpr 1.67 4.15 0.66 2.96 0.32
## LetPeopleExpr 0.64 1.60 0.25 1.14 0.12
## PersonalValues0 0.16 0.03 0.34 0.62 0.72
## PersonalValues 0.12 0.02 0.26 0.48 0.56
## PeersOnStaff0 0.27 0.48 0.14 0.04 0.19
## PeersOnStaff 0.69 1.22 0.37 0.09 0.48
## EditSupervis0 0.81 1.16 1.59 0.50 2.44
## EditSupervis 0.68 0.96 1.32 0.42 2.03
## Managers0 1.08 0.79 0.45 0.16 2.45
## Managers 1.48 1.08 0.61 0.21 3.34
## Owners0 1.05 0.69 0.16 0.09 1.76
## Owners 1.87 1.22 0.28 0.16 3.13
## EdPolicy0 0.84 0.37 1.79 0.77 1.19
## EdPolicy 0.59 0.26 1.26 0.54 0.84
## AdvertCons0 0.62 0.55 0.02 0.01 0.004
## AdvertCons 1.93 1.71 0.06 0.02 0.01
## ProfitExp0 0.50 0.71 0.03 0.02 0.08
## ProfitExp 1.46 2.08 0.10 0.05 0.24
## AudienceRes0 0.60 0.51 0.50 0.45 0.58
## AudienceRes 1.03 0.88 0.85 0.76 1.00
## AvailResour0 0.53 0.34 3.75 1.15 0.54
## AvailResour 0.35 0.22 2.48 0.76 0.36
## TimeLimits0 0.33 0.20 3.41 0.28 1.03
## TimeLimits 0.18 0.11 1.85 0.15 0.56
## JournEthics0 0.29 0.42 3.22 1.04 0.58
## JournEthics 0.08 0.12 0.94 0.30 0.17
## FriendsPeerFamily0 0.38 0.22 0.0001 0.37 0.002
## FriendsPeerFamily 1.66 0.94 0.001 1.62 0.01
## ColleaguesOtherMedia0 0.37 0.33 0.0005 0.10 0.01
## ColleaguesOtherMedia 1.71 1.49 0.002 0.46 0.05
## AudienceFeedb0 1.06 0.83 0.47 0.94 0.002
## AudienceFeedb 1.28 1.01 0.57 1.14 0.002
## CompetingNewsorg0 0.95 0.56 0.50 0.04 0.03
## CompetingNewsorg 1.68 1.00 0.89 0.07 0.06
## MediaLawsRegul0 1.01 0.05 1.77 0.51 0.14
## MediaLawsRegul 0.84 0.04 1.47 0.42 0.11
## InformAccess0 0.81 0.13 3.71 0.50 0.04
## InformAccess 0.37 0.06 1.71 0.23 0.02
## Censorshp0 1.33 0.28 0.02 0.08 0.08
## Censorshp 2.79 0.58 0.05 0.16 0.17
## GovOfficials0 0.72 0.19 0.01 1.55 0.02
## GovOfficials 2.90 0.77 0.05 6.26 0.08
## Politician0 0.61 0.12 0.003 1.61 0.01
## Politician 2.90 0.55 0.02 7.63 0.03
## PressureGroups0 0.44 0.14 0.01 1.19 0.01
## PressureGroups 2.61 0.81 0.07 7.09 0.04
## BusinessPeople0 0.39 0.32 0.02 0.94 0.05
## BusinessPeople 2.22 1.83 0.12 5.30 0.29
## PublRelat0 0.47 0.53 0.0004 0.34 0.02
## PublRelat 1.82 2.05 0.001 1.30 0.07
## RelationshSources0 0.70 0.36 1.27 0.09 0.12
## RelationshSources 0.83 0.43 1.51 0.10 0.15
## -----------------------------------------------------#stargazer(res$partial.axes$cor.between, type="text", digits=2)
res$group$RV## ROLES INFLUENCES WEIGHT COUNTRY MFA
## ROLES 1.00000000 0.13712512 0.03092037 0.09049198 0.78007073
## INFLUENCES 0.13712512 1.00000000 0.05217540 0.07699509 0.72674819
## WEIGHT 0.03092037 0.05217540 1.00000000 0.12500000 0.05439889
## COUNTRY 0.09049198 0.07699509 0.12500000 1.00000000 0.11138665
## MFA 0.78007073 0.72674819 0.05439889 0.11138665 1.00000000res$group$correlation## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## ROLES 0.8460466 0.7628701 0.718328 0.7427509 0.8960130
## INFLUENCES 0.8497983 0.4815977 0.567806 0.7305044 0.5758713fviz_contrib(res, choice = "quali.var", axes = 1, top = 30, palette = "jco")
fviz_contrib(res, choice = "quali.var", axes = 2, top = 30, palette = "jco")
fviz_contrib(res, choice = "quali.var", axes = 3, top = 30, palette = "jco")
MFA First dimension: Domination by elites (political, economic) MFA Second dimension: Political / interventionist role MFA Third dimension: Variant of second, emphasise goverment support (seems unstable)
# individuals, axes and groups
print(plot.MFA(res, axes=c(1, 2), choix="ind", new.plot=TRUE, lab.ind=FALSE,
lab.par=FALSE, lab.var=FALSE, habillage="group", title=""))
print(plot.MFA(res, axes=c(1, 2), choix="axes", new.plot=TRUE,
habillage="group", title=""))
print(plot.MFA(res, axes=c(1, 2), choix="group", new.plot=TRUE,
lab.grpe=TRUE, title=""))
# active variables
fviz_mfa_var(res, "quali.var", palette = "jco",
col.var.sup = "violet", repel = TRUE, labelsize=3,
geom = c("point", "text"), legend = "bottom") + ylab("2: NONPOLITICAL/NONMONITORAL ROLE --->") +
xlab("1: SUBJUGATION BY ELITES --->") 
fviz_mfa_var(res, "quali.var", palette = "jco",
col.var.sup = "violet", repel = TRUE, labelsize=3, axes = c(1,3), select.var=list(contrib=50),
geom = c("point", "text"), legend = "bottom") 
fviz_mfa_var(res, "quali.var", axes=c(1,2), col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
col.var.sup = "violet", repel = TRUE,
geom = c("point", "text"), select.var=list(contrib=30))
fviz_mfa_var(res, "quali.var", axes=c(1,3), col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
col.var.sup = "violet", repel = TRUE,
geom = c("point", "text"), select.var=list(contrib=30))
# COUNTRIES / SUPPLEMENTARY
fviz_mfa_ind(res, geom="pint", repel=TRUE) # pint is a typo, but it works
plot(res)
PCA ROLES AND INFLUENCES, WORLD (equal country weight)
# https://f0nzie.github.io/machine_learning_compilation/detailed-study-of-principal-component-analysis.html
roleinflnum<-wjs2000[,c(38:55,75:86, 88:100,201,1)]
roleinflnum<-roleinflnum %>% filter(complete.cases(.))
x<-c(1:43) # convert factors to numeric
roleinflnum[x] = lapply(roleinflnum[x], FUN = function(y){as.numeric(y)})
res.pca<-PCA(roleinflnum, quanti.sup=44, quali.sup=45, scale.unit=TRUE, ncp=3, graph = FALSE, row.w = roleinflnum$nworldweight)
fviz_pca_ind(res.pca, geom="point") # individuals
fviz_pca_var(res.pca, geom = c("point", "text"), col.var = "contrib", repel=TRUE) # active variables
fviz_pca_var(res.pca, geom = c("point", "text"), col.var = "contrib", repel=TRUE, axes=c(1,3)) # active variables
fviz_pca_var(res.pca, geom = c("point", "text"), col.var = "contrib", repel=TRUE, select.var=list(contrib=20), axes=c(1,2))
fviz_pca_var(res.pca, geom = c("point", "text"), col.var = "contrib", repel=TRUE, select.var=list(contrib=20), axes=c(1,3))
fviz_pca_var(res.pca, geom = c("point", "text"), col.var = "contrib", repel=TRUE, select.var=list(contrib=20), axes=c(3,2))
p<-fviz_pca_ind(res.pca, invisible = "ind") # countries
p<-fviz_add(p, res.pca$quali.sup$coord, labelsize = 2, pointsize = 1, repel = TRUE)
p
stargazer(res.pca$var$contrib, type="text", digits=2)##
## =================================================
## Dim.1 Dim.2 Dim.3
## -------------------------------------------------
## Detached.observer 0.0001 1.51 2.03
## Report.reality 0.002 4.80 5.41
## Analyse.current.affairs 0.37 5.80 1.02
## Monitor.political.leaders 0.95 11.55 0.03
## Monitor.business 1.04 7.98 0.03
## Set.political.agenda 1.92 5.62 2.66
## Influence.public.opinion 2.07 2.90 0.80
## Advocate.for.social.change 1.80 6.18 0.27
## Adversary.government 1.37 2.86 1.67
## Sup.national.development 2.97 4.23 0.07
## Pos.image.politicians 3.33 0.19 6.85
## Sup.gov.policy 4.08 0.08 5.43
## Entertain 1.52 0.40 0.26
## Large.audience 2.20 0.09 0.02
## Advice.for.daily.life 2.18 0.08 1.09
## Info.people.pol.decisions 1.15 9.52 0.10
## Motivate.pol.particip. 1.88 4.95 2.34
## Let.people.express.themselves 0.97 6.47 0.81
## Personal.values 0.42 0.0004 2.68
## Peers.on.staff 1.37 1.38 1.47
## Editorial.supervisors 2.13 1.31 4.09
## Managers.of.news.org 3.49 1.02 0.91
## Owners 3.81 0.57 0.12
## Editorial.policy 1.90 0.05 6.45
## Advertising.considerations 3.62 1.86 0.06
## Profit.expectations 3.00 2.66 0.11
## Audience.research 2.45 0.26 3.10
## Availability.of.resources 1.24 0.01 8.98
## Time.limits 0.58 0.0003 9.37
## Journalism.ethics 0.14 3.43 7.68
## Friends.peers.family 2.34 1.58 0.30
## Colleagues.other.media 2.82 1.95 0.01
## Audience.feedback 3.00 0.75 1.81
## Competing.newsorg 3.30 0.97 1.27
## Media.laws.and.regul 2.05 0.14 4.06
## Information.acceess 1.39 0.21 6.65
## Censorship 5.04 0.23 0.08
## Government.officials 5.58 0.35 2.06
## Politicians 5.21 0.26 2.10
## Pressure.groups 4.76 0.88 2.22
## Business.people 4.67 1.71 1.47
## Public.relations 3.87 2.83 0.31
## Relationships.sources 2.04 0.40 1.75
## -------------------------------------------------stargazer(res.pca$quali.sup$v.test, type="text", digits=2)##
## =======================================
## Dim.1 Dim.2 Dim.3
## ---------------------------------------
## Albania -0.51 0.26 0.62
## Argentina -0.20 1.00 -1.97
## Australia -0.88 -2.40 3.38
## Austria -4.07 0.56 -0.54
## Bangladesh 2.03 -1.11 -2.59
## Belgium -4.05 -1.33 -0.16
## Bhutan 7.73 -0.72 -1.67
## Botswana 4.14 2.80 3.30
## Brazil -0.59 -0.76 0.03
## Bulgaria -0.18 -0.09 1.91
## Canada -2.13 1.14 2.88
## Chile 1.45 -0.91 0.87
## China 1.95 -2.00 -3.19
## Colombia 2.69 1.65 -1.54
## Croatia 1.42 2.27 1.12
## Cyprus -0.07 1.42 3.96
## Czech Republic -2.84 -0.91 4.23
## Denmark -2.98 0.45 -0.18
## Ecuador 3.09 -0.26 -1.56
## Egypt 3.85 1.55 0.39
## El Salvador 3.35 0.001 -0.63
## Estonia -1.26 -0.10 0.29
## Ethiopia 1.54 -2.60 -4.15
## Finland -3.40 1.29 3.25
## France -6.04 1.54 -0.17
## Germany -3.26 -1.34 0.41
## Greece -2.45 2.19 0.90
## Hong Kong -1.08 -1.66 -1.74
## Hungary -0.54 -3.98 0.02
## Iceland -3.62 -3.12 1.23
## India 2.05 0.22 -0.19
## Indonesia 0.07 0.69 -2.80
## Ireland -1.97 0.01 2.14
## Israel -2.26 0.89 1.06
## Italy -4.88 -1.77 1.68
## Kenya 3.31 0.68 -1.12
## Kosovo -0.85 3.69 -2.95
## Latvia -0.17 0.77 2.13
## Malawi 4.08 3.15 -1.11
## Malaysia 4.32 -2.64 -4.11
## Mexico 1.61 2.79 -2.65
## Moldova -0.74 0.55 2.23
## Netherlands -2.58 -1.67 -1.51
## New Zealand -1.44 -1.02 1.59
## Norway -1.74 0.14 0.0001
## Oman 3.06 -0.12 -2.83
## Philippines 1.94 2.00 1.74
## Qatar 0.42 1.59 -1.25
## Romania -0.90 -0.10 1.92
## Russia -0.23 -1.91 -3.33
## Serbia 1.84 2.25 0.61
## Sierra Leone 3.39 0.01 0.29
## Singapore -2.83 -4.60 -11.44
## South Africa -0.47 -2.32 3.82
## South Korea 1.47 -0.27 -0.34
## Spain 0.54 2.82 1.53
## Sudan 2.25 -1.56 0.66
## Sweden -2.39 2.37 0.75
## Switzerland -4.04 -1.04 -1.88
## Tanzania 1.01 0.87 -1.88
## Thailand 6.79 -1.07 -2.20
## Turkey -2.02 1.57 -0.47
## United Arab Emirates 5.19 -0.57 -1.04
## UK -1.64 -1.96 3.70
## USA -2.98 -0.66 1.07
## ---------------------------------------fviz_contrib(res.pca, choice = "var", axes = 1, top = 30, palette = "jco")
fviz_contrib(res.pca, choice = "var", axes = 2, top = 30, palette = "jco")
fviz_contrib(res.pca, choice = "var", axes = 3, top = 30, palette = "jco")
PCA 1: Pressure PCA 2: POLITICAL vs MARKET PCA 3: INTERNAL vs. EXTERNAL
MCA ROLES AND INFLUENCES, WORLD (equal country weight)
mca <- speMCA(mcadata[,225:270], row.w=mcadata$nworldweight) #weighted for equal country weight
mca<-flip.mca(mca,dim=c(1,2)) # reverse axes
# Eigenvalues
mca$eig$eigen[1:10]## [1] 0.15784289 0.06813965 0.05515366 0.04613312 0.03926188 0.03389422
## [7] 0.03169028 0.02869685 0.02752477 0.02514639plot(mca$eig$eigen[1:10])
mca$eig$rate[1:10]## [1] 14.953064 6.455131 5.224918 4.370368 3.719429 3.210930 3.002142
## [8] 2.718563 2.607527 2.382214modif.rate(mca)$modif #Benzecri## mrate cum.mrate
## 1 80.401307918 80.40131
## 2 9.344769365 89.74608
## 3 4.846113140 94.59219
## 4 2.582787029 97.17498
## 5 1.332685176 98.50766
## 6 0.641267437 99.14893
## 7 0.429802590 99.57873
## 8 0.210114878 99.78885
## 9 0.145286532 99.93413
## 10 0.050388730 99.98452
## 11 0.013255413 99.99778
## 12 0.002221792 100.00000# Contributions first four axes
tabcontrib(mca, dim=1)## var moda ctr1 ctr2 weight ctrtot cumctrtot
## 8 Censorshp Censorshp0 -1.4 1329.0 4.66 4.66
## 9 Censorshp 3.26 574.7
## 27 Owners Owners0 -1.34 1184.7 3.98 8.64
## 28 Owners 2.64 646.5
## 23 Managers Managers0 -1.43 1112.7 3.7 12.34
## 24 Managers 2.27 764.5
## 29 Politician Politician 3.46 334.5 3.46 15.8
## 17 GovOfficials GovOfficials 3.45 368.3 3.45 19.25
## 31 PressureGroups PressureGroups 3.41 263.2 3.41 22.66
## 37 SupNatDevelop notSupNatDevelop -1.67 923.4 3.27 25.93
## 38 SupNatDevelop 1.6 1009.4
## 4 AudienceFeedb AudienceFeedb0 -1.44 1099.1 3.23 29.16
## 5 AudienceFeedb 1.79 854.5
## 11 CompetingNewsorg CompetingNewsorg0 -1.15 1265.9 3.22 32.38
## 12 CompetingNewsorg 2.07 669.9
## 7 BusinessPeople BusinessPeople 3.08 282.5 3.08 35.46
## 25 MediaLawsRegul MediaLawsRegul0 -1.42 877.4 2.58 38.04
## 26 MediaLawsRegul 1.16 1055.8
## 1 AdvertCons AdvertCons 2.56 432.8 2.56 40.6
## 33 PublRelat PublRelat 2.5 384.7 2.5 43.1
## 22 LargeAudience notLargeAudience -1.33 877.9 2.44 45.54
## 21 LargeAudience 1.11 1075.3
## 18 InflPublOpinion notInflPublOpinion -1.2 960.3 2.43 47.97
## 19 InflPublOpinion 1.23 961.8
## 14 EditSupervis EditSupervis0 -1.13 914.9 2.3 50.27
## 13 EditSupervis 1.17 982.5
## 32 ProfitExp ProfitExp 2.23 439.1 2.23 52.5
## 36 SupGovPolicy SupGovPolicy 2.22 327.9 2.22 54.72
## 10 ColleaguesOtherMedia ColleaguesOtherMedia 2.19 346.3 2.19 56.91
## 16 FriendsPeerFamily FriendsPeerFamily 1.98 336.1 1.98 58.89
## 30 PosImagePolit PosImagePolit 1.77 279.3 1.77 60.66
## 6 AudienceRes AudienceRes 1.61 694.2 1.61 62.27
## 3 AdvSocialChange notAdvSocialChange -1.38 774.9 1.38 63.65
## 34 RelationshSources RelationshSources 1.24 828.6 1.24 64.89
## 15 EdPolicy EdPolicy0 -1.2 807.4 1.2 66.09
## 20 InformAccess InformAccess0 -1.14 645.1 1.14 67.23
## 35 SetPolAgenda SetPolAgenda 1.12 706.7 1.12 68.35
## 2 AdvGoverment AdvGoverment 1.1 450.4 1.1 69.45tabcontrib(mca, dim=2)## var moda ctr1 ctr2 weight ctrtot cumctrtot
## 19 MonPolLeaders MonPolLeaders -3.3 1206.0 8.46 8.46
## 20 notMonPolLeaders 5.16 739.2
## 13 InfoMakePolDec InfoMakePolDec -2.91 1284.8 8.39 16.85
## 14 notInfoMakePolDec 5.48 666.1
## 15 LetPeopleExpr LetPeopleExpr -1.8 1411.9 6.29 23.14
## 16 notLetPeopleExpr 4.49 541.7
## 21 MotivPolPart MotivPolPart -3.53 858.3 6.23 29.37
## 22 notMotivPolPart 2.7 1077.3
## 3 AdvSocialChange AdvSocialChange -2.36 1155.6 5.99 35.36
## 4 notAdvSocialChange 3.63 774.9
## 26 PromTolerance PromTolerance -1.9 1116.9 5.91 41.27
## 27 notPromTolerance 4.01 420.2
## 18 MonBusiness MonBusiness -2.42 1012.6 5.09 46.36
## 17 notMonBusiness 2.67 934.8
## 30 SetPolAgenda SetPolAgenda -2.9 706.7 4.69 51.05
## 31 notSetPolAgenda 1.79 1193.2
## 32 SupNatDevelop SupNatDevelop -1.67 1009.4 3.57 54.62
## 33 notSupNatDevelop 1.9 923.4
## 8 EduAudience EduAudience -1.12 1079.6 3.29 57.91
## 9 notEduAudience 2.17 461.5
## 5 AnalysCurrAffairs notAnalysCurrAffairs 3.1 477.7 3.1 61.01
## 11 InflPublOpinion InflPublOpinion -1.5 961.8 3.03 64.04
## 12 notInflPublOpinion 1.53 960.3
## 28 PublRelat PublRelat 2.6 384.7 2.6 66.64
## 6 BusinessPeople BusinessPeople 2.53 282.5 2.53 69.17
## 29 ReportReality notReportReality 2.25 179.7 2.25 71.42
## 24 PressureGroups PressureGroups 1.67 263.2 1.67 73.09
## 25 ProfitExp ProfitExp 1.65 439.1 1.65 74.74
## 34 TellStories notTellStories 1.42 450.0 1.42 76.16
## 7 ColleaguesOtherMedia ColleaguesOtherMedia 1.39 346.3 1.39 77.55
## 1 AdvertCons AdvertCons 1.36 432.8 1.36 78.91
## 23 Politician Politician 1.28 334.5 1.28 80.19
## 2 AdvGoverment AdvGoverment -1.23 450.4 1.23 81.42
## 10 GovOfficials GovOfficials 1.13 368.3 1.13 82.55tabcontrib(mca, dim=3)## var moda ctr1 ctr2 weight ctrtot cumctrtot
## 17 JournEthics JournEthics -2.07 1481.6 7.9 7.9
## 16 JournEthics0 5.83 445.4
## 5 AvailResour AvailResour -3.26 1124.7 7.57 15.47
## 6 AvailResour0 4.31 783.4
## 28 TimeLimits TimeLimits -2.49 1244.9 6.54 22.01
## 27 TimeLimits0 4.05 705.3
## 11 EdPolicy EdPolicy -2.59 1120.3 5.92 27.93
## 10 EdPolicy0 3.33 807.4
## 14 InformAccess InformAccess -1.98 1289.3 5.84 33.77
## 15 InformAccess0 3.86 645.1
## 21 Politician Politician0 -1.16 1555.6 5.79 39.56
## 22 Politician 4.63 334.5
## 12 GovOfficials GovOfficials0 -1.22 1556.6 5.72 45.28
## 13 GovOfficials 4.5 368.3
## 24 PressureGroups PressureGroups 5.15 263.2 5.15 50.43
## 8 EditSupervis EditSupervis -2.57 982.5 5.11 55.54
## 9 EditSupervis0 2.54 914.9
## 23 PosImagePolit PosImagePolit 4.36 279.3 4.36 59.9
## 3 AudienceRes AudienceRes -2.77 694.2 4.14 64.04
## 4 AudienceRes0 1.37 1187.6
## 19 MediaLawsRegul MediaLawsRegul -1.91 1055.8 3.9 67.94
## 20 MediaLawsRegul0 1.99 877.4
## 26 SupGovPolicy SupGovPolicy 3.58 327.9 3.58 71.52
## 7 BusinessPeople BusinessPeople 3.5 282.5 3.5 75.02
## 1 AdvGoverment AdvGoverment 2 450.4 2 77.02
## 2 AudienceFeedb AudienceFeedb -1.49 854.5 1.49 78.51
## 25 PublRelat PublRelat 1.47 384.7 1.47 79.98
## 18 Managers Managers -1.17 764.5 1.17 81.15tabcontrib(mca, dim=4)## var moda ctr1 ctr2 weight ctrtot cumctrtot
## 8 Entertainment Entertainment -5.49 753.3 9.21 9.21
## 9 notEntertainment 3.72 1186.3
## 17 MonBusiness notMonBusiness -3.84 934.8 7.48 16.69
## 18 MonBusiness 3.64 1012.6
## 20 MonPolLeaders notMonPolLeaders -3.87 739.2 6.26 22.95
## 19 MonPolLeaders 2.39 1206.0
## 15 LargeAudience LargeAudience -2.56 1075.3 5.87 28.82
## 16 notLargeAudience 3.31 877.9
## 6 DetachedObserver notDetachedObserver -3.93 510.6 5.61 34.43
## 7 DetachedObserver 1.68 1414.1
## 2 AdvDailyLife AdvDailyLife -2.76 956.3 5.45 39.88
## 1 notAdvDailyLife 2.69 999.9
## 25 RelationshSources RelationshSources0 -1.64 1055.3 3.8 43.68
## 24 RelationshSources 2.16 828.6
## 21 Politician Politician 3.65 334.5 3.65 47.33
## 26 ReportReality notReportReality -3.62 179.7 3.62 50.95
## 23 PressureGroups PressureGroups 3.45 263.2 3.45 54.4
## 12 InformAccess InformAccess0 -2.24 645.1 3.41 57.81
## 13 InformAccess 1.17 1289.3
## 28 SupNatDevelop SupNatDevelop -1.6 1009.4 3.27 61.08
## 29 notSupNatDevelop 1.67 923.4
## 30 TimeLimits TimeLimits0 -2.01 705.3 3.24 64.32
## 31 TimeLimits 1.23 1244.9
## 22 PosImagePolit PosImagePolit -2.82 279.3 2.82 67.14
## 5 BusinessPeople BusinessPeople 2.57 282.5 2.57 69.71
## 27 SupGovPolicy SupGovPolicy -2.33 327.9 2.33 72.04
## 10 GovOfficials GovOfficials 2.29 368.3 2.29 74.33
## 4 AnalysCurrAffairs notAnalysCurrAffairs -1.61 477.7 1.61 75.94
## 14 JournEthics JournEthics0 -1.27 445.4 1.27 77.21
## 3 AdvSocialChange notAdvSocialChange 1.22 774.9 1.22 78.43
## 11 InflPublOpinion notInflPublOpinion 1.19 960.3 1.19 79.62#dimdescr(mca) # ETA-values for active variablesFirst axis= influences (owners, censorship, political etc) low - high Second axis = not vs. democratic/political roles (monitor, inform perople, adv. social change) Third axis = organisational constraints
# typicality tests
v<-varsup(mca,wjs2000$COUNTRY)
v$typic## dim.1 dim.2 dim.3 dim.4 dim.5
## Albania 0.609828 0.663252 -2.551328 -0.676752 -4.424477
## Argentina -0.224331 -2.005458 -0.250931 -1.653963 3.063564
## Australia -0.914457 1.599931 -4.699878 2.428158 -3.253063
## Austria -3.930997 -0.767804 0.237187 -0.487637 -1.525401
## Bangladesh 1.604042 0.161284 1.557273 -2.717077 0.101500
## Belgium -3.697298 1.009405 -0.111641 1.051393 0.385801
## Bhutan 8.736047 1.404584 3.219039 1.391624 0.122887
## Botswana 5.550916 -3.287107 -2.981638 -0.086983 -1.807872
## Brazil -2.850628 0.776306 -0.280875 0.643533 1.769959
## Bulgaria -0.876597 -0.254339 -2.879192 0.345927 -2.431951
## Canada -2.527093 -1.573996 -2.378136 3.238444 -0.506966
## Chile 1.071748 0.768381 -0.406135 -1.285955 1.063937
## China 0.309544 2.813559 3.638063 -1.351320 0.253921
## Colombia 3.064435 -2.227432 1.264643 -1.621344 2.463111
## Croatia 1.839582 -1.231243 -0.360609 3.452478 1.268240
## Cyprus -2.326569 -0.028783 0.162847 -0.774401 6.757890
## Czech Republic -2.509833 3.194692 -4.871430 1.484541 -0.950213
## Denmark -3.855453 -0.337816 1.170816 4.843987 4.001284
## Ecuador 4.259161 -1.105376 0.788240 -2.643388 0.285871
## Egypt 3.184875 -2.018839 0.509806 0.717483 2.394440
## El Salvador 3.618075 0.005169 -1.371544 -3.139853 2.083282
## Estonia -1.082006 0.718234 -2.353965 -0.530921 -3.812521
## Ethiopia 2.473004 1.304287 3.964313 -5.431667 -0.174354
## Finland -4.372810 -0.612857 -2.069256 3.102049 -1.543948
## France -6.181214 -2.577255 0.546311 3.574383 0.327392
## Germany -3.674586 0.960279 -0.472563 -0.667901 -1.967458
## Greece -2.285012 -0.856279 -0.621975 1.227137 0.966004
## Hong Kong -2.528427 0.849758 1.363310 1.386931 5.582550
## Hungary -0.679674 1.792487 -1.528892 -3.251551 -1.939819
## Iceland -4.783529 2.831326 -0.823999 2.030891 -2.397918
## India 2.598837 1.456459 -1.345399 -1.347925 -2.473532
## Indonesia 0.660227 -3.806421 -0.150628 -4.234880 -3.075496
## Ireland -3.880102 0.227825 -1.595372 2.600478 -2.625982
## Israel -0.688539 -0.000323 -0.396181 -0.371212 0.606909
## Italy -3.897420 1.539592 0.197003 1.552255 -1.511533
## Japan -3.091733 -0.050890 1.849367 1.689125 5.207404
## Kenya 5.694185 -0.718654 0.277339 -1.805765 -0.783484
## Kosovo 0.255544 -2.763212 1.684084 -0.435968 -2.850092
## Latvia -0.682890 -2.328119 -0.691134 -0.422198 -0.023717
## Malawi 5.929530 -2.998513 -1.052448 -3.154749 -1.866971
## Malaysia 3.753795 3.014223 4.402086 -0.756840 -2.261414
## Mexico 2.256923 -2.127282 0.928741 -3.529598 3.453338
## Moldova -1.671119 1.227033 -1.037675 0.937554 -2.907322
## Netherlands -4.723788 3.200242 1.433549 -2.145660 -1.624996
## New Zealand -2.040422 0.517101 -1.068800 2.847952 -0.014971
## Norway -3.681802 -0.830683 1.469396 -0.481305 -0.167012
## Oman 3.015304 1.869339 3.980298 0.046602 -1.302410
## Philippines 2.086449 -2.796272 -2.692984 1.660671 2.168306
## Portugal -2.173420 -0.587870 -2.156339 3.065882 -0.258017
## Qatar -0.406566 4.024897 5.019980 -3.513129 -0.776026
## Romania -1.392792 0.505232 -1.581415 -1.223642 -4.276938
## Russia -1.180949 3.237722 2.937171 -5.129861 0.533957
## Serbia 1.519831 -2.874639 -1.281133 -0.939324 -0.074130
## Sierra Leone 5.220685 -0.089616 1.635575 -2.518079 -1.252434
## Singapore -2.626126 2.661922 5.962137 -4.397940 0.798882
## South Africa 0.953228 2.117831 -3.617431 -1.058435 -1.220297
## South Korea -0.484267 0.681882 -0.387492 3.380015 5.651889
## Spain -0.048048 -2.427911 -1.971024 1.727637 1.592363
## Sudan 8.529316 0.701838 1.624086 1.071977 0.255744
## Sweden -2.486159 -3.433786 -0.273943 3.743290 2.508055
## Switzerland -4.962584 -0.037619 1.543442 0.717441 -1.398526
## Tanzania 2.284270 -3.589940 -2.940453 -6.190206 6.059554
## Thailand 7.624189 2.213144 4.909783 1.106424 2.006325
## Turkey 0.599707 -0.258797 3.395834 7.406748 1.872051
## United Arab Emirates 4.787283 2.652538 5.073883 -3.333576 -0.730361
## UK -2.602564 2.332126 -3.939730 2.931470 -2.899410
## USA -2.703941 -0.773690 -0.500668 2.140347 0.814074ggcloud_indiv(mca) # cloud of individuals## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
ggcloud_variables(mca, shapes=FALSE, legend="none",vlab=FALSE) # cloud of active vars
#Countries
plot(mca,col='white')
textvarsup(mca,wjs2000$COUNTRY,col='black',vname='COUNTRY',app=1)