#weight=country+5th year /avg. pr country+5th year *10
weights_all <- c(33,20,22,15,15,12,8,7,5,16,20,14,10,9,9,8,9,4,53,49,35,12,7,7,8,7,5)
weight_sansletters <- c(37,25,27,18,18,16,10,11,7,17,24,15,11,11,11,9,10,5,56,65,58,19,11,11,11,11,9)
# make weigting variable - with all or with all without letters to editor
for (i in 1:27) {
scanpub$weightall[scanpub$country2year5==levels(scanpub$country2year5)[i]] <- weights_all[i]
scanpub$weight_sansletters[scanpub$country2year5==levels(scanpub$country2year5)[i]] <- weight_sansletters[i]
}
#scanpub$r09length<-recode(scanpub$n09length,"'<25%'='<25%';'25-50%'='25-50%';'50-75%'='50-99%';'75-100%'='50-99%'; NA=NA; else='>1 page'")
# sans letters
scanpub<-scanpub %>% filter(n10format!="Lettertoeditor") # drop letters
# duplicate rows
#scanpub_expandedsansletters<-expandRows(scanpub,"weight_sansletters") # if "no letters" # why error?
#scanpub<-scanpub_expandedsansletters
# with letters
#scanpub<-scanpub_copy # all
#scanpub_weightall<-expandRows(scanpub,"weightall") # if "no letters"
#scanpub<-scanpub_scanpub_weightall
count(scanpub)
## # A tibble: 1 x 1
## n
## <int>
## 1 3327
active_themes <- colnames(scanpub[(302:326)])
passive_themes <- active_themes[c(11,13,18,20,25,22,23,25)]
active_themes <- active_themes[-c(11,13,18,20,25,22,23,25)]
active_frames <- colnames(scanpub[551:563])
active_sources1 <- colnames(scanpub[(658:671)])
active_sources2 <- colnames(scanpub[(813:814)])
active_sources <- c(active_sources1,active_sources2)
passive_sources <- active_sources[c(1,2,3,6,7,11,12,13,14)]
active_sources <- active_sources[-c(1,2,3,6,7,11,12,13,14)]
passive_other <-colnames(scanpub[c("COUNTRY2","country2year5","r10genre5","r10genre3", "r02year15", "r02year5","r02year10","v03publication", "n09length", "weight_sansletters", "weightall")])
vars <- c(active_themes, active_frames, active_sources, passive_themes, passive_sources, passive_other)
vars_facto<-vars[-c(57,59,60)]
vars <- vars[-c(46)]
scanpub<-scanpub[,vars]
data<-scanpub[c(1:38,55:62)]
data$country3 <- paste0(data$COUNTRY2,data$r02year15)
data$pub3 <- paste0(data$v03publication,data$r02year15)
data<-data %>% filter(complete.cases(.))
active<-data[c(1:38)]
passive<-data[c(39:48)]
passsimple <-data[c(39,42,45:47)]
passsimplepubyear <-data[c(48)]
result<-soc.mca(active, passsimple)
result<-invert(result,2)
#result<-invert(result,3)
result
## Specific Multiple Correspondence Analysis:
##
## Statistics Scree plot
## Active dimensions: 14 | 1. 42.9% **********************
## Dimensions explaining 80% of inertia: 3 | 2. 26.3% *************
## Active modalities: 76 | 3. 11.3% ******
## Supplementary modalities: 26 | 4. 8.7% ****
## Individuals: 3316 | 5. 6.1% ***
## Share of passive mass: 0 | 6. 2.1% *
## Number of passive modalities: 0 | 7. 1.3%
##
## The 38 active variables: [No. modalities - share of variance]
##
## v12social [2 - 3%] v12edu [2 - 3%]
## v12work [2 - 3%] v12integration [2 - 3%]
## v12familycustom [2 - 3%] v12attitudes [2 - 3%]
## v12racism [2 - 3%] v12religion [2 - 3%]
## v12natsecurity [2 - 3%] v12crime [2 - 3%]
## v12economy [2 - 3%] v12legalimm [2 - 3%]
## v12illegalimm [2 - 3%] v12civilsociety [2 - 3%]
## v12multicult [2 - 3%] v12culture [2 - 3%]
## v12immdebate [2 - 3%] v12civilrights [2 - 3%]
## v20victim_humanitarian [2 - 3%] v20victim_war [2 - 3%]
## v20victim_racismdiscrim [2 - 3%] v20victim_other [2 - 3%]
## v20hero_diversity [2 - 3%] v20hero_integration [2 - 3%]
## v20hero_goodworker [2 - 3%] v20hero_other [2 - 3%]
## v20threat_jobs [2 - 3%] v20threat_publicorder [2 - 3%]
## v20threat_fiscal [2 - 3%] v20threat_socialcohesion [2 - 3%]
## v20threat_other [2 - 3%] r13immigr [2 - 3%]
## r13nonimmigr [2 - 3%] r13ngo [2 - 3%]
## r13expert [2 - 3%] r13media [2 - 3%]
## r13pol [2 - 3%] r13civtot [2 - 3%]
contribution(result)
##
## Dimension 1. (+)
## Ctr Coord
## v12multicult: 1 9.7 1.52
## v20hero_integration: 1 9.6 1.72
## v20hero_goodworker: 1 7.1 1.36
## v12edu: 1 6.1 1.23
## v12attitudes: 1 5.6 1.42
## v20hero_diversity: 1 5.3 1.99
## v12work: 1 4.8 0.81
## v12integration: 1 3.9 0.62
## v12culture: 1 2.9 1.13
## v12immdebate: 1 2.7 1.08
## v12familycustom: 1 2.4 0.71
## v20threat_socialcohesion: 1 2.4 1.09
## v20victim_racismdiscrim: 1 2.1 0.50
## v12racism: 1 1.7 0.47
## v12religion: 1 1.5 0.63
##
## Dimension 1. (-)
## Ctr Coord
## v12crime: 1 2.7 -0.50
## r13civtot: 1 2.0 -0.46
## v12integration: 0 1.6 -0.26
## v20victim_humanitarian: 1 1.6 -0.46
## v12illegalimm: 1 1.5 -0.77
## v12legalimm: 1 1.4 -0.31
## v20threat_publicorder: 1 1.3 -0.51
contribution(result, 2)
##
## Dimension 2. (+)
## Ctr Coord
## v12racism: 1 9.1 1.01
## v20victim_racismdiscrim: 1 6.4 0.82
## v12crime: 1 5.1 0.64
## v12legalimm: 0 4.1 0.41
## v12religion: 1 3.7 0.92
## v20victim_humanitarian: 0 1.7 0.23
## v12integration: 0 1.4 0.22
##
## Dimension 2. (-)
## Ctr Coord
## v20victim_humanitarian: 1 6.3 -0.85
## v12legalimm: 1 5.9 -0.59
## v12social: 1 5.7 -0.93
## v12economy: 1 5.0 -1.14
## v20threat_fiscal: 1 3.4 -1.27
## v12work: 1 3.3 -0.63
## v12integration: 1 3.3 -0.53
## v12civilrights: 1 3.0 -0.69
## v12racism: 0 2.5 -0.28
## v12crime: 0 2.3 -0.28
## v20victim_racismdiscrim: 0 2.0 -0.25
## v12edu: 1 1.9 -0.63
## v20victim_war: 1 1.9 -0.88
## v20hero_goodworker: 1 1.7 -0.61
## r13ngo: 1 1.5 -0.57
contribution(result, 3)
##
## Dimension 3. (+)
## Ctr Coord
## v20threat_socialcohesion: 1 10.1 1.83
## v12immdebate: 1 7.5 1.51
## v20threat_fiscal: 1 6.7 1.59
## r13pol: 1 5.6 0.69
## v12social: 1 4.7 0.75
## v12economy: 1 4.5 0.96
## v20threat_publicorder: 1 4.4 0.77
## v12integration: 1 3.1 0.46
## v12religion: 1 3.1 0.74
## r13media: 1 3.1 0.88
## v12familycustom: 1 1.9 0.53
## v12natsecurity: 1 1.6 0.56
##
## Dimension 3. (-)
## Ctr Coord
## v20hero_goodworker: 1 8.3 -1.22
## r13immigr: 1 4.8 -0.66
## v12culture: 1 3.7 -1.05
## v20hero_integration: 1 3.0 -0.79
## v12work: 1 2.5 -0.48
## v20hero_diversity: 1 2.2 -1.06
## r13pol: 0 1.6 -0.20
contribution(result, 1:3, mode="variable")
## The contribution of the active variables
##
## r13civtot Dim.1 Dim.2 Dim.3 Freq
## r13civtot: 0 0.7 0.0 0.0 2434
## r13civtot: 1 2.0 0.1 0.0 882
## Total 2.7 0.1 0.0 3316
##
## r13expert Dim.1 Dim.2 Dim.3 Freq
## r13expert: 0 0.0 0.0 0.1 3126
## r13expert: 1 0.1 0.1 1.1 190
## Total 0.1 0.1 1.2 3316
##
## r13immigr Dim.1 Dim.2 Dim.3 Freq
## r13immigr: 0 0.1 0.1 1.3 2606
## r13immigr: 1 0.4 0.2 4.8 710
## Total 0.5 0.3 6.1 3316
##
## r13media Dim.1 Dim.2 Dim.3 Freq
## r13media: 0 0.1 0.1 0.3 3056
## r13media: 1 0.9 1.3 3.1 260
## Total 1.0 1.4 3.4 3316
##
## r13ngo Dim.1 Dim.2 Dim.3 Freq
## r13ngo: 0 0.0 0.2 0.0 2941
## r13ngo: 1 0.0 1.5 0.4 375
## Total 0.0 1.7 0.4 3316
##
## r13nonimmigr Dim.1 Dim.2 Dim.3 Freq
## r13nonimmigr: 0 0.0 0.1 0.0 3031
## r13nonimmigr: 1 0.1 0.7 0.5 285
## Total 0.1 0.8 0.5 3316
##
## r13pol Dim.1 Dim.2 Dim.3 Freq
## r13pol: 0 0.0 0.3 1.6 2571
## r13pol: 1 0.1 1.1 5.6 745
## Total 0.1 1.4 7.2 3316
##
## v12attitudes Dim.1 Dim.2 Dim.3 Freq
## v12attitudes: 0 0.5 0.0 0.1 3057
## v12attitudes: 1 5.6 0.6 0.9 259
## Total 6.1 0.6 1.0 3316
##
## v12civilrights Dim.1 Dim.2 Dim.3 Freq
## v12civilrights: 0 0.1 0.6 0.1 2798
## v12civilrights: 1 0.4 3.0 0.5 518
## Total 0.5 3.6 0.6 3316
##
## v12civilsociety Dim.1 Dim.2 Dim.3 Freq
## v12civilsociety: 0 0.1 0.0 0.0 3142
## v12civilsociety: 1 0.9 0.4 0.7 174
## Total 1.0 0.4 0.7 3316
##
## v12crime Dim.1 Dim.2 Dim.3 Freq
## v12crime: 0 1.2 2.3 0.2 2294
## v12crime: 1 2.7 5.1 0.4 1022
## Total 3.9 7.4 0.6 3316
##
## v12culture Dim.1 Dim.2 Dim.3 Freq
## v12culture: 0 0.2 0.1 0.3 3101
## v12culture: 1 2.9 0.9 3.7 215
## Total 3.1 1.0 4.0 3316
##
## v12economy Dim.1 Dim.2 Dim.3 Freq
## v12economy: 0 0.1 0.5 0.5 3002
## v12economy: 1 1.3 5.0 4.5 314
## Total 1.4 5.5 5.0 3316
##
## v12edu Dim.1 Dim.2 Dim.3 Freq
## v12edu: 0 0.8 0.2 0.0 2934
## v12edu: 1 6.1 1.9 0.0 382
## Total 6.9 2.1 0.0 3316
##
## v12familycustom Dim.1 Dim.2 Dim.3 Freq
## v12familycustom: 0 0.4 0.0 0.3 2873
## v12familycustom: 1 2.4 0.0 1.9 443
## Total 2.8 0.0 2.2 3316
##
## v12illegalimm Dim.1 Dim.2 Dim.3 Freq
## v12illegalimm: 0 0.1 0.1 0.0 3073
## v12illegalimm: 1 1.5 0.8 0.2 243
## Total 1.6 0.9 0.2 3316
##
## v12immdebate Dim.1 Dim.2 Dim.3 Freq
## v12immdebate: 0 0.2 0.0 0.5 3103
## v12immdebate: 1 2.7 0.5 7.5 213
## Total 2.9 0.5 8.0 3316
##
## v12integration Dim.1 Dim.2 Dim.3 Freq
## v12integration: 0 1.6 1.4 1.3 2342
## v12integration: 1 3.9 3.3 3.1 974
## Total 5.5 4.7 4.4 3316
##
## v12legalimm Dim.1 Dim.2 Dim.3 Freq
## v12legalimm: 0 1.0 4.1 0.1 1953
## v12legalimm: 1 1.4 5.9 0.1 1363
## Total 2.4 10.0 0.2 3316
##
## v12multicult Dim.1 Dim.2 Dim.3 Freq
## v12multicult: 0 1.3 0.2 0.0 2921
## v12multicult: 1 9.7 1.1 0.1 395
## Total 11.0 1.3 0.1 3316
##
## v12natsecurity Dim.1 Dim.2 Dim.3 Freq
## v12natsecurity: 0 0.1 0.1 0.2 2996
## v12natsecurity: 1 1.3 1.3 1.6 320
## Total 1.4 1.4 1.8 3316
##
## v12racism Dim.1 Dim.2 Dim.3 Freq
## v12racism: 0 0.5 2.5 0.1 2590
## v12racism: 1 1.7 9.1 0.4 726
## Total 2.2 11.6 0.5 3316
##
## v12religion Dim.1 Dim.2 Dim.3 Freq
## v12religion: 0 0.2 0.5 0.4 2956
## v12religion: 1 1.5 3.7 3.1 360
## Total 1.7 4.2 3.5 3316
##
## v12social Dim.1 Dim.2 Dim.3 Freq
## v12social: 0 0.1 1.1 0.9 2785
## v12social: 1 0.7 5.7 4.7 531
## Total 0.8 6.8 5.6 3316
##
## v12work Dim.1 Dim.2 Dim.3 Freq
## v12work: 0 1.3 0.9 0.7 2623
## v12work: 1 4.8 3.3 2.5 693
## Total 6.1 4.2 3.2 3316
##
## v20hero_diversity Dim.1 Dim.2 Dim.3 Freq
## v20hero_diversity: 0 0.2 0.0 0.1 3190
## v20hero_diversity: 1 5.3 0.1 2.2 126
## Total 5.5 0.1 2.3 3316
##
## v20hero_goodworker Dim.1 Dim.2 Dim.3 Freq
## v20hero_goodworker: 0 0.9 0.2 1.0 2955
## v20hero_goodworker: 1 7.1 1.7 8.3 361
## Total 8.0 1.9 9.3 3316
##
## v20hero_integration Dim.1 Dim.2 Dim.3 Freq
## v20hero_integration: 0 1.0 0.1 0.3 3011
## v20hero_integration: 1 9.6 0.6 3.0 305
## Total 10.6 0.7 3.3 3316
##
## v20hero_other Dim.1 Dim.2 Dim.3 Freq
## v20hero_other: 0 0 0 0 3236
## v20hero_other: 1 0.0 0.0 0.8 80
## Total 0.0 0.0 0.8 3316
##
## v20threat_fiscal Dim.1 Dim.2 Dim.3 Freq
## v20threat_fiscal: 0 0.0 0.2 0.4 3144
## v20threat_fiscal: 1 0.1 3.4 6.7 172
## Total 0.1 3.6 7.1 3316
##
## v20threat_jobs Dim.1 Dim.2 Dim.3 Freq
## v20threat_jobs: 0 0 0 0 3262
## v20threat_jobs: 1 0.2 1.2 0.1 54
## Total 0.2 1.2 0.1 3316
##
## v20threat_other Dim.1 Dim.2 Dim.3 Freq
## v20threat_other: 0 0 0 0 3144
## v20threat_other: 1 0.2 0.4 0.3 172
## Total 0.2 0.4 0.3 3316
##
## v20threat_publicorder Dim.1 Dim.2 Dim.3 Freq
## v20threat_publicorder: 0 0.2 0.2 0.8 2831
## v20threat_publicorder: 1 1.3 1.2 4.4 485
## Total 1.5 1.4 5.2 3316
##
## v20threat_socialcohesion Dim.1 Dim.2 Dim.3 Freq
## v20threat_socialcohesion: 0 0.2 0.0 0.6 3121
## v20threat_socialcohesion: 1 2.4 0.1 10.1 195
## Total 2.6 0.1 10.7 3316
##
## v20victim_humanitarian Dim.1 Dim.2 Dim.3 Freq
## v20victim_humanitarian: 0 0.4 1.7 0.0 2598
## v20victim_humanitarian: 1 1.6 6.3 0.0 718
## Total 2 8 0 3316
##
## v20victim_other Dim.1 Dim.2 Dim.3 Freq
## v20victim_other: 0 0 0 0 2782
## v20victim_other: 1 0.2 0.1 0.2 534
## Total 0.2 0.1 0.2 3316
##
## v20victim_racismdiscrim Dim.1 Dim.2 Dim.3 Freq
## v20victim_racismdiscrim: 0 0.6 2.0 0.1 2541
## v20victim_racismdiscrim: 1 2.1 6.4 0.3 775
## Total 2.7 8.4 0.4 3316
##
## v20victim_war Dim.1 Dim.2 Dim.3 Freq
## v20victim_war: 0 0.0 0.1 0.0 3113
## v20victim_war: 1 0.6 1.9 0.3 203
## Total 0.6 2.0 0.3 3316
## Average contribution per modality: 1.3
## Total number of individuals: 3316
balance(result)
## + Contrib. - Contrib. Balance (+/-)
## [1,] 0.7657 0.2343 3.2682
## [2,] 0.4442 0.5558 0.7992
## [3,] 0.6538 0.3462 1.8885
## [4,] 0.5933 0.4067 1.4591
## [5,] 0.6679 0.3321 2.0115
## [6,] 0.5078 0.4922 1.0315
## [7,] 0.6134 0.3866 1.5865
## [8,] 0.7733 0.2267 3.4103
## [9,] 0.5569 0.4431 1.2570
## [10,] 0.5571 0.4429 1.2577
## [11,] 0.4422 0.5578 0.7929
## [12,] 0.5864 0.4136 1.4176
## [13,] 0.5890 0.4110 1.4329
## [14,] 0.5915 0.4085 1.4479
# trim variabelnamn
result$names.sup <- sapply(strsplit(result$names.sup, ": "), tail, 1)
# result$names.mod <- sapply(strsplit(result$names.mod, ": "), tail, 1) # fjernar variabelnamn i kategorinamnet
map.ind(result, dim=c(1,2))
map.ind(result, dim=c(3,2))
map.ctr(result, dim = c(1,2), ctr.dim=c(1), label.size = 3)
map.ctr(result, dim = c(3,2), ctr.dim=c(2), label.size = 3)
map.ctr(result, dim = c(3,2), ctr.dim=c(3), label.size = 3)
map <- map.sup(result, dim = c(1, 2), label.size = 2)
map.path(result,data$r02year10, map, dim=c(1,2))
map <- map.sup(result, dim = c(3, 2), label.size = 2)
map.path(result,data$r02year10, map, dim=c(3,2))
map.active(result, dim=c(1,2), point.size=result$ctr.mod[, 1]) # size acc.to contribution
map.active(result, dim=c(3,2), point.size=result$ctr.mod[, 1]) # size acc.to contribution
map.active(result, dim=c(1,2), point.size=result$ctr.mod[, 1], label.size=3, point.shape = 21, label.repel = FALSE)
map.active(result, dim=c(3,2), point.size=result$ctr.mod[, 1], label.size=3, point.shape = 21, label.repel = FALSE)
# map.array(c(nor_ind, nor_ctr))
class <- which(data$r10genre3 == 'news')
csa.res <- soc.csa(result, class, sup=passsimple)
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
contribution(csa.res,1)
##
## Dimension 1. (+)
## Ctr Coord
## v20hero_integration: 1 11.9 1.93
## v20hero_goodworker: 1 11.7 1.76
## v12work: 1 7.6 1.02
## v12edu: 1 7.1 1.33
## v12multicult: 1 5.9 1.20
## v20hero_diversity: 1 4.5 1.84
## v12integration: 1 3.7 0.60
## v12culture: 1 3.4 1.23
## v12attitudes: 1 3.1 1.08
## v12familycustom: 1 2.6 0.75
## v12crime: 0 2.3 0.31
## v20threat_socialcohesion: 1 1.7 0.91
##
## Dimension 1. (-)
## Ctr Coord
## v12crime: 1 5.2 -0.69
## v20threat_publicorder: 1 2.5 -0.70
## r13civtot: 1 2.2 -0.49
## v12natsecurity: 1 2.1 -0.79
## v12work: 0 2.0 -0.27
## v12integration: 0 1.5 -0.25
## v20hero_goodworker: 0 1.4 -0.22
contribution(csa.res,2)
##
## Dimension 2. (+)
## Ctr Coord
## v20victim_humanitarian: 1 10.9 1.08
## v12legalimm: 1 8.7 0.70
## v12social: 1 5.3 0.87
## v12civilrights: 1 4.8 0.84
## v20victim_war: 1 3.6 1.16
## v20threat_fiscal: 1 3.6 1.26
## v12economy: 1 3.3 0.90
## v12integration: 1 2.8 0.47
## v12crime: 0 2.3 0.28
## r13ngo: 1 2.2 0.67
## r13pol: 1 2.2 0.48
## v12racism: 0 2.0 0.24
## v20victim_racismdiscrim: 0 1.5 0.22
##
## Dimension 2. (-)
## Ctr Coord
## v12racism: 1 7.1 -0.87
## v12legalimm: 0 6.1 -0.49
## v12crime: 1 5.1 -0.62
## v20victim_racismdiscrim: 1 5.1 -0.71
## v20victim_humanitarian: 0 3.0 -0.30
## v12culture: 1 2.7 -0.99
## v12religion: 1 2.1 -0.67
## v12multicult: 1 1.5 -0.53
csa.measures(csa.res, correlations = T, cosines = F, cosine.angles = F, dim=1:3)
##
## Measures for Class Specific Multiple Correspondence Analysis:
##
##
## Correlations:
##
## MCA: 1 MCA: 2 MCA: 3
## CSA: 1 0.97 -0.26 -0.18
## CSA: 2 -0.18 -0.94 0.20
## CSA: 3 0.11 0.05 0.89
##
##
map.active(csa.res, label.size=3, label.repel = T, map.title = "News 1-2")
map.sup(csa.res, label.size=3, label.repel = T, map.title = "News 1-2")
## Warning: Removed 13 rows containing missing values (geom_point).
## Warning: Removed 13 rows containing missing values (geom_text_repel).
#map.csa.mca.array(csa.res, ndim = 3)
class <- which(data$r10genre3 == 'column')
csa.res <- soc.csa(result, class,sup=passsimple)
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
contribution(csa.res,1)
##
## Dimension 1. (+)
## Ctr Coord
## r13media: 1 25.5 3.45
## v12immdebate: 1 20.4 3.41
## v12attitudes: 1 8.9 2.05
## v12multicult: 1 8.0 1.56
## v12religion: 1 5.3 1.34
## v20victim_racismdiscrim: 1 3.7 0.76
## v12racism: 1 3.6 0.78
## v20threat_socialcohesion: 1 2.2 1.17
## v20hero_diversity: 1 1.5 1.21
##
## Dimension 1. (-)
## Ctr Coord
## r13media: 0 2.2 -0.29
## v20victim_humanitarian: 1 1.7 -0.53
## v12legalimm: 1 1.4 -0.35
## v12immdebate: 0 1.4 -0.23
contribution(csa.res,2)
##
## Dimension 2. (+)
## Ctr Coord
## r13media: 1 14.8 2.41
## v12economy: 1 11.2 1.91
## v12immdebate: 1 11.2 2.32
## v12social: 1 5.8 1.06
## v20threat_fiscal: 1 4.9 1.70
## v12legalimm: 1 3.4 0.51
## v12civilrights: 1 2.7 0.73
## v20victim_humanitarian: 1 2.7 0.62
## v12illegalimm: 1 2.5 1.03
## v12racism: 0 1.5 0.25
## v20threat_jobs: 1 1.5 1.70
##
## Dimension 2. (-)
## Ctr Coord
## v12multicult: 1 5.6 -1.20
## v12racism: 1 5.5 -0.88
## v12religion: 1 4.0 -1.07
## v20victim_racismdiscrim: 1 2.7 -0.60
## v12legalimm: 0 2.4 -0.36
## v20hero_diversity: 1 1.7 -1.18
csa.measures(csa.res, correlations = T, cosines = F, cosine.angles = F, dim=1:3)
##
## Measures for Class Specific Multiple Correspondence Analysis:
##
##
## Correlations:
##
## MCA: 1 MCA: 2 MCA: 3
## CSA: 1 0.61 0.62 0.51
## CSA: 2 -0.12 -0.64 0.51
## CSA: 3 0.73 -0.40 -0.05
##
##
map.active(csa.res, label.size=3, label.repel = T, map.title = "Column")
map.sup(csa.res, label.size=3, label.repel = T, map.title = "Column")
## Warning: Removed 13 rows containing missing values (geom_point).
## Warning: Removed 13 rows containing missing values (geom_text_repel).
csa.res <-csa.all(result, data$COUNTRY2)
csa.res$measures$NOR$cor
## MCA: 1 MCA: 2 MCA: 3 MCA: 4 MCA: 5
## CSA: 1 0.959735 0.13168 -0.3693 0.1606 -0.03056
## CSA: 2 0.070666 -0.90583 -0.2881 -0.1944 0.45072
## CSA: 3 0.004845 -0.02601 0.4467 0.2547 0.11054
## CSA: 4 0.023009 -0.25537 -0.2699 0.3057 -0.60228
## CSA: 5 0.003030 0.02647 -0.1134 -0.1201 0.01322
csa.res$measures$SWE$cor
## MCA: 1 MCA: 2 MCA: 3 MCA: 4 MCA: 5
## CSA: 1 0.91658 0.31900 0.1935 -0.31103 0.1008
## CSA: 2 0.29271 -0.86935 -0.1536 0.30979 0.3097
## CSA: 3 0.00629 0.31118 -0.3245 0.34357 0.3885
## CSA: 4 -0.17295 0.02293 0.7291 -0.01278 0.6589
## CSA: 5 0.13281 0.11404 0.1252 0.70406 -0.2117
csa.res$measures$DEN$cor
## MCA: 1 MCA: 2 MCA: 3 MCA: 4 MCA: 5
## CSA: 1 0.84204 -0.4374 0.2558 0.008826 -0.3783
## CSA: 2 0.45158 0.6755 -0.1935 0.550585 0.3570
## CSA: 3 -0.19037 0.2350 0.8631 0.387005 -0.1865
## CSA: 4 0.09603 0.2570 0.2364 -0.635763 0.2826
## CSA: 5 -0.05775 -0.3138 0.1821 -0.047566 0.5660
class <- which(data$COUNTRY2 == 'NOR')
csa.res <- soc.csa(result, class,sup=passsimple)
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
contribution(csa.res,1)
##
## Dimension 1. (+)
## Ctr Coord
## v20hero_diversity: 1 13.9 2.91
## v12multicult: 1 8.8 1.31
## v20hero_integration: 1 7.5 1.37
## v20hero_goodworker: 1 5.8 1.12
## v12edu: 1 4.4 0.94
## v12integration: 1 4.4 0.59
## v12culture: 1 3.2 1.07
## v12work: 1 3.0 0.58
## v12religion: 1 2.9 0.78
## v12legalimm: 0 2.5 0.31
## v12familycustom: 1 2.3 0.64
## r13immigr: 1 2.0 0.47
## r13civtot: 0 1.7 0.23
##
## Dimension 1. (-)
## Ctr Coord
## v12illegalimm: 1 5.4 -1.30
## r13civtot: 1 4.7 -0.64
## v12legalimm: 1 3.5 -0.45
## v12crime: 1 2.6 -0.44
## v20victim_humanitarian: 1 2.6 -0.53
## v20threat_publicorder: 1 2.4 -0.61
## v12integration: 0 1.8 -0.24
contribution(csa.res,2)
##
## Dimension 2. (+)
## Ctr Coord
## v20victim_humanitarian: 1 13.5 1.09
## v20victim_war: 1 13.5 2.06
## v12legalimm: 1 6.3 0.54
## v12crime: 0 5.0 0.37
## r13ngo: 1 3.9 0.81
## v12social: 1 3.3 0.63
## v12work: 1 3.1 0.53
## v20hero_goodworker: 1 2.2 0.63
## v12civilrights: 1 1.5 0.43
##
## Dimension 2. (-)
## Ctr Coord
## v12crime: 1 11.2 -0.83
## v20threat_publicorder: 1 6.1 -0.90
## v12legalimm: 0 4.4 -0.38
## v20victim_humanitarian: 0 3.7 -0.30
## v12racism: 1 3.3 -0.54
## v20victim_racismdiscrim: 1 2.8 -0.48
## v12natsecurity: 1 1.9 -0.62
## v12religion: 1 1.7 -0.54
map.active(csa.res, label.size=3, label.repel = T, map.title = "Norway CSA 1-2")
map.sup(csa.res, label.size=3, label.repel = T, map.title = "Norway CSA 1-2")
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing missing values (geom_text_repel).
csa.res <- soc.csa(result, class,sup=passive)
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(1), label.size = 3, map.title = "Norway CSA ctr axis 1")
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(2), label.size = 3, map.title = "Norway CSA ctr axis 2")
class <- which(data$COUNTRY2 == 'SWE')
csa.res <- soc.csa(result, class, sup=passsimple)
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
contribution(csa.res,1)
##
## Dimension 1. (+)
## Ctr Coord
## v12attitudes: 1 17.5 2.78
## v12multicult: 1 12.5 1.90
## v20victim_racismdiscrim: 1 8.2 1.10
## v12racism: 1 7.9 1.12
## r13media: 1 4.3 1.38
## v12immdebate: 1 3.5 1.38
## v12culture: 1 3.1 1.29
## v20hero_goodworker: 1 3.1 0.99
## v20hero_integration: 1 3.0 1.06
## v12work: 1 2.0 0.58
## v12edu: 1 1.6 0.68
## v20hero_diversity: 1 1.5 1.18
##
## Dimension 1. (-)
## Ctr Coord
## v20victim_other: 1 3.6 -0.88
## v20victim_racismdiscrim: 0 2.5 -0.34
## v12racism: 0 2.2 -0.31
## v12multicult: 0 1.7 -0.26
## v12attitudes: 0 1.5 -0.24
## v20victim_humanitarian: 1 1.5 -0.48
## r13civtot: 1 1.5 -0.45
contribution(csa.res,2)
##
## Dimension 2. (+)
## Ctr Coord
## v12civilsociety: 1 5.4 1.75
## v20victim_humanitarian: 1 4.9 0.83
## v20hero_goodworker: 1 4.8 1.15
## v12work: 1 4.5 0.81
## v12legalimm: 1 4.5 0.57
## v12integration: 1 4.2 0.66
## v20hero_integration: 1 3.8 1.11
## v12edu: 1 3.7 0.99
## v20hero_other: 1 3.4 2.06
## v12economy: 1 3.0 0.97
## v12civilrights: 1 3.0 0.76
## r13immigr: 1 2.9 0.64
## v12racism: 0 2.7 0.32
## v20victim_racismdiscrim: 0 1.9 0.28
## v12social: 1 1.8 0.59
## v20victim_war: 1 1.8 0.94
## v12crime: 0 1.6 0.27
##
## Dimension 2. (-)
## Ctr Coord
## v12racism: 1 9.6 -1.15
## v20victim_racismdiscrim: 1 6.4 -0.91
## v12crime: 1 3.6 -0.60
## v12legalimm: 0 3.1 -0.40
## v20threat_other: 1 2.6 -1.23
## v12integration: 0 1.8 -0.27
## v20victim_humanitarian: 0 1.4 -0.23
map.active(csa.res, label.size=3, label.repel = T, map.title = "Sweden CSA 1-2")
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(1), label.size = 3, map.title = "Sweden CSA ctr axis 1")
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(2), label.size = 3, map.title = "Sweden CSA ctr axis 2")
map.sup(csa.res, dim = c(1,2), label.size = 3, map.title = "Sweden CSA")
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing missing values (geom_text_repel).
class <- which(data$COUNTRY2 == 'DEN')
csa.res <- soc.csa(result, class,sup=passsimple)
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
contribution(csa.res,1)
##
## Dimension 1. (+)
## Ctr Coord
## v20threat_fiscal: 1 11.5 2.81
## v12edu: 1 8.3 1.61
## v20hero_integration: 1 7.1 1.66
## v20threat_socialcohesion: 1 6.7 2.01
## v12social: 1 6.0 1.16
## v12economy: 1 5.5 1.44
## v12integration: 1 5.2 0.79
## v12work: 1 3.8 0.81
## v20hero_goodworker: 1 3.2 1.02
## v12immdebate: 1 2.4 1.15
## v20hero_diversity: 1 2.4 1.49
## v12crime: 0 1.8 0.31
## v12familycustom: 1 1.7 0.67
## v12multicult: 1 1.6 0.69
## r13pol: 1 1.4 0.48
##
## Dimension 1. (-)
## Ctr Coord
## v20threat_publicorder: 1 4.6 -1.06
## v12crime: 1 4.1 -0.69
## v12natsecurity: 1 2.5 -0.97
## v12integration: 0 2.1 -0.33
## v20victim_humanitarian: 1 1.4 -0.47
contribution(csa.res,2)
##
## Dimension 2. (+)
## Ctr Coord
## v12religion: 1 9.1 1.70
## v20hero_integration: 1 6.2 1.52
## v20threat_socialcohesion: 1 6.2 1.91
## v20hero_diversity: 1 4.4 2.01
## v12familycustom: 1 3.3 0.93
## v12immdebate: 1 3.2 1.32
## v12multicult: 1 2.7 0.89
## v12culture: 1 2.0 1.04
## v12legalimm: 0 1.9 0.33
## v12edu: 1 1.5 0.67
## v20threat_fiscal: 0 1.4 0.23
##
## Dimension 2. (-)
## Ctr Coord
## v20threat_fiscal: 1 25.3 -4.12
## v12economy: 1 6.4 -1.54
## v12social: 1 2.9 -0.79
## v12legalimm: 1 2.7 -0.47
## v20victim_humanitarian: 1 2.2 -0.60
contribution(csa.res,3)
##
## Dimension 3. (+)
## Ctr Coord
## v20threat_socialcohesion: 1 19.8 3.14
## v20threat_publicorder: 1 16.1 1.79
## v20threat_fiscal: 1 6.4 1.90
## v12religion: 1 3.7 1.00
## v12crime: 1 2.9 0.52
## v12familycustom: 1 2.5 0.73
## v12immdebate: 1 2.2 1.01
## v12natsecurity: 1 1.7 0.71
##
## Dimension 3. (-)
## Ctr Coord
## v20hero_goodworker: 1 7.4 -1.41
## v20hero_integration: 1 5.1 -1.27
## v12work: 1 3.4 -0.69
## v20hero_diversity: 1 3.0 -1.52
## v20threat_publicorder: 0 2.8 -0.31
## r13immigr: 1 2.7 -0.61
## v20victim_humanitarian: 1 1.4 -0.44
map.active(csa.res, label.size=3, label.repel = T, map.title = "Denmark CSA 1-2")
map.active(csa.res, label.size=3, label.repel = T, dim=c(3,2), map.title = "Denmark CSA 3-2")
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(1), label.size = 3, map.title = "Denmark CSA ctr axis 1")
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(2), label.size = 3, map.title = "Denmark CSA ctr axis 2")
map.ctr(csa.res, dim = c(3,2), ctr.dim=c(3), label.size = 3, map.title = "Denmark CSA ctr axis 3")
map.sup(csa.res, dim = c(1,2), label.size = 3, map.title = "Denmark CSA 1-2")
## Warning: Removed 18 rows containing missing values (geom_point).
## Warning: Removed 18 rows containing missing values (geom_text_repel).
map.sup(csa.res, dim = c(3,2), label.size = 3, map.title = "Denmark CSA 3-2")
## Warning: Removed 18 rows containing missing values (geom_point).
## Warning: Removed 18 rows containing missing values (geom_text_repel).
csa.res <-csa.all(result, data$r02year15)
csa.res$measures$`70-84`$cor
## MCA: 1 MCA: 2 MCA: 3 MCA: 4 MCA: 5
## CSA: 1 0.92549 -0.3472 -0.20127 -0.23405841 -0.1699
## CSA: 2 0.24474 0.1605 -0.56923 0.22021743 0.5597
## CSA: 3 -0.10608 -0.6904 0.08565 -0.10832927 0.5319
## CSA: 4 0.21479 0.3650 0.53898 -0.17783185 0.2768
## CSA: 5 0.02248 0.3997 -0.13591 -0.00003358 -0.1018
csa.res$measures$`85-99`$cor
## MCA: 1 MCA: 2 MCA: 3 MCA: 4 MCA: 5
## CSA: 1 0.96421 0.25320 0.0605 0.05317 -0.13073
## CSA: 2 0.19282 -0.90533 0.4407 0.06739 0.02642
## CSA: 3 0.12373 -0.13377 -0.4548 0.12147 0.85957
## CSA: 4 -0.03494 0.23337 0.6785 -0.43906 0.43491
## CSA: 5 -0.08860 0.06814 0.2912 0.82424 0.08796
csa.res$measures$`00-`$cor
## MCA: 1 MCA: 2 MCA: 3 MCA: 4 MCA: 5
## CSA: 1 0.89254 0.38874 0.1893 0.10411 0.16093
## CSA: 2 0.42526 -0.87273 -0.2205 -0.06242 -0.12350
## CSA: 3 -0.08683 -0.22391 0.5847 0.77130 0.04814
## CSA: 4 -0.01280 -0.13633 0.7122 -0.60788 0.17457
## CSA: 5 -0.03099 -0.03824 -0.1096 0.07765 0.78366
class <- which(data$r02year15 == '70-84')
csa.res <- soc.csa(result, class,sup=passsimple)
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
maptitle<-"1970-84"
map.active(csa.res, label.size=3, label.repel = T, map.title = maptitle)
map.active(csa.res, label.size=3, label.repel = T, dim=c(3,2), map.title = maptitle)
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(1), label.size = 3, map.title = maptitle)
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(2), label.size = 3, map.title = maptitle)
map.ctr(csa.res, dim = c(3,2), ctr.dim=c(3), label.size = 3, map.title = maptitle)
map.sup(csa.res, dim = c(1,2), label.size = 3, map.title = maptitle)
## Warning: Removed 15 rows containing missing values (geom_point).
## Warning: Removed 15 rows containing missing values (geom_text_repel).
map.sup(csa.res, dim = c(3,2), label.size = 3, map.title = maptitle)
## Warning: Removed 15 rows containing missing values (geom_point).
## Warning: Removed 15 rows containing missing values (geom_text_repel).
class <- which(data$r02year15 == '00-')
csa.res <- soc.csa(result, class,sup=passsimple)
## Warning in lapply(obj, as.numeric): NAs introduced by coercion
maptitle<-"2000-"
map.active(csa.res, label.size=3, label.repel = T, map.title = maptitle)
map.active(csa.res, label.size=3, label.repel = T, dim=c(3,2), map.title = maptitle)
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(1), label.size = 3, map.title = maptitle)
map.ctr(csa.res, dim = c(1,2), ctr.dim=c(2), label.size = 3, map.title = maptitle)
map.ctr(csa.res, dim = c(3,2), ctr.dim=c(3), label.size = 3, map.title = maptitle)
map.sup(csa.res, dim = c(1,2), label.size = 3, map.title = maptitle)
## Warning: Removed 15 rows containing missing values (geom_point).
## Warning: Removed 15 rows containing missing values (geom_text_repel).
map.sup(csa.res, dim = c(3,2), label.size = 3, map.title = maptitle)
## Warning: Removed 15 rows containing missing values (geom_point).
## Warning: Removed 15 rows containing missing values (geom_text_repel).
