First names, cognitive ability and social status in Denmark
Emil O. W. Kirkegaard
Study
Study to be published in Mankind Quarterly:
- Emil O. W. Kirkegaard, Mila Batin. (2019). First names, cognitive ability and social status in Denmark. Mankind Quarterly.
Study files
Data sources:
- Social data: Kirkegaard and Tranberg (2015)
- Cognitive data: Danish military recruits about year 2005 (sensitive data from Thomas)
Startup
Load packages and set options.
options(digits = 2)
library(pacman)
p_load(mediation, kirkegaard, readr, rms, lavaan)Data
Load data and merge with existing dataset. This is a little tricky because we have to use the gendered versions of the names to merge, but we don’t necessarily want these for other purposes.
#ad hoc gender function
genderize = function(x, gender = NULL, remove = F) {
if (remove) {
x = str_replace(x, " \\(.+\\)", "")
x = str_replace(x, "_.$", "")
} else {
#check missing
if (is.null(gender)) stop("`gender` was NULL!")
#identify gender
.gender = (gender == "Male" | gender == 1)
#make a vec to add
.gender_str = if_else(.gender, true = "_M", false = "_F", missing = "_?")
#add
x = x + .gender_str
}
x
}
#load main name data from previous study
kirk_tran = read_rds("data/kirkegaard_tranberg_2015.rds") %>%
#rename name to firstname
rename(
firstname = name
) %>%
#remove gender tag from firstnames
mutate(
firstname = genderize(firstname, remove = T),
firstname_gendered = genderize(firstname, gender)
)
#Thomas
thomas_data = read_csv("data/thomas_firstnames_data.csv") %>%
#remove na name
filter(!is.na(firstname)) %>%
#fix gender
mutate(
firstname_gendered = genderize(firstname, sex)
)## Parsed with column specification:
## cols(
## sex = col_double(),
## firstname = col_character(),
## region = col_character(),
## IQ_raw = col_double(),
## n = col_double()
## )#merge names in Thomas' data
thomas_data %<>% plyr::ddply("firstname", .fun = function(.d) {
#assert equal sex
assert_that(kirkegaard::all_elements_the_same(.d$sex))
#use first row as default values
..d = .d[1, ]
#calc wtd mean IQ
..d$IQ_raw = wtd_mean(.d$IQ_raw, .d$n)
#sum n
..d$n = sum(.d$n)
#out
..d
})
#sample sizes for IQ data
#total
thomas_data$n %>% sum()## [1] 65137#descrip
thomas_data$n %>% describe()## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 265 246 429 65 132 58 21 2140 2119 2.6 6.5 26#recode sex
thomas_data$sex = if_else(thomas_data$sex == 1, true = "male", false = "female")
#sex dist
thomas_data$sex %>% table2()## # A tibble: 3 x 3
## Group Count Percent
## <chr> <dbl> <dbl>
## 1 male 226 85.3
## 2 female 39 14.7
## 3 <NA> 0 0#any duplicates in thomas' data?
assert_that(!any(duplicated(thomas_data$firstname)))## [1] TRUE#merge data
d = full_join(kirk_tran, thomas_data %>% select(-firstname), by = c("firstname_gendered" = "firstname_gendered"))
#synonyms
d$S = d$S.both.no.age
d$CA = d$IQ_raw %>% standardize()
d$sqrtn = sqrt(d$n)
d$nonwestern = (d$region != "W Europe")
d %>% filter(!is.na(nonwestern)) %>% .$nonwestern %>% table2()## # A tibble: 3 x 3
## Group Count Percent
## <chr> <dbl> <dbl>
## 1 FALSE 255 96.2
## 2 TRUE 10 3.77
## 3 <NA> 0 0d$male = d$sex == "male"
#conver to IQ, assuming Danish = 100, 15
#data from previous study
d$IQ = (((d$IQ_raw - 42.79833641) / 9.832409119) * 15) + 100
d$IQ_std = d$CA
#descrip
d$IQ %>% describe()## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 265 97 4.9 98 98 4.1 76 107 31 -1.2 2.8 0.3d$male %>% describe()## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 265 0.85 0.35 1 0.94 0 0 1 1 -2 1.9 0.02Note that the S scores are standardized at the firstname level, not the individual level. An S score of -2 does NOT mean that the group mean is on the 2th centile of society. However, the IQs are scaled to individual-level Danish norms, so they do have the usual interpretation. It’s not easy to rescale the S scores.
Plot cognitive ability x S
Now we are ready to examine the relationship between the mean cognitive ability and S score of each first name.
#labels for reuse
S_ylab = ylab("Average S\n(general socioeconomic factor based on 5 indicators; z-score)")
CA_xlab = xlab("Average IQ on Danish military test (Danish norms)")
#plot
GG_scatter(d, "IQ", "S", case_names = "firstname", color = "gender") +
scale_color_manual("Sex", values = c("Red", "Blue")) +
CA_xlab +
S_ylab
GG_save("figs/CA_S.png")
GG_scatter(d, "IQ", "S", case_names = "firstname", weights = sqrt(d$n), color = "gender", alpha = .2) +
scale_color_manual("Sex", values = c("Red", "Blue")) +
CA_xlab +
S_ylab
GG_save("figs/CA_S_wtd.png")
#S by sex
GG_denhist(d, "S", group = "gender") + scale_x_continuous("S\n(generalized social status scored from 5 indicators)")## Warning in GG_denhist(d, "S", group = "gender"): There were groups without
## any data. These were removed## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
GG_save("figs/S_sex.png")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.#IQ by western status
#which names?
d %>% filter(nonwestern) %>% pull(firstname)## [1] "Ahmet" "Mohamad" "Mehmet" "Ahmed" "Mohammed" "Mustafa"
## [7] "Ahmad" "Mohamed" "Mohammad" "Ali"#distribution
GG_denhist(d, "IQ", "nonwestern")## Warning in GG_denhist(d, "IQ", "nonwestern"): Grouping variable contained
## missing values. These were removed. If you want an NA group, convert to
## explicit value.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#numbers
describeBy(d$IQ, d$nonwestern)##
## Descriptive statistics by group
## group: FALSE
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 255 98 3.9 98 98 3.6 86 107 20 -0.19 -0.2 0.24
## --------------------------------------------------------
## group: TRUE
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 10 82 3.4 82 82 3.6 76 87 11 -0.27 -1.3 1.1plyr::ddply(d, "nonwestern", function(dd) {
tibble(
n = nrow(dd),
mean = wtd.mean(dd$IQ),
wmean = wtd.mean(dd$IQ, dd$number)
)
})## nonwestern n mean wmean
## 1 FALSE 255 98 98
## 2 TRUE 10 82 81
## 3 NA 2093 NaN NaNWe see a good correspondance. One would in fact expect this to be near perfect if it wasn’t for a few problems:
- The first names have limited sample size, down to 25. This should be improved by weighting the datapoints, but in this case it actually depreciated the fit a bit.
- The cognitive data concern differnet age groups within each first name. The cognitive data concern people entering military service around 2005, whereas the social data concern all persons with that name. While one can expect name averages to be quite stable, they are not entirely stable over time (cf. The Son also Rises).
- The military recruits are mostly self-selected, so there may be selection bias.
By region
We can also see that there’s some outliers in the bottom which are obviously Muslim names. We can remove these since we have estimated geographical origin too.
#plot with geo colors
GG_scatter(d, "IQ", "S", case_names = "firstname", color = "region", weights = "sqrtn") +
scale_color_discrete("Region of origin") +
CA_xlab +
S_ylab
GG_save("figs/CA_S_region.png")
#excluding nonwestern
d %>% filter(!nonwestern) %>%
GG_scatter("IQ", "S", case_names = "firstname", weights = "sqrtn") +
CA_xlab +
S_ylab
GG_save("figs/CA_S_WEurope.png")
d %>% filter(!nonwestern) %>%
GG_scatter("IQ", "S", case_names = "firstname") +
CA_xlab +
S_ylab
GG_save("figs/CA_S_WEurope_unwtd.png")
#nonwestern only
d %>% filter(nonwestern) %>%
GG_scatter("IQ", "S", case_names = "firstname", weights = "sqrtn") +
CA_xlab +
S_ylab
GG_save("figs/CA_S_nonwestern.png")
d %>% filter(nonwestern) %>%
GG_scatter("IQ", "S", case_names = "firstname") +
CA_xlab +
S_ylab
GG_save("figs/CA_S_nonwestern_unwtd.png")
#numbers
describeBy(d$S, d$nonwestern)##
## Descriptive statistics by group
## group: FALSE
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 254 0.74 0.47 0.78 0.75 0.47 -0.59 2.2 2.8 -0.04 -0.02
## se
## X1 0.03
## --------------------------------------------------------
## group: TRUE
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 10 -1.9 0.38 -1.8 -1.8 0.4 -2.5 -1.4 1.1 -0.39 -1.4
## se
## X1 0.12plyr::ddply(d, "nonwestern", function(dd) {
tibble(
n = nrow(dd),
mean = wtd.mean(dd$S),
wmean = wtd.mean(dd$S, dd$number)
)
})## nonwestern n mean wmean
## 1 FALSE 255 0.74 0.93
## 2 TRUE 10 -1.85 -1.84
## 3 NA 2093 -0.10 0.46In other words, we see that Muslim immigrant names are increasing the cognitive and social inequality, as modeled by Kirkegaard and Tranberg (2015).
Regressions
#regression by sex
model1 = rms::ols(S ~ IQ_std, data = d, weights = sqrtn)
model1## Frequencies of Missing Values Due to Each Variable
## S IQ_std (weights)
## 455 2093 2093
##
## Linear Regression Model
##
## rms::ols(formula = S ~ IQ_std, data = d, weights = sqrtn)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 264 LR chi2 140.45 R2 0.413
## sigma1.5203 d.f. 1 R2 adj 0.410
## d.f. 262 Pr(> chi2) 0.0000 g 0.475
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.62062 -0.37594 -0.05183 0.24766 1.27846
##
##
## Coef S.E. t Pr(>|t|)
## Intercept 0.7188 0.0269 26.71 <0.0001
## IQ_std 0.4458 0.0329 13.57 <0.0001
## model2 = rms::ols(S ~ IQ_std, data = d %>% filter(!nonwestern), weights = sqrtn)
model2## Frequencies of Missing Values Due to Each Variable
## S IQ_std (weights)
## 1 0 0
##
## Linear Regression Model
##
## rms::ols(formula = S ~ IQ_std, data = d %>% filter(!nonwestern),
## weights = sqrtn)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 254 LR chi2 50.01 R2 0.179
## sigma1.3757 d.f. 1 R2 adj 0.175
## d.f. 252 Pr(> chi2) 0.0000 g 0.242
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.55086 -0.37058 -0.06407 0.22950 1.03405
##
##
## Coef S.E. t Pr(>|t|)
## Intercept 0.7684 0.0251 30.60 <0.0001
## IQ_std 0.2726 0.0368 7.41 <0.0001
## model3 = rms::ols(S ~ IQ_std + nonwestern, data = d, weights = sqrtn)
model3## Frequencies of Missing Values Due to Each Variable
## S IQ_std nonwestern (weights)
## 455 2093 2093 2093
##
## Linear Regression Model
##
## rms::ols(formula = S ~ IQ_std + nonwestern, data = d, weights = sqrtn)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 264 LR chi2 201.10 R2 0.533
## sigma1.3579 d.f. 2 R2 adj 0.530
## d.f. 261 Pr(> chi2) 0.0000 g 0.419
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.55191 -0.36400 -0.05903 0.21491 1.03153
##
##
## Coef S.E. t Pr(>|t|)
## Intercept 0.7681 0.0248 31.00 <0.0001
## IQ_std 0.2746 0.0360 7.62 <0.0001
## nonwestern -1.7301 0.2107 -8.21 <0.0001
## model4 = rms::ols(S ~ IQ_std * nonwestern, data = d, weights = sqrtn)
model4## Frequencies of Missing Values Due to Each Variable
## S IQ_std nonwestern (weights)
## 455 2093 2093 2093
##
## Linear Regression Model
##
## rms::ols(formula = S ~ IQ_std * nonwestern, data = d, weights = sqrtn)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 264 LR chi2 201.26 R2 0.533
## sigma1.3601 d.f. 3 R2 adj 0.528
## d.f. 260 Pr(> chi2) 0.0000 g 0.417
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.55086 -0.35662 -0.06407 0.21443 1.03405
##
##
## Coef S.E. t Pr(>|t|)
## Intercept 0.7684 0.0248 30.95 <0.0001
## IQ_std 0.2726 0.0364 7.49 <0.0001
## nonwestern -1.3816 0.8889 -1.55 0.1213
## IQ_std * nonwestern 0.1098 0.2721 0.40 0.6868
## model5 = rms::ols(S ~ nonwestern, data = d, weights = sqrtn)
model5## Frequencies of Missing Values Due to Each Variable
## S nonwestern (weights)
## 455 2093 2093
##
## Linear Regression Model
##
## rms::ols(formula = S ~ nonwestern, data = d, weights = sqrtn)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 264 LR chi2 148.01 R2 0.429
## sigma1.4987 d.f. 1 R2 adj 0.427
## d.f. 262 Pr(> chi2) 0.0000 g 0.195
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.40538 -0.38410 -0.02692 0.25536 1.38287
##
##
## Coef S.E. t Pr(>|t|)
## Intercept 0.8108 0.0266 30.44 <0.0001
## nonwestern -2.6608 0.1896 -14.03 <0.0001
## #path model
path_model = "
IQ_std ~ nonwestern
S ~ IQ_std + nonwestern
"
path_fit = sem(path_model, data = d)
parameterestimates(path_fit)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 IQ_std ~ nonwestern -3.285 0.251 -13 0 -3.777 -2.793
## 2 S ~ IQ_std 0.238 0.034 7 0 0.171 0.304
## 3 S ~ nonwestern -1.811 0.178 -10 0 -2.159 -1.463
## 4 IQ_std ~~ IQ_std 0.607 0.053 11 0 0.503 0.710
## 5 S ~~ S 0.184 0.016 11 0 0.153 0.216
## 6 nonwestern ~~ nonwestern 0.036 0.000 NA NA 0.036 0.036#mediation package
#recode type
d$nonwestern2 = d$nonwestern %>% as.numeric()
mediate_fit = mediation::mediate(
model.m = lm(IQ_std ~ nonwestern2, data = d),
model.y = lm(S ~ nonwestern2 + IQ_std, data = d),
treat = 'nonwestern2',
mediator = 'IQ_std',
boot = TRUE,
sims = 500,
dropobs = T
)
summary(mediate_fit)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.782 -1.072 -0.54 <2e-16 ***
## ADE -1.811 -2.130 -1.53 <2e-16 ***
## Total Effect -2.593 -2.841 -2.36 <2e-16 ***
## Prop. Mediated 0.301 0.209 0.40 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 264
##
##
## Simulations: 500List of names
Sorted by social status.
d %>%
filter(!is.na(IQ)) %>%
arrange(-IQ) %>%
select(firstname, gender, IQ, S, n) %>%
as_tibble() %>%
print(n = Inf)## # A tibble: 265 x 5
## firstname gender IQ S n
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Nils Male 107. 1.07 25
## 2 Viktor Male 106. 0.866 30
## 3 Tor Male 106. 0.639 25
## 4 Jonatan Male 106. 0.283 65
## 5 Lauge Male 106. 2.13 22
## 6 Esben Male 105. 1.08 170
## 7 Malte Male 105. 0.753 89
## 8 William Male 105. 1.56 54
## 9 Aske Male 105. 0.173 107
## 10 Bastian Male 105. 0.807 35
## 11 Asger Male 105. 1.65 136
## 12 Johannes Male 105. 1.08 117
## 13 Erik Male 105. 0.773 114
## 14 Pelle Male 104. 0.581 44
## 15 Gustav Male 104. 2.19 75
## 16 Malthe Male 104. 1.33 71
## 17 Bjarke Male 104. 0.973 160
## 18 Anton Male 104. 1.82 81
## 19 Ida Female 104. 0.967 28
## 20 Markus Male 103. 0.899 45
## 21 Johan Male 103. 1.02 205
## 22 August Male 103. 1.75 24
## 23 Hjalte Male 103. 0.779 34
## 24 Kristina Female 103. 0.558 28
## 25 Ditte Female 103. 0.301 21
## 26 Niels Male 103. 1.18 349
## 27 Tue Male 102. 0.710 42
## 28 Matias Male 102. 1.11 87
## 29 Carl Male 102. 1.19 42
## 30 Signe Female 102. 0.688 30
## 31 Steen Male 102. 1.24 36
## 32 Lau Male 102. 1.40 29
## 33 Oskar Male 102. 1.39 46
## 34 Magnus Male 102. 1.24 302
## 35 Jonathan Male 102. 0.442 197
## 36 Ulrik Male 102. 1.53 103
## 37 Anja Female 102. 0.982 26
## 38 Kåre Male 102. 0.976 23
## 39 Thorbjørn Male 102. 1.05 37
## 40 Filip Male 102. 0.721 55
## 41 Troels Male 102. 0.905 193
## 42 Jens Male 102. 1.27 484
## 43 Sigurd Male 102. 1.20 45
## 44 Laurits Male 102. 1.82 35
## 45 Lukas Male 102. 0.534 74
## 46 Jakob Male 102. 1.21 726
## 47 Asbjørn Male 102. 1.38 106
## 48 Jeppe Male 101. 1.21 630
## 49 Marie Female 101. 0.218 34
## 50 Andreas Male 101. 0.825 1268
## 51 Kristoffer Male 101. 0.717 325
## 52 Frederik Male 101. 1.41 1129
## 53 Caspar Male 101. 0.305 27
## 54 Hans Male 101. 0.968 160
## 55 Joakim Male 101. 1.09 103
## 56 Sune Male 101. 0.682 145
## 57 Marcus Male 101. 1.09 116
## 58 Tore Male 101. 0.569 25
## 59 Adrian Male 101. -0.595 27
## 60 Christoffer Male 101. 0.875 481
## 61 Emil Male 101. 0.926 1020
## 62 Aleksander Male 100. -0.147 41
## 63 Silas Male 100. 0.359 43
## 64 Oliver Male 100. 1.10 256
## 65 Poul Male 100. 0.838 46
## 66 Mette Female 100. 1.28 69
## 67 Matthias Male 100. 0.559 36
## 68 Joachim Male 100. 0.933 182
## 69 Uffe Male 100. 1.33 22
## 70 Maja Female 100. 0.697 29
## 71 Julian Male 100. -0.179 45
## 72 Laura Female 100. 0.853 21
## 73 Ruben Male 99.8 0.971 30
## 74 Aksel Male 99.8 1.34 21
## 75 Bjørn Male 99.8 0.807 144
## 76 Rune Male 99.8 0.487 340
## 77 Flemming Male 99.8 1.07 44
## 78 Christian Male 99.7 1.12 2042
## 79 Søren Male 99.7 1.39 1064
## 80 Louis Male 99.5 0.796 59
## 81 Philip Male 99.5 0.987 360
## 82 Peder Male 99.5 1.32 36
## 83 Alexander Male 99.5 0.741 741
## 84 Tobias Male 99.5 0.745 593
## 85 Mathias Male 99.5 1.15 1619
## 86 Rasmus Male 99.5 0.934 2140
## 87 Peter Male 99.5 1.24 961
## 88 Adam Male 99.4 0.0963 69
## 89 Mikael Male 99.4 1.00 148
## 90 Kristian Male 99.4 1.14 808
## 91 Charlotte Female 99.4 1.15 33
## 92 Anna Female 99.4 0.135 31
## 93 Katrine Female 99.2 0.556 30
## 94 Svend Male 99.2 0.482 45
## 95 Anders Male 99.2 1.14 1824
## 96 Sean Male 99.1 0.276 33
## 97 Robin Male 99.1 0.159 48
## 98 Lucas Male 99.1 1.03 59
## 99 Sofie Female 98.9 0.659 31
## 100 Anne Female 98.9 0.936 56
## 101 Toke Male 98.8 0.336 47
## 102 Samuel Male 98.8 -0.314 37
## 103 Christopher Male 98.8 0.441 175
## 104 Niklas Male 98.8 0.647 344
## 105 Simone Female 98.8 0.345 30
## 106 Nikolaj Male 98.8 1.02 783
## 107 Sebastian Male 98.8 0.710 625
## 108 Cecilie Female 98.8 0.465 40
## 109 Stephan Male 98.6 0.391 62
## 110 Thor Male 98.6 0.794 127
## 111 Carina Female 98.6 0.431 22
## 112 Stig Male 98.6 1.06 59
## 113 David Male 98.6 0.162 297
## 114 Victor Male 98.5 0.830 157
## 115 Nanna Female 98.5 0.663 48
## 116 Julie Female 98.5 0.500 51
## 117 Mads Male 98.5 1.24 2011
## 118 Nickolai Male 98.3 0.900 30
## 119 Theis Male 98.3 0.523 135
## 120 Simon Male 98.3 0.716 1567
## 121 Claus Male 98.3 1.43 246
## 122 Maria Female 98.3 0.301 86
## 123 Christina Female 98.2 0.701 52
## 124 Kris Male 98.2 0.918 59
## 125 Janus Male 98.2 0.531 80
## 126 Oscar Male 98.2 1.61 42
## 127 Alex Male 98.2 0.931 170
## 128 Karl Male 98.2 0.921 28
## 129 Sarah Female 98.2 0.0473 29
## 130 Lasse Male 98.2 0.957 1155
## 131 Jacob Male 98.2 1.33 1000
## 132 Jonas Male 98.2 0.532 1548
## 133 Nikolai Male 98.0 0.844 90
## 134 Julius Male 98.0 0.716 40
## 135 Thomas Male 98.0 1.06 1551
## 136 Henrik Male 98.0 1.47 550
## 137 Lennart Male 97.9 0.677 47
## 138 Trine Female 97.9 1.04 53
## 139 Mikkel Male 97.9 0.804 1695
## 140 Jon Male 97.7 0.449 97
## 141 Morten Male 97.7 1.14 1717
## 142 Nicolai Male 97.7 0.901 833
## 143 Niclas Male 97.6 0.840 159
## 144 Marco Male 97.6 0.154 107
## 145 Mick Male 97.4 0.339 54
## 146 Rolf Male 97.4 0.670 38
## 147 Pernille Female 97.4 0.929 63
## 148 Kian Male 97.3 0.669 22
## 149 Jes Male 97.3 1.26 27
## 150 Mie Female 97.3 0.697 23
## 151 Torben Male 97.3 1.51 74
## 152 Jesper Male 97.3 1.28 1260
## 153 Hasse Male 97.1 0.724 26
## 154 Mia Female 97.1 0.471 50
## 155 Benjamin Male 97.0 0.401 486
## 156 Casper Male 97.0 0.707 1006
## 157 Ole Male 97.0 1.10 95
## 158 Bjarne Male 96.8 1.18 31
## 159 Martin Male 96.8 0.886 2056
## 160 Jørgen Male 96.8 0.883 30
## 161 Lars Male 96.8 1.50 542
## 162 Nicholai Male 96.6 0.445 25
## 163 Robert Male 96.5 0.249 48
## 164 Kasper Male 96.5 0.514 1733
## 165 Mikki Male 96.3 NA 21
## 166 Nicolas Male 96.3 -0.00208 59
## 167 Jannik Male 96.3 0.754 178
## 168 Phillip Male 96.3 0.774 95
## 169 Line Female 96.3 0.805 69
## 170 Klaus Male 96.2 1.50 123
## 171 Daniel Male 96.2 0.0677 1406
## 172 Henning Male 96.2 0.924 23
## 173 Camilla Female 96.2 0.428 112
## 174 Jan Male 96.2 1.06 126
## 175 Marc Male 96.0 0.283 287
## 176 Nicklas Male 96.0 0.472 610
## 177 Dan Male 95.9 0.644 173
## 178 Stine Female 95.9 0.570 48
## 179 Louise Female 95.9 0.612 100
## 180 Pierre Male 95.7 -0.127 21
## 181 Nicholas Male 95.7 0.365 91
## 182 Chris Male 95.7 0.824 193
## 183 Nicolaj Male 95.7 1.01 274
## 184 Max Male 95.7 1.25 74
## 185 Steffen Male 95.7 0.943 538
## 186 Rikke Female 95.7 1.26 43
## 187 Michael Male 95.7 0.926 1238
## 188 Stefan Male 95.4 0.366 415
## 189 Nichlas Male 95.3 0.869 142
## 190 Tom Male 95.3 0.909 28
## 191 Kenneth Male 95.3 0.715 613
## 192 Kim Male 95.3 0.981 426
## 193 Leon Male 95.1 0.482 26
## 194 Mark Male 95.1 0.277 548
## 195 Mickey Male 95.0 -0.322 37
## 196 Glenn Male 95.0 0.788 56
## 197 Danny Male 95.0 0.125 102
## 198 Tomas Male 94.8 0.347 21
## 199 Kent Male 94.8 1.32 58
## 200 Carsten Male 94.8 1.45 121
## 201 Tim Male 94.7 0.584 155
## 202 Nicky Male 94.5 -0.140 37
## 203 Ronnie Male 94.5 0.235 62
## 204 Elias Male 94.5 0.246 24
## 205 Michelle Female 94.5 -0.0518 41
## 206 Patrick Male 94.5 0.117 866
## 207 Karsten Male 94.5 1.56 92
## 208 Sara Female 94.4 0.113 29
## 209 Bo Male 94.4 1.24 104
## 210 Jeff Male 94.2 0.256 35
## 211 Ricki Male 94.1 -0.0388 25
## 212 Jim Male 94.1 0.676 34
## 213 Per Male 94.1 1.19 118
## 214 Kenni Male 93.9 0.0343 92
## 215 Malene Female 93.9 1.05 41
## 216 Heidi Female 93.9 1.08 23
## 217 Steffan Male 93.7 0.228 105
## 218 Paw Male 93.7 0.646 50
## 219 Jimmi Male 93.7 0.492 101
## 220 Rene Male 93.7 0.752 393
## 221 Allan Male 93.7 1.05 146
## 222 André Male 93.6 0.269 86
## 223 Nick Male 93.4 0.000856 323
## 224 Kevin Male 93.4 0.110 211
## 225 Tommy Male 93.4 0.958 98
## 226 Jimmy Male 93.0 0.424 62
## 227 Frank Male 93.0 0.984 71
## 228 Jannick Male 92.8 0.370 109
## 229 Steven Male 92.7 0.0944 65
## 230 Dennis Male 92.5 0.386 697
## 231 John Male 92.5 0.685 62
## 232 Nicki Male 92.4 0.0215 93
## 233 Brian Male 92.4 1.15 185
## 234 Andre Male 92.2 0.207 31
## 235 Dannie Male 92.1 0.653 25
## 236 Sandra Female 92.1 -0.209 24
## 237 Charlie Male 91.9 0.317 36
## 238 Danni Male 91.9 -0.107 145
## 239 Andy Male 91.8 0.857 23
## 240 Miki Male 91.5 -0.0236 25
## 241 Ken Male 91.3 0.829 23
## 242 Michel Male 91.2 -0.234 23
## 243 Stephanie Female 91.2 -0.310 21
## 244 Claes Male 91.0 0.864 32
## 245 Sonny Male 90.8 -0.0135 40
## 246 Jeanette Female 90.8 0.856 22
## 247 Johnny Male 90.8 0.656 48
## 248 Kenny Male 90.7 0.418 60
## 249 Jack Male 90.4 0.560 77
## 250 Mike Male 90.1 0.0940 415
## 251 Micki Male 89.8 0.516 28
## 252 Ronni Male 88.9 0.395 73
## 253 Kennet Male 88.6 0.790 29
## 254 Sabrina Female 87.3 -0.127 21
## 255 Ahmet Male 86.6 -1.38 24
## 256 Rico Male 86.3 1.00 22
## 257 Mustafa Male 84.4 -1.50 25
## 258 Mohammed Male 84.3 -1.74 28
## 259 Mehmet Male 83.8 -1.57 29
## 260 Ali Male 83.5 -1.79 69
## 261 Ahmed Male 81.4 -2.29 27
## 262 Mohamad Male 79.6 -2.13 30
## 263 Mohammad Male 79.2 -1.51 83
## 264 Ahmad Male 78.6 -2.05 81
## 265 Mohamed Male 75.7 -2.53 37Versions
sessionInfo()## R version 3.5.3 (2019-03-11)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 19.1
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lavaan_0.6-3 rms_5.1-3 SparseM_1.77
## [4] kirkegaard_2018.05 metafor_2.0-0 psych_1.8.12
## [7] magrittr_1.5 assertthat_0.2.1 weights_1.0
## [10] mice_3.4.0 gdata_2.18.0 Hmisc_4.2-0
## [13] Formula_1.2-3 survival_2.43-3 lattice_0.20-38
## [16] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.0.1
## [19] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3
## [22] tibble_2.1.1 ggplot2_3.1.1 tidyverse_1.2.1
## [25] mediation_4.4.6 sandwich_2.5-0 mvtnorm_1.0-10
## [28] Matrix_1.2-17 MASS_7.3-51.1 pacman_0.5.0
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.0-10 minqa_1.2.4 colorspace_1.4-1
## [4] htmlTable_1.13.1 base64enc_0.1-3 rstudioapi_0.10
## [7] MatrixModels_0.4-1 fansi_0.4.0 lubridate_1.7.4
## [10] xml2_1.2.0 codetools_0.2-16 splines_3.5.3
## [13] mnormt_1.5-5 knitr_1.22 jsonlite_1.6
## [16] nloptr_1.2.1 broom_0.5.2 cluster_2.0.8
## [19] curry_0.1.1 compiler_3.5.3 httr_1.4.0
## [22] backports_1.1.4 lazyeval_0.2.2 cli_1.1.0
## [25] acepack_1.4.1 htmltools_0.3.6 quantreg_5.38
## [28] tools_3.5.3 gtable_0.3.0 glue_1.3.1
## [31] Rcpp_1.0.1 cellranger_1.1.0 nlme_3.1-139
## [34] multilevel_2.6 xfun_0.6 lme4_1.1-21
## [37] rvest_0.3.3 lpSolve_5.6.13 gtools_3.8.1
## [40] polspline_1.1.14 pan_1.6 zoo_1.8-4
## [43] scales_1.0.0 hms_0.4.2 parallel_3.5.3
## [46] RColorBrewer_1.1-2 psychometric_2.2 yaml_2.2.0
## [49] gridExtra_2.3 rpart_4.1-13 latticeExtra_0.6-28
## [52] stringi_1.4.3 checkmate_1.9.1 boot_1.3-20
## [55] rlang_0.3.4 pkgconfig_2.0.2 evaluate_0.13
## [58] labeling_0.3 htmlwidgets_1.3 tidyselect_0.2.5
## [61] plyr_1.8.4 R6_2.4.0 generics_0.0.2
## [64] mitml_0.3-7 multcomp_1.4-10 pillar_1.3.1
## [67] haven_2.1.0 foreign_0.8-70 withr_2.1.2
## [70] nnet_7.3-12 modelr_0.1.4 crayon_1.3.4
## [73] jomo_2.6-7 utf8_1.1.4 rmarkdown_1.12
## [76] grid_3.5.3 readxl_1.3.1 data.table_1.12.2
## [79] pbivnorm_0.6.0 digest_0.6.18 stats4_3.5.3
## [82] munsell_0.5.0