Angst und Persönlichkeit
Unit, Examples, etc
Peter Zezula
Einführendes Beispiel Angst und Persönlichkeit
Die Daten entstammen dem Paket Psych.
Kurzbeschreibung bzw. über help(epi.bfi) [http://rgm3.lab.nig.ac.jp/RGM/R_rdfile?f=psych/man/epi.bfi.Rd&d=R_CC]
Big Five Dimensions and Facet (and correlated trait adjective)
Extraversion vs. introversion * Gregariousness (sociable) * Assertiveness (forceful) * Activity (energetic) * Excitement-seeking (adventurous) * Positive emotions (enthusiastic) * Warmth (outgoing)
Agreeableness vs. antagonism * Trust (forgiving) * Straightforwardness (not demanding) * Altruism (warm) * Compliance (not stubborn) * Modesty (not show-off) * Tender-mindedness (sympathetic)
Conscientiousness vs. lack of direction * Competence (efficient) * Order (organized) * Dutifulness (not careless) * Achievement striving (thorough) * Self-discipline (not lazy) * Deliberation (not impulsive)
Neuroticism vs. emotional stability * Anxiety (tense) * Angry hostility (irritable) * Depression (not contented) * Self-consciousness (shy) * Impulsiveness (moody) * Vulnerability (not self-confident)
Openness vs. closedness to experience * Ideas (curious) * Fantasy (imaginative) * Aesthetics (artistic) * Actions (wide interests) * Feelings (excitable) * Values (unconventional)
| Faktor | schwach ausgeprägt | stark ausgeprägt |
|---|---|---|
| Neurotizismus | selbstsicher, ruhig | emotional, verletzlich |
| Extraversion | zurückhaltend, reserviert | gesellig |
| Offenheit für Erfahrungen | konsistent, vorsichtig | erfinderisch, neugierig |
| Verträglichkeit | kompetitiv, misstrauisch | kooperativ, freundlich, mitfühlend |
| Gewissenhaftigkeit | unbekümmert, nachlässig | effektiv, organisiert |
Fragestellung: Wie gut kann Trait-Angst durch die Big-Five-Skalen Neurotizismus und Offenheit für Erfahrungen erklärt werden.
Daten einlesen, deskriptive Daten, erster grafischer Eindruck. Die Skalen sind bereits gebildet, die Einzelitems sind nicht im Datensatz enthalten.
# we need package psych
require("psych")## Loading required package: psych# read data
ddf <- psychTools::epi.bfi
# get data into namespace
attach(ddf)
# psych package gives a nice, short description of the data
psych:::describe(ddf)## vars n mean sd median trimmed mad min max range skew
## epiE 1 231 13.33 4.14 14 13.49 4.45 1 22 21 -0.33
## epiS 2 231 7.58 2.69 8 7.77 2.97 0 13 13 -0.57
## epiImp 3 231 4.37 1.88 4 4.36 1.48 0 9 9 0.06
## epilie 4 231 2.38 1.50 2 2.27 1.48 0 7 7 0.66
## epiNeur 5 231 10.41 4.90 10 10.39 4.45 0 23 23 0.06
## bfagree 6 231 125.00 18.14 126 125.26 17.79 74 167 93 -0.21
## bfcon 7 231 113.25 21.88 114 113.42 22.24 53 178 125 -0.02
## bfext 8 231 102.18 26.45 104 102.99 22.24 8 168 160 -0.41
## bfneur 9 231 87.97 23.34 90 87.70 23.72 34 152 118 0.07
## bfopen 10 231 123.43 20.51 125 123.78 20.76 73 173 100 -0.16
## bdi 11 231 6.78 5.78 6 5.97 4.45 0 27 27 1.29
## traitanx 12 231 39.01 9.52 38 38.36 8.90 22 71 49 0.67
## stateanx 13 231 39.85 11.48 38 38.92 10.38 21 79 58 0.72
## kurtosis se
## epiE -0.06 0.27
## epiS -0.02 0.18
## epiImp -0.62 0.12
## epilie 0.24 0.10
## epiNeur -0.50 0.32
## bfagree -0.27 1.19
## bfcon 0.23 1.44
## bfext 0.51 1.74
## bfneur -0.55 1.54
## bfopen -0.16 1.35
## bdi 1.50 0.38
## traitanx 0.47 0.63
## stateanx -0.01 0.76# correlation matrix
cor(ddf)## epiE epiS epiImp epilie epiNeur bfagree
## epiE 1.0000000 0.84791012 0.80149700 -0.22219097 -0.17673625 0.17594316
## epiS 0.8479101 1.00000000 0.42625103 -0.05175109 -0.21670706 0.20427259
## epiImp 0.8014970 0.42625103 1.00000000 -0.24242865 -0.07395424 0.07893713
## epilie -0.2221910 -0.05175109 -0.24242865 1.00000000 -0.25089261 0.17185824
## epiNeur -0.1767363 -0.21670706 -0.07395424 -0.25089261 1.00000000 -0.08219006
## bfagree 0.1759432 0.20427259 0.07893713 0.17185824 -0.08219006 1.00000000
## bfcon -0.1141498 0.04621194 -0.24000238 0.22556543 -0.13347321 0.44989862
## bfext 0.5434974 0.57735132 0.34720618 -0.04413118 -0.17054999 0.47848190
## bfneur -0.0944616 -0.06895418 -0.08947612 -0.21946993 0.62747180 -0.04461721
## bfopen 0.1375564 0.15364990 0.06817649 -0.02527999 0.08750596 0.39415383
## bdi -0.1563745 -0.13363483 -0.10602555 -0.20178646 0.57860862 -0.14145278
## traitanx -0.2328380 -0.25516199 -0.11826051 -0.23262712 0.72868850 -0.30545039
## stateanx -0.1311297 -0.11865925 -0.08908171 -0.15490999 0.48974620 -0.19449579
## bfcon bfext bfneur bfopen bdi traitanx
## epiE -0.11414984 0.54349742 -0.09446160 0.13755642 -0.15637447 -0.2328380
## epiS 0.04621194 0.57735132 -0.06895418 0.15364990 -0.13363483 -0.2551620
## epiImp -0.24000238 0.34720618 -0.08947612 0.06817649 -0.10602555 -0.1182605
## epilie 0.22556543 -0.04413118 -0.21946993 -0.02527999 -0.20178646 -0.2326271
## epiNeur -0.13347321 -0.17054999 0.62747180 0.08750596 0.57860862 0.7286885
## bfagree 0.44989862 0.47848190 -0.04461721 0.39415383 -0.14145278 -0.3054504
## bfcon 1.00000000 0.26675235 0.04450273 0.30518284 -0.17900732 -0.2918801
## bfext 0.26675235 1.00000000 0.03712981 0.45876070 -0.13940254 -0.3932522
## bfneur 0.04450273 0.03712981 1.00000000 0.29347850 0.46616629 0.5930101
## bfopen 0.30518284 0.45876070 0.29347850 1.00000000 -0.07651051 -0.1052987
## bdi -0.17900732 -0.13940254 0.46616629 -0.07651051 1.00000000 0.6547648
## traitanx -0.29188008 -0.39325224 0.59301013 -0.10529872 0.65476479 1.0000000
## stateanx -0.13752551 -0.14809702 0.49205672 -0.04246252 0.60873997 0.5711474
## stateanx
## epiE -0.13112969
## epiS -0.11865925
## epiImp -0.08908171
## epilie -0.15490999
## epiNeur 0.48974620
## bfagree -0.19449579
## bfcon -0.13752551
## bfext -0.14809702
## bfneur 0.49205672
## bfopen -0.04246252
## bdi 0.60873997
## traitanx 0.57114741
## stateanx 1.00000000# an overview of relevant scales
pairs.panels(ddf[,c("traitanx", "bfneur", "bfopen")])
Wie gut kann Trait-Angst durch die Big-Five-Skalen Neurotizismus und Offenheit für Erfahrungen erklärt werden?
Gesamtmodell anpassen
require("psych")
ddf <- psychTools::epi.bfi
# adapt model with big five scale values as explanatory variables
m.no <- lm(traitanx ~ bfneur + bfopen, data=ddf)
# summary of model
summary(m.no)##
## Call:
## lm(formula = traitanx ~ bfneur + bfopen, data = ddf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.7307 -5.3200 -0.9284 3.8739 28.8526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.02119 3.05213 10.49 < 2e-16 ***
## bfneur 0.27856 0.02121 13.13 < 2e-16 ***
## bfopen -0.14192 0.02413 -5.88 1.44e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.175 on 228 degrees of freedom
## Multiple R-squared: 0.437, Adjusted R-squared: 0.4321
## F-statistic: 88.5 on 2 and 228 DF, p-value: < 2.2e-16Beispiel EPI BFI
Die Daten entstammen dem Paket Psych.
Kurzbeschreibung bzw. über help(epi.bfi) [http://rgm3.lab.nig.ac.jp/RGM/R_rdfile?f=psych/man/epi.bfi.Rd&d=R_CC]
Daten einlesen, deskriptive Daten, erster grafischer Eindruck
# we need package psych
require("psych")
# read data
ddf <- psychTools::epi.bfi
# psych package gives a nice, short description of the data
psych:::describe(ddf)## vars n mean sd median trimmed mad min max range skew
## epiE 1 231 13.33 4.14 14 13.49 4.45 1 22 21 -0.33
## epiS 2 231 7.58 2.69 8 7.77 2.97 0 13 13 -0.57
## epiImp 3 231 4.37 1.88 4 4.36 1.48 0 9 9 0.06
## epilie 4 231 2.38 1.50 2 2.27 1.48 0 7 7 0.66
## epiNeur 5 231 10.41 4.90 10 10.39 4.45 0 23 23 0.06
## bfagree 6 231 125.00 18.14 126 125.26 17.79 74 167 93 -0.21
## bfcon 7 231 113.25 21.88 114 113.42 22.24 53 178 125 -0.02
## bfext 8 231 102.18 26.45 104 102.99 22.24 8 168 160 -0.41
## bfneur 9 231 87.97 23.34 90 87.70 23.72 34 152 118 0.07
## bfopen 10 231 123.43 20.51 125 123.78 20.76 73 173 100 -0.16
## bdi 11 231 6.78 5.78 6 5.97 4.45 0 27 27 1.29
## traitanx 12 231 39.01 9.52 38 38.36 8.90 22 71 49 0.67
## stateanx 13 231 39.85 11.48 38 38.92 10.38 21 79 58 0.72
## kurtosis se
## epiE -0.06 0.27
## epiS -0.02 0.18
## epiImp -0.62 0.12
## epilie 0.24 0.10
## epiNeur -0.50 0.32
## bfagree -0.27 1.19
## bfcon 0.23 1.44
## bfext 0.51 1.74
## bfneur -0.55 1.54
## bfopen -0.16 1.35
## bdi 1.50 0.38
## traitanx 0.47 0.63
## stateanx -0.01 0.76# correlation matrix
cor(ddf)## epiE epiS epiImp epilie epiNeur bfagree
## epiE 1.0000000 0.84791012 0.80149700 -0.22219097 -0.17673625 0.17594316
## epiS 0.8479101 1.00000000 0.42625103 -0.05175109 -0.21670706 0.20427259
## epiImp 0.8014970 0.42625103 1.00000000 -0.24242865 -0.07395424 0.07893713
## epilie -0.2221910 -0.05175109 -0.24242865 1.00000000 -0.25089261 0.17185824
## epiNeur -0.1767363 -0.21670706 -0.07395424 -0.25089261 1.00000000 -0.08219006
## bfagree 0.1759432 0.20427259 0.07893713 0.17185824 -0.08219006 1.00000000
## bfcon -0.1141498 0.04621194 -0.24000238 0.22556543 -0.13347321 0.44989862
## bfext 0.5434974 0.57735132 0.34720618 -0.04413118 -0.17054999 0.47848190
## bfneur -0.0944616 -0.06895418 -0.08947612 -0.21946993 0.62747180 -0.04461721
## bfopen 0.1375564 0.15364990 0.06817649 -0.02527999 0.08750596 0.39415383
## bdi -0.1563745 -0.13363483 -0.10602555 -0.20178646 0.57860862 -0.14145278
## traitanx -0.2328380 -0.25516199 -0.11826051 -0.23262712 0.72868850 -0.30545039
## stateanx -0.1311297 -0.11865925 -0.08908171 -0.15490999 0.48974620 -0.19449579
## bfcon bfext bfneur bfopen bdi traitanx
## epiE -0.11414984 0.54349742 -0.09446160 0.13755642 -0.15637447 -0.2328380
## epiS 0.04621194 0.57735132 -0.06895418 0.15364990 -0.13363483 -0.2551620
## epiImp -0.24000238 0.34720618 -0.08947612 0.06817649 -0.10602555 -0.1182605
## epilie 0.22556543 -0.04413118 -0.21946993 -0.02527999 -0.20178646 -0.2326271
## epiNeur -0.13347321 -0.17054999 0.62747180 0.08750596 0.57860862 0.7286885
## bfagree 0.44989862 0.47848190 -0.04461721 0.39415383 -0.14145278 -0.3054504
## bfcon 1.00000000 0.26675235 0.04450273 0.30518284 -0.17900732 -0.2918801
## bfext 0.26675235 1.00000000 0.03712981 0.45876070 -0.13940254 -0.3932522
## bfneur 0.04450273 0.03712981 1.00000000 0.29347850 0.46616629 0.5930101
## bfopen 0.30518284 0.45876070 0.29347850 1.00000000 -0.07651051 -0.1052987
## bdi -0.17900732 -0.13940254 0.46616629 -0.07651051 1.00000000 0.6547648
## traitanx -0.29188008 -0.39325224 0.59301013 -0.10529872 0.65476479 1.0000000
## stateanx -0.13752551 -0.14809702 0.49205672 -0.04246252 0.60873997 0.5711474
## stateanx
## epiE -0.13112969
## epiS -0.11865925
## epiImp -0.08908171
## epilie -0.15490999
## epiNeur 0.48974620
## bfagree -0.19449579
## bfcon -0.13752551
## bfext -0.14809702
## bfneur 0.49205672
## bfopen -0.04246252
## bdi 0.60873997
## traitanx 0.57114741
## stateanx 1.00000000# an overview of EPI scales
pairs.panels(ddf[,1:5])
Gesamtmodell: Wie gut kann Trait-Angst durch die Big-Five-Skalen erklärt werden?
Wir schätzen die Trait-Angst aus einer Linearkombination der Big-Five-Skalen. Wir betrachten das summary()
require("psych")
ddf <- psychTools::epi.bfi
# adapt model with big five scale values as explanatory variables
m.bf <- lm(traitanx ~ bfagree + bfcon + bfext + bfneur + bfopen, data=ddf)
# summary of model
summary(m.bf)##
## Call:
## lm(formula = traitanx ~ bfagree + bfcon + bfext + bfneur + bfopen,
## data = ddf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.2952 -4.2436 -0.7314 3.3558 21.2960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.74134 3.55405 12.026 < 2e-16 ***
## bfagree 0.00325 0.02876 0.113 0.910
## bfcon -0.09197 0.02144 -4.290 2.66e-05 ***
## bfext -0.11751 0.01893 -6.208 2.56e-09 ***
## bfneur 0.26054 0.01889 13.793 < 2e-16 ***
## bfopen -0.03756 0.02494 -1.506 0.133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.273 on 225 degrees of freedom
## Multiple R-squared: 0.5753, Adjusted R-squared: 0.5659
## F-statistic: 60.97 on 5 and 225 DF, p-value: < 2.2e-16Standardisierte Gewichte, vergleichbar
Wir berechnen die standartisierten Gewichte, indem wir die Variablen im Spiel vorher standardisieren (z-transformieren).
require("psych")
ddf <- psychTools::epi.bfi
# do the same with standardized variables
ms.bf <- lm(scale(traitanx) ~ scale(bfagree) + scale(bfcon) + scale(bfext) + scale(bfneur) + scale(bfopen), data=ddf)
# compare the coefficients
ms.bf$coefficients## (Intercept) scale(bfagree) scale(bfcon) scale(bfext) scale(bfneur)
## -1.823477e-16 6.192817e-03 -2.113170e-01 -3.264464e-01 6.385508e-01
## scale(bfopen)
## -8.088944e-02m.bf$coefficients## (Intercept) bfagree bfcon bfext bfneur bfopen
## 42.741336772 0.003249864 -0.091968547 -0.117513857 0.260540623 -0.037558314# better to read
round(ms.bf$coefficients, digits=3)## (Intercept) scale(bfagree) scale(bfcon) scale(bfext) scale(bfneur)
## 0.000 0.006 -0.211 -0.326 0.639
## scale(bfopen)
## -0.081Eine Streudiagramm-Matrix
require("psych")
ddf <- psychTools::epi.bfi
# scatterplot matrix
pairs.panels(ddf[,c('traitanx', 'bfagree', 'bfcon', 'bfext', 'bfneur', 'bfopen')])
Qualität der Analyse: NV der Residuen, Residualanalyse
# again the model
require("psych")
ddf <- psychTools::epi.bfi
m.bf <- lm(traitanx ~ bfagree + bfcon + bfext + bfneur + bfopen, data=ddf)
# all, used by ggplot has to reside in one dataframe
# we prepare adequate dataframe
# fitted values and studentized.residuals in columns
#ddf.plot <- cbind(ddf[,c('traitanx', 'bfagree', 'bfcon', 'bfext', 'bfneur', 'bfopen')])
ddf.plot = data.frame(subj = 1:length(traitanx), ddf[,c('traitanx', 'bfagree', 'bfcon', 'bfext', 'bfneur', 'bfopen')])
ddf.plot['fitted.values'] <- m.bf$fitted.values
ddf.plot['studentized.residuals'] <- rstudent(m.bf)
ddf.plot['standardized.residuals'] <- rstandard(m.bf)
library(tidyverse)## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## Warning: package 'ggplot2' was built under R version 3.6.2## Warning: package 'tibble' was built under R version 3.6.2## Warning: package 'purrr' was built under R version 3.6.2## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x ggplot2::%+%() masks psych::%+%()
## x ggplot2::alpha() masks psych::alpha()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()pplot <- ggplot(ddf.plot, aes(studentized.residuals)) +
# {} opts(legend.position = 'none') +
geom_histogram(aes(y=..density..), solor = 'black', fill = 'white') +
labs(x = "Studentized Residuals", y = "Density")## Warning: Ignoring unknown parameters: solorpplot +
stat_function(fun = dnorm, args = list(mean = mean(ddf.plot$studentized.residuals, na.rm=TRUE), sd=sd(ddf$studentized.residuals, na.rm=TRUE)), color = "red", size=1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 101 row(s) containing missing values (geom_path).
# qq-plot studentized residuals
pplot <- ggplot(ddf.plot, aes(sample=ddf.plot$studentized.residuals)) +
stat_qq(color="firebrick2", alpha=1) +
geom_abline(intercept = mean(ddf.plot$studentized.residuals), slope = sd(ddf.plot$studentized.residuals))
# show it
pplot## Warning: Use of `ddf.plot$studentized.residuals` is discouraged. Use
## `studentized.residuals` instead.
# get the extreme value subjects
extremes <- ddf.plot$subj[abs(ddf.plot$standardized.residuals) > 2]
extremes.exist <- ifelse(length(extremes), 1, 0)
if (length(extremes)){
print(paste('extreme studentized values in observation: ', extremes))
ddf.plot[extremes,]
} ## [1] "extreme studentized values in observation: 6"
## [2] "extreme studentized values in observation: 31"
## [3] "extreme studentized values in observation: 32"
## [4] "extreme studentized values in observation: 47"
## [5] "extreme studentized values in observation: 76"
## [6] "extreme studentized values in observation: 92"
## [7] "extreme studentized values in observation: 93"
## [8] "extreme studentized values in observation: 96"
## [9] "extreme studentized values in observation: 138"
## [10] "extreme studentized values in observation: 150"## subj traitanx bfagree bfcon bfext bfneur bfopen fitted.values
## 6 6 51 110 113 61 54 149 34.01104
## 31 31 23 112 90 87 77 143 39.29524
## 32 32 64 113 121 53 100 84 48.65131
## 47 47 60 112 102 83 105 125 46.63286
## 76 76 60 120 143 118 112 141 39.99801
## 92 92 36 120 98 54 97 118 48.61321
## 93 93 71 124 116 32 98 121 49.70395
## 96 96 71 100 103 121 152 158 53.04235
## 138 138 52 136 111 124 82 104 35.86136
## 150 150 53 134 120 68 69 112 37.92042
## studentized.residuals standardized.residuals
## 6 2.838336 2.794851
## 31 -2.667954 -2.632405
## 32 2.522436 2.492903
## 47 2.159492 2.142123
## 76 3.294959 3.225074
## 92 -2.049586 -2.035161
## 93 3.568556 3.478987
## 96 3.003102 2.950978
## 138 2.631457 2.597481
## 150 2.456279 2.429258# plot residuals against predicted values using ggplot
pplot <- ggplot(ddf.plot, aes(fitted.values, studentized.residuals))
pplot +
geom_point() +
geom_smooth(method="lm", color="Blue") +
labs(x="Fitted Values", y="Studentized Residual")## `geom_smooth()` using formula 'y ~ x'
Extremwerte und einflussreiche Fälle?
# again the model
m.bf <- lm(traitanx ~ bfagree + bfcon + bfext + bfneur + bfopen)
# we prepare adequate dataframe
ddf.plot = data.frame(subj = 1:length(traitanx), ddf[,c('traitanx', 'bfagree', 'bfcon', 'bfext', 'bfneur', 'bfopen')])
ddf.plot['fitted.values'] <- m.bf$fitted.values
ddf.plot['studentized.residuals'] <- rstudent(m.bf)
ddf.plot['cooks.distance'] <- cooks.distance(m.bf)
ddf.plot['dfbeta'] <- dfbeta(m.bf)
ddf.plot['dffits'] <- dffits(m.bf)
ddf.plot['hatvalues'] <- hatvalues(m.bf)
ddf.plot['covratio'] <- covratio(m.bf)
# example: plot cooks distance against subject number - you have to identify the person
pplot <- ggplot(ddf.plot, aes(subj, cooks.distance, ymin=0, ymax=cooks.distance), title="Cook's Distance") +
geom_point() +
geom_linerange() +
scale_x_continuous("Observation Number") +
scale_y_continuous("Cook's distance")
#opts(title="Cook's Distance")
pplot
# get at persons above Cook's distance above 0.5
# find observations with cooks distance > 1
extremes <- ddf.plot$subj[cooks.distance(m.bf) > 0.5]
if(length(extremes)) {
print(paste('extreme cooks distances in observation: ', extremes))
ddf.plot[extremes,]
}Voraussetzung Unabhängigkeit der Fehler
Daumenregel: Je näher an 2, desto besser. Werte unter 1 und über 3 sollten aufmerksam machen.
# again the model
m.bf <- lm(traitanx ~ bfagree + bfcon + bfext + bfneur + bfopen)
car::durbinWatsonTest(m.bf)## lag Autocorrelation D-W Statistic p-value
## 1 -0.1172569 2.234047 0.072
## Alternative hypothesis: rho != 0# or dwt(m.bf)
if (car::dwt(m.bf)[2] < 1 | car::dwt(m.bf)[2] > 3){
message("Durbin Watson Test zur Unabhängigkeit der Residuen vom vorhergesagten Wert ist auffällig")
}Multikollinearität
VIF (Variation Inflation Factor) oder tolerance. Sie sind ineinander überführbar: \(tolerance = \frac{1}{vif}\)
Empfehlungen bei VIF
- Achtung wenn es VIF größer 10 gibt bzw. wenn es Toleranzwerte < 0.1 gibt
- Achtung wenn der durchschnittliche VIF deutlich größer als 1 ist.
- Toleranzwerte < 0.2 sollten Verdacht auf Multikollinearität wecken
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## some## The following object is masked from 'package:psych':
##
## logit# again the model
m.bf <- lm(traitanx ~ bfagree + bfcon + bfext + bfneur + bfopen)
# output vif
message(paste("vif: ", vif(m.bf)))## vif: 1.5914495483492vif: 1.28584057965325vif: 1.46505293753843vif: 1.13562678127135vif: 1.52840419706987# output tolerance
message(paste("tolerance: ", 1 / vif(m.bf)))## tolerance: 0.628357965250793tolerance: 0.77770138524456tolerance: 0.682569192127753tolerance: 0.880570990832472tolerance: 0.654277187878126# mean of vif
message(paste("mean tolerance: ", mean(vif(m.bf))))## mean tolerance: 1.40127480877642if (length(vif(m.bf)[vif(m.bf) > 5])) {
message("suspicious:")
vif(m.bf)[vif(m.bf) > 5]
}
# {} schräges ModellVersion: 26 Juni, 2020 21:58