# Libraries ----
library(car) # Function Recode
library(psych) # Function Describe
##
## Attaching package: 'psych'
##
## The following object is masked from 'package:car':
##
## logit
library(mirt)
## Loading required package: stats4
## Loading required package: lattice
# Import data ----
## Import dataframe
praticasPro <- read.csv("praticasprofissionais_df.csv")
## Summing scales to remove NA's
praticasPro$scaleSum <- rowSums(praticasPro[,32:68])
## Subset completed observations and consented participation
praticasPro <- subset(praticasPro, subset=praticasPro$termo=="Sim" & praticasPro$estado=="Finalizadas" & !is.na(praticasPro$scaleSum))
# Demographics
## Age
# Demographics
## Age
### Clean data
praticasPro$idade <- as.numeric(as.character(praticasPro$idade))
## Warning: NAs introduzidos por coerção
praticasPro$idade[praticasPro$idade < 18 | praticasPro$idade > 68 ] <- NA
### Descriptives
summary(praticasPro$idade) # all
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 19.0 34.0 41.0 40.8 47.0 68.0 332
by(praticasPro$idade, praticasPro$sexo, describe) #by sex
## praticasPro$sexo: Feminino
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 2335 40.86 8.76 41 40.77 10.38 19 68 49 0.08 -0.64
## se
## 1 0.18
## --------------------------------------------------------
## praticasPro$sexo: Masculino
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 396 40.12 9.95 40 39.64 10.38 21 67 46 0.38 -0.59 0.5
## Sex
cbind(round(prop.table(sort(table(praticasPro$sexo), decreasing = TRUE)),2))
## [,1]
## Feminino 0.86
## Masculino 0.14
## Degree
cbind(round(prop.table(sort(table(praticasPro$escolaridade), decreasing = TRUE)),2))
## [,1]
## Pós-graduação 0.65
## Ensino Superior Completo 0.29
## Ensino Superior Incompleto 0.05
## Ensino Médio Completo 0.01
## Ensino Fundamental Incompleto 0.00
## Ensino Médio Incompleto 0.00
## Ensino Fundamental Completo 0.00
## Marital Staus
cbind(round(prop.table(sort(table(praticasPro$estadocivil), decreasing = TRUE)),2))
## [,1]
## Casado (a) 0.58
## Solteiro (a) 0.22
## Divorciado (a) 0.09
## União Estável 0.07
## Outros 0.02
## Viúvo (a) 0.02
## Education
#cbind(round(prop.table(table(praticasPro$formacao)),2)) # Broken, needs manual recoding
## Ocupação
#cbind(round(prop.table(table(praticasPro$ocupacao)),2)) # Broken, needs manual recoding
## Time working
timeWorking <- as.numeric(as.character(praticasPro$tempodeservico))
## Warning: NAs introduzidos por coerção
timeWorking[timeWorking > 59] <- NA
summary(timeWorking)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 5 12 13 20 48 760
## Religion
cbind(round(prop.table(sort(table(praticasPro$religiao), decreasing = TRUE)),2))
## [,1]
## Católica 0.66
## Evangélica 0.19
## EspÃrita 0.08
## Sem religião 0.04
## Outras 0.02
## Umbanda 0.00
## Budismo 0.00
## Candomblé 0.00
## Contact
cbind(round(prop.table(sort(table(praticasPro$contatoanterior), decreasing = TRUE)),2))
## [,1]
## Sim 0.63
## Não 0.37
## Deal with
cbind(round(prop.table(sort(table(praticasPro$lidadiretamente), decreasing = TRUE)),2))
## [,1]
## Sim 0.64
## Não 0.36
## Where deal with
cbind(round(prop.table(sort(table(praticasPro$lida.onde), decreasing = TRUE)),2))
## [,1]
## Escola 0.35
## FamÃlia 0.23
## Comunidade 0.19
## Outros 0.13
## Amigos 0.05
## Serviços de atuação 0.04
## Serviços de saúde 0.02
# Scale analysis ---
# Full scale
fullScale <- praticasPro[,32:68]
# descriptives
describe(fullScale)
## vars n mean sd median trimmed mad min max range skew kurtosis
## pp001 1 3064 2.09 0.92 2 1.97 0.00 1 5 4 0.87 0.48
## pp002 2 3064 4.33 0.60 4 4.36 0.00 1 5 4 -0.76 2.35
## pp003 3 3064 4.38 0.61 4 4.41 0.00 1 5 4 -0.90 2.47
## pp004 4 3064 4.54 0.53 5 4.57 0.00 1 5 4 -0.79 1.24
## pp005 5 3064 3.88 0.88 4 3.98 0.00 1 5 4 -0.85 0.41
## pp006 6 3064 4.51 0.59 5 4.56 0.00 1 5 4 -1.25 3.15
## pp007 7 3064 4.44 0.69 5 4.52 0.00 1 5 4 -1.53 3.96
## pp008 8 3064 1.97 0.89 2 1.86 1.48 1 5 4 1.00 1.06
## pp009 9 3064 2.38 0.90 2 2.32 0.00 1 5 4 0.77 0.18
## pp010 10 3064 3.89 0.78 4 3.96 0.00 1 5 4 -1.01 1.68
## pp011 11 3064 3.53 0.96 4 3.56 0.00 1 5 4 -0.58 -0.39
## pp012 12 3064 3.54 0.91 4 3.58 0.00 1 5 4 -0.63 -0.13
## pp013 13 3064 4.08 0.70 4 4.14 0.00 1 5 4 -0.95 2.29
## pp014 14 3064 2.87 0.92 3 2.84 1.48 1 5 4 0.23 -0.86
## pp015 15 3064 3.23 1.01 3 3.25 1.48 1 5 4 -0.31 -0.68
## pp016 16 3064 3.34 0.96 4 3.36 1.48 1 5 4 -0.34 -0.64
## pp017 17 3064 4.29 0.62 4 4.33 0.00 1 5 4 -0.84 2.54
## pp018 18 3064 2.29 1.03 2 2.19 0.00 1 5 4 0.82 0.04
## pp019 19 3064 3.75 0.83 4 3.82 0.00 1 5 4 -0.82 0.65
## pp020 20 3064 3.74 0.80 4 3.79 0.00 1 5 4 -0.78 0.69
## pp021 21 3064 3.76 0.82 4 3.83 0.00 1 5 4 -0.74 0.45
## pp022 22 3064 3.59 0.92 4 3.63 0.00 1 5 4 -0.66 -0.06
## pp023 23 3064 4.21 0.65 4 4.26 0.00 1 5 4 -0.90 2.55
## pp024 24 3064 1.70 0.76 2 1.59 1.48 1 5 4 1.41 3.09
## pp025 25 3064 2.08 0.90 2 1.98 1.48 1 5 4 0.82 0.51
## pp026 26 3064 2.75 0.99 3 2.72 1.48 1 5 4 0.33 -0.63
## pp027 27 3064 3.26 0.92 3 3.29 1.48 1 5 4 -0.32 -0.61
## pp028 28 3064 1.72 0.92 2 1.53 1.48 1 5 4 1.79 3.50
## pp029 29 3064 3.88 0.76 4 3.93 0.00 1 5 4 -0.71 0.91
## pp030 30 3064 3.91 0.75 4 3.97 0.00 1 5 4 -0.78 0.98
## pp031 31 3064 3.74 0.78 4 3.77 0.00 1 5 4 -0.59 0.36
## pp032 32 3064 3.63 0.90 4 3.67 0.00 1 5 4 -0.56 -0.21
## pp033 33 3064 1.97 0.78 2 1.90 0.00 1 5 4 0.89 1.36
## pp034 34 3064 1.87 0.78 2 1.77 0.00 1 5 4 1.02 1.48
## pp035 35 3064 2.44 0.93 2 2.41 1.48 1 5 4 0.56 -0.25
## pp036 36 3064 2.34 0.91 2 2.28 0.00 1 5 4 0.74 0.10
## pp037 37 3064 4.29 0.71 4 4.37 0.00 1 5 4 -1.38 3.79
## se
## pp001 0.02
## pp002 0.01
## pp003 0.01
## pp004 0.01
## pp005 0.02
## pp006 0.01
## pp007 0.01
## pp008 0.02
## pp009 0.02
## pp010 0.01
## pp011 0.02
## pp012 0.02
## pp013 0.01
## pp014 0.02
## pp015 0.02
## pp016 0.02
## pp017 0.01
## pp018 0.02
## pp019 0.01
## pp020 0.01
## pp021 0.01
## pp022 0.02
## pp023 0.01
## pp024 0.01
## pp025 0.02
## pp026 0.02
## pp027 0.02
## pp028 0.02
## pp029 0.01
## pp030 0.01
## pp031 0.01
## pp032 0.02
## pp033 0.01
## pp034 0.01
## pp035 0.02
## pp036 0.02
## pp037 0.01
# correlations
round(cor(fullScale, method="kendal", use="complete.obs"),2) # kendall correlation coef
## pp001 pp002 pp003 pp004 pp005 pp006 pp007 pp008 pp009 pp010 pp011
## pp001 1.00 -0.34 -0.25 -0.23 -0.19 -0.19 -0.16 0.19 0.06 -0.12 -0.03
## pp002 -0.34 1.00 0.53 0.44 0.26 0.37 0.32 -0.20 0.01 0.21 0.06
## pp003 -0.25 0.53 1.00 0.54 0.36 0.41 0.37 -0.18 -0.01 0.20 0.08
## pp004 -0.23 0.44 0.54 1.00 0.36 0.48 0.44 -0.20 0.01 0.21 0.07
## pp005 -0.19 0.26 0.36 0.36 1.00 0.28 0.22 -0.06 0.07 0.17 0.15
## pp006 -0.19 0.37 0.41 0.48 0.28 1.00 0.51 -0.19 0.03 0.22 0.12
## pp007 -0.16 0.32 0.37 0.44 0.22 0.51 1.00 -0.14 -0.01 0.19 0.11
## pp008 0.19 -0.20 -0.18 -0.20 -0.06 -0.19 -0.14 1.00 0.14 -0.06 0.06
## pp009 0.06 0.01 -0.01 0.01 0.07 0.03 -0.01 0.14 1.00 0.21 0.18
## pp010 -0.12 0.21 0.20 0.21 0.17 0.22 0.19 -0.06 0.21 1.00 0.31
## pp011 -0.03 0.06 0.08 0.07 0.15 0.12 0.11 0.06 0.18 0.31 1.00
## pp012 -0.16 0.18 0.14 0.13 0.15 0.14 0.10 -0.02 0.20 0.42 0.20
## pp013 -0.21 0.31 0.33 0.31 0.27 0.29 0.27 -0.15 0.07 0.25 0.12
## pp014 -0.06 0.11 0.09 0.08 0.14 0.09 0.06 0.03 0.43 0.23 0.17
## pp015 -0.06 0.09 0.09 0.07 0.14 0.10 0.08 0.11 0.17 0.24 0.18
## pp016 -0.15 0.16 0.15 0.13 0.17 0.11 0.05 -0.06 0.17 0.27 0.14
## pp017 -0.23 0.37 0.40 0.40 0.35 0.38 0.35 -0.16 0.04 0.23 0.12
## pp018 0.28 -0.24 -0.21 -0.20 -0.15 -0.18 -0.14 0.17 0.08 -0.16 0.01
## pp019 -0.15 0.19 0.18 0.16 0.16 0.16 0.14 -0.10 0.11 0.31 0.15
## pp020 -0.17 0.27 0.21 0.19 0.20 0.20 0.15 -0.09 0.17 0.31 0.16
## pp021 -0.17 0.21 0.18 0.17 0.16 0.18 0.15 -0.08 0.15 0.43 0.17
## pp022 -0.13 0.15 0.14 0.12 0.14 0.12 0.07 -0.05 0.14 0.26 0.16
## pp023 -0.21 0.35 0.31 0.31 0.20 0.32 0.30 -0.16 0.03 0.29 0.10
## pp024 0.20 -0.31 -0.28 -0.29 -0.11 -0.29 -0.28 0.37 0.12 -0.08 0.07
## pp025 0.20 -0.24 -0.23 -0.19 -0.12 -0.19 -0.17 0.17 -0.01 -0.17 0.02
## pp026 -0.04 0.06 0.02 0.05 0.10 0.03 -0.01 0.02 0.34 0.22 0.13
## pp027 -0.09 0.13 0.10 0.08 0.13 0.12 0.08 0.01 0.20 0.34 0.20
## pp028 0.19 -0.28 -0.29 -0.32 -0.14 -0.32 -0.29 0.23 0.05 -0.16 -0.01
## pp029 -0.20 0.26 0.22 0.21 0.19 0.23 0.18 -0.13 0.14 0.40 0.17
## pp030 -0.14 0.24 0.21 0.21 0.16 0.23 0.16 -0.08 0.21 0.35 0.22
## pp031 -0.12 0.19 0.17 0.15 0.15 0.17 0.12 -0.04 0.23 0.40 0.24
## pp032 -0.13 0.21 0.17 0.17 0.18 0.18 0.14 -0.03 0.25 0.33 0.17
## pp033 0.22 -0.31 -0.26 -0.27 -0.18 -0.26 -0.20 0.17 -0.08 -0.24 -0.07
## pp034 0.24 -0.33 -0.28 -0.28 -0.19 -0.26 -0.22 0.17 -0.03 -0.23 -0.04
## pp035 0.15 -0.19 -0.15 -0.15 -0.14 -0.16 -0.08 0.08 -0.23 -0.33 -0.14
## pp036 0.15 -0.18 -0.15 -0.16 -0.15 -0.15 -0.08 0.06 -0.21 -0.33 -0.13
## pp037 -0.15 0.29 0.30 0.35 0.19 0.34 0.40 -0.16 -0.01 0.17 0.06
## pp012 pp013 pp014 pp015 pp016 pp017 pp018 pp019 pp020 pp021 pp022
## pp001 -0.16 -0.21 -0.06 -0.06 -0.15 -0.23 0.28 -0.15 -0.17 -0.17 -0.13
## pp002 0.18 0.31 0.11 0.09 0.16 0.37 -0.24 0.19 0.27 0.21 0.15
## pp003 0.14 0.33 0.09 0.09 0.15 0.40 -0.21 0.18 0.21 0.18 0.14
## pp004 0.13 0.31 0.08 0.07 0.13 0.40 -0.20 0.16 0.19 0.17 0.12
## pp005 0.15 0.27 0.14 0.14 0.17 0.35 -0.15 0.16 0.20 0.16 0.14
## pp006 0.14 0.29 0.09 0.10 0.11 0.38 -0.18 0.16 0.20 0.18 0.12
## pp007 0.10 0.27 0.06 0.08 0.05 0.35 -0.14 0.14 0.15 0.15 0.07
## pp008 -0.02 -0.15 0.03 0.11 -0.06 -0.16 0.17 -0.10 -0.09 -0.08 -0.05
## pp009 0.20 0.07 0.43 0.17 0.17 0.04 0.08 0.11 0.17 0.15 0.14
## pp010 0.42 0.25 0.23 0.24 0.27 0.23 -0.16 0.31 0.31 0.43 0.26
## pp011 0.20 0.12 0.17 0.18 0.14 0.12 0.01 0.15 0.16 0.17 0.16
## pp012 1.00 0.21 0.27 0.26 0.46 0.18 -0.16 0.37 0.31 0.49 0.34
## pp013 0.21 1.00 0.18 0.14 0.23 0.42 -0.21 0.27 0.28 0.21 0.21
## pp014 0.27 0.18 1.00 0.27 0.25 0.16 -0.02 0.22 0.30 0.24 0.20
## pp015 0.26 0.14 0.27 1.00 0.21 0.16 -0.06 0.20 0.22 0.22 0.20
## pp016 0.46 0.23 0.25 0.21 1.00 0.16 -0.13 0.38 0.29 0.39 0.36
## pp017 0.18 0.42 0.16 0.16 0.16 1.00 -0.25 0.25 0.28 0.23 0.18
## pp018 -0.16 -0.21 -0.02 -0.06 -0.13 -0.25 1.00 -0.18 -0.15 -0.22 -0.16
## pp019 0.37 0.27 0.22 0.20 0.38 0.25 -0.18 1.00 0.32 0.41 0.29
## pp020 0.31 0.28 0.30 0.22 0.29 0.28 -0.15 0.32 1.00 0.40 0.26
## pp021 0.49 0.21 0.24 0.22 0.39 0.23 -0.22 0.41 0.40 1.00 0.36
## pp022 0.34 0.21 0.20 0.20 0.36 0.18 -0.16 0.29 0.26 0.36 1.00
## pp023 0.20 0.32 0.13 0.12 0.17 0.38 -0.22 0.21 0.30 0.27 0.24
## pp024 -0.03 -0.22 0.01 0.05 -0.03 -0.29 0.24 -0.09 -0.13 -0.10 -0.04
## pp025 -0.13 -0.22 -0.08 -0.04 -0.10 -0.22 0.25 -0.11 -0.19 -0.17 -0.09
## pp026 0.25 0.10 0.35 0.18 0.23 0.07 0.00 0.19 0.22 0.22 0.19
## pp027 0.36 0.16 0.25 0.24 0.29 0.16 -0.15 0.28 0.29 0.41 0.35
## pp028 -0.11 -0.24 -0.06 -0.05 -0.09 -0.32 0.23 -0.16 -0.17 -0.16 -0.13
## pp029 0.44 0.28 0.22 0.22 0.37 0.28 -0.20 0.37 0.40 0.52 0.34
## pp030 0.28 0.26 0.26 0.20 0.20 0.25 -0.15 0.25 0.40 0.34 0.24
## pp031 0.37 0.22 0.29 0.25 0.30 0.21 -0.14 0.32 0.36 0.41 0.29
## pp032 0.29 0.22 0.31 0.24 0.23 0.22 -0.12 0.23 0.34 0.32 0.24
## pp033 -0.19 -0.28 -0.19 -0.13 -0.20 -0.29 0.26 -0.24 -0.28 -0.24 -0.17
## pp034 -0.18 -0.33 -0.13 -0.13 -0.19 -0.34 0.29 -0.23 -0.24 -0.23 -0.18
## pp035 -0.35 -0.21 -0.30 -0.18 -0.30 -0.20 0.19 -0.29 -0.33 -0.36 -0.28
## pp036 -0.33 -0.19 -0.27 -0.18 -0.27 -0.21 0.19 -0.27 -0.31 -0.36 -0.27
## pp037 0.09 0.28 0.06 0.07 0.06 0.39 -0.16 0.14 0.17 0.14 0.09
## pp023 pp024 pp025 pp026 pp027 pp028 pp029 pp030 pp031 pp032 pp033
## pp001 -0.21 0.20 0.20 -0.04 -0.09 0.19 -0.20 -0.14 -0.12 -0.13 0.22
## pp002 0.35 -0.31 -0.24 0.06 0.13 -0.28 0.26 0.24 0.19 0.21 -0.31
## pp003 0.31 -0.28 -0.23 0.02 0.10 -0.29 0.22 0.21 0.17 0.17 -0.26
## pp004 0.31 -0.29 -0.19 0.05 0.08 -0.32 0.21 0.21 0.15 0.17 -0.27
## pp005 0.20 -0.11 -0.12 0.10 0.13 -0.14 0.19 0.16 0.15 0.18 -0.18
## pp006 0.32 -0.29 -0.19 0.03 0.12 -0.32 0.23 0.23 0.17 0.18 -0.26
## pp007 0.30 -0.28 -0.17 -0.01 0.08 -0.29 0.18 0.16 0.12 0.14 -0.20
## pp008 -0.16 0.37 0.17 0.02 0.01 0.23 -0.13 -0.08 -0.04 -0.03 0.17
## pp009 0.03 0.12 -0.01 0.34 0.20 0.05 0.14 0.21 0.23 0.25 -0.08
## pp010 0.29 -0.08 -0.17 0.22 0.34 -0.16 0.40 0.35 0.40 0.33 -0.24
## pp011 0.10 0.07 0.02 0.13 0.20 -0.01 0.17 0.22 0.24 0.17 -0.07
## pp012 0.20 -0.03 -0.13 0.25 0.36 -0.11 0.44 0.28 0.37 0.29 -0.19
## pp013 0.32 -0.22 -0.22 0.10 0.16 -0.24 0.28 0.26 0.22 0.22 -0.28
## pp014 0.13 0.01 -0.08 0.35 0.25 -0.06 0.22 0.26 0.29 0.31 -0.19
## pp015 0.12 0.05 -0.04 0.18 0.24 -0.05 0.22 0.20 0.25 0.24 -0.13
## pp016 0.17 -0.03 -0.10 0.23 0.29 -0.09 0.37 0.20 0.30 0.23 -0.20
## pp017 0.38 -0.29 -0.22 0.07 0.16 -0.32 0.28 0.25 0.21 0.22 -0.29
## pp018 -0.22 0.24 0.25 0.00 -0.15 0.23 -0.20 -0.15 -0.14 -0.12 0.26
## pp019 0.21 -0.09 -0.11 0.19 0.28 -0.16 0.37 0.25 0.32 0.23 -0.24
## pp020 0.30 -0.13 -0.19 0.22 0.29 -0.17 0.40 0.40 0.36 0.34 -0.28
## pp021 0.27 -0.10 -0.17 0.22 0.41 -0.16 0.52 0.34 0.41 0.32 -0.24
## pp022 0.24 -0.04 -0.09 0.19 0.35 -0.13 0.34 0.24 0.29 0.24 -0.17
## pp023 1.00 -0.31 -0.24 0.08 0.19 -0.32 0.36 0.31 0.24 0.27 -0.30
## pp024 -0.31 1.00 0.30 0.08 -0.01 0.38 -0.16 -0.10 -0.04 -0.08 0.27
## pp025 -0.24 0.30 1.00 -0.06 -0.11 0.25 -0.23 -0.18 -0.15 -0.16 0.30
## pp026 0.08 0.08 -0.06 1.00 0.27 0.00 0.22 0.25 0.29 0.28 -0.12
## pp027 0.19 -0.01 -0.11 0.27 1.00 -0.09 0.37 0.29 0.38 0.32 -0.19
## pp028 -0.32 0.38 0.25 0.00 -0.09 1.00 -0.23 -0.17 -0.15 -0.16 0.31
## pp029 0.36 -0.16 -0.23 0.22 0.37 -0.23 1.00 0.37 0.43 0.35 -0.28
## pp030 0.31 -0.10 -0.18 0.25 0.29 -0.17 0.37 1.00 0.55 0.43 -0.29
## pp031 0.24 -0.04 -0.15 0.29 0.38 -0.15 0.43 0.55 1.00 0.46 -0.27
## pp032 0.27 -0.08 -0.16 0.28 0.32 -0.16 0.35 0.43 0.46 1.00 -0.29
## pp033 -0.30 0.27 0.30 -0.12 -0.19 0.31 -0.28 -0.29 -0.27 -0.29 1.00
## pp034 -0.32 0.27 0.33 -0.09 -0.19 0.33 -0.27 -0.26 -0.25 -0.25 0.60
## pp035 -0.22 0.09 0.23 -0.29 -0.34 0.16 -0.36 -0.38 -0.42 -0.41 0.42
## pp036 -0.23 0.10 0.21 -0.28 -0.35 0.16 -0.37 -0.38 -0.42 -0.40 0.39
## pp037 0.31 -0.31 -0.19 0.01 0.10 -0.33 0.22 0.20 0.15 0.18 -0.27
## pp034 pp035 pp036 pp037
## pp001 0.24 0.15 0.15 -0.15
## pp002 -0.33 -0.19 -0.18 0.29
## pp003 -0.28 -0.15 -0.15 0.30
## pp004 -0.28 -0.15 -0.16 0.35
## pp005 -0.19 -0.14 -0.15 0.19
## pp006 -0.26 -0.16 -0.15 0.34
## pp007 -0.22 -0.08 -0.08 0.40
## pp008 0.17 0.08 0.06 -0.16
## pp009 -0.03 -0.23 -0.21 -0.01
## pp010 -0.23 -0.33 -0.33 0.17
## pp011 -0.04 -0.14 -0.13 0.06
## pp012 -0.18 -0.35 -0.33 0.09
## pp013 -0.33 -0.21 -0.19 0.28
## pp014 -0.13 -0.30 -0.27 0.06
## pp015 -0.13 -0.18 -0.18 0.07
## pp016 -0.19 -0.30 -0.27 0.06
## pp017 -0.34 -0.20 -0.21 0.39
## pp018 0.29 0.19 0.19 -0.16
## pp019 -0.23 -0.29 -0.27 0.14
## pp020 -0.24 -0.33 -0.31 0.17
## pp021 -0.23 -0.36 -0.36 0.14
## pp022 -0.18 -0.28 -0.27 0.09
## pp023 -0.32 -0.22 -0.23 0.31
## pp024 0.27 0.09 0.10 -0.31
## pp025 0.33 0.23 0.21 -0.19
## pp026 -0.09 -0.29 -0.28 0.01
## pp027 -0.19 -0.34 -0.35 0.10
## pp028 0.33 0.16 0.16 -0.33
## pp029 -0.27 -0.36 -0.37 0.22
## pp030 -0.26 -0.38 -0.38 0.20
## pp031 -0.25 -0.42 -0.42 0.15
## pp032 -0.25 -0.41 -0.40 0.18
## pp033 0.60 0.42 0.39 -0.27
## pp034 1.00 0.36 0.35 -0.30
## pp035 0.36 1.00 0.74 -0.13
## pp036 0.35 0.74 1.00 -0.12
## pp037 -0.30 -0.13 -0.12 1.00
cor.plot(cor(fullScale, method="kendal", use="complete.obs"), numbers= TRUE)
# alpha
cronbach <- alpha(fullScale)
## Warning: Some items were negatively correlated with total scale and were
## automatically reversed.
cronbach
##
## Reliability analysis
## Call: alpha(x = fullScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.9 0.9 0.92 0.2 9.2 0.0033 3.8 0.38
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.9 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## pp001- 0.90 0.9 0.92 0.20 9.1 0.0034
## pp002 0.90 0.9 0.92 0.20 8.9 0.0034
## pp003 0.90 0.9 0.92 0.20 9.0 0.0034
## pp004 0.90 0.9 0.92 0.20 9.0 0.0034
## pp005 0.90 0.9 0.92 0.20 9.1 0.0034
## pp006 0.90 0.9 0.92 0.20 9.0 0.0034
## pp007 0.90 0.9 0.92 0.20 9.1 0.0034
## pp008- 0.90 0.9 0.92 0.21 9.4 0.0033
## pp009 0.90 0.9 0.92 0.20 9.2 0.0034
## pp010 0.89 0.9 0.92 0.20 8.8 0.0035
## pp011 0.90 0.9 0.92 0.20 9.3 0.0033
## pp012 0.89 0.9 0.92 0.20 8.8 0.0035
## pp013 0.89 0.9 0.92 0.20 8.9 0.0034
## pp014 0.89 0.9 0.92 0.20 9.0 0.0034
## pp015 0.90 0.9 0.92 0.20 9.1 0.0034
## pp016 0.89 0.9 0.92 0.20 8.9 0.0035
## pp017 0.89 0.9 0.92 0.20 8.9 0.0034
## pp018- 0.90 0.9 0.92 0.20 9.2 0.0034
## pp019 0.89 0.9 0.92 0.20 8.9 0.0034
## pp020 0.89 0.9 0.92 0.20 8.8 0.0035
## pp021 0.89 0.9 0.92 0.20 8.7 0.0035
## pp022 0.89 0.9 0.92 0.20 8.9 0.0034
## pp023 0.89 0.9 0.92 0.20 8.9 0.0034
## pp024- 0.90 0.9 0.92 0.21 9.3 0.0033
## pp025- 0.90 0.9 0.92 0.20 9.1 0.0034
## pp026 0.90 0.9 0.92 0.20 9.1 0.0034
## pp027 0.89 0.9 0.92 0.20 8.9 0.0035
## pp028- 0.90 0.9 0.92 0.20 9.2 0.0033
## pp029 0.89 0.9 0.92 0.19 8.7 0.0035
## pp030 0.89 0.9 0.92 0.20 8.8 0.0035
## pp031 0.89 0.9 0.92 0.20 8.7 0.0035
## pp032 0.89 0.9 0.92 0.20 8.8 0.0035
## pp033- 0.89 0.9 0.92 0.20 8.8 0.0035
## pp034- 0.89 0.9 0.92 0.20 8.9 0.0034
## pp035- 0.89 0.9 0.91 0.20 8.7 0.0035
## pp036- 0.89 0.9 0.92 0.20 8.8 0.0035
## pp037 0.90 0.9 0.92 0.20 9.1 0.0034
##
## Item statistics
## n r r.cor r.drop mean sd
## pp001- 3064 0.37 0.34 0.31 3.9 0.92
## pp002 3064 0.50 0.48 0.43 4.3 0.60
## pp003 3064 0.48 0.46 0.40 4.4 0.61
## pp004 3064 0.48 0.46 0.40 4.5 0.53
## pp005 3064 0.41 0.38 0.34 3.9 0.88
## pp006 3064 0.44 0.42 0.37 4.5 0.59
## pp007 3064 0.35 0.32 0.28 4.4 0.69
## pp008- 3064 0.20 0.16 0.12 4.0 0.89
## pp009 3064 0.33 0.30 0.29 2.4 0.90
## pp010 3064 0.56 0.55 0.53 3.9 0.78
## pp011 3064 0.29 0.25 0.24 3.5 0.96
## pp012 3064 0.57 0.56 0.55 3.5 0.91
## pp013 3064 0.50 0.48 0.44 4.1 0.70
## pp014 3064 0.48 0.46 0.45 2.9 0.92
## pp015 3064 0.37 0.34 0.33 3.2 1.01
## pp016 3064 0.52 0.51 0.49 3.3 0.96
## pp017 3064 0.54 0.52 0.48 4.3 0.62
## pp018- 3064 0.35 0.31 0.29 3.7 1.03
## pp019 3064 0.51 0.50 0.48 3.7 0.83
## pp020 3064 0.59 0.58 0.56 3.7 0.80
## pp021 3064 0.61 0.61 0.59 3.8 0.82
## pp022 3064 0.49 0.47 0.46 3.6 0.92
## pp023 3064 0.51 0.49 0.45 4.2 0.65
## pp024- 3064 0.26 0.22 0.18 4.3 0.76
## pp025- 3064 0.38 0.35 0.32 3.9 0.90
## pp026 3064 0.41 0.38 0.38 2.7 0.99
## pp027 3064 0.54 0.52 0.51 3.3 0.92
## pp028- 3064 0.31 0.27 0.24 4.3 0.92
## pp029 3064 0.65 0.64 0.62 3.9 0.76
## pp030 3064 0.59 0.58 0.55 3.9 0.75
## pp031 3064 0.62 0.61 0.59 3.7 0.78
## pp032 3064 0.57 0.56 0.54 3.6 0.90
## pp033- 3064 0.55 0.53 0.50 4.0 0.78
## pp034- 3064 0.53 0.52 0.48 4.1 0.78
## pp035- 3064 0.61 0.62 0.59 3.6 0.93
## pp036- 3064 0.59 0.59 0.57 3.7 0.91
## pp037 3064 0.36 0.33 0.29 4.3 0.71
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## pp001 0.26 0.51 0.14 0.09 0.01 0
## pp002 0.00 0.01 0.03 0.57 0.39 0
## pp003 0.00 0.01 0.03 0.53 0.43 0
## pp004 0.00 0.00 0.01 0.43 0.56 0
## pp005 0.00 0.10 0.11 0.56 0.21 0
## pp006 0.00 0.01 0.02 0.42 0.55 0
## pp007 0.01 0.02 0.03 0.44 0.51 0
## pp008 0.31 0.49 0.13 0.06 0.01 0
## pp009 0.11 0.56 0.19 0.13 0.02 0
## pp010 0.01 0.06 0.13 0.63 0.17 0
## pp011 0.02 0.17 0.19 0.51 0.11 0
## pp012 0.02 0.14 0.22 0.52 0.10 0
## pp013 0.00 0.03 0.09 0.64 0.24 0
## pp014 0.03 0.38 0.30 0.26 0.03 0
## pp015 0.04 0.22 0.27 0.39 0.07 0
## pp016 0.02 0.20 0.26 0.43 0.08 0
## pp017 0.00 0.01 0.04 0.58 0.36 0
## pp018 0.20 0.51 0.13 0.13 0.03 0
## pp019 0.01 0.09 0.18 0.59 0.13 0
## pp020 0.01 0.08 0.20 0.59 0.12 0
## pp021 0.01 0.09 0.18 0.58 0.14 0
## pp022 0.02 0.14 0.20 0.53 0.11 0
## pp023 0.00 0.02 0.06 0.61 0.31 0
## pp024 0.43 0.48 0.05 0.03 0.01 0
## pp025 0.26 0.50 0.16 0.07 0.01 0
## pp026 0.07 0.40 0.28 0.21 0.04 0
## pp027 0.02 0.21 0.31 0.41 0.05 0
## pp028 0.48 0.43 0.03 0.04 0.03 0
## pp029 0.00 0.05 0.18 0.59 0.18 0
## pp030 0.00 0.06 0.15 0.61 0.18 0
## pp031 0.00 0.07 0.23 0.56 0.13 0
## pp032 0.01 0.13 0.21 0.52 0.13 0
## pp033 0.26 0.56 0.13 0.04 0.01 0
## pp034 0.32 0.54 0.09 0.04 0.00 0
## pp035 0.12 0.50 0.22 0.15 0.02 0
## pp036 0.13 0.55 0.17 0.13 0.01 0
## pp037 0.01 0.02 0.04 0.53 0.40 0
# EFA ----
## All items ----
## KMO
KMO(fullScale)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = fullScale)
## Overall MSA = 0.93
## MSA for each item =
## pp001 pp002 pp003 pp004 pp005 pp006 pp007 pp008 pp009 pp010 pp011 pp012
## 0.93 0.93 0.92 0.93 0.92 0.93 0.91 0.81 0.88 0.95 0.89 0.95
## pp013 pp014 pp015 pp016 pp017 pp018 pp019 pp020 pp021 pp022 pp023 pp024
## 0.96 0.92 0.95 0.94 0.95 0.92 0.96 0.97 0.95 0.96 0.96 0.86
## pp025 pp026 pp027 pp028 pp029 pp030 pp031 pp032 pp033 pp034 pp035 pp036
## 0.94 0.95 0.97 0.94 0.97 0.94 0.95 0.97 0.91 0.91 0.88 0.88
## pp037
## 0.93
# Barlett test of homogeneity
bartlett.test(fullScale)
##
## Bartlett test of homogeneity of variances
##
## data: fullScale
## Bartlett's K-squared = 5723, df = 36, p-value < 2.2e-16
# Defining factors
fa.parallel(fullScale, fm="minres", fa="both", ylabel="Eigenvalues") # yields 4 components and 4 factors
## Loading required package: parallel
## Loading required package: MASS
## Parallel analysis suggests that the number of factors = 8 and the number of components = 5
VSS(fullScale, rotate="none") # VSS = 2; MAP = 4 factors
##
## Very Simple Structure
## Call: vss(x = x, n = n, rotate = rotate, diagonal = diagonal, fm = fm,
## n.obs = n.obs, plot = plot, title = title)
## VSS complexity 1 achieves a maximimum of 0.69 with 1 factors
## VSS complexity 2 achieves a maximimum of 0.8 with 2 factors
##
## The Velicer MAP achieves a minimum of 0.01 with 4 factors
## BIC achieves a minimum of -1706 with 7 factors
## Sample Size adjusted BIC achieves a minimum of -346.1 with 7 factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC
## 1 0.69 0.00 0.0143 629 14478 0.0e+00 34 0.69 0.085 9428 11427
## 2 0.65 0.80 0.0072 593 8101 0.0e+00 22 0.80 0.064 3340 5224
## 3 0.64 0.80 0.0068 558 5610 0.0e+00 19 0.82 0.055 1131 2904
## 4 0.64 0.79 0.0064 524 3887 0.0e+00 17 0.84 0.046 -319 1346
## 5 0.64 0.79 0.0070 491 3089 0.0e+00 16 0.85 0.042 -852 708
## 6 0.60 0.79 0.0079 459 2252 8.6e-234 16 0.86 0.036 -1432 26
## 7 0.61 0.79 0.0087 428 1730 1.2e-155 15 0.87 0.032 -1706 -346
## 8 0.44 0.63 0.0096 398 1697 1.5e-159 15 0.87 0.033 -1498 -233
## complex eChisq eRMS eCRMS eBIC
## 1 1.0 33627 0.091 0.093 28578
## 2 1.5 9541 0.048 0.051 4781
## 3 1.8 6187 0.039 0.043 1708
## 4 2.0 3475 0.029 0.033 -732
## 5 2.2 2662 0.026 0.030 -1279
## 6 2.3 2052 0.022 0.027 -1633
## 7 2.5 1516 0.019 0.024 -1919
## 8 3.7 1509 0.019 0.025 -1686
# Factor Analysis using polychoric correlations
faAll <- fa.poly(fullScale, nfactors = 2, rotate = "oblimin", fm="minres")
## Loading required package: mvtnorm
## Loading required package: GPArotation
faAll$fa
## Factor Analysis using method = minres
## Call: fa.poly(x = fullScale, nfactors = 2, rotate = "oblimin", fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## pp001 -0.07 -0.42 0.20 0.80 1.1
## pp002 0.07 0.68 0.51 0.49 1.0
## pp003 -0.01 0.73 0.53 0.47 1.0
## pp004 -0.02 0.77 0.58 0.42 1.0
## pp005 0.13 0.42 0.24 0.76 1.2
## pp006 0.03 0.68 0.47 0.53 1.0
## pp007 -0.06 0.64 0.38 0.62 1.0
## pp008 0.14 -0.45 0.17 0.83 1.2
## pp009 0.57 -0.28 0.27 0.73 1.4
## pp010 0.62 0.11 0.45 0.55 1.1
## pp011 0.39 -0.05 0.14 0.86 1.0
## pp012 0.71 -0.03 0.49 0.51 1.0
## pp013 0.22 0.49 0.37 0.63 1.4
## pp014 0.63 -0.11 0.35 0.65 1.1
## pp015 0.47 -0.06 0.20 0.80 1.0
## pp016 0.61 0.00 0.37 0.63 1.0
## pp017 0.14 0.64 0.50 0.50 1.1
## pp018 -0.08 -0.40 0.19 0.81 1.1
## pp019 0.53 0.13 0.35 0.65 1.1
## pp020 0.58 0.18 0.44 0.56 1.2
## pp021 0.70 0.07 0.54 0.46 1.0
## pp022 0.56 0.03 0.33 0.67 1.0
## pp023 0.23 0.51 0.41 0.59 1.4
## pp024 0.22 -0.67 0.38 0.62 1.2
## pp025 -0.11 -0.40 0.21 0.79 1.1
## pp026 0.63 -0.22 0.33 0.67 1.2
## pp027 0.70 -0.08 0.45 0.55 1.0
## pp028 0.01 -0.57 0.32 0.68 1.0
## pp029 0.64 0.18 0.54 0.46 1.2
## pp030 0.61 0.15 0.47 0.53 1.1
## pp031 0.75 0.01 0.57 0.43 1.0
## pp032 0.64 0.07 0.45 0.55 1.0
## pp033 -0.30 -0.43 0.38 0.62 1.8
## pp034 -0.24 -0.49 0.40 0.60 1.5
## pp035 -0.71 -0.04 0.52 0.48 1.0
## pp036 -0.69 -0.04 0.50 0.50 1.0
## pp037 -0.02 0.60 0.35 0.65 1.0
##
## MR1 MR2
## SS loadings 8.04 6.33
## Proportion Var 0.22 0.17
## Cumulative Var 0.22 0.39
## Proportion Explained 0.56 0.44
## Cumulative Proportion 0.56 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.0 0.4
## MR2 0.4 1.0
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 666 and the objective function was 18.31 with Chi Square of 55833
## The degrees of freedom for the model are 593 and the objective function was 4.44
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.06
##
## The harmonic number of observations is 3064 with the empirical chi square 12413 with prob < 0
## The total number of observations was 3064 with MLE Chi Square = 13522 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.737
## RMSEA index = 0.085 and the 90 % confidence intervals are NA NA
## BIC = 8761
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy
## MR1 MR2
## Correlation of scores with factors 0.97 0.96
## Multiple R square of scores with factors 0.93 0.92
## Minimum correlation of possible factor scores 0.87 0.83
# Diagram
fa.diagram(faAll)
# Items per factor #
# MR1 : 9,10,11,12,14,15,16,19,20,21,22,26,27,29,30,31,32,-35,-36
# MR2 : -1,2,3,4,5,6,7,-8,13,17,-18,23,-24,-25,-28,-33,-34,37
# Recode negative items
for (i in c(1,8,18,24,25,28,33,34,35,36)){
fullScale[,i] <- Recode(fullScale[,i], "5=1 ; 4=2 ; 3 = 3; 2 = 4; 1 = 5; else = NA")
}
# Factor Analysis using polychoric correlations
faAll <- fa.poly(fullScale, nfactors = 2, rotate = "oblimin", fm="minres")
faAll$fa
## Factor Analysis using method = minres
## Call: fa.poly(x = fullScale, nfactors = 2, rotate = "oblimin", fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## pp001 0.07 0.42 0.20 0.80 1.1
## pp002 0.07 0.68 0.51 0.49 1.0
## pp003 -0.01 0.73 0.53 0.47 1.0
## pp004 -0.02 0.77 0.58 0.42 1.0
## pp005 0.13 0.42 0.24 0.76 1.2
## pp006 0.03 0.68 0.47 0.53 1.0
## pp007 -0.06 0.64 0.38 0.62 1.0
## pp008 -0.14 0.45 0.17 0.83 1.2
## pp009 0.57 -0.28 0.27 0.73 1.4
## pp010 0.62 0.11 0.45 0.55 1.1
## pp011 0.39 -0.05 0.14 0.86 1.0
## pp012 0.71 -0.03 0.49 0.51 1.0
## pp013 0.22 0.49 0.37 0.63 1.4
## pp014 0.63 -0.11 0.35 0.65 1.1
## pp015 0.47 -0.06 0.20 0.80 1.0
## pp016 0.61 0.00 0.37 0.63 1.0
## pp017 0.14 0.64 0.50 0.50 1.1
## pp018 0.08 0.40 0.19 0.81 1.1
## pp019 0.53 0.13 0.35 0.65 1.1
## pp020 0.58 0.18 0.44 0.56 1.2
## pp021 0.70 0.07 0.54 0.46 1.0
## pp022 0.56 0.03 0.33 0.67 1.0
## pp023 0.23 0.51 0.41 0.59 1.4
## pp024 -0.22 0.67 0.38 0.62 1.2
## pp025 0.11 0.40 0.21 0.79 1.1
## pp026 0.63 -0.22 0.33 0.67 1.2
## pp027 0.70 -0.08 0.45 0.55 1.0
## pp028 -0.01 0.57 0.32 0.68 1.0
## pp029 0.64 0.18 0.54 0.46 1.2
## pp030 0.61 0.15 0.47 0.53 1.1
## pp031 0.75 0.01 0.57 0.43 1.0
## pp032 0.64 0.07 0.45 0.55 1.0
## pp033 0.30 0.43 0.38 0.62 1.8
## pp034 0.24 0.49 0.40 0.60 1.5
## pp035 0.71 0.04 0.52 0.48 1.0
## pp036 0.69 0.04 0.50 0.50 1.0
## pp037 -0.02 0.60 0.35 0.65 1.0
##
## MR1 MR2
## SS loadings 8.04 6.33
## Proportion Var 0.22 0.17
## Cumulative Var 0.22 0.39
## Proportion Explained 0.56 0.44
## Cumulative Proportion 0.56 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.0 0.4
## MR2 0.4 1.0
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 666 and the objective function was 18.31 with Chi Square of 55833
## The degrees of freedom for the model are 593 and the objective function was 4.44
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.06
##
## The harmonic number of observations is 3064 with the empirical chi square 12413 with prob < 0
## The total number of observations was 3064 with MLE Chi Square = 13522 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.737
## RMSEA index = 0.085 and the 90 % confidence intervals are NA NA
## BIC = 8761
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy
## MR1 MR2
## Correlation of scores with factors 0.97 0.96
## Multiple R square of scores with factors 0.93 0.92
## Minimum correlation of possible factor scores 0.87 0.83
# Diagram
fa.diagram(faAll)
# CFA ---- Not implemented yet.
### Exploratory factor analysis
### Bifactor Model
library(mirt)
factors <- c(2,2,2,2,2,2,2,2,1,1,1,1,2,1,1,1,2,2,1,1,1,1,2,2,2,1,1,2,1,1,1,1,2,2,1,1,2) # based on efa scores
mbi <- bfactor(fullScale, factors, verbose = FALSE)
summary(mbi)
##
## Factor loadings metric:
## G S1 S2 h2
## pp001 0.4496 0.000 0.19861 0.242
## pp002 0.5629 0.000 0.49677 0.564
## pp003 0.4852 0.000 0.63628 0.640
## pp004 0.4847 0.000 0.70689 0.735
## pp005 0.3111 0.000 0.46704 0.315
## pp006 0.4655 0.000 0.60963 0.588
## pp007 0.3706 0.000 0.60472 0.503
## pp008 0.3588 0.000 0.16444 0.156
## pp009 0.0056 0.585 0.00000 0.343
## pp010 0.4325 0.576 0.00000 0.519
## pp011 0.0922 0.450 0.00000 0.211
## pp012 0.3656 0.654 0.00000 0.562
## pp013 0.5451 0.000 0.35343 0.422
## pp014 0.2159 0.595 0.00000 0.401
## pp015 0.1949 0.468 0.00000 0.257
## pp016 0.3443 0.550 0.00000 0.421
## pp017 0.5767 0.000 0.48701 0.570
## pp018 0.5307 0.000 0.10051 0.292
## pp019 0.4353 0.472 0.00000 0.412
## pp020 0.5100 0.491 0.00000 0.501
## pp021 0.4776 0.623 0.00000 0.616
## pp022 0.3597 0.506 0.00000 0.385
## pp023 0.6003 0.000 0.30331 0.452
## pp024 0.5171 0.000 0.28027 0.346
## pp025 0.5611 0.000 0.06070 0.319
## pp026 0.1346 0.589 0.00000 0.365
## pp027 0.3484 0.612 0.00000 0.495
## pp028 0.5667 0.000 0.26628 0.392
## pp029 0.5552 0.548 0.00000 0.608
## pp030 0.5093 0.517 0.00000 0.526
## pp031 0.4708 0.642 0.00000 0.634
## pp032 0.4685 0.535 0.00000 0.506
## pp033 0.8229 0.000 -0.04990 0.680
## pp034 0.8357 0.000 -0.00883 0.698
## pp035 0.6135 0.481 0.00000 0.608
## pp036 0.5990 0.478 0.00000 0.588
## pp037 0.4796 0.000 0.40614 0.395
##
## SS loadings: 8.573 5.734 2.958
##
## Factor covariance:
## F1 F2 F3
## F1 1 0 0
## F2 0 1 0
## F3 0 0 1
residuals(mbi)
## LD matrix (lower triangle) and standardized values:
## pp001 pp002 pp003 pp004 pp005 pp006 pp007
## pp001 NA 0.125 0.102 -0.098 0.100 -0.108 -0.114
## pp002 192.56 NA 0.142 0.115 0.100 0.104 0.114
## pp003 127.59 247.371 NA 0.143 0.122 0.100 0.128
## pp004 -117.30 161.085 249.714 NA 0.124 0.122 0.152
## pp005 122.55 121.885 182.941 188.307 NA 0.124 0.133
## pp006 -143.09 133.245 123.610 182.750 187.670 NA 0.197
## pp007 -160.26 157.934 202.225 284.320 216.936 477.391 NA
## pp008 144.27 99.353 -121.850 90.326 -163.527 136.819 -186.935
## pp009 -230.38 -177.559 -272.523 -263.248 -158.407 -224.766 -252.205
## pp010 -176.21 -263.786 -336.251 -293.350 -151.278 -259.616 -341.447
## pp011 -137.09 -213.548 -195.607 -159.784 141.496 -157.721 -216.133
## pp012 -113.33 -345.873 -565.574 -691.944 -212.041 -503.858 -539.360
## pp013 -79.06 134.093 152.077 154.612 165.914 175.555 169.364
## pp014 -156.11 -150.724 -271.609 -334.865 -167.161 -253.113 -292.864
## pp015 -138.31 -207.523 -299.255 -274.061 -160.612 -217.430 -275.392
## pp016 -144.70 -236.712 -259.158 -351.484 -125.986 -324.241 -348.971
## pp017 -78.53 59.243 142.943 135.870 156.585 144.873 219.209
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