podatki <- read.table("/cloud/project/Poglavje 1/Naloga 2/Anketa.csv", header=TRUE, sep=";", dec=",")

Opis spremenljivk:

Zamenjava vrednosti

#Zamenjava mankajočih vrednosti s povprečjem (mean) spremenljivke

podatki_imputacija <- sapply(X = podatki, FUN = function(x) {
  x[is.na(x)] <- mean(x, na.rm = TRUE)
  x
})

podatki_imputacija <- as.data.frame(podatki_imputacija)

Odstranitev nepopolnih odgovorov

podatki <- na.omit(podatki) #Odstranitev enot z manjkajočimi vrednostmi
podatki_MGK <- podatki[-1]
library(pastecs) 
round(stat.desc(podatki_MGK, basic=FALSE), 2) 
##                T1   T2   T3   T4   T5   T6   T7   T8   T9  T10  T11
## median       5.00 5.00 5.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00
## mean         4.46 4.53 4.45 4.28 4.17 3.93 4.08 3.78 3.77 3.61 3.81
## SE.mean      0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.02 0.03 0.03 0.03
## CI.mean.0.95 0.04 0.04 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.06 0.05
## var          0.53 0.49 0.54 0.69 0.80 1.07 0.93 0.83 0.97 1.25 0.92
## std.dev      0.73 0.70 0.73 0.83 0.89 1.03 0.96 0.91 0.98 1.12 0.96
## coef.var     0.16 0.15 0.16 0.19 0.21 0.26 0.24 0.24 0.26 0.31 0.25
##               T12
## median       4.00
## mean         3.67
## SE.mean      0.03
## CI.mean.0.95 0.05
## var          0.86
## std.dev      0.93
## coef.var     0.25
R <- cor(podatki_MGK) 
library(psych)
cortest.bartlett(R, n=nrow(podatki_MGK)) 
## $chisq
## [1] 8676.712
## 
## $p.value
## [1] 0
## 
## $df
## [1] 66
library(psych)
KMO(R)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = R)
## Overall MSA =  0.93
## MSA for each item = 
##   T1   T2   T3   T4   T5   T6   T7   T8   T9  T10  T11  T12 
## 0.92 0.92 0.94 0.94 0.95 0.93 0.92 0.96 0.96 0.96 0.91 0.91
library(FactoMineR) 
mgk <- PCA(podatki_MGK,  
           scale.unit = TRUE, 
           graph = FALSE) 


library(factoextra) 
get_eigenvalue(mgk) 
##        eigenvalue variance.percent cumulative.variance.percent
## Dim.1   6.2491466        52.076222                    52.07622
## Dim.2   1.2294783        10.245652                    62.32187
## Dim.3   0.7189891         5.991576                    68.31345
## Dim.4   0.6131295         5.109412                    73.42286
## Dim.5   0.5611649         4.676374                    78.09924
## Dim.6   0.5029911         4.191592                    82.29083
## Dim.7   0.4712636         3.927196                    86.21802
## Dim.8   0.3888152         3.240127                    89.45815
## Dim.9   0.3679037         3.065864                    92.52402
## Dim.10  0.3282004         2.735004                    95.25902
## Dim.11  0.3173777         2.644814                    97.90383
## Dim.12  0.2515399         2.096166                   100.00000
library(psych)
fa.parallel(podatki_MGK,
            sim = FALSE, 
            fa = "pc")

## Parallel analysis suggests that the number of factors =  NA  and the number of components =  2
library(FactoMineR)
mgk <- PCA(podatki_MGK, 
           scale.unit = TRUE, 
           graph = FALSE,
           ncp = 2) 
print(mgk$var$cor)
##         Dim.1        Dim.2
## T1  0.7272862 -0.449186917
## T2  0.7237804 -0.407656811
## T3  0.7462246 -0.308126055
## T4  0.6851108 -0.281370367
## T5  0.8064691 -0.105254309
## T6  0.7551239  0.365934593
## T7  0.6409799  0.497003643
## T8  0.5926928  0.377917114
## T9  0.7633547  0.134538044
## T10 0.6513753  0.364483297
## T11 0.8191389 -0.040292200
## T12 0.7137101  0.004668726
print(mgk$var$contrib)
##         Dim.1        Dim.2
## T1   8.464279 16.410935243
## T2   8.382874 13.516633611
## T3   8.910834  7.722109966
## T4   7.511055  6.439258329
## T5  10.407700  0.901070779
## T6   9.124640 10.891459216
## T7   6.574581 20.090848536
## T8   5.621324 11.616418635
## T9   9.324640  1.472208603
## T10  6.789563 10.805239477
## T11 10.737283  0.132044741
## T12  8.151226  0.001772866
library(factoextra)
fviz_pca_var(mgk, 
             repel = TRUE) 

podatki$GK1 <- mgk$ind$coord[ , 1]
podatki$GK2 <- mgk$ind$coord[ , 2]

podatki[34, c(1, 14, 15)]
##    ID       GK1       GK2
## 34 34 0.1166287 -1.470207
podatki_std <- scale(podatki[ , 2:13])
podatki_std[34, ]
##          T1          T2          T3          T4          T5 
##  0.73741110  0.67816982  0.75745547  0.86774507 -0.18499331 
##          T6          T7          T8          T9         T10 
##  0.06726110 -0.07981577  0.24583366 -0.78700936 -1.43920227 
##         T11         T12 
##  0.19519579 -0.71913015