library(tidyverse)
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## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.5
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library(ltm)
## Warning: package 'ltm' was built under R version 4.3.3
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: msm
## Warning: package 'msm' was built under R version 4.3.3
## Loading required package: polycor
## Warning: package 'polycor' was built under R version 4.3.3
library(dplyr)
library(stats)
library(fastDummies)
## Warning: package 'fastDummies' was built under R version 4.3.3
## Thank you for using fastDummies!
## To acknowledge our work, please cite the package:
## Kaplan, J. & Schlegel, B. (2023). fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables. Version 1.7.1. URL: https://github.com/jacobkap/fastDummies, https://jacobkap.github.io/fastDummies/.
df <- read.csv2("basak_sayisal_veriler.csv")
#glimpse(df)
extract_factors <- function (df,what,howmany,reduce=0,rotat="promax"){
cat("________________ START --> ", what, "_____________________")
cat("\n")
center <- function(x) { return (x - mean(x))}
df_sub <- df %>% dplyr::select(starts_with(what)) %>% mutate(across(everything(), center))
CA <- round(cronbach.alpha(df_sub) $ alpha,2)
cat("\n")
cat("cronbach_alpa =", CA)
cat("\n")
if (reduce != 0) df_sub=df_sub[,-reduce]
FA<- df_sub%>%factanal(.,howmany, scores ="regression",rotation=rotat)
print(FA $ loadings)
explained <- 1-FA $ uniquenesses
barplot(explained,cex.names=0.7, col=1:length(explained),
main="explained proportions by factor analysis")
cat("\n")
cat("explained_proportions:");cat("\n")
explained_props <- as.data.frame(1-FA $ uniquenesses)
colnames(explained_props) ="explained_variances"
print(explained_props);cat("\n")
cat("likelihood ratio test | p-value:", FA $ PVAL); cat("\n")
if(FA $ PVAL<0.05) print("factors are not sufficient")
else cat("\n", "factors are sufficient")
cat("\n")
cat("________________ END _____________________")
outcome <-list(FA,df_sub)
return(outcome)
}
see_sur <- extract_factors(df,"far_sur",2)
## ________________ START --> far_sur _____________________
##
## cronbach_alpa = 0.92
##
## Loadings:
## Factor1 Factor2
## far_sur_kaynak 1.044
## far_sur_gelecek 0.542 0.365
## far_sur_adil_is 0.791
## far_sur_toplum 0.906 -0.110
## far_sur_cevre_koruma 0.888
## far_sur_paydas 0.608 0.216
## far_sur_eko_performans 0.160 0.506
## far_sur_calisan_hak 0.808
## far_sur_tarim 0.678 -0.119
##
## Factor1 Factor2
## SS loadings 4.040 1.562
## Proportion Var 0.449 0.174
## Cumulative Var 0.449 0.622
##
## explained_proportions:
## explained_variances
## far_sur_kaynak 0.9950000
## far_sur_gelecek 0.7264461
## far_sur_adil_is 0.7079855
## far_sur_toplum 0.6819977
## far_sur_cevre_koruma 0.7664654
## far_sur_paydas 0.6143465
## far_sur_eko_performans 0.4042705
## far_sur_calisan_hak 0.7504223
## far_sur_tarim 0.3518575
##
## likelihood ratio test | p-value: 0.1037971
##
## factors are sufficient
## ________________ END _____________________
see_sur_scores <- see_sur[[1]] $ scores
colnames(see_sur_scores) <- c("sosyal_cevresel_donusum","verimlilik_boyutu")
see_sur_scores %>% boxplot(.,horizontal=TRUE, cex.axis=0.7,col=1:dim(see_sur_scores)[2])
df_sub_2 <- df %>%
dplyr::select(starts_with("isletmenin")) %>%
dplyr::select(c(5)) %>%
dummy_cols %>%
dplyr::select(where(is.numeric))
df_pilot <- cbind(see_sur_scores,df_sub_2)
df_pilot %>% head(4)
## sosyal_cevresel_donusum verimlilik_boyutu isletmenin.olcegi._Buyuk
## 1 -1.0300490 1.3127042 1
## 2 -4.0775289 2.1202934 0
## 3 0.9681238 -0.1379685 1
## 4 -0.7814591 1.1287485 0
## isletmenin.olcegi._Kucuk isletmenin.olcegi._Orta
## 1 0 0
## 2 0 1
## 3 0 0
## 4 0 1
model_sosyal_cevresel_donusum <- lm(df_pilot $sosyal_cevresel_donusum~.,data = df_pilot [,-c(2,3)])
cat(rep("__",20),"\n",colnames(df_pilot)[1],"\n")
## __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
## sosyal_cevresel_donusum
model_sosyal_cevresel_donusum%>% summary %>% print
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2, 3)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3985 -0.8574 0.4428 0.9172 3.8892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05096 0.20596 0.247 0.805
## isletmenin.olcegi._Kucuk -0.13362 0.41192 -0.324 0.746
## isletmenin.olcegi._Orta -0.07033 0.27844 -0.253 0.801
##
## Residual standard error: 1.427 on 119 degrees of freedom
## Multiple R-squared: 0.001054, Adjusted R-squared: -0.01573
## F-statistic: 0.0628 on 2 and 119 DF, p-value: 0.9392
model_sosyal_verimlilik_boyutu<-lm(df_pilot $ verimlilik_boyutu~.,data = df_pilot [,-c(1,3)])
cat(rep("__",20),"\n",colnames(df_pilot)[2],"\n")
## __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
## verimlilik_boyutu
model_sosyal_verimlilik_boyutu%>% summary %>% print
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1, 3)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8541 -0.3437 0.0588 0.8463 5.1661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2057 0.2112 0.974 0.332
## isletmenin.olcegi._Kucuk -0.1096 0.4223 -0.259 0.796
## isletmenin.olcegi._Orta -0.4024 0.2855 -1.410 0.161
##
## Residual standard error: 1.463 on 119 degrees of freedom
## Multiple R-squared: 0.01707, Adjusted R-squared: 0.0005541
## F-statistic: 1.034 on 2 and 119 DF, p-value: 0.3589
see_eko<- extract_factors(df,"far_eko",2)
## ________________ START --> far_eko _____________________
##
## cronbach_alpa = 0.82
##
## Loadings:
## Factor1 Factor2
## far_eko_verimlilik 0.851
## far_eko_satin_alma 0.787 0.104
## far_eko_teknoloji 0.861 -0.126
## far_eko_maliyet 1.002
## far_eko_karlilik 0.862
##
## Factor1 Factor2
## SS loadings 2.087 1.775
## Proportion Var 0.417 0.355
## Cumulative Var 0.417 0.772
##
## explained_proportions:
## explained_variances
## far_eko_verimlilik 0.7471410
## far_eko_satin_alma 0.6940722
## far_eko_teknoloji 0.6737947
## far_eko_maliyet 0.9950000
## far_eko_karlilik 0.7549521
##
## likelihood ratio test | p-value: 0.5832917
##
## factors are sufficient
## ________________ END _____________________
see_eko_scores <- see_eko[[1]] $ scores
colnames(see_eko_scores) <- c("verim_satin","tek_kar_maliyet")
see_eko_scores %>% boxplot(.,horizontal=TRUE,cex.axis=0.7,col=1:dim(see_eko_scores)[2])
df_sub_2 <- df %>%
dplyr::select(starts_with("isletmenin")) %>%
dplyr::select(c(5)) %>%
dummy_cols %>%
dplyr::select(where(is.numeric))
df_pilot <- cbind(see_eko_scores,df_sub_2)
# filter(verim_satin > -2 & tek_kar_maliyet >-3)
#df_pilot[,1:2] %>% boxplot(.,horizontal=TRUE, cex.axis=0.7,col=1:dim(see_eko_scores)[2])
df_pilot %>% head(4)
## verim_satin tek_kar_maliyet isletmenin.olcegi._Buyuk isletmenin.olcegi._Kucuk
## 1 -0.2049273 1.3420314 1 0
## 2 -1.7484247 0.8438739 0 0
## 3 1.3329562 -0.3392091 1 0
## 4 -0.3420535 0.2909294 0 0
## isletmenin.olcegi._Orta
## 1 0
## 2 1
## 3 0
## 4 1
model_sosyal_verim_satin <- lm(df_pilot$verim_satin~.,data = df_pilot[,-c(2,3)])
cat(rep("__",20),"\n",colnames(df_pilot)[1],"\n")
## __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
## verim_satin
model_sosyal_cevresel_donusum %>% summary %>% print
##
## Call:
## lm(formula = df_pilot$sosyal_cevresel_donusum ~ ., data = df_pilot[,
## -c(2, 3)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3985 -0.8574 0.4428 0.9172 3.8892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05096 0.20596 0.247 0.805
## isletmenin.olcegi._Kucuk -0.13362 0.41192 -0.324 0.746
## isletmenin.olcegi._Orta -0.07033 0.27844 -0.253 0.801
##
## Residual standard error: 1.427 on 119 degrees of freedom
## Multiple R-squared: 0.001054, Adjusted R-squared: -0.01573
## F-statistic: 0.0628 on 2 and 119 DF, p-value: 0.9392
model_sosyal_tek_kar_maliyet<-lm(df_pilot $ tek_kar_maliyet~.,data = df_pilot[,-c(1,3)])
cat(rep("__",20),"\n",colnames(df_pilot)[2],"\n")
## __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __
## tek_kar_maliyet
model_sosyal_verimlilik_boyutu%>% summary %>% print
##
## Call:
## lm(formula = df_pilot$verimlilik_boyutu ~ ., data = df_pilot[,
## -c(1, 3)])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8541 -0.3437 0.0588 0.8463 5.1661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2057 0.2112 0.974 0.332
## isletmenin.olcegi._Kucuk -0.1096 0.4223 -0.259 0.796
## isletmenin.olcegi._Orta -0.4024 0.2855 -1.410 0.161
##
## Residual standard error: 1.463 on 119 degrees of freedom
## Multiple R-squared: 0.01707, Adjusted R-squared: 0.0005541
## F-statistic: 1.034 on 2 and 119 DF, p-value: 0.3589