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Data and first models
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‎BD final - estudantes.sav

3.62 MB
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‎_RData.gz

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‎_Rhistory

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library()
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install.packages(c("GGally", "ggplot2"))
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install.packages("tidyverse")
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library(ggplot2)
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install.packages(ggplot2)
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install.packages("ggplot2")
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mean(1,2,3)
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mean(c(1,2,3))
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??diamonds
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ggplot(data = diamonds, aes(x = carat, y = price)) +
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geom_point()
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library(ggplot2)
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ggplot(data = diamonds, aes(x = carat, y = price)) +
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geom_point()
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ggplot(data = diamonds, aes(x = carat, y = price, color = cut)) +
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geom_point()
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ggplot(data = diamonds, aes(x = carat, y = price, color = cut)) +
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geom_point() +
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facet_grid(. ~ clarity)
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ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
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geom_jitter()
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ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
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geom_jitter()
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plot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
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geom_jitter()
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geom_point() +
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facet_grid(. ~ clarity)
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ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
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geom_jitter()
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geom_point() +
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facet_grid(. ~ clarity)
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ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
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geom_jitter() +
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geom_point() +
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facet_grid(. ~ clarity
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ggplot(data = diamonds, aes(x = carat, y = price, color = cut, size = color)) +
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geom_jitter() +
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geom_point() +
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facet_grid(. ~ clarity)
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ggplot(data = diamonds, aes(x = carat, y = log(price), color = cut, size = color)) +
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geom_jitter() +
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geom_point() +
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facet_grid(. ~ clarity)
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ggplot(data = diamonds, aes(x = carat, y = log(price), color = cut, size = color)) +
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geom_jitter() +
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geom_hex() +
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facet_grid(. ~ clarity)
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install.packages("hexbin")
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ggplot(data = diamonds, aes(x = carat, y = log(price), color = cut, size = color)) +
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geom_jitter() +
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geom_hex() +
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facet_grid(. ~ clarity)
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install.packages(lmtest)
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install.packages("lmtest")
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library(lmtest)
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model = lm(diamonds, price ~ carat + cut + color + clarity + x + y + z + depth + table)
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model = lm(data = diamonds, price ~ carat + cut + color + clarity + x + y + z + depth + table)
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summary(model)
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library(dplyr)
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library(memisc)
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library(haven)
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library(lmtest)
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library(ggplot2)
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library(ggfortify)
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df = as.data.frame(read_spss("BD final - estudantes.sav"))
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data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
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pressao = as.numeric(df$losango_motivacao),
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racionalizacao = as.numeric(df$tri_dist_moral),
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fraudenctx = as.numeric(df$norma_contexto),
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fraudencop = as.numeric(df$norma_copiar),
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fraudenplg = as.numeric(df$norma_plagio),
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fraudeneud = as.numeric(df$norma_eu_desonesto),
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fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
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fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
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setwd("~/Documents/Comportamento Organizacional")
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df = as.data.frame(read_spss("BD final - estudantes.sav"))
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data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
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pressao = as.numeric(df$losango_motivacao),
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racionalizacao = as.numeric(df$tri_dist_moral),
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fraudenctx = as.numeric(df$norma_contexto),
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fraudencop = as.numeric(df$norma_copiar),
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fraudenplg = as.numeric(df$norma_plagio),
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fraudeneud = as.numeric(df$norma_eu_desonesto),
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fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
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fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
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data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
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pressao = as.numeric(df$losango_motivacao),
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racionalizacao = as.numeric(df$tri_dist_moral),
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fraudenctx = as.numeric(df$norma_contexto),
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fraudencop = as.numeric(df$norma_copiar),
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fraudenplg = as.numeric(df$norma_plagio),
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fraudeneud = as.numeric(df$norma_eu_desonesto),
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fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
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fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
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data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
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pressao = as.numeric(df$losango_motivacao),
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racionalizacao = as.numeric(df$tri_dist_moral),
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fraudenctx = as.numeric(df$norma_contexto),
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fraudencop = as.numeric(df$norma_copiar),
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fraudenplg = as.numeric(df$norma_plagio),
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fraudeneud = as.numeric(df$norma_eu_desonesto),
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fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
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fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
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data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
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pressao = as.numeric(df$losango_motivacao),
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racionalizacao = as.numeric(df$tri_dist_moral),
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fraudenctx = as.numeric(df$norma_contexto),
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fraudencop = as.numeric(df$norma_copiar),
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fraudenplg = as.numeric(df$norma_plagio),
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fraudeneud = as.numeric(df$norma_eu_desonesto),
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fraudenavg = rowMeans(data[4:7], na.rm = TRUE),
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fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
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View(df)
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data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
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pressao = as.numeric(df$losango_motivacao),
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racionalizacao = as.numeric(df$tri_dist_moral),
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fraudenctx = as.numeric(df$norma_contexto),
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fraudencop = as.numeric(df$norma_copiar),
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fraudenplg = as.numeric(df$norma_plagio),
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fraudeneud = as.numeric(df$norma_eu_desonesto),
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fraudenavg = rowMeans(df[68:71], na.rm = TRUE),
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fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
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GGally::ggpairs(data)
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model11 = lm(data = data, fraudenctx ~ oportunidade + pressao + racionalizacao)
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model12 = lm(data = data, fraudencop ~ oportunidade + pressao + racionalizacao)
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model13 = lm(data = data, fraudenplg ~ oportunidade + pressao + racionalizacao)
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model14 = lm(data = data, fraudeneud ~ oportunidade + pressao + racionalizacao)
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model15 = lm(data = data, fraudenavg ~ oportunidade + pressao + racionalizacao)
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### Comparing Models
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AIC(model11, model12, model13, model14, model15)
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model21 = lm(data = data, fraudkfreq ~ oportunidade + pressao + racionalizacao)
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summary(model15)
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summary(model14)
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summary(model13)
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summary(model12)
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summary(model12)
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summary(model11)
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### Comparing Models
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AIC(model11, model12, model13, model14, model15)
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bptest(model15)
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bptest(model14)

‎script.R

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install.packages("ggfortify")
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install.packages("haven")
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install.packages("dplyr")
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install.packages("memisc")
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install.packages("GGally")
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install.packages("tidyr")
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library(dplyr)
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library(memisc)
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library(haven)
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library(lmtest)
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library(ggplot2)
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library(ggfortify)
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df = as.data.frame(read_spss("BD final - estudantes.sav"))
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# Base variables
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data = transmute(df, oportunidade = as.numeric(df$tri_oportunidade),
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pressao = as.numeric(df$losango_motivacao),
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racionalizacao = as.numeric(df$tri_dist_moral),
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fraudenctx = as.numeric(df$norma_contexto),
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fraudencop = as.numeric(df$norma_copiar),
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fraudenplg = as.numeric(df$norma_plagio),
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fraudeneud = as.numeric(df$norma_eu_desonesto),
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fraudenavg = rowMeans(df[68:71], na.rm = TRUE),
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fraudkfreq = rowMeans(df[34:50], na.rm = TRUE))
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GGally::ggpairs(data)
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# Models
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## Fraude by the Norm
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### Norma Contexto
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model11 = lm(data = data, fraudenctx ~ oportunidade + pressao + racionalizacao)
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summary(model11)
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autoplot(model11)
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### Norma Copiar
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model12 = lm(data = data, fraudencop ~ oportunidade + pressao + racionalizacao)
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summary(model12)
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### Norma Plágio
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model13 = lm(data = data, fraudenplg ~ oportunidade + pressao + racionalizacao)
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summary(model13)
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### Norma Eu desonesto
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model14 = lm(data = data, fraudeneud ~ oportunidade + pressao + racionalizacao)
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summary(model14)
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### Norma Avg
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model15 = lm(data = data, fraudenavg ~ oportunidade + pressao + racionalizacao)
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summary(model15)
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autoplot(model15) + theme_bw()
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autoplot(prcomp(model15), scale = TRUE)
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### Comparing Models
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AIC(model11, model12, model13, model14, model15)
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## Fraud by Frequency
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model21 = lm(data = data, fraudkfreq ~ oportunidade + pressao + racionalizacao)
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summary(model21)
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# Tests
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# Multicollinearity - bptest, gqtest
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bptest(model11)
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bptest(model12)
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bptest(model13)
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bptest(model14)
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bptest(model15)
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gqtest(model13)
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gqtest(model15)
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# Heteroskedascitity - PCA?
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