
R Studio on Quantitative Methods for Risk Analysis
- 13th May, 2022
- 14:51 PM
## load the data #question1 datao<-read.csv("C:/Users/Ashwini Khandelwal/Downloads/move_au.csv") View(datao) library(factoextra) data<-datao[5:10] res.pca <- prcomp(data, scale = TRUE) fviz_eig(res.pca) library(factoextra) # Eigenvalues eig.val <- get_eigenvalue(res.pca) eig.val # Results for Variables res.var <- get_pca_var(res.pca) res.var$coord # Coordinates res.var$contrib # Contributions to the PCs res.var$cos2 # Quality of representation # Results for individuals res.ind <- get_pca_ind(res.pca) res.ind$coord # Coordinates res.ind$contrib # Contributions to the PCs res.ind$cos2 fit <- factanal(data, 3, rotation="varimax") print(fit, digits=2, cutoff=.3, sort=TRUE) # plot factor 1 by factor 2 load <- fit$loadings[,1:2] plot(load,type="n") # set up plot text(load,labels=names(data),cex=.7) library(psych) fit <- factor.pa(data, nfactors=3) fit # print results #install.packages("nFactors") library(nFactors) ev <- eigen(cor(data)) # get eigenvalues ev$vectors ap <- parallel(subject=nrow(data),var=ncol(data), rep=100,cent=.05) nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea) plotnScree(nS) boxplot(ev$vectors) a<-ev$vectors[,1] View(a) data1<-head(datao) data1$a<-a data1 library(lubridate) data1$date<-as.Date(data1$date,format="%Y-%m-%d") plot(data1$date, data1$a, main = "Main title", xlab = "X axis title", ylab = "Y axis title", pch = 19, frame = FALSE) ggplot(data1, aes(x=data1$country_region, y=data1$a)) + geom_boxplot(notch=TRUE)