36 lines
933 B
R
36 lines
933 B
R
## Tests and visualisation
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source("data_format.R")
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## Box-Cox power transformation performs comparably to logarithmic transformation. The latter is much easier to explain.
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library(MASS)
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dta_bc<-dta_backup|>
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dplyr::select(all_of(c("mdi_6_newobs_enr",vars)))|>
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mutate(pase_0=sqrt(pase_0),
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mdi_6_newobs_enr=mdi_6_newobs_enr+1)#|>
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# na.omit()
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bc<-boxcox(mdi_6_newobs_enr~.,data=dta_bc)
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lambda <- bc$x[which.max(bc$y)]
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## Q-Q plots to compare the two different approaches, and the non-transformed
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q1 <- qqnorm(lm(((mdi_6_newobs_enr^lambda-1)/lambda) ~ .,data=dta_bc)$residuals)
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q2 <- qqnorm(lm(log(mdi_6_newobs_enr) ~ .,data=dta_bc)$residuals)
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library(patchwork)
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plot(q1); plot(q2)
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## Histograms for reference
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h1 <- hist(dta_bc$pase_0,40); hist(sqrt(dta_bc$pase_0),40)
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h2 <- hist(log(dta_bc$mdi_6_newobs_enr),40); hist((dta_bc$mdi_6_newobs_enr),40) ## Observed MDI, and log transformed MDI
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plot(h1); plot(h2)
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