Table of contents for DECR Review
Just as a quick look at the data provided from CSIRO for the Drought Exceptional Circumstances Report, I made density plots (frequency histograms) for the rainfall data over two periods, 1900-2010 and 2010-2040 for the South-west of Western Australia, the area with the highest drought predictions. The plot below is the result, with the past in blue and the future in red. You can see the extremely skewed distribution of the data, with a large number of zeros (no drought areas). The future data (red) has a bump at the right hand end of the plot indicating a higher frequency of 100% drought declared areas than the past data.
The question in: What is the appropriate test of the difference between these two populations?
Here is the R code. You will have to delete the summary rows at the bottom and save a file as csv, until I develop a script for processing all the data files in native format.
d< -read.table("RSWWA.csv",header=T,sep=",")
pr<-as.matrix(d[1:110,2:14])
dim(pr)<-NULL
fr<-as.matrix(d[111:141,2:14])
dim(fr)<-NULL
plot(density(fr),col="red",xlab="%Area low rainfall",main="SWWA blue->red”)
lines(density(pr),col=”blue”)


7 responses so far ↓
David, can you post up scripts for the calcs to save time?
David:
I am not sure I understand the negative % portion of the curve - is it simply the way R draws the chart?
DAvid, do you know a way to read from tar files into R directly? I think that I saw something like that at Bioconductor but didn’t note it at the time.
Bernie, That is just because density() is a smoothed histogram.
Steve, No I don’t. I’ll just untar them.
David, I’ve collated the nastily organized CSIRO data into some R objects and made them available for download. See post at CA.
I can’t tell exactly which series are being compared in your graphic here. In my first cut at the 5 pct minimum rainfall information, I observed very low intermodel correlation in Qld and Mur-Darl and very low (negligible) correlation of models to history. I need to quadruple check the collation to make sure that I’ve collated apples and apples though.
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