#I am having a lot of trouble reshaping this data.
#This is just an examination of sample size on the margin of error that I
did for a colleague.
#Nothing complicated.
#But restructuring the data...another story
#Here's code to produce the dataset:
n <- seq(10000, 100000, by=10000)
P <- seq(0.1, 0.5, by=0.1)
Meff <- seq(3, 8, by=1)
# 4 age groups by 2 sex categories - these are population distribution
sizes - order is males 18-29, females 18-29, males 30-44,females 30-44,
males 45-59, females 45-59, males 60+, females 60+
Age1 <- data.frame(matrix(c(0.196598808, 0.224390935, 0.149289474,
0.151446387,0.08750253, 0.076399683, 0.060799283, 0.053572898), nrow=4,
ncol=2, byrow=T, dimnames=list(seq(1,4, by=1), c("Males", "Females"))))
MOE <- NULL
for (i in n){
for (p in P){
for (m in Meff){
for (a in Age1[,1]){
ni <- i * a
M <- sqrt((1.92^2)*p*(1-p)*m/ni)
dta <- data.frame(a, i, ni, p, m, M)
colnames(dta) <- c("Proportion", "Total_n", "Stratum_n", "P",
"Meff", "MOE")
dta$Sex <- "Males"
MOE <- rbind(MOE, dta)
}
for (a in Age1[,2]){
ni <- i * a
M <- sqrt((1.92^2)*p*(1-p)*m/ni)
dta <- data.frame(a, i, ni, p, m, M)
colnames(dta) <- c("Proportion", "Total_n", "Stratum_n", "P",
"Meff", "MOE")
dta$Sex <- "Females"
MOE <- rbind(MOE, dta)
}
}
}
}
# What I want is data that looks like:
# Meff Proportion Sample Size Males_0.1 Females_0.1 Males_0.2
Females_0.2 ..... Males_0.5 Females_0.5
#I'm stumped on how to reshape this.
#I tried this but it gave me the attached output (yuck):
library(reshape2)
library(plyr)
tmp <- ddply(MOE, .(Meff), mutate, id=paste(Meff, Sex, seq_along(MOE)))
wd <- dcast(tmp, id + Meff ~ P + Sex, value.var="MOE")
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