interval

post_impact.r

npost = nrow(filter(df, Impact==1))
mm = mean(filter(df, Impact==1)$AbundanceRate)
ll = mm - qnorm(1 - alpha/2) * sd(filter(df, Impact==1)$AbundanceRate)/sqrt(npost)
uu = mm + qnorm(1 - alpha/2) * sd(filter(df, Impact==1)$AbundanceRate)/sqrt(npost)
print(paste("lower", round(ll, 2)))
print(paste("upper", round(uu, 2)))

quilt.r

library(fields)
par(mrow=c(2,2))
quilt.plot(df$XPos[df$Impact==0], df$YPos[df$Impact==0], df$Nhat[df$Impact==0]/df$Area[df$Impact==0], pch=20, 
           main="Impact = 0", xlab="X co-ordinate", ylab="Y co-ordinate", 
           nrow=20, ncol=20, asp=1)
quilt.plot(df$XPos[df$Impact==1], df$YPos[df$Impact==1], df$Nhat[df$Impact==1]/df$Area[df$Impact==1], pch=20, 
           main="Impact = 1", xlab="X co-ordinate", ylab="Y co-ordinate", 
           nrow=20, ncol=20, asp=1)

standard_deviation.r

alpha = 0.02
quant = qt(p=alpha/2, df=n-1, lower.tail=F)
n = nrow(filter(df, Impact==0))
m = mean(filter(df, Impact==0)$AbundanceRate)
s = sd(filter(df, Impact==0)$AbundanceRate)
lower = m - quant * s/sqrt(n)
upper = m + quant * s/sqrt(n)
print(paste("lower", round(lower, 2)))
print(paste("upper", round(upper, 2)))