# Required fxns: 'c2.unb','c2.rat','c2.prop','c2.pps'
# Need two datasets here because of calculations
# read in 'sewing machines1.csv' and 'sewing machines2.csv'
# sewing machines1 has individual observations from each plant
# sewing machines2 has cluster information and proportions

sew1=read.csv('http://webpages.uidaho.edu/~renaes/Data/sewing%20v2.csv',header=T)
sew2=read.csv('http://webpages.uidaho.edu/~renaes/Data/sewing%20v3.csv',header=T)

# because of the nature of the c2.unb fxn, you will need to calculate 
# cluster means and variances (ybari and s2i)

ybari=apply(sew1,2,mean,na.rm=T); ybari
s2i=apply(sew1,2,var,na.rm=T); s2i
sew2$phati

# estimation of mean
c2.unb('sewing',ybari,s2i,sew2$mi,sew2$Mi,4500,10,90,'mean')

# estimation of total
c2.unb('sewing',ybari,s2i,sew2$mi,sew2$Mi,4500,10,90,'total')

# estimation of proportion
c2.prop('sewing',sew2$phati,sew2$mi,sew2$Mi,10,90)

# estimation of mean (ratio)
c2.rat('sewing',ybari,s2i,sew2$mi,sew2$Mi,10,90,'mean')

# estimation of total (ratio)
c2.rat('sewing',ybari,s2i,sew2$mi,sew2$Mi,10,90,'total')

# estimation of mean (pps)
M=328+109+432+220+280+190
n=3; N=6; Mi=c(432,280,190)
mi=c(43,28,19)
yi=c(25,15,8)
ybari=yi/mi
# This example is a proportion but can use the formula for mean
c2.pps('hospital',ybari,M,'mean')