Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates Horvitz-Thompson estimate with different methods for variance estimation such as Yates and Grundy, Hansen-Hurwitz and Hajek.
1 | htestimate(y, N, PI, pk, pik, method = 'yg')
|
y |
vector of observations |
N |
integer for population size |
PI |
square matrix of second order inclusion probabilities with |
pk |
vector of first order inclusion probabilities of length |
pik |
an optional vector of first order inclusion probabilities of length |
method |
method to be used for variance estimation. Options are |
For using methods 'yg'
or 'ht'
has to be provided matrix PI
, and for 'hh'
and 'ha'
has to be specified vector pk
of inclusion probabilities.
Additionally, for Hajek method 'ha'
can be specified pik
. Unless, an approximate Hajek method is used.
The function htestimate
returns a value, which is a list consisting of the components
call |
is a list of call components: |
mean |
mean estimate |
se |
standard error of the mean estimate |
Juliane Manitz
Kauermann, Goeran/Kuechenhoff, Helmut (2010): Stichproben. Methoden und praktische Umsetzung mit R. Springer.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | data(influenza)
summary(influenza)
# pps.sampling()
set.seed(108506)
pps <- pps.sampling(z=influenza$population,n=20,method='midzuno')
sample <- influenza[pps$sample,]
# htestimate()
N <- nrow(influenza)
# exact variance estimate
PI <- pps$PI
htestimate(sample$cases, N=N, PI=PI, method='yg')
htestimate(sample$cases, N=N, PI=PI, method='ht')
# approximate variance estimate
pk <- pps$pik[pps$sample]
htestimate(sample$cases, N=N, pk=pk, method='hh')
pik <- pps$pik
htestimate(sample$cases, N=N, pk=pk, pik=pik, method='ha')
# without pik just approximate calculation of Hajek method
htestimate(sample$cases, N=N, pk=pk, method='ha')
# calculate confidence interval based on normal distribution for number of cases
est.ht <- htestimate(sample$cases, N=N, PI=PI, method='ht')
est.ht$mean*N
lower <- est.ht$mean*N - qnorm(0.975)*N*est.ht$se
upper <- est.ht$mean*N + qnorm(0.975)*N*est.ht$se
c(lower,upper)
# true number of influenza cases
sum(influenza$cases)
|
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