Description Usage Arguments Details Value References See Also Examples
Computes the variance estimation by the ultimate cluster method.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
Y |
Variables of interest. Object convertible to |
H |
The unit stratum variable. One dimensional object convertible to one-column |
PSU |
Primary sampling unit variable. One dimensional object convertible to one-column |
w_final |
Weight variable. One dimensional object convertible to one-column |
N_h |
Number of primary sampling units in population for each stratum (and period if |
fh_zero |
by default FALSE; |
PSU_level |
by default TRUE; if PSU_level is TRUE, in each strata |
PSU_sort |
optional; if PSU_sort is defined, then variance is calculated for systematic sample. |
period |
Optional variable for the survey periods. If supplied, the values for each period are computed independently. Object convertible to |
dataset |
an optional name of the individual dataset |
msg |
an optional printed text, when function print error. |
checking |
Optional variable if this variable is TRUE, then function checks data preparation errors, otherwise not checked. This variable by default is TRUE. |
If we assume that n_h>=2 for all h, that is, two or more PSUs are selected from each stratum, then the variance of θ can be estimated from the variation among the estimated PSU totals of the variable Z:
V(θ)=∑ h=1...H (1-f_h)*n_h/(n_h-1)* ∑ i=1...n_h ( z_hi.- z_h..)^2,
where z_hi.=∑ j=1...m_hi ω_hij * z_hij
z_h..=(∑ i=1...n_h z_hi.)/n_h
f_h is the sampling fraction of PSUs within stratum
h is the stratum number, with a total of H strata
i is the primary sampling unit (PSU) number within stratum h, with a total of n_h PSUs
j is the household number within cluster i of stratum h, with a total of m_hi household
w_hij is the sampling weight for household j in PSU i of stratum h
z_hij denotes the observed value of the analysis variable z for household j in PSU i of stratum h
a data.table
containing the values of the variance estimation by totals.
Morris H. Hansen, William N. Hurwitz, William G. Madow, (1953), Sample survey methods and theory Volume I Methods and applications, 257-258, Wiley.
Guillaume Osier and Emilio Di Meglio. The linearisation approach implemented by Eurostat for the first wave of EU-SILC: what could be done from the second onwards? 2012
Eurostat Methodologies and Working papers, Standard error estimation for the EU-SILC indicators of poverty and social exclusion, 2013, URL http://ec.europa.eu/eurostat/documents/3859598/5927001/KS-RA-13-029-EN.PDF.
Yves G. Berger, Tim Goedeme, Guillame Osier (2013). Handbook on standard error estimation and other related sampling issues in EU-SILC, URL https://ec.europa.eu/eurostat/cros/content/handbook-standard-error-estimation-and-other-related-sampling-issues-ver-29072013_en
Eurostat Methodologies and Working papers, Handbook on precision requirements and variance estimation for ESS household surveys, 2013, URL http://ec.europa.eu/eurostat/documents/3859598/5927001/KS-RA-13-029-EN.PDF.
domain
, lin.ratio
, linarpr
,
linarpt
, lingini
, lingini2
,
lingpg
, linpoormed
, linqsr
,
linrmpg
, residual_est
, vardom
,
vardomh
, varpoord
, variance_othstr
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | Ys <- rchisq(10, 3)
w <- rep(2, 10)
PSU <- 1 : length(Ys)
H <- rep("Strata_1", 10)
# by default without using fh_zero (finite population correction)
variance_est(Y = Ys, H = H, PSU = PSU, w_final = w)
## Not run:
# without using fh_zero (finite population correction)
variance_est(Y = Ys, H = H, PSU = PSU, w_final = w, fh_zero = FALSE)
# with using fh_zero (finite population correction)
variance_est(Y = Ys, H = H, PSU = PSU, w_final = w, fh_zero = TRUE)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.