Computes the variance estimation for measures of annual net change or annual for single stratified sampling designs.
vardchangstrs(
Y,
H,
PSU,
w_final,
Dom = NULL,
periods = NULL,
dataset,
periods1,
periods2,
in_sample,
in_frame,
confidence = 0.95,
percentratio = 1
)Variables of interest. Object convertible to data.table or variable names as character, column numbers.
The unit stratum variable. One dimensional object convertible to one-column data.table or variable name as character, column number.
Primary sampling unit variable. One dimensional object convertible to one-column data.table or variable name as character, column number.
Weight variable. One dimensional object convertible to one-column data.table or variable name as character, column number.
Optional variables used to define population domains. If supplied, variables are calculated for each domain. An object convertible to data.table or variable names as character vector, column numbers.
Variable for the all survey periods. The values for each period are computed independently. Object convertible to data.table or variable names as character, column numbers.
Optional survey data object convertible to data.table.
The vector of periods from variable periods describes the first period for measures of change.
The vector of periods from variable periods describes the second period for measures of change.
Sample variable. One dimensional object convertible to one-column data.table or variable name as character, column number.
Frame variable. One dimensional object convertible to one-column data.table or variable name as character, column number.
optional; either a positive value for confidence interval. This variable by default is 0.95.
Positive numeric value. All linearized variables are multiplied with percentratio value, by default - 1.
A list with objects are returned by the function:
crossectional_results - a data.table containing:
year - survey years,
subperiods - survey sub-periods,
variable - names of variables of interest,
Dom - optional variable of the population domains,
estim - the estimated value,
var - the estimated variance of cross-sectional and longitudinal measures,
sd_w - the estimated weighted variance of simple random sample,
se - the estimated standard error of cross-sectional or longitudinal,
rse - the estimated relative standard error (coefficient of variation),
cv - the estimated relative standard error (coefficient of variation) in percentage,
absolute_margin_of_error - the estimated absolute margin of error,
relative_margin_of_error - the estimated relative margin of error,
CI_lower - the estimated confidence interval lower bound,
CI_upper - the estimated confidence interval upper bound,
confidence_level - the positive value for confidence interval.
annual_results - a data.table containing:
year_1 - survey years of years1 for measures of annual net change,
year_2 - survey years of years2 for measures of annual net change,
Dom - optional variable of the population domains,
variable - names of variables of interest,
estim_2 - the estimated value for period2 for measures of annual net change,
estim_1 - the estimated value for period1 for measures of annual net change,
estim - the estimated value,
var - the estimated variance,
se - the estimated standard error,
rse - the estimated relative standard error (coefficient of variation),
cv - the estimated relative standard error (coefficient of variation) in percentage,
absolute_margin_of_error - the estimated absolute margin of error for period1 for measures of annual,
relative_margin_of_error - the estimated relative margin of error in percentage for measures of annual,
CI_lower - the estimated confidence interval lower bound,
CI_upper - the estimated confidence interval upper bound,
confidence_level - the positive value for confidence interval,
significant - is the the difference significant
Guillaume OSIER, Virginie RAYMOND, (2015), Development of methodology for the estimate of variance of annual net changes for LFS-based indicators. Deliverable 1 - Short document with derivation of the methodology.