Package 'surveyplanning' reference manual

Title:	Survey Planning Tools
Description:	Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.
Authors:	Juris Breidaks [aut, cre], Martins Liberts [aut], Janis Jukams [aut]
Maintainer:	Juris Breidaks <[email protected]>
License:	GPL (>= 2)
Version:	4.0
Built:	2025-02-10 04:13:25 UTC
Source:	https://github.com/csblatvia/surveyplanning

Survey Planning Tools

Description

Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.

Details

Package:	surveyplanning
Version:	2.9
Date:	2017-10-26
Depends:	R (>= 3.0.0), data.table (>= 1.10.4), stats, laeken
License:	GPL (>= 2)
URL:	https://github.com/CSBLatvia/surveyplanning/
BugReports:	https://github.com/CSBLatvia/surveyplanning/issues/

Index:

dom_optimal_allocation  Optimal sample size allocation
expsize                 Sample size calculation
expvar                  Expected precision for the estimates of totals
min_count               Minimal count of respondents for the given relative margin of error
min_prop                Minimal proportion for the given relative margin of error
MoE_Y                   Margin of error for count
MoE_P                   Margin of error for proportion
optsize                 Optimal sample size allocation
s2                      Population variance estimation
surveyplanning-package  Survey Planning Tools

Author(s)

Juris Breidaks [aut, cre], Martins Liberts [aut], Janis Jukams [aut]

Maintainer: Juris Breidaks <[email protected]>

Optimal sample size allocation

Description

The function computes optimal sample size allocation over strata and domain for population.

Usage

dom_optimal_allocation(
  id,
  Dom,
  H,
  Y,
  Rh = NULL,
  deffh = NULL,
  indicator,
  sup_w,
  sup_cv,
  min_size = 3,
  correction_before = FALSE,
  dataset = NULL
)
dom_optimal_allocation(
  id,
  Dom,
  H,
  Y,
  Rh = NULL,
  deffh = NULL,
  indicator,
  sup_w,
  sup_cv,
  min_size = 3,
  correction_before = FALSE,
  dataset = NULL
)

Arguments

`id`	Variable for unit ID codes. One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`Dom`	Optional variables used to define population domains. If supplied, values are calculated for each domain. An object convertible to `data.table` or variable names as character vector, column numbers.
`H`	The unit stratum variable. One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`Y`	Variable of interest. Object convertible to `data.table` or variable names as character, column numbers.
`Rh`	The expected response rate in each stratum (optional). If not defined, it is assumed to be 1 in each stratum (full-response). Object convertible to one-column `data.table`, variable name as character, or column number.
`deffh`	The expected design effect for the estimate of variable (optional). If not defined, it is assumed to be 1 for each variable in each stratum. If is defined, then variables is defined the same arrangement as `Yh`. Object convertible to `data.table`, variable name as character vector, or column numbers.
`indicator`	Variable for detection fully surveyed units. Object convertible to `data.table` or variable names as character, column numbers.
`sup_w`	Variable for weight limit in domain of stratum. Object convertible to `data.table` or variable names as character, column numbers.
`sup_cv`	Variable for maximum coeficient of variation (CV) in percentage for domain. Object convertible to `data.table` or variable names as character, column numbers.
`min_size`	A numeric value for sample size.
`correction_before`	by default FALSE; correction of sample size is made before ending, if true, correction of sample size is made at the end.
`dataset`	Optional survey data object convertible to `data.table` with one row for each stratum.

Value

A list with eights data objects:

`data`	An object as `data.table`, with variables: `id` - variable with unit ID codes, `Dom` - optional variables used to define population domains, `H` - the unit stratum variable, `Y` - variable of interest, `Rh` - the expected response rate in each stratum, `deffh` - the expected design effect, `indicator` - variable for full surveys, `sup_w` - variable for weight limit in domain of stratum, `sup_cv` - Variable for maximum coeficient of variation, `poph` - population size, `nh` - sample size .
`nh_larger_then_Nh`	An object as `data.table`, with variables: `H` - the stratum variable, `nh` - sample size, `poph` - population size.
`dom_strata_size`	An object as `data.table`, with variables: `H` - the unit stratum variable, `Dom` - optional variables used to define population domains, `sup_w` - variable for weight limit in domain of stratum, `poph` - population size, `nh` - sample size, `sample100` - sample size for fully surveyed units, `design_weights` - design weigts.
`dom_size`	An object as `data.table`, with variables: `Dom` - optional variables used to define population domains, `poph` - population size, `nh` - sample size, `sample100` - sample size for fully surveyed units, `design_weights` - design weigts.
`size`	An object as `data.table`, with variables: `poph` - population size, `nh` - sample size, `sample100` - sample size for fully surveyed units.
`dom_strata_expected_precision`	An object as `data.table`, with variables: `H` - stratum, `variable` - the name of variable of interest, `estim` - total value, `deffh` - the expected design effect, `s2h` - population variance $S^2$ , `nh` - sample size, `Rh` - the expected response rate, `deffh` - the expected design effect, `poph` - population size, `nrh` - expected number of respondents, `var` - expected variance, `se` - expected standard error, `cv` - expected coeficient of variance.
`dom_expected_precision`	An object as `data.table`, with variables: `Dom` - domain, `variable` - the name of variable of interest, `poph` - the population size, `nh` - sample size, `nrh` - expected number of respondents, `estim` - total value, `var` - the expected variance, `se` - the expected standart error, `cv` - the expected coeficient of variance.
`total_expected_precision`	An object as `data.table`, with variables: `variable` - the name of variable of interest, `poph` - the population size, `nh` - sample size, `nrh` - expected number of respondents, `estim` - total value, `var` - the expected variance, `se` - the expected standart error, `cv` - the expected coeficient of variance.

Examples

library("laeken")
library("data.table")
data("ses")
data <- data.table(ses)
data[, H := paste(location, NACE1, size, sep = "_")]
data[, id := .I]
data[, full := 0]
data[, sup_cv := 10]
data[, sup_w := 20]
#vars <- dom_optimal_allocation(id = "id", dom = "sex",
#                                H = "H", Y = "earnings",
#                                indicator = "full",
#                                sup_w = "sup_w",
#                                sup_cv = "sup_cv",
#                                min_size = 3,
#                                correction_before = FALSE,
#                                dataset = data)
#                                dataset=data)
#vars
library("laeken")
library("data.table")
data("ses")
data <- data.table(ses)
data[, H := paste(location, NACE1, size, sep = "_")]
data[, id := .I]
data[, full := 0]
data[, sup_cv := 10]
data[, sup_w := 20]
#vars <- dom_optimal_allocation(id = "id", dom = "sex",
#                                H = "H", Y = "earnings",
#                                indicator = "full",
#                                sup_w = "sup_w",
#                                sup_cv = "sup_cv",
#                                min_size = 3,
#                                correction_before = FALSE,
#                                dataset = data)
#                                dataset=data)
#vars

The function computes minimum sample size for each stratum to achieve defined precision (CV) for the estimates of totals in each stratum. The calculation takes into account expected totals, population variance, expected response rate and design effect in each stratum.

Usage

expsize(Yh, H, s2h, poph, Rh = NULL, deffh = NULL, CVh, dataset = NULL)
expsize(Yh, H, s2h, poph, Rh = NULL, deffh = NULL, CVh, dataset = NULL)

Arguments

`Yh`	The expected totals for variables of interest in each stratum. Object convertible to `data.table`, variable names as character vector, or column numbers.
`H`	The stratum variable. One dimensional object convertible to one-column `data.table`, variable name as character, or column number.
`s2h`	The expected population variance $S^2$ for variables of interest in each stratum. Object convertible to `data.table`, variable name as character vector, or column numbers.
`poph`	Population size in each stratum. One dimensional object convertible to one-column `data.table`, variable name as character, or column number.
`Rh`	The expected response rate in each stratum (optional). If not defined, it is assumed to be 1 in each stratum (full-response). Object convertible to one-column `data.table`, variable name as character, or column number.
`deffh`	The expected design effect for the estimates of totals (optional). If not defined, it is assumed to be 1 for each variable in each stratum. Object convertible to `data.table`, variable name as character vector, or column numbers.
`CVh`	Coefficient of variation (in percentage) to be achieved for each stratum. One dimensional object convertible to one-column `data.table`, variable name as character, or column number.
`dataset`	Optional survey data object convertible to `data.table` with one row for each stratum.

Value

A data.table is returned by the function, with variables:
H - stratum,
variable - the name of variable of interest,
estim - total value,
deffh - the expected design effect,
s2h - population variance $S^2$ ,
CVh - the expected coefficient of variation,
Rh - the expected response rate,
poph - population size,
nh - minimal sample size to achieve defined precision (CV).

Examples

library("data.table")
data <- data.table(H = 1:3, Yh = 10 * 1:3,
                   Yh1 = 10 * 4:6, s2h = 10 * runif(3),
                   s2h2 = 10 * runif(3), CVh = rep(4.9,3),
                   poph = 8 * 1:3, Rh = rep(1, 3),
                   deffh = rep(2, 3), deffh2 = rep(3, 3))

size <- expsize(Yh = c("Yh", "Yh1"), H = "H",
                s2h = c("s2h", "s2h2"), poph = "poph",
                Rh = "Rh", deffh = c("deffh", "deffh2"),
                CVh = "CVh", dataset = data)

size


library("data.table")
data <- data.table(H = 1:3, Yh = 10 * 1:3,
                   Yh1 = 10 * 4:6, s2h = 10 * runif(3),
                   s2h2 = 10 * runif(3), CVh = rep(4.9,3),
                   poph = 8 * 1:3, Rh = rep(1, 3),
                   deffh = rep(2, 3), deffh2 = rep(3, 3))

size <- expsize(Yh = c("Yh", "Yh1"), H = "H",
                s2h = c("s2h", "s2h2"), poph = "poph",
                Rh = "Rh", deffh = c("deffh", "deffh2"),
                CVh = "CVh", dataset = data)

size

Expected precision for the estimates of totals

Description

The function computes expected precision as variance, standard error, and coefficient of variation for the estimates.

Usage

expvar(
  Yh,
  Zh = NULL,
  H,
  s2h,
  nh,
  poph,
  Rh = NULL,
  deffh = NULL,
  Dom = NULL,
  dataset = NULL
)
expvar(
  Yh,
  Zh = NULL,
  H,
  s2h,
  nh,
  poph,
  Rh = NULL,
  deffh = NULL,
  Dom = NULL,
  dataset = NULL
)

Arguments

`Yh`	The expected totals for variables of interest in each stratum. Object convertible to `data.table`, variable names as character vector, or column numbers.
`Zh`	Optional variables of denominator for the expected ratio estimation in each stratum. Object convertible to `data.table`, variable names as character vector, or column numbers.
`H`	The stratum variable. One dimensional object convertible to one-column `data.table`, variable name as character, or column number.
`s2h`	The expected population variance $S^2$ for variables of interest in each stratum. Variables is defined the same arrangement as `Yh`. Object convertible to `data.table`, variable name as character vector, or column numbers.
`nh`	Sample size in each stratum. One dimensional object convertible to one-column `data.table`, variable name as character, or column number.
`poph`	Population size in each stratum. One dimensional object convertible to one-column `data.table`, variable name as character, or column number.
`Rh`	The expected response rate in each stratum (optional). If not defined, it is assumed to be 1 in each stratum (full-response). Object convertible to one-column `data.table`, variable name as character, or column number.
`deffh`	The expected design effect for the estimates of totals (optional). If not defined, it is assumed to be 1 for each variable in each stratum. If is defined, then variables is defined the same arrangement as `Yh`. Object convertible to `data.table`, variable name as character vector, or column numbers.
`Dom`	Optional variables used to define population domains. Only domains as unions of strata can be defined. If supplied, estimated precision is calculated for each domain. An object convertible to `data.table`, variable names as character vector, or column numbers.
`dataset`	Optional survey data object convertible to `data.table` with one row for each stratum.

Value

A list with three data objects:

`resultH`	An object as `data.table`, with variables: `H` - stratum, `variableY` - the name of variable of interest, `variableZ` - the name of optional variable of denominator for the expected ratio estimation, `estim` - total value, `deffh` - the expected design effect, `s2h` - population variance $S^2$ , `nh` - sample size, `Rh` - the expected response rate, `poph` - population size, `nrh` - expected number of respondents, `var` - expected variance, `se` - expected standard error, `cv` - expected coeficient of variance.
`resultDom`	An object as `data.table`, with variables: `Dom` - domain, `variableY` - the name of variable of interest, `variableZ` - the name of optional variable of denominator for the expected ratio estimation, `poph` - the population size, `nh` - sample size, `nrh` - expected number of respondents, `estim` - total value, `var` - the expected variance, `se` - the expected standart error, `cv` - the expected coeficient of variance.
`result`	An object as `data.table`, with variables: `variableY` - the name of variable of interest, `variableZ` - the name of optional variable of denominator for the expected ratio estimation, `poph` - the population size, `nh` - sample size, `nrh` - expected number of respondents, `estim` - total value, `var` - the expected variance, `se` - the expected standart error, `cv` - the expected coeficient of variance.

Examples

library("data.table")
data <- data.table(H = 1:3, Yh = 10 * 1:3,
                   Yh1 = 10 * 4:6, s2h = 10 * runif(3),
                   s2h2 = 10 * runif(3), nh = rep(4 * 1:3),
                   poph = 8 * 1:3, Rh = rep(1, 3),
                   deffh = rep(2, 3), deffh2 = rep(3, 3))

vars <- expvar(Yh = c("Yh", "Yh1"), H = "H",
               s2h = c("s2h", "s2h2"),
               nh = "nh", poph = "poph",
               Rh = "Rh", deffh = c("deffh", "deffh2"),
               dataset = data)
vars

library("data.table")
data <- data.table(H = 1:3, Yh = 10 * 1:3,
                   Yh1 = 10 * 4:6, s2h = 10 * runif(3),
                   s2h2 = 10 * runif(3), nh = rep(4 * 1:3),
                   poph = 8 * 1:3, Rh = rep(1, 3),
                   deffh = rep(2, 3), deffh2 = rep(3, 3))

vars <- expvar(Yh = c("Yh", "Yh1"), H = "H",
               s2h = c("s2h", "s2h2"),
               nh = "nh", poph = "poph",
               Rh = "Rh", deffh = c("deffh", "deffh2"),
               dataset = data)
vars

Minimal count of respondents for the given relative margin of error

Description

The function computes minimal proportion for the given relative margin of error. The calculation takes into sample size, population size, margin of error, expected response rate and design effect.

Usage

min_count(n, pop, RMoE, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)
min_count(n, pop, RMoE, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)

Arguments

`n`	The expected sample size.
`pop`	Population size.
`RMoE`	The expected relative margin of error.
`confidence`	Optional positive value for confidence interval. This variable by default is 0.95.
`R`	The expected response rate (optional). If not defined, it is assumed to be 1 (full-response).
`deff_sam`	The expected design effect of sample design for the estimates (optional). If not defined, it is assumed to be 1.
`deff_est`	The estimated design effect of estimator for the estimates (optional). If not defined, it is assumed to be 1.

Value

The estimate of minimal count of respondents for the given relative margin of error.

Examples

min_count(n = 15e3, pop = 2e6, RMoE = 0.1)

## Not run: 
library("data.table")
min_count(n = c(10e3, 15e3, 20e3), pop = 2e6, 0.1)

n <- seq(10e3, 30e3, length.out = 11)
# n <- sort(c(n, 22691))
n

RMoE <- seq(.02, .2, length.out = 10)
RMoE

dt <- data.table(n = rep(n, each = length(RMoE)), RMoE = RMoE)
dt[, Y := min_count(n = n, pop = 2.1e6, RMoE = RMoE, R = 1) / 1e3]
dt

## End(Not run)

min_count(n = 15e3, pop = 2e6, RMoE = 0.1)

## Not run: 
library("data.table")
min_count(n = c(10e3, 15e3, 20e3), pop = 2e6, 0.1)

n <- seq(10e3, 30e3, length.out = 11)
# n <- sort(c(n, 22691))
n

RMoE <- seq(.02, .2, length.out = 10)
RMoE

dt <- data.table(n = rep(n, each = length(RMoE)), RMoE = RMoE)
dt[, Y := min_count(n = n, pop = 2.1e6, RMoE = RMoE, R = 1) / 1e3]
dt

## End(Not run)

Minimal proportion for the given relative margin of error

Description

The function computes minimal proportion for the given relative margin of error. The calculation takes into sample size, population size, margin of error, expected response rate and design effect.

Usage

min_prop(n, pop, RMoE, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)
min_prop(n, pop, RMoE, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)

Arguments

`n`	The expected sample size.
`pop`	Population size.
`RMoE`	The expected relative margin of error.
`confidence`	Optional positive value for confidence interval. This variable by default is 0.95.
`R`	The expected response rate (optional). If not defined, it is assumed to be 1 (full-response).
`deff_sam`	The expected design effect of sample design for the estimates (optional). If not defined, it is assumed to be 1.
`deff_est`	The estimated design effect of estimator for the estimates (optional). If not defined, it is assumed to be 1.

Value

The estimate of minimal proportion for the given relative margin of error.

Examples

min_prop(n = 100, pop = 1000, RMoE = 0.1)

min_prop(n = 100, pop = 1000, RMoE = 0.1)

Margin of error for proportion

Description

The function computes margin of error for proportion. The calculation takes into proportion, expected response rate and design effect.

Usage

MoE_P(P = 0.5, n, pop, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)
MoE_P(P = 0.5, n, pop, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)

Arguments

`P`	The expected proportion for variable of interest.
`n`	The expected sample size.
`pop`	Population size.
`R`	The expected response rate (optional). If not defined, it is assumed to be 1 (full-response).
`deff_sam`	The expected design effect of sample design for the estimates (optional). If not defined, it is assumed to be 1.
`deff_est`	The estimated design effect of estimator for the estimates (optional). If not defined, it is assumed to be 1.
`confidence`	Optional

positive value for confidence interval. This variable by default is 0.95.

Value

The estimate of margin of error for proportion.

Examples

library("data.table")
n <- 100
pop <- 1000

MoE_P(P = 0.5, n = n, pop = pop)

DT <- data.table(P = seq(0, 1, 0.01))
DT[, Y := round(pop * P)]
DT[, AMoE := MoE_P(P, n = 100, pop = 1000)]
DT[Y > 0, RMoE := AMoE / Y]
DT

library("data.table")
n <- 100
pop <- 1000

MoE_P(P = 0.5, n = n, pop = pop)

DT <- data.table(P = seq(0, 1, 0.01))
DT[, Y := round(pop * P)]
DT[, AMoE := MoE_P(P, n = 100, pop = 1000)]
DT[Y > 0, RMoE := AMoE / Y]
DT

Margin of error for count

Description

The function computes margin of error for count. The calculation takes into proportion, expected response rate and design effect.

Usage

MoE_Y(P = 0.5, n, pop, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)
MoE_Y(P = 0.5, n, pop, confidence = 0.95, R = 1, deff_sam = 1, deff_est = 1)

Arguments

`P`	The expected proportion for variable of interest.
`n`	The expected sample size.
`pop`	Population size.
`confidence`	Optional positive value for confidence interval. This variable by default is 0.95.
`R`	The expected response rate (optional). If not defined, it is assumed to be 1 (full-response).
`deff_sam`	The expected design effect of sample design for the estimates (optional). If not defined, it is assumed to be 1.
`deff_est`	The estimated design effect of estimator for the estimates (optional). If not defined, it is assumed to be 1.

Value

The estimate of margin of error for count.

Examples

library("data.table")
n <- 100
pop <- 1000

MoE_Y(P = 0.5, n = n, pop = pop)

DT <- data.table(P = seq(0, 1, 0.01))
DT[, Y := round(pop * P)]
DT[, AMoE := MoE_Y(P, n = 100, pop = 1000)]
DT[Y > 0, RMoE := AMoE / Y]
DT

library("data.table")
n <- 100
pop <- 1000

MoE_Y(P = 0.5, n = n, pop = pop)

DT <- data.table(P = seq(0, 1, 0.01))
DT[, Y := round(pop * P)]
DT[, AMoE := MoE_Y(P, n = 100, pop = 1000)]
DT[Y > 0, RMoE := AMoE / Y]
DT

Optimal sample size allocation

Description

The function computes optimal sample size allocation over strata.

Usage

optsize(
  H,
  n,
  poph,
  s2h = NULL,
  Rh = NULL,
  deffh = NULL,
  fullsampleh = NULL,
  dataset = NULL
)
optsize(
  H,
  n,
  poph,
  s2h = NULL,
  Rh = NULL,
  deffh = NULL,
  fullsampleh = NULL,
  dataset = NULL
)

Arguments

`H`	The stratum variable. One dimensional object convertible to one-column `data.table`, variable name as character, or column number.
`n`	Total sample size. One dimensional object with length one.
`poph`	Population size in each stratum. One dimensional object convertible to one-column `data.table`, variable name as character, or column number.
`s2h`	The expected population variance $S^2$ for variables of interest in each stratum (optional). If not defined, it is assumed to be 1 in each stratum. Object convertible to `data.table`, variable name as character vector, or column numbers.
`Rh`	The expected response rate in each stratum (optional). If not defined, it is assumed to be 1 in each stratum (full-response). Object convertible to one-column `data.table`, variable name as character, or column number.
`deffh`	The expected design effect for the estimate of variable (optional). If not defined, it is assumed to be 1 for each variable in each stratum. If is defined, then variables is defined the same arrangement as `Yh`. Object convertible to `data.table`, variable name as character vector, or column numbers.
`fullsampleh`	Variable for detection fully surveyed stratum (optinal). If not defined, it is assumed to be 1 in each stratum (full-response). Object convertible to one-column `data.table`, variable name as character, or column number.
`dataset`	Optional survey data object convertible to `data.table` with one row for each stratum.

Value

An object as data.table, with variables:
H - stratum,
variable - the name of variable for population variance $S^2$ ,
s2h - population variance $S^2$ ,
Rh - the expectedresponse rate,
deffh - the expected design effect,
poph - population size,
deffh - design effect,
fullsampleh - full sample indicator,
nh - sample size.

Details

If s2h and Rh is not defined, the sample allocation will be calculated as proportional allocation (proportional to the population size). If Rh is not defined, the sample allocation will be calculated as Neyman allocation.

Examples

library("data.table")
data <- data.table(H = 1 : 3,
                   s2h=10 * runif(3),
                   s2h2 = 10 * runif(3),
                   poph = 8 * 1 : 3,
                   Rh = rep(1, 3),
                   dd = c(1, 1, 1))

vars <- optsize(H = "H",
                s2h = c("s2h", "s2h2"),
                n = 10, poph = "poph",
                Rh = "Rh",
                fullsampleh = NULL,
                dataset = data)
vars

library("data.table")
data <- data.table(H = 1 : 3,
                   s2h=10 * runif(3),
                   s2h2 = 10 * runif(3),
                   poph = 8 * 1 : 3,
                   Rh = rep(1, 3),
                   dd = c(1, 1, 1))

vars <- optsize(H = "H",
                s2h = c("s2h", "s2h2"),
                n = 10, poph = "poph",
                Rh = "Rh",
                fullsampleh = NULL,
                dataset = data)
vars

Optimal sample size allocation for proportion

Description

The function computes optimal sample size allocation over strata and domain for proportion.

Usage

prop_dom_optimal_allocation(
  H,
  Dom,
  pop = NULL,
  R = NULL,
  deff = NULL,
  se_max = 0.5,
  prop = 0.5,
  min_size = 3,
  step = 1,
  unit_level = TRUE,
  dataset = NULL
)
prop_dom_optimal_allocation(
  H,
  Dom,
  pop = NULL,
  R = NULL,
  deff = NULL,
  se_max = 0.5,
  prop = 0.5,
  min_size = 3,
  step = 1,
  unit_level = TRUE,
  dataset = NULL
)

Arguments

`H`	The stratum variable. One dimensional object convertible to one-column `data.table` or variable name as character, column number.
`Dom`	Variables

used to define population domains. An object convertible to data.table or variable names as character vector, column numbers.

pop

The

population size in each stratum.

R

The

expected response rate in each stratum (optional). If not defined, it is assumed to be 1 in each stratum (full-response). Object convertible to one-column data.table, variable name as character, or column number.

deff

The

expected design effect for the estimate of variable (optional). If not defined, it is assumed to be 1 for each variable in each stratum. If is defined, then variables is defined the same arrangement as Yh. Object convertible to data.table, variable name as character vector, or column numbers.

se_max

Variable

for maximum standarterror (se) in domain.

prop

The

excepted ratio proportion.

min_size

numeric value for minimal sample size.

step

value for pace.

unit_level

logical value, if dataset is prepared for unit level then value TRUE, othercase FALSE.

dataset

Optional

agrregated survey data object convertible to data.table with one row for each stratum.

Value

A list with two data objects:

datah

An object as data.table, with variables:
H - the unit stratum variable,
Dom - variables used to define population domains,
poph - the population size in each stratum,
Rh - the expected response rate in each stratum,
deffh - the expected design effect,
s2h - variance in domain of stratum,
sup_cv - Variable for maximum coeficient of variation,
poph - population size,
nh - sample size .

aggr_Dom

An object as data.table, with variables:
Dom - optional variables used to define population domains,
pop_Dom - population size,
sample_size_Dom - optional variables used to define population domains,
sample_size - optional variables used to define population domains,
pop - sample size

Examples

library("data.table")
library("laeken")
data("eusilc")
eusilc <- data.table(eusilc)
dataset <- eusilc[, .(poph = sum(db090)), by = c("db040")]
dataset[, dom := "1"]
res <- prop_dom_optimal_allocation(H = "db040", Dom = "dom",
                                   pop = "poph", R = NULL,
                                   deff = NULL, se_max = 0.5,
                                   prop = 0.5, min_size = 3,
                                   step = 1, unit_level = FALSE,
                                   dataset = dataset)

library("data.table")
library("laeken")
data("eusilc")
eusilc <- data.table(eusilc)
dataset <- eusilc[, .(poph = sum(db090)), by = c("db040")]
dataset[, dom := "1"]
res <- prop_dom_optimal_allocation(H = "db040", Dom = "dom",
                                   pop = "poph", R = NULL,
                                   deff = NULL, se_max = 0.5,
                                   prop = 0.5, min_size = 3,
                                   step = 1, unit_level = FALSE,
                                   dataset = dataset)

Rounding numbers

Description

The function rounds the values in its first argument to the specified number of decimal places (default 0).

Usage

round2(x, n)
round2(x, n)

Arguments

`x`	a numeric vector.
`n`	integer indicating the number of decimal places.

Value

Rounded value

Examples

dar <- 100 * runif(3)
dar
round2(dar, 1)

dar <- 100 * runif(3)
dar
round2(dar, 1)

Population variance

Description

The function to estimate population variance $S^2$ .

Usage

s2(y, w = NULL)
s2(y, w = NULL)

Arguments

`y`	Study variable.
`w`	Survey weight (optional). If not defined, it is assumed to be 1 for each element.

Value

Population variance $S^2$ or the estimate of population variance $s^2$ .

Details

If w is not defined, the result is equal to the result of the function var.

Examples

s2(1:10)
s2(1:10, rep(1:2, each = 5))
all.equal(s2(1:10), var(1:10))

s2(1:10)
s2(1:10, rep(1:2, each = 5))
all.equal(s2(1:10), var(1:10))

Package 'surveyplanning'

Help Index

Survey Planning Tools

Description

Details

Author(s)

Optimal sample size allocation

Description

Usage

Arguments

Value

See Also

Examples

Sample size calculation

Description

Usage

Arguments

Value

See Also

Examples

Expected precision for the estimates of totals

Description

Usage

Arguments

Value

See Also

Examples

Minimal count of respondents for the given relative margin of error

Description

Usage

Arguments

Value

See Also

Examples

Minimal proportion for the given relative margin of error

Description

Usage

Arguments

Value

See Also

Examples

Margin of error for proportion

Description

Usage

Arguments

Value

See Also

Examples

Margin of error for count

Description

Usage

Arguments

Value

See Also

Examples

Optimal sample size allocation

Description

Usage

Arguments

Value

Details

See Also

Examples

Optimal sample size allocation for proportion

Description

Usage

Arguments

Value

See Also

Examples

Rounding numbers

Description

Usage

Arguments

Value

See Also

Examples

Population variance

Description

Usage