Package 'sleacr'

Title: Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) Tools in R
Description: In the recent past, measurement of coverage has been mainly through two-stage cluster sampled surveys either as part of a nutrition assessment or through a specific coverage survey known as Centric Systematic Area Sampling (CSAS). However, such methods are resource intensive and often only used for final programme evaluation meaning results arrive too late for programme adaptation. SLEAC, which stands for Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage, is a low resource method designed specifically to address this limitation and is used regularly for monitoring, planning and importantly, timely improvement to programme quality, both for agency and Ministry of Health (MoH) led programmes. SLEAC is designed to complement the Semi-quantitative Evaluation of Access and Coverage (SQUEAC) method. This package provides functions for use in conducting a SLEAC assessment.
Authors: Ernest Guevarra [aut, cre]
Maintainer: Ernest Guevarra <[email protected]>
License: GPL-3
Version: 0.0.0.9000
Built: 2024-10-29 16:18:30 UTC
Source: https://github.com/nutriverse/sleacr

Help Index

Classify coverage results

Description

Classify coverage results

Usage

classify_coverage(n_in, n_total, standard = c(0.2, 0.5))

Arguments

n_in

Number (integer) of cases found in the programme
n_total

Number (integer) of children under 5 years sampled
standard

Decision rule standard/s. Should be between 0 and 1. At least one standard should be provided for a two-tier coverage classifier. Two standards should be provided for a three-tier coverage classifier. Default is a three-tier classifier with rule set at 0.2 and 0.5.

Value

A character value or vector indicating coverage classification. If standard is a single value, returns "Satisfactory" if coverage is above standard and "Not satisfactory" if coverage is below or equal to standard. If standard is two values, returns "Low" if coverage is below or equal to lower standard, "High" if coverage is above the higher standard, and "Moderate" for all other coverage values.

Author(s)

Ernest Guevarra

Examples

classify_coverage(n_in = 6, n_total = 40)
with(survey_data,
  classify_coverage(n_in = in_cases, n_total = n)
)

Calculate the binomial coefficient "n-choose-k"

Description

Calculate the binomial coefficient "n-choose-k"

Usage

get_binom_hypergeom(n, k)

Arguments

n

Total population
k

Number of sample drawn from total population

Value

A numeric vector of binomial probability

Examples

get_binom_hypergeom(n = 600, k = 40)

Function to produce misclassification probabilities

Description

Function to produce misclassification probabilities

Usage

get_class_prob(x)

Arguments

x

Simulated results data produced by test_lqas_classifier()

Value

A list of LQAS misclassification probabilities results

Examples

sim <- test_lqas_classifier(replicates = 5, runs = 5,
                            pop = 10000, n = 40,
                            d.lower = 60, d.upper = 90)
get_class_prob(x = sim)

Calculate decision rule for a specified sample size and lower and upper triage thresholds

Description

Calculate decision rule for a specified sample size and lower and upper triage thresholds

Usage

get_d(N, n, dLower, dUpper, alpha = 0.1, beta = 0.1)

Arguments

N

Total population size of cases in the specified survey area
n

Sample size
dLower

Lower triage threshold. Values from 0 to 1.
dUpper

Upper triage threshold. Values from 0 to 1.
alpha

Maximum tolerable alpha error. Values from 0 to 1. Default is 0.1
beta

Maximum tolerable beta error. Values from 0 to 1. Default is 0.1

Value

A list of values providing the LQAS sampling plan for the specified parameters. The list includes sample size, decision rule, alpha error and beta error for the specified classification scheme

Examples

get_d(N = 600, n = 40, dLower = 0.7, dUpper = 0.9)

Calculate hypergeometric probability

Description

Calculate hypergeometric probability

Usage

get_hypergeom(k, m, n, N)

Arguments

k

Number of cases in the sample
m

Number of cases in the population
n

Sample size
N

Population size

Value

A numeric value of hypergeometric probability given specified parameters

Examples

get_hypergeom(k = 5, m = 600, n = 25, N = 10000)

Calculate cumulative hypergeometric probabilities

Description

Calculate cumulative hypergeometric probabilities

Usage

get_hypergeom_cumulative(k, m, n, N, tail = "lower")

Arguments

k

Number of cases in the sample
m

Number of cases in the population
n

Sample size
N

Population size
tail

A character vector indicating "lower" (default) or "upper" tail

Examples

get_hypergeom_cumulative(k = 5, m = 600, n = 25, N = 10000)

Calculate sample size of number of cases to be found to assess coverage

Description

Calculate sample size of number of cases to be found to assess coverage

Usage

get_n(N, dLower, dUpper, alpha = 0.1, beta = 0.1)

Arguments

N

Total population size of cases in the specified survey area
dLower

Lower triage threshold. Values from 0 to 1.
dUpper

Upper triage threshold. Values from 0 to 1.
alpha

Maximum tolerable alpha error. Values from 0 to 1. Default is 0.1
beta

Maximum tolerable beta error. Values from 0 to 1. Default is 0.1

Value

A list of values providing the LQAS sampling plan for the specified parameters. The list includes sample size, decision rule, alpha error and beta error for the specified classification scheme

Examples

get_n(N = 600, dLower = 0.7, dUpper = 0.9)

Calculate estimated number of cases for a condition affecting children under 5 years old in a specified survey area

Description

Calculate estimated number of cases for a condition affecting children under 5 years old in a specified survey area

Usage

get_n_cases(N, u5, p)

Arguments

N

Population for all ages in the specified survey area
u5

Proportion (value from 0 to 1) of population that are aged 6-59 months
p

Prevalence of condition that is to be assessed

Value

Numeric value of the estimated number of cases in the specified survey area

Examples

## Calculate number of SAM cases in a population of 100000 persons of all
## ages with an under-5 population of 17% and a prevalence of 2%
get_n_cases(N = 100000, u5 = 0.17, p = 0.02)

Calculate number of clusters to sample to reach target sample size

Description

Calculate number of clusters to sample to reach target sample size

Usage

get_n_clusters(n, N, u5, p)

Arguments

n

Target sample size of cases for the coverage survey
N

Average cluster population for all ages in the specified survey area
u5

Proportion (value from 0 to 1) of population that are aged 6-59 months
p

Prevalence of condition that is to be assessed

Value

Numeric value of the estimated number of clusters to sample to reach target sample size

Examples

## Calculate number of villages to sample given an average village population
## of 600 persons of all ages with an under-5 population of 17% and a
## prevalence of SAM of 2% if the target sample size is 40
get_n_clusters(n = 40, N = 600, u5 = 0.17, p = 0.02)

Select sampling clusters using systematic sampling

Description

Select sampling clusters using systematic sampling

Usage

get_sampling_interval(N_clusters, n_clusters)

select_random_start(interval)

select_sampling_clusters(N_clusters, n_clusters)

create_sampling_list(cluster_list, n_clusters)

Arguments

N_clusters

Total number of clusters in survey area
n_clusters

Number of sampling clusters to be selected
interval

Sampling interval usually calculated using get_sampling_interval()
cluster_list

A data.frame containing at least the name or any other identifier for the entire set of clusters to sample from.

Value

A numeric value for get_sampling_interval() and select_random_start(); An integer vector for select_sampling_clusters() giving the row index for selected clusters; A data.frame for create_sampling_list() which is a subset of cluster_list

Examples

get_sampling_interval(N_clusters = 211, n_clusters = 35)
interval <- get_sampling_interval(N_clusters = 211, n_clusters = 35)
select_random_start(interval)
select_sampling_clusters(N_clusters = 211, n_clusters = 35)
create_sampling_list(cluster_list = village_list, n_clusters = 70)

Function to simulate survey data of covered/cases and non-covered/non-cases given specified parameters

Description

Function to simulate survey data of covered/cases and non-covered/non-cases given specified parameters

Usage

make_data(proportion, pop)

Arguments

proportion

A numeric value of a coverage proportion to simulate on
pop

Population size from which simulated coverage survey data is to be taken from

Value

A numeric vector of cases and non-cases (1s and 0s)

Examples

make_data(proportion = 0.3, pop = 10000)

plot helper function for test_lqas_classifier() function

Description

plot helper function for test_lqas_classifier() function

Usage

## S3 method for class 'lqasSim'
plot(x, ...)

Arguments

x

An object of class lqasSim produced by test_lqas_classifier() function
...

Additional plot parameters

Value

An LQAS probability of classification plot

Examples

x <- test_lqas_classifier(replicates = 5, runs = 5,
                          pop = 10000, n = 40, d.lower = 60, d.upper = 90)
plot(x)

print helper function for get_class_prob() function

Description

print helper function for get_class_prob() function

Usage

## S3 method for class 'lqasClass'
print(x, ...)

Arguments

x

An object resulting from applying the get_class_prob() function.
...

Additional print parameters

Value

Printed output of get_class_prob() function

Examples

sim <- test_lqas_classifier(replicates = 5, runs = 5,
                            pop = 10000, n = 40,
                            d.lower = 60, d.upper = 90)
x <- get_class_prob(x = sim)
print(x)

Function to perform LQAS based on data based on specified decision rules

Description

Function to perform LQAS based on data based on specified decision rules

Usage

run_lqas(data, n, d.lower, d.upper)

Arguments

data

A vector of simulated data produced by make_data()
n

Sample size of actual or test coverage data
d.lower

A numeric value for the lower classification threshold
d.upper

A numeric value for the upper classification threshold

Value

A list of coverage proportions and LQAS outcomes

Examples

run_lqas(data = make_data(proportion = 0.3, pop = 10000),
         n = 40, d.lower = 60, d.upper = 90)

Function to perform a series of LQAS analysis on simulated coverage survey data

Description

Function to perform a series of LQAS analysis on simulated coverage survey data

Usage

simulate_lqas(
  runs = 50,
  pop = NULL,
  n,
  d.lower,
  d.upper,
  p.lower = 0,
  p.upper = 100,
  fine = 1,
  progress = TRUE
)

Arguments

runs

Number of simulation runs to perform per coverage proportion. Default is 50 runs
pop

Population size from which simulated coverage survey data is to be taken from
n

Sample size of actual or test coverage data
d.lower

A numeric value for the lower classification threshold
d.upper

A numeric value for the upper classification threshold
p.lower

Starting proportion for simulations. Default is 0
p.upper

Ending proportion for simulations. Default is 100
fine

Granularity of simulated proportions; Default to 1
progress

Logical. Should simulation progress be shown? Default is TRUE

Value

A data.frame of coverage proportions and LQAS outcomes

Examples

simulate_lqas(runs = 10, pop = 10000, n = 40, d.lower = 60, d.upper = 90)

SLEAC survey data from Sierra Leone

Description

SLEAC survey data from Sierra Leone

Usage

survey_data

Format

A tibble with 14 rows and 6 columns:

country

Country

province

Province

district

District

in_cases

Cases found who are in the programme

out_cases

Cases found who are not in the programme

n

Total number of under 5 children sampled

Source

Ministry of Health, Sierra Leone

Examples

survey_data

Function to test performance of LQAS classifier

Description

Function to test performance of LQAS classifier

Usage

test_lqas_classifier(
  replicates = 20,
  runs = 50,
  pop = NULL,
  n = NULL,
  d.lower = NULL,
  d.upper = NULL,
  p.lower = 0,
  p.upper = 100,
  fine = 1,
  progress = TRUE
)

Arguments

replicates

Number of replicate LQAS simulations to perform. Default is set to 20 replicates
runs

Number of simulation runs to perform per coverage proportion. Default is 50 runs
pop

Population size from which simulated coverage survey data is to be taken from
n

Sample size of actual or test coverage data
d.lower

A numeric value for the lower classification threshold
d.upper

A numeric value for the upper classification threshold
p.lower

Starting proportion for simulations. Default is 0
p.upper

Ending proportion for simulations. Default is 100
fine

Granularity of simulated proportions; Defaul to 1
progress

Logical. Should simulation progress be shown? Default is TRUE

Value

A sleac object

Examples

test_lqas_classifier(replicates = 5, runs = 5,
                     pop = 10000, n = 40, d.lower = 60, d.upper = 90)

List of villages in Bo District, Sierra Leone

Description

List of villages in Bo District, Sierra Leone

Usage

village_list

Format

A tibble with 1001 rows and 4 columns:

id

Unique identifier

chiefdom

Chiefdom

section

Section

village

Village

Source

Ministry of Health, Sierra Leone

Examples

village_list