View source: R/utils-ajustes.R
| amostragem_ajustar_deff_resposta | R Documentation |
Inflates a base sample size (computed under a simple-random-sampling
assumption) by a design effect (deff, accounting for the extra
variability introduced by a more complex design such as clustering or
multi-stage sampling) and by an expected response rate, following
Eq. (6.6) of the course notes (Espinosa et al., 2019).
amostragem_ajustar_deff_resposta(n_prime, deff = 1.5, taxa_resposta = 0.85)
n_prime |
Base sample size (e.g. from |
deff |
Design effect ( |
taxa_resposta |
Expected response rate, default |
Although this formula is first introduced in the course notes' cluster
sampling chapter, it is a general-purpose adjustment: it only needs
a base sample size, a design effect, and a response rate – nothing
specific to clusters. Course exercises apply it to plain AAS and
stratified problems just as often as to cluster problems (e.g. to
scale up a prevalence-survey sample size for an anticipated design
effect and non-response), so it is exposed here as a cross-cutting
utility rather than tucked away under the cong1_ prefix.
cong1_ajustar_deff() remains available as an alias, for continuity
with the cluster-sampling chapter and its documentation.
A sampling_result object with the adjusted sample size.
Espinosa et al. (2019), as cited in the course notes, Eq. (6.6).
# Cap. 3 (AAS) + deff + resposta -- ex.: planejamento de prevalencia
n_prime <- aas_tamanho_proporcao(p = 0.20, d = 0.05)$estimativa$n
amostragem_ajustar_deff_resposta(n_prime, deff = 1.2, taxa_resposta = 0.85)
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