Sampling Design Objects

All sampling functions in sondage return objects inheriting from class "sondage_sample". These objects store the realized sample together with the design-defining quantities needed by the query generics.

Details

All sondage_sample objects contain:

$sample

Sample indices (integer vector, or matrix/list when nrep > 1).

$n

Sample size. For designs with $fixed_size = TRUE, an integer equal to the realized sample size. For random-size designs ($fixed_size = FALSE), a double equal to the expected sample size – sum(pik) for "poisson", the user-supplied target for "bernoulli".

$N

Population size.

$method

Sampling method name.

$fixed_size

Whether the sample size is fixed by design.

Without-replacement ("wor") objects also contain:

$pik

Inclusion probability vector. For most methods this equals the true first-order probabilities. For "sps" and "pareto", this is the target vector.

With-replacement ("wr") objects also contain:

$prob

Per-draw selection probability vector.

$hits

Realized selection counts (integer vector, or N x nrep matrix when nrep > 1).

The class vector records the design family:

c("equal_prob", "wor", "sondage_sample")

Equal probability, without replacement.

c("equal_prob", "wr", "sondage_sample")

Equal probability, with replacement.

c("unequal_prob", "wor", "sondage_sample")

Unequal probability, without replacement.

c("balanced", "unequal_prob", "wor", "sondage_sample")

Balanced sampling (cube method); dispatches to unequal_prob / wor methods unless a balanced-specific method is defined.

c("unequal_prob", "wr", "sondage_sample")

Unequal probability, with replacement.

See also

equal_prob_wor(), equal_prob_wr(), unequal_prob_wor(), unequal_prob_wr(), balanced_wor(), inclusion_prob(), expected_hits(), joint_inclusion_prob(), joint_expected_hits(), sampling_cov(), print.sondage_sample()