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Summary Statistics for Models of the Nonprob Class

Source: R/summary.R
summary.nonprob.Rd

Summarises the nonprob class object. The summary depends on the type of the estimator (i.e. IPW, MI, DR)

Usage

# S3 method for class 'nonprob'
summary(object, ...)

Arguments

object

object of the nonprob class

...

Additional optional arguments

Value

An object of nonprob_summary class containing:

  • call call

  • estimator type of estimator

  • control list of controls

  • ipw_weights estimated IPW weights

  • ipw_weights_total estimated IPW total (sum)

  • ps_scores_nonprob estimated propensity scores for non-probability sample

  • ps_scores_prob estimated propensity scores for probability sample

  • case_weights case weights

  • output estimated means and standard errors

  • SE estimated standard errors of V1 and V2

  • confidence_interval confidence intervals

  • nonprob_size size of the non-probability sample

  • prob_size size of the probability sample

  • pop_size population size

  • pop_size_fixed whether the population size is treated as fixed

  • no_prob whether probability sample was provided

  • outcome model details

  • selection selection details

  • estimator_method estimator method

  • selection_formula selection formula

  • outcome_formula outcome formula

  • vars_selection whether variable selection algorithm was applied

  • vars_outcome variables of the outcome models

  • ys_rand_pred predicted values for the random sample (if applies)

  • ys_nons_pred predicted values for the non-probability sample

  • ys_resid residuals for the non-probability sample

Examples


data(admin)
data(jvs)

jvs_svy <- svydesign(ids = ~ 1,  weights = ~ weight,
strata = ~ size + nace + region, data = jvs)

ipw_est1 <- nonprob(selection = ~ region + private + nace + size,
target = ~ single_shift,
svydesign = jvs_svy,
data = admin, method_selection = "logit"
)
summary(ipw_est1)
#> A nonprob_summary object
#>  - call: nonprob(data = admin, selection = ~region + private + nace + 
#>     size, target = ~single_shift, svydesign = jvs_svy, method_selection = "logit")
#>  - estimator type: inverse probability weighting
#>  - nonprob sample size: 9344 (18%)
#>  - prob sample size: 6523 (12.6%)
#>  - population size: 51870 (fixed: false)
#>  - detailed information about models are stored in list element(s): "selection"
#> ----------------------------------------------------------------
#>  - sum of IPW weights: 52898.13 
#>  - distribution of IPW weights (nonprob sample):
#>    - min: 1.1693; mean: 5.6612; median: 4.3334; max: 49.9504
#>  - distribution of IPW probabilities (nonprob sample):
#>    - min: 0.0189; mean: 0.2894; median: 0.2525; max: 0.8552
#>  - distribution of IPW probabilities (prob sample):
#>    - min: 0.0200; mean: 0.2687; median: 0.2291; max: 0.8552
#> ----------------------------------------------------------------