Updating population size estimation results.

A function that applies all post-hoc procedures that were taken (such as heteroscedastic consistent covariance matrix estimation or bias reduction) to population size estimation and standard error estimation.

Usage

redoPopEstimation(object, newdata, ...)

# S3 method for class 'singleRStaticCountData'
redoPopEstimation(
  object,
  newdata,
  cov,
  weights,
  coef,
  control,
  popVar,
  offset,
  weightsAsCounts,
  ...
)

Arguments

object

object for which update of population size estimation results will be done.

newdata

optional data.frame with new data for pop size estimation.

...

additional optional arguments, currently not used in singleRStaticCountData class method.

cov

an updated covariance matrix estimate.

weights

optional vector of weights to use in population size estimation.

coef

optional vector of coefficients of regression on which to base population size estimation. If missing it is set to coef(object).

control

similar to controlPopVar in estimatePopsize(). If missing set to controls provided on call to object.

popVar

similar to popVar in estimatePopsize(). If missing set to "analytic".

offset

offset argument for new data

weightsAsCounts

for singleRStaticCountData method used to specify whether weights should be treated as number of occurrences for rows in data

Value

An object of class popSizeEstResults containing updated population size estimation results.

Details

Any non specified arguments will be inferred from the object

Examples

# Create simple model
Model <- estimatePopsize(
  formula = capture ~ nation + gender, 
  data = netherlandsimmigrant, 
  model = ztpoisson, 
  method = "IRLS"
)
# Apply heteroscedasticity consistent covariance matrix estimation
require(sandwich)
#> Loading required package: sandwich
cov <- vcovHC(Model, type = "HC3")
summary(Model, cov = cov,
popSizeEst = redoPopEstimation(Model, cov = cov))
#> 
#> Call:
#> estimatePopsize.default(formula = capture ~ nation + gender, 
#>     data = netherlandsimmigrant, model = ztpoisson, method = "IRLS")
#> 
#> Pearson Residuals:
#>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
#> -0.479668 -0.479668 -0.351833  0.000449 -0.225717 14.058858 
#> 
#> Coefficients:
#> -----------------------
#> For linear predictors associated with: lambda 
#>                      Estimate Std. Error z value  P(>|z|)    
#> (Intercept)           -1.3977     0.2553  -5.475 4.37e-08 ***
#> nationAsia            -1.0560     0.3940  -2.680  0.00736 ** 
#> nationNorth Africa     0.2327     0.2399   0.970  0.33200    
#> nationRest of Africa  -0.8864     0.3514  -2.523  0.01164 *  
#> nationSurinam         -2.3519     1.0273  -2.289  0.02205 *  
#> nationTurkey          -1.6845     0.6110  -2.757  0.00583 ** 
#> gendermale             0.3833     0.2014   1.904  0.05695 .  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> AIC: 1718.993
#> BIC: 1757.766
#> Residual deviance: 1136.645
#> 
#> Log-likelihood: -852.4963 on 1873 Degrees of freedom 
#> Number of iterations: 8
#> -----------------------
#> Population size estimation results: 
#> Point estimate 11879.92
#> Observed proportion: 15.8% (N obs = 1880)
#> Std. Error 2531.196
#> 95% CI for the population size:
#>           lowerBound upperBound
#> normal      6918.872   16840.98
#> logNormal   8015.862   18177.38
#> 95% CI for the share of observed population:
#>           lowerBound upperBound
#> normal      11.16325   27.17206
#> logNormal   10.34252   23.45350
# Compare to results with usual covariance matrix estimation
summary(Model)
#> 
#> Call:
#> estimatePopsize.default(formula = capture ~ nation + gender, 
#>     data = netherlandsimmigrant, model = ztpoisson, method = "IRLS")
#> 
#> Pearson Residuals:
#>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
#> -0.479668 -0.479668 -0.351833  0.000449 -0.225717 14.058858 
#> 
#> Coefficients:
#> -----------------------
#> For linear predictors associated with: lambda 
#>                      Estimate Std. Error z value  P(>|z|)    
#> (Intercept)           -1.3977     0.2146  -6.514 7.33e-11 ***
#> nationAsia            -1.0560     0.3016  -3.501 0.000464 ***
#> nationNorth Africa     0.2327     0.1939   1.200 0.230002    
#> nationRest of Africa  -0.8864     0.3009  -2.946 0.003224 ** 
#> nationSurinam         -2.3519     1.0137  -2.320 0.020337 *  
#> nationTurkey          -1.6845     0.6029  -2.794 0.005203 ** 
#> gendermale             0.3833     0.1630   2.352 0.018686 *  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> AIC: 1718.993
#> BIC: 1757.766
#> Residual deviance: 1136.645
#> 
#> Log-likelihood: -852.4963 on 1873 Degrees of freedom 
#> Number of iterations: 8
#> -----------------------
#> Population size estimation results: 
#> Point estimate 11879.92
#> Observed proportion: 15.8% (N obs = 1880)
#> Std. Error 2448.792
#> 95% CI for the population size:
#>           lowerBound upperBound
#> normal      7080.380   16679.47
#> logNormal   8111.333   17927.69
#> 95% CI for the share of observed population:
#>           lowerBound upperBound
#> normal      11.27134   26.55225
#> logNormal   10.48657   23.17745

## get confidence interval with larger significance level
redoPopEstimation(Model, control = controlPopVar(alpha = .000001))
#> Point estimate: 11879.92
#> Variance: 5996583
#> 99.9999% confidence intervals:
#>           lowerBound upperBound
#> normal      1880.000   23858.53
#> logNormal   4951.313   34438.87