This dataset represents a synthetic nonprobability sample generated via
Poisson sampling from a finite population constructed from the National
Health and Nutrition Examination Survey (NHANES) cycles 1999–2010. It is
intended to illustrate the pseudo-weighting methods implemented in the
nonprobsampling package.
Usage
data(sc)Format
A data frame with 2404 observations and 8 variables:
- psa_level
Outcome variable: serum prostate-specific antigen level (numeric)
- BMI
Body mass index category (factor with 4 levels: "Normal", "Overweight", "Obese", "Morbidly Obese")
- race
Race category (factor with 4 levels: 1 = White, 2 = Black, 3 = Hispanic, 4 = Other)
- agecat
Age category (factor with 4 levels: 1 = 55–59, 2 = 60–64, 3 = 65–69, 4 = 70+)
- education
Education level (factor with 5 levels: 1 = Less Than 8 Years, 2 = 8–11 Years, 3 = 12 Years Or Completed High School, 4 = College Graduate, 5 = Postgraduate)
- pros_enlarged
Prostate enlargement indicator (factor with 2 levels: 0 = No, 1 = Yes)
- comorbidity
General comorbidity indicator (factor with 2 levels: 0 = No, 1 = Yes)
- diabetes
Diabetes diagnosis indicator (factor with 2 levels: 0 = No, 1 = Yes)
Source
Synthetic data generated by the package authors. The underlying finite population was constructed from the National Health and Nutrition Examination Survey (NHANES), 1999–2010 cycles, conducted by the U.S. National Center for Health Statistics (NCHS).
Details
The dataset has 2,404 complete-case observations, with
psa_level serving as the outcome variable. Auxiliary variables
shared with the probability reference surveys sp1 and sp2
are used to construct pseudo-weights aimed at correcting for participation
bias.
Examples
data(sc)
str(sc)
#> 'data.frame': 2404 obs. of 8 variables:
#> $ psa_level : num 0.3 1 1 1.2 0.3 1 0.3 1 1 0.3 ...
#> $ BMI : Factor w/ 4 levels "Morbidly Obese",..: 1 1 4 3 2 1 2 4 4 1 ...
#> $ race : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
#> $ agecat : Factor w/ 4 levels "1","2","3","4": 1 2 2 4 2 3 2 2 2 1 ...
#> $ education : Factor w/ 5 levels "1","2","3","4",..: 4 3 4 4 1 3 1 4 4 4 ...
#> $ pros_enlarged: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> $ comorbidity : Factor w/ 2 levels "0","1": 2 2 1 2 2 2 2 1 1 2 ...
#> $ diabetes : Factor w/ 2 levels "0","1": 2 2 1 1 1 2 1 1 1 2 ...
summary(sc)
#> psa_level BMI race agecat education
#> Min. : 0.070 Morbidly Obese: 195 1:1694 1:853 1:276
#> 1st Qu.: 0.600 Normal : 618 2: 276 2:678 2:294
#> Median : 1.000 Obese : 573 3: 299 3:523 3:503
#> Mean : 1.536 Overweight :1018 4: 135 4:350 4:558
#> 3rd Qu.: 1.860 5:773
#> Max. :34.960
#> pros_enlarged comorbidity diabetes
#> 0:1838 0: 981 0:1982
#> 1: 566 1:1423 1: 422
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