Disclaimer

This package is a work in progress. It has been released to get feedback from users that we can incorporate in future releases.

serosurvey

The goal of serosurvey is to gather Serological Survey Analysis functions and workflow templates for Prevalence Estimation Under Misclassification.

Installation

You can install the developmental version of serosurvey from GitHub with:

if(!require("remotes")) install.packages("remotes")
remotes::install_github("avallecam/serosurvey")

Brief description

The current workflow is divided in two steps:

survey: Estimate multiple prevalences, and
serology: Estimate prevalence Under misclassification for a device with Known or Unknown test performance

In the Introductory article we provide detailed definitions and references of the methods available.
In the Workflow article we provide a reproducible example with this package.

Contributing

Feel free to fill an issue or contribute with your functions or workflows in a pull request.

Code of Conduct

Please note that the serosurvey project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Contact

Andree Valle Campos | @avallecam | [email protected]

Project Link: https://github.com/avallecam/serosurvey

Acknowledgements

Many thanks to the Centro Nacional de Epidemiología, Prevención y Control de Enfermedades (CDC Perú) for the opportunity to work on this project.

How to cite this R package

citation("serosurvey")
#> 
#> To cite package ‘serosurvey’ in publications use:
#> 
#> Valle Campos A (2020). "serosurvey: Serological Survey Analysis For
#> Prevalence Estimation Under Misclassification." _Zenodo_. doi:
#> 10.5281/zenodo.4065080 (URL: https://doi.org/10.5281/zenodo.4065080), R
#> package version 1.0, <URL: https://avallecam.github.io/serosurvey/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     author = {Andree {Valle Campos}},
#>     title = {serosurvey: Serological Survey Analysis For Prevalence Estimation Under Misclassification},
#>     journal = {Zenodo},
#>     month = {oct},
#>     year = {2020},
#>     doi = {10.5281/zenodo.4065080},
#>     note = {R package version 1.0},
#>     url = {https://avallecam.github.io/serosurvey/},
#>   }

error when using pmap and srvyr_prop_step_02

library(serosurvey)
library(tidyverse)
library(srvyr)
#> 
#> Attaching package: 'srvyr'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(survey)
#> Loading required package: grid
#> Loading required package: Matrix
#> 
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#> 
#>     expand, pack, unpack
#> Loading required package: survival
#> 
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#> 
#>     dotchart
data(api)
dstrata <- apistrat %>% as_survey_design(strata = stype, weights = pw)
dstrata2 <- apistrat %>%
  mutate(pw2=1) %>%
  as_survey_design(strata = stype, weights = pw2)
dstrata %>%
  summarise(pct = survey_mean(awards=="Yes",proportion = TRUE))
#> # A tibble: 1 × 2
#>     pct pct_se
#>   <dbl>  <dbl>
#> 1 0.639 0.0349
dstrata2 %>%
  summarise(pct = survey_mean(awards=="Yes",proportion = TRUE))
#> # A tibble: 1 × 2
#>     pct pct_se
#>   <dbl>  <dbl>
#> 1 0.565 0.0331

srvyr_prop_step_01(design = dstrata,
                     numerator = awards,
                     denominator = stype) %>%
  mutate(resultado=pmap(.l = select(.,design=design,
                                    numerator = numerator,
                                    denominator = denominator,
                                    numerator_level=numerator_level),
                       .f = srvyr_prop_step_02))
#> Error in `mutate()`:
#> ! Problem while computing `resultado = pmap(...)`.
#> ℹ The error occurred in group 1: stype = E.
#> Caused by error in `pmap()`:
#> ℹ In index: 1.
#> Caused by error in `dplyr::summarise()`:
#> ! Problem while computing `prop = survey_mean(...)`.
#> ℹ The error occurred in group 1: stype = E.
#> Caused by error in `.data$numerator_level`:
#> ! Column `numerator_level` not found in `.data`.

#> Backtrace:
#>      ▆
#>   1. ├─... %>% ...
#>   2. ├─dplyr::mutate(...)
#>   3. ├─dplyr:::mutate.data.frame(...)
#>   4. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), caller_env = caller_env())
#>   5. │   ├─base::withCallingHandlers(...)
#>   6. │   └─mask$eval_all_mutate(quo)
#>   7. ├─purrr::pmap(...)
#>   8. │ └─purrr:::pmap_("list", .l, .f, ..., .progress = .progress)
#>   9. │   ├─purrr:::with_indexed_errors(...)
#>  10. │   │ └─base::withCallingHandlers(...)
#>  11. │   ├─purrr:::call_with_cleanup(...)
#>  12. │   └─serosurvey (local) .f(...)
#>  13. │     └─... %>% ... at serosurvey/R/serosurvey_srvyr.R:154:2
#>  14. ├─dplyr::rename_at(...)
#>  15. │ └─dplyr:::tbl_at_vars(.tbl, .vars, .include_group_vars = TRUE)
#>  16. │   └─dplyr::tbl_vars(tbl)
#>  17. │     ├─dplyr:::new_sel_vars(tbl_vars_dispatch(x), group_vars(x))
#>  18. │     │ └─base::structure(...)
#>  19. │     └─dplyr:::tbl_vars_dispatch(x)
#>  20. ├─dplyr::ungroup(.)
#>  21. ├─dplyr::summarize(...)
#>  22. ├─srvyr:::summarise.grouped_svy(...)
#>  23. │ ├─dplyr::summarise(.data$variables, !!!.dots, .groups = .groups)
#>  24. │ └─dplyr:::summarise.grouped_df(.data$variables, !!!.dots, .groups = .groups)
#>  25. │   └─dplyr:::summarise_cols(.data, dplyr_quosures(...), caller_env = caller_env())
#>  26. │     ├─base::withCallingHandlers(...)
#>  27. │     └─dplyr:::map(quosures, summarise_eval_one, mask = mask)
#>  28. │       └─base::lapply(.x, .f, ...)
#>  29. │         └─dplyr (local) FUN(X[[i]], ...)
#>  30. │           └─mask$eval_all_summarise(quo)
#>  31. ├─srvyr::survey_mean(...)
#>  32. │ └─srvyr:::stop_for_factor(x)
#>  33. │   └─base::is.factor(x)
#>  34. ├─numerator_level
#>  35. ├─rlang:::`$.rlang_data_pronoun`(.data, numerator_level)
#>  36. │ └─rlang:::data_pronoun_get(...)
#>  37. └─rlang:::abort_data_pronoun(x, call = y)
#>  38.   └─rlang::abort(msg, "rlang_error_data_pronoun_not_found", call = call)
ᘀ娂Ȼ
#> Error in eval(expr, envir, enclos): object 'ᘀ娂Ȼ' not found

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