`tibble` outputs for `incidence_fit` objects using broom-like `tidy` and `glance` functions

[avallecam]

Currently get_info generate vector class outputs, isolated from their metadata.

For tidyverse users, this output could be cleaner, more explicit and easy to manage if the output is a dataframe (or tibble).

Here I share some broom-like implementations as tidy_incidence and glance_incidence in a currently personal package here.

These functions prove to be easy to manage to any level of stratification, if required, when using purrr with map family of functions.

Feel free to check them and evaluate their addition to the core package!

# packages ----------------------------------------------------------------

if(!require("devtools")) install.packages("devtools")
if(!require("avallecam")) devtools::install_github("avallecam/avallecam") #improvements

library(tidyverse) #magrittr and purrr packages
library(lubridate) #ymd
library(outbreaks) #sample data
library(incidence) #core functions

# example outbreak --------------------------------------------------------

dat <- ebola_sim$linelist$date_of_onset
i.7 <- incidence(dat, interval=7)
# plot(i.7)
f1 <- fit(i.7[1:20])
f2 <- fit_optim_split(i.7)

# broom like functions ----------------------------------------------------

# tidy
f1 %>% tidy_incidence()
#> # A tibble: 2 x 5
#>   mark  parameter estimate conf_lower conf_upper
#>   <chr> <chr>        <dbl>      <dbl>      <dbl>
#> 1 1     r           0.0318     0.0260     0.0376
#> 2 1     doubling   21.8       18.5       26.7
f2 %>% pluck("fit") %>% tidy_incidence()
#> # A tibble: 4 x 5
#>   mark   parameter estimate conf_lower conf_upper
#>   <chr>  <chr>        <dbl>      <dbl>      <dbl>
#> 1 before r           0.0298     0.0261    0.0336 
#> 2 after  r          -0.0102    -0.0110   -0.00930
#> 3 before doubling   23.2       20.7      26.6    
#> 4 after  halving    68.2       62.9      74.5

# glance
f1 %>% glance_incidence()
#> # A tibble: 1 x 11
#>   r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC   BIC
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <int>  <dbl> <dbl> <dbl>
#> 1     0.880         0.874 0.498      133. 9.81e-10     2  -13.4  32.8  35.7
#> # ... with 2 more variables: deviance <dbl>, df.residual <int>
f2 %>% pluck("fit") %>% glance_incidence()
#> # A tibble: 2 x 12
#>   names r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC
#>   <chr>     <dbl>         <dbl> <dbl>     <dbl>    <dbl> <int>  <dbl> <dbl>
#> 1 befo~     0.922         0.919 0.455      273. 2.95e-14     2  -14.8  35.5
#> 2 after     0.951         0.949 0.155      578. 3.72e-21     2   15.4 -24.8
#> # ... with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>

# using purrr -------------------------------------------------------------

# using purrr::map family function allows easy stratification
# for gender and could be extrapolated to administrative levels
# in country level analysis

incidence_purrr <- ebola_sim$linelist %>% 
  as_tibble() %>% 
  #filter observations explicitly before incidence()
  filter(date_of_onset<lubridate::ymd(20141007)) %>% 
  #stratify by any group of covariates
  group_by(gender) %>% 
  nest() %>% 
  mutate(incidence_strata=map(.x = data,
                              .f = ~incidence(.x %>% pull(date_of_onset),
                                              interval=7))) %>% 
  mutate(strata_fit=map(.x = incidence_strata,
                            .f = fit)) %>% 
  mutate(strata_fit_tidy=map(.x = strata_fit,
                                  .f = tidy_incidence)) %>% 
  mutate(strata_fit_glance=map(.x = strata_fit,
                                       .f = glance_incidence))

# keep only the tibbles
incidence_purrr_tibble <- incidence_purrr %>% 
  select(-data,-incidence_strata,-strata_fit)

# tidy_incidence output
incidence_purrr_tibble %>% 
  unnest(cols = c(strata_fit_tidy))
#> # A tibble: 4 x 7
#> # Groups:   gender [2]
#>   gender mark  parameter estimate conf_lower conf_upper strata_fit_glance
#>   <fct>  <chr> <chr>        <dbl>      <dbl>      <dbl> <list>           
#> 1 f      1     r           0.0228     0.0189     0.0267 <tibble [1 x 11]>
#> 2 f      1     doubling   30.3       25.9       36.6    <tibble [1 x 11]>
#> 3 m      1     r           0.0241     0.0188     0.0294 <tibble [1 x 11]>
#> 4 m      1     doubling   28.7       23.6       36.8    <tibble [1 x 11]>

# glance_incidence output
incidence_purrr_tibble %>% 
  unnest(cols = c(strata_fit_glance))
#> # A tibble: 2 x 13
#> # Groups:   gender [2]
#>   gender strata_fit_tidy r.squared adj.r.squared sigma statistic  p.value
#>   <fct>  <list>              <dbl>         <dbl> <dbl>     <dbl>    <dbl>
#> 1 f      <tibble [2 x 5~     0.859         0.853 0.510     146.  1.05e-11
#> 2 m      <tibble [2 x 5~     0.795         0.786 0.657      89.0 2.26e- 9
#> # ... with 6 more variables: df <int>, logLik <dbl>, AIC <dbl>, BIC <dbl>,
#> #   deviance <dbl>, df.residual <int>

^{Created on 2020-06-12 by the reprex package (v0.3.0)}

PD.- this is a RECON workshop alumni, thanks for all you work!

[avallecam]

this content was added as a pull request. I will close the issue.