Input data

library(serosv)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(magrittr)

Input data format

Most *_model() functions in serosv require data argument as the input data to be fitted.

The package can handle both linelisting and aggregated data, and it infers the format from the column names of the input data frame. This means that input data is expected to follow a specific format.

For linelisting data: data must have age, pos and tot columns, where

  • age is the age vector

  • pos is the vector of counts of sero positives of that age group

  • tot is the vector is the total population of that age group

For aggregated data: data must have age, status columns, where

  • age is the age vector of individuals

  • status is the vector for the sero positivity of that individual

Example: Fitting linelisting and aggregated data using polynomial_model()

linelisting <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ]
aggregated <- hav_bg_1964

# View the 2 different data format
head(linelisting)
#>     age seropositive year gender parvouml
#> 1     1            0 2001      m        8
#> 2     1            0 2001      m        7
#> 3     1            0 2001      m        6
#> 406   1            0 2001      f       11
#> 407   1            0 2001      f        7
#> 408   1            0 2001      f        5
head(aggregated)
#>   age pos tot
#> 1   1   3  16
#> 2   2   3  15
#> 3   3   3  16
#> 4   4   4  13
#> 5   5   7  12
#> 6   6   4  15

# fit with aggregated data
model1 <- polynomial_model(aggregated, type = "Muench")
plot(model1)

# fit with linelisting data
model2 <- linelisting %>% 
  rename(status = seropositive) %>% 
  polynomial_model(type = "Muench")
plot(model2)

Data transformation

serosv also offers function transform_data() to convert from linelisting to aggregated data. For more information, refer to Data transformation

transform_data(
  linelisting$age, 
  linelisting$seropositive,
  heterogeneity_col = "age") %>% 
  polynomial_model(type = "Muench") %>% 
  plot()