Visualize the results of the extrapolation method for estimating tau.

Usage

# S3 method for projoint_tau
plot(x, ...)

Arguments

x: An object of class projoint_tau derived from predict_tau
...: Optional arguments; currently none accepted

Examples

library(projoint)
library(stringr)

## Example 1: repeated, flipped task
data("exampleData1")
head(exampleData1)
#> # A tibble: 6 × 185
#>   ResponseId      choice1_repeated_fli…¹ choice1 choice2 choice3 choice4 choice5
#>   <chr>           <chr>                  <chr>   <chr>   <chr>   <chr>   <chr>  
#> 1 R_1M3TDihZzq9z… Community B            Commun… Commun… Commun… Commun… Commun…
#> 2 R_3HtXzkcSSlfi… Community B            Commun… Commun… Commun… Commun… Commun…
#> 3 R_yjYj0jtOY98X… Community B            Commun… Commun… Commun… Commun… Commun…
#> 4 R_1dKd05O6FTOV… Community B            Commun… Commun… Commun… Commun… Commun…
#> 5 R_1otDp642wWYl… Community A            Commun… Commun… Commun… Commun… Commun…
#> 6 R_2BnD3fuJMRKZ… Community A            Commun… Commun… Commun… Commun… Commun…
#> # ℹ abbreviated name: ¹choice1_repeated_flipped
#> # ℹ 178 more variables: choice6 <chr>, choice7 <chr>, choice8 <chr>,
#> #   race <chr>, party_1 <chr>, party_2 <chr>, party_3 <chr>, party_4 <chr>,
#> #   ideology <chr>, honesty <chr>, `K-1-1` <chr>, `K-1-1-1` <chr>,
#> #   `K-1-2` <chr>, `K-1-1-2` <chr>, `K-1-3` <chr>, `K-1-1-3` <chr>,
#> #   `K-1-4` <chr>, `K-1-1-4` <chr>, `K-1-5` <chr>, `K-1-1-5` <chr>,
#> #   `K-1-6` <chr>, `K-1-1-6` <chr>, `K-1-7` <chr>, `K-1-1-7` <chr>, …

outcomes <- paste0("choice", seq(from = 1, to = 8, by = 1))
outcomes <- c(outcomes, "choice1_repeated_flipped")
reshaped_data <- reshape_projoint(
  .dataframe = exampleData1, 
  .idvar = "ResponseId", 
  .outcomes = outcomes,
  .outcomes_ids = c("A", "B"),
  .alphabet = "K", 
  .repeated = TRUE,
  .flipped = TRUE)
  
tau1 <- predict_tau(reshaped_data)
plot(tau1)