pspforecast

Shellfish toxicity forecast serving package

Requirements

• R v4+

• rlang

• dplyr

• readr

• tidyr

• httr

Installation

remotes::install_github("BigelowLab/pspforecast")

Reading the forecast database

Variables:

• version - the version/configuration of the model used to make the prediction

• ensemble_n - number of ensemble members used to generate prediction

• location - the sampling station the forecast is for

• date - the date the forecast was made on

• name - site name

• lat - latitude

• lon - longitude

• class_bins - the bins used to classify shellfish total toxicity (i.e. 0: 0-10, 1: 10-30, 2: 30-80, 3: >80)

• forecast_date - the date the forecast is valid for (i.e. one week ahead of when it was made)

• predicted_class - the predicted classification at the location listed on the forecast_date (in this case 0-3)

• p_0 - class 0 probability

• p_1 - class 1 probability

• p_2 - class 2 probability

• p_3 - class 3 probability

• p3_sd - class 3 probability standard deviation

• p_3_min - class 3 minimum probability (from ensemble run)

• p_3_max - class 3 maximum probability (from ensemble run)

• predicted_class - the predicted classification

predictions <- read_forecast(year = "2024") |>
  distinct()

glimpse(predictions)

## Rows: 464
## Columns: 19
## $ version             <chr> "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", …
## $ ensemble_n          <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10…
## $ location            <chr> "PSP10.11", "PSP10.33", "PSP12.01", "PSP12.03", "P…
## $ date                <date> 2024-05-06, 2024-05-06, 2024-05-08, 2024-05-08, 2…
## $ name                <chr> "Ogunquit River", "Spurwink River", "Basin Pt.", "…
## $ lat                 <dbl> 43.25030, 43.56632, 43.73848, 43.73064, 43.79553, …
## $ lon                 <dbl> -70.59540, -70.27305, -70.04343, -70.02556, -69.94…
## $ class_bins          <chr> "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30…
## $ forecast_start_date <date> 2024-05-10, 2024-05-10, 2024-05-12, 2024-05-12, 2…
## $ forecast_end_date   <date> 2024-05-16, 2024-05-16, 2024-05-18, 2024-05-18, 2…
## $ p_0                 <dbl> 93, 100, 100, 99, 31, 3, 95, 94, 95, 95, 100, 99, …
## $ p_1                 <dbl> 6, 0, 0, 1, 44, 13, 4, 5, 4, 5, 0, 1, 0, 42, 9, 40…
## $ p_2                 <dbl> 1, 0, 0, 0, 18, 43, 0, 1, 0, 0, 0, 0, 0, 2, 0, 17,…
## $ p_3                 <dbl> 0, 0, 0, 0, 7, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 3…
## $ p3_sd               <dbl> 2.537746e-02, 1.702311e-04, 5.835063e-07, 3.170006…
## $ p_3_min             <dbl> 2.803591e-02, 1.613240e-06, 4.298889e-09, 3.494154…
## $ p_3_max             <dbl> 1.114067e-01, 5.424280e-04, 1.839769e-06, 9.452227…
## $ predicted_class     <dbl> 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,…
## $ f_id                <chr> "PSP10.11_2024-05-06", "PSP10.33_2024-05-06", "PSP…

2024 Season Results

Metrics

• tp - The model predicted class 3 and the following week’s measurement was class 3
• fp - The model predicted class 3 and the following week’s measurement was not class 3
• tn - The model predicted class 0,1,2 and the following week’s measurement was in class 0,1,2
• fn - The model predicted class 0,1,2 and the following week’s measurement was class 3
• accuracy - Measure of how many correct classifications were predicted
• cl_accuracy - Considering predictions are those that correctly predicted toxicity above or below the closure limit or not
• precision - TP/(TP+FP)
• sensitivity - TP/(TP+FN)
• specificity - TN/(TN+FP)
• f_1

## # A tibble: 1 × 10
##      tp    fp    tn    fn accuracy cl_accuracy   f_1 precision sensitivity
##   <int> <int> <int> <int>    <dbl>       <dbl> <dbl>     <dbl>       <dbl>
## 1     2     4   397     7    0.717       0.973 0.267     0.333       0.222
## # ℹ 1 more variable: specificity <dbl>

2023 Season Results

predictions <- read_forecast(year = "2023")

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

## # A tibble: 1 × 10
##      tp    fp    tn    fn accuracy cl_accuracy   f_1 precision sensitivity
##   <int> <int> <int> <int>    <dbl>       <dbl> <dbl>     <dbl>       <dbl>
## 1     0     0   550     0    0.993           1   NaN       NaN         NaN
## # ℹ 1 more variable: specificity <dbl>

2022 Season Results

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

## # A tibble: 1 × 10
##      tp    fp    tn    fn accuracy cl_accuracy   f_1 precision sensitivity
##   <int> <int> <int> <int>    <dbl>       <dbl> <dbl>     <dbl>       <dbl>
## 1    16    20   603    12    0.799       0.951   0.5     0.444       0.571
## # ℹ 1 more variable: specificity <dbl>

Timing of initial closure-level predictions

2021 Season Results

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

## # A tibble: 1 × 10
##      tp    fp    tn    fn accuracy cl_accuracy   f_1 precision sensitivity
##   <int> <int> <int> <int>    <dbl>       <dbl> <dbl>     <dbl>       <dbl>
## 1     2     3   463     0    0.938       0.994 0.571       0.4           1
## # ℹ 1 more variable: specificity <dbl>

Closure-level accuracy

Timing of initial closure-level predictions

Possible manuscript plot(s)

Last Updated

## [1] "2024-10-02"