pspforecast

Shellfish toxicity (PSP) forecast serving package

For the current 2026 Maine PSP predictions, click here

For past season results, check out this shiny application

Installation

remotes::install_github("BigelowLab/pspforecast")

Reading the forecast database

library(pspforecast)
fc = read_forecast()
glimpse(fc)

## Rows: 712
## Columns: 20
## $ version             <chr> "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v0.4.0", "v…
## $ ensemble_n          <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, …
## $ location            <chr> "PSP10.11", "PSP10.33", "PSP12.01", "PSP12.03", "PSP12.13", "PSP12.28", "PSP12.34", "PSP21.09", "PSP26.07", "PSP27.05", "PSP27.46", …
## $ date                <date> 2026-04-13, 2026-04-13, 2026-04-14, 2026-04-14, 2026-04-14, 2026-04-14, 2026-04-14, 2026-04-14, 2026-04-14, 2026-04-14, 2026-04-14,…
## $ species             <chr> "mytilus", "mytilus", "mytilus", "mytilus", "mytilus", "mytilus", "mytilus", "mytilus", "mytilus", "mytilus", "mytilus", "mytilus", …
## $ name                <chr> "Ogunquit River", "Spurwink River", "Basin Pt.", "Potts Pt.", "Lumbos Hole", "Bear Island", "Head Beach", "Bass Hbr.", "Cutler Hbr."…
## $ lat                 <dbl> 43.25030, 43.56632, 43.73848, 43.73064, 43.79553, 43.78556, 43.71711, 44.23824, 44.65701, 44.90545, 44.97084, 44.15419, 44.39319, 44…
## $ lon                 <dbl> -70.59540, -70.27305, -70.04343, -70.02556, -69.94557, -69.87415, -69.84999, -68.34792, -67.20525, -67.05621, -67.05254, -68.65947, …
## $ class_bins          <chr> "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,…
## $ forecast_start_date <date> 2026-04-17, 2026-04-17, 2026-04-18, 2026-04-18, 2026-04-18, 2026-04-18, 2026-04-18, 2026-04-18, 2026-04-18, 2026-04-18, 2026-04-18,…
## $ forecast_end_date   <date> 2026-04-23, 2026-04-23, 2026-04-24, 2026-04-24, 2026-04-24, 2026-04-24, 2026-04-24, 2026-04-24, 2026-04-24, 2026-04-24, 2026-04-24,…
## $ p_0                 <dbl> 99, 99, 98, 98, 94, 93, 94, 98, 81, 96, 81, 98, 98, 97, 99, 99, 99, 97, 96, 97, 89, 99, 98, 96, 95, 59, 30, 89, 96, 94, 95, 93, 96, …
## $ p_1                 <dbl> 1, 1, 2, 2, 6, 6, 6, 2, 16, 3, 16, 2, 2, 3, 1, 1, 1, 3, 4, 3, 9, 1, 2, 4, 5, 26, 36, 9, 4, 5, 5, 7, 4, 8, 10, 21, 12, 2, 3, 5, 3, 1,…
## $ p_2                 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 11, 24, 1, 0, 0, 0, 1, 0, 1, 2, 6, 3, 0, 0, 0, 0, 0, 0, 0…
## $ p_3                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 11, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0,…
## $ p3_sd               <dbl> 0.0012371945, 0.0023733521, 0.0024041385, 0.0041712376, 0.0669436602, 0.0467839217, 0.0317355271, 0.0031925299, 0.5697455237, 0.0215…
## $ p_3_min             <dbl> 0.0005414331, 0.0009854686, 0.0019742312, 0.0012217212, 0.0037948987, 0.0943924708, 0.0315538928, 0.0009424035, 0.1029437175, 0.0027…
## $ p_3_max             <dbl> 4.035514e-03, 7.677227e-03, 9.584654e-03, 1.410544e-02, 2.017008e-01, 2.584866e-01, 1.390330e-01, 1.091267e-02, 1.650765e+00, 5.6975…
## $ predicted_class     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ f_id                <chr> "PSP10.11_2026-04-13_mytilus", "PSP10.33_2026-04-13_mytilus", "PSP12.01_2026-04-14_mytilus", "PSP12.03_2026-04-14_mytilus", "PSP12.1…

Forecast 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

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 - (2TP)/(2TP-FP+FN)

Predictions evaluated:

## [1] 2634

Metrics (overall):

## # A tibble: 1 × 11
##      tp    fp    tn    fn cl_accuracy accuracy brier   f_1 precision sensitivity specificity
##   <int> <int> <int> <int>       <dbl>    <dbl> <dbl> <dbl>     <dbl>       <dbl>       <dbl>
## 1    69    65  2449    51       0.956    0.806 0.028 0.543     0.515       0.575       0.974

Individual seasons:

## # A tibble: 5 × 12
##    year    tp    fp    tn    fn cl_accuracy accuracy brier     f_1 precision sensitivity specificity
##   <int> <int> <int> <int> <int>       <dbl>    <dbl> <dbl>   <dbl>     <dbl>       <dbl>       <dbl>
## 1  2021     2     3   463     0       0.994    0.938 0.005   0.571     0.4         1           0.994
## 2  2022    16    20   603    12       0.951    0.799 0.03    0.5       0.444       0.571       0.968
## 3  2023     0     0   405     0       1        0.99  0     NaN       NaN         NaN           1    
## 4  2024     2     4   397     7       0.973    0.717 0.017   0.267     0.333       0.222       0.990
## 5  2025    49    38   581    32       0.9      0.671 0.066   0.583     0.563       0.605       0.939

2026 (current season) Results

Predictions evaluated

## [1] 630

Metrics

## # A tibble: 1 × 11
##      tp    fp    tn    fn cl_accuracy accuracy brier   f_1 precision sensitivity specificity
##   <int> <int> <int> <int>       <dbl>    <dbl> <dbl> <dbl>     <dbl>       <dbl>       <dbl>
## 1    13    10   596    11       0.967     0.61 0.023 0.553     0.565       0.542       0.983

Last Updated

## [1] "2026-09-22"

Requirements

• R v4+

• rlang

• dplyr

• readr

• tidyr

• httr

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