pspforecast

Shellfish toxicity forecast serving package

Requirements

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: 313
## Columns: 19
## $ version             �[3m�[38;5;246m<chr>�[39m�[23m "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0", "v0.3.0",…
## $ ensemble_n          �[3m�[38;5;246m<dbl>�[39m�[23m 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, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…
## $ location            �[3m�[38;5;246m<chr>�[39m�[23m "PSP10.11", "PSP10.33", "PSP12.01", "PSP12.03", "PSP12.13", "PSP12.28", "PSP12.34", "PSP15.25", "PSP16.41", "PSP19.15", "PSP21.09", "PSP27.05", "PSP27.46", "PSP10.11", "PSP10.33", "PSP11.110", "PSP11.115", "PSP11.117", …
## $ date                �[3m�[38;5;246m<date>�[39m�[23m 2024-05-06, 2024-05-06, 2024-05-08, 2024-05-08, 2024-05-08, 2024-05-06, 2024-05-06, 2024-05-06, 2024-05-06, 2024-05-06, 2024-05-06, 2024-05-07, 2024-05-07, 2024-05-14, 2024-05-14, 2024-05-13, 2024-05-13, 2024-05-13, 20…
## $ name                �[3m�[38;5;246m<chr>�[39m�[23m "Ogunquit River", "Spurwink River", "Basin Pt.", "Potts Pt.", "Lumbos Hole", "Bear Island", "Head Beach", "Christmas Cove Town Landing", "Port Clyde", "Stonington", "Bass Hbr.", "Gove Pt.", "Gleason Cove", "Ogunquit Riv…
## $ lat                 �[3m�[38;5;246m<dbl>�[39m�[23m 43.25030, 43.56632, 43.73848, 43.73064, 43.79553, 43.78556, 43.71711, 43.84476, 43.92526, 44.15419, 44.23824, 44.90545, 44.97084, 43.25030, 43.56632, 43.72800, 43.73316, 43.71190, 43.73848, 43.73064, 43.74995, 43.81755,…
## $ lon                 �[3m�[38;5;246m<dbl>�[39m�[23m -70.59540, -70.27305, -70.04343, -70.02556, -69.94557, -69.87415, -69.84999, -69.55365, -69.25900, -68.65947, -68.34792, -67.05621, -67.05254, -70.59540, -70.27305, -70.09500, -70.16358, -70.18787, -70.04343, -70.02556,…
## $ class_bins          �[3m�[38;5;246m<chr>�[39m�[23m "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", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,80", "0,10,30,…
## $ forecast_start_date �[3m�[38;5;246m<date>�[39m�[23m 2024-05-10, 2024-05-10, 2024-05-12, 2024-05-12, 2024-05-12, 2024-05-10, 2024-05-10, 2024-05-10, 2024-05-10, 2024-05-10, 2024-05-10, 2024-05-11, 2024-05-11, 2024-05-18, 2024-05-18, 2024-05-17, 2024-05-17, 2024-05-17, 20…
## $ forecast_end_date   �[3m�[38;5;246m<date>�[39m�[23m 2024-05-16, 2024-05-16, 2024-05-18, 2024-05-18, 2024-05-18, 2024-05-16, 2024-05-16, 2024-05-16, 2024-05-16, 2024-05-16, 2024-05-16, 2024-05-17, 2024-05-17, 2024-05-24, 2024-05-24, 2024-05-23, 2024-05-23, 2024-05-23, 20…
## $ p_0                 �[3m�[38;5;246m<dbl>�[39m�[23m 93, 100, 100, 99, 31, 3, 95, 94, 95, 95, 100, 99, 100, 55, 91, 38, 39, 53, 98, 91, 69, 57, 37, 2, 93, 97, 66, 98, 100, 99, 98, 19, 6, 17, 75, 60, 86, 89, 63, 94, 46, 5, 61, 99, 83, 41, 93, 6, 26, 72, 96, 95, 95, 98, 89,…
## $ p_1                 �[3m�[38;5;246m<dbl>�[39m�[23m 6, 0, 0, 1, 44, 13, 4, 5, 4, 5, 0, 1, 0, 42, 9, 40, 46, 37, 2, 9, 26, 32, 36, 10, 6, 3, 28, 2, 0, 1, 2, 52, 29, 43, 21, 33, 13, 10, 30, 6, 42, 24, 31, 1, 15, 39, 7, 20, 39, 23, 4, 5, 5, 2, 10, 4, 2, 9, 9, 8, 15, 31, 11,…
## $ p_2                 �[3m�[38;5;246m<dbl>�[39m�[23m 1, 0, 0, 0, 18, 43, 0, 1, 0, 0, 0, 0, 0, 2, 0, 17, 12, 8, 0, 0, 4, 9, 21, 39, 0, 0, 5, 0, 0, 0, 0, 27, 44, 34, 3, 7, 1, 1, 6, 0, 11, 40, 7, 0, 1, 16, 0, 41, 26, 4, 0, 0, 0, 0, 1, 0, 0, 1, 38, 38, 46, 32, 1, 11, 5, 25, 0…
## $ p_3                 �[3m�[38;5;246m<dbl>�[39m�[23m 0, 0, 0, 0, 7, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 2, 0, 0, 1, 2, 6, 50, 0, 0, 1, 0, 0, 0, 0, 2, 21, 5, 1, 1, 0, 0, 1, 0, 1, 32, 1, 0, 0, 4, 0, 33, 10, 1, 0, 0, 0, 0, 0, 0, 0, 0, 52, 54, 38, 17, 0, 5, 1, 8, 0, 1, 1, 4,…
## $ p3_sd               �[3m�[38;5;246m<dbl>�[39m�[23m 2.537746e-02, 1.702311e-04, 5.835063e-07, 3.170006e-04, 2.573652e+00, 1.032039e+01, 6.801030e-03, 1.598628e-02, 8.719488e-03, 4.358502e-03, 1.378183e-06, 5.185523e-05, 5.231048e-07, 2.781944e-01, 2.931956e-02, 2.222504e…
## $ p_3_min             �[3m�[38;5;246m<dbl>�[39m�[23m 2.803591e-02, 1.613240e-06, 4.298889e-09, 3.494154e-05, 3.757856e+00, 2.148448e+01, 6.643038e-03, 1.782123e-02, 9.030735e-03, 2.118392e-03, 9.372612e-10, 3.377466e-06, 2.084870e-10, 1.508329e-02, 3.404170e-04, 2.190988e…
## $ p_3_max             �[3m�[38;5;246m<dbl>�[39m�[23m 1.114067e-01, 5.424280e-04, 1.839769e-06, 9.452227e-04, 1.157185e+01, 5.338209e+01, 3.128168e-02, 7.217547e-02, 3.986240e-02, 1.588983e-02, 4.431532e-06, 1.838427e-04, 1.674185e-06, 9.820972e-01, 9.217632e-02, 9.631202e…
## $ predicted_class     �[3m�[38;5;246m<dbl>�[39m�[23m 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ f_id                �[3m�[38;5;246m<chr>�[39m�[23m "PSP10.11_2024-05-06", "PSP10.33_2024-05-06", "PSP12.01_2024-05-08", "PSP12.03_2024-05-08", "PSP12.13_2024-05-08", "PSP12.28_2024-05-06", "PSP12.34_2024-05-06", "PSP15.25_2024-05-06", "PSP16.41_2024-05-06", "PSP19.15_20…

2024 Season Results

Metrics

Season Accuracy:

## # A tibble: 1 × 1
##   accuracy
##      <dbl>
## 1    0.711

Closure-level (Class 3) Predictions

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
precision - TP/(TP+FP)
sensitivity - TP/(TP+FN)
specificity - TN/(TN+FP)

## # A tibble: 1 × 8
##      tp    fp    tn    fn cl_accuracy precision sensitivity specificity
##   <int> <int> <int> <int>       <dbl>     <dbl>       <dbl>       <dbl>
## 1     2     4   251     6       0.962     0.333        0.25       0.984

2023 Season Results

predictions <- read_forecast(year = "2023")

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

Season Accuracy:

## # A tibble: 1 × 1
##   accuracy
##      <dbl>
## 1    0.993

Closure-level (Class 3) Predictions

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
precision - TP/(TP+FP)
sensitivity - TP/(TP+FN)
specificity - TN/(TN+FP)

## # A tibble: 1 × 8
##      tp    fp    tn    fn cl_accuracy precision sensitivity specificity
##   <int> <int> <int> <int>       <dbl>     <dbl>       <dbl>       <dbl>
## 1     0     0   554     0           1       NaN         NaN           1

2022 Season Results

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

Season Accuracy:

## # A tibble: 1 × 1
##   accuracy
##      <dbl>
## 1    0.799

Closure-level (Class 3) Predictions

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
precision - TP/(TP+FP)
sensitivity - TP/(TP+FN)
specificity - TN/(TN+FP)

## # A tibble: 1 × 8
##      tp    fp    tn    fn cl_accuracy precision sensitivity specificity
##   <int> <int> <int> <int>       <dbl>     <dbl>       <dbl>       <dbl>
## 1    16    20   603    12       0.951     0.444       0.571       0.968

Timing of initial closure-level predictions

2021 Season Results

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

Season Accuracy:

## # A tibble: 1 × 1
##   accuracy
##      <dbl>
## 1    0.938

Closure-level (Class 3) Predictions

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
precision - TP/(TP+FP)
sensitivity - TP/(TP+FN)
specificity - TN/(TN+FP)

## # A tibble: 1 × 8
##      tp    fp    tn    fn cl_accuracy precision sensitivity specificity
##   <int> <int> <int> <int>       <dbl>     <dbl>       <dbl>       <dbl>
## 1     2     3   463     0       0.994       0.4           1       0.994

Closure-level accuracy

Timing of initial closure-level predictions

Possible manuscript plot(s)

Last Updated

## [1] "2024-07-12"

Name		Name	Last commit message	Last commit date
Latest commit History 141 Commits
R		R
README_files/figure-gfm		README_files/figure-gfm
inst		inst
man		man
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.html		README.html
README.md		README.md

License

BigelowLab/pspforecast

Folders and files

Latest commit

History

Repository files navigation

pspforecast

Requirements

Installation

Reading the forecast database

Variables:

2024 Season Results

Metrics

Season Accuracy:

Closure-level (Class 3) Predictions

2023 Season Results

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

Season Accuracy:

Closure-level (Class 3) Predictions

2022 Season Results

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

Season Accuracy:

Closure-level (Class 3) Predictions

Timing of initial closure-level predictions

2021 Season Results

Confusion Matrix

Probability of Closure-level Toxicity vs Measured Toxicity

Metrics

Season Accuracy:

Closure-level (Class 3) Predictions

Closure-level accuracy

Timing of initial closure-level predictions

Possible manuscript plot(s)

Last Updated

About

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Contributors 2

Languages

Packages