R_Scripts/Run Predict Wrapper CLPR_Combo_1985__2024_Str_Wgt_Sex.R
In John-R-Wallace-NOAA/FishNIRS: NIRS on fish structures.
sourceFunctionURL <- function (URL, type = c("function", "script")[1]) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
if (!any(installed.packages()[, 1] %in% "httr")) install.packages("httr")
File.ASCII <- tempfile()
if(type == "function")
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(gsub(' ', '%20', URL))
if(type == "function") {
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
if(type == "script") {
fileName <- strsplit(URL, "/")[[1]]
fileName <- rev(fileName)[1]
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), fileName)
}
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/panel.xyplot.loess.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/agreementFigure.R")
# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Predict_NN_Age_Wrapper.R")
# source("C:/SIDT/Train_NN_Model/agreementFigure.R")
# source("C:/SIDT/Predict_NN_Ages/Predict_NN_Age.R")
source("C:/SIDT/Predict_NN_Ages/Predict_NN_Age_Wrapper.R")
# Spectra_Set needs to be in position '1' as in: assign("Spectra_Set", "Sable_Combo_2022", pos = 1)
# Spectra_Set <- "CLPR_Combo_1985__2024"
Spectra_Set <- "CLPR_SWFSC_1985__2024"
# =============================== NOT Bias Correction ========================================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
# !!! After moving C:\SIDT\Predict_NN_Ages\Predicted_Ages run "C:\SIDT\Chilipepper\Fit Von Bert & Double Reads\Add Double Reads to New Ages.R" for double read figures !!!
Estimate Std. Error t value Pr(>|t|)
Linf 48.7030996 0.156198260 311.80309 0
K 0.2037147 0.002702931 75.36808 0
t0 -0.2244905 0.003609395 62.19617 0
Males
Estimate Std. Error t value Pr(>|t|)
Linf 35.6674871 0.112558762 316.87881 0
K 0.3165704 0.005529543 57.25074 0
t0 -0.3335626 0.005954828 56.01549 0
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_Combo_1985__2024_Str_Wgt_Sex\\Predicted_Ages NO BIAS Corr\\NN Predicted Ages, 26 Mar 2025.RData")
Table(!New_Ages$Used_NN_Model)
New_Ages$Used_NN_Model[is.na(New_Ages$Used_NN_Model)] <- FALSE
save(New_Ages, file = "C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_Combo_1985__2024_Str_Wgt_Sex\\Predicted_Ages NO BIAS Corr\\NN Predicted Ages, 26 Mar 2025.RData")
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_Combo_1985__2024_Str_Wgt_Sex\\Predicted_Ages NO BIAS Corr\\NN Predicted Ages, 26 Mar 2025.RData")
Table(!New_Ages$Used_NN_Model)
change(New_Ages[New_Ages$Used_NN_Model, ])
Table(New_Ages$TMA, New_Ages$Pred_Age_Bias_Corr_plus_Delta_rounded)
sum(Table(New_Ages$Used_NN_Model))
Table(!is.na(New_Ages$TMA))
Table(New_Ages$Used_NN_Model, !is.na(New_Ages$TMA))
change(New_Ages[New_Ages$Used_NN_Model, ])
Spectra in NN Model 4,618 # Red circles in Metadata_Length_cm_vs_NN_Pred_Median.png and Metadata_structure_weight_g_vs_NN_Pred_Median.png
Spectra with TMA but not in the NN model 198 # Purple circles in the same figures
Spectra without TMA predicted by NN model 2,980 # Red circles in the same figures
Total Spectra 7,796
Percent predicted ages without TMA: 38.2% (100 * 2,980/7,796)
# =============================== Bias Correction ========================================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = c(12, 13:15), Bias_Reduction_Factor = 1.25, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.4386, k = 0.2064, t0 = 0.2800), M_vonBert = list(Linf = 35.6959, k = 0.3156, t0 = 0.3796),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
# !!! After moving C:\SIDT\Predict_NN_Ages\Predicted_Ages run "C:\SIDT\Chilipepper\Fit Von Bert & Double Reads\Add Double Reads to New Ages.R" for double read figures !!!
# ============ 2004 Triennial Only - NOT Bias Correction ====================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = FALSE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData", Corr_Calc = FALSE, # All TMA are missing so need Corr_Calc = FALSE
Model_Spectra_Meta_Path = "C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
# ============ With 2004 Triennial - Low scans only - NOT Bias Correction ====================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = FALSE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA_Low_Tri.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA_Low_Tri.RData", main = "Metadata: Str Wgt, Sex")
# ============ With 2004 Triennial - All Tri scans - NOT Bias Correction ====================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
# ----------- Look at high/low absorbance by tray
load("C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
plotly.Spec(Model_Spectra_Meta, N_Samp = 'All', colorGroup = 'Tray', htmlPlotFolder = paste0(Predicted_Ages_Path, '/Spectra Figure Triennial by Tray'), Debug = TRUE)
# metadata <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_2004_Triennial_Otolith_Weights_SWFSC.xlsx", sheet = 1, detectDates = TRUE) # Load in ancillary data
New_Ages <- match.f(New_Ages, Model_Spectra_Meta, 'specimen_id', 'specimen_id', 'Tray')
Table(New_Ages$Scan_Pos, New_Ages$Tray)
1 2 3 4 5 6 7 <NA>
High 41 15 79 69 0 0 0 146
Low 59 80 20 30 98 97 50 7650
# ------------ Predicted age by high/low absorbance by 2004 Triennial vs all other year/fishery groups -----------------
Table(New_Ages$Pred_Age_Bias_Corr_plus_Delta_rounded, New_Ages$Scan_Pos, New_Ages$Year %in% '2004_Triennial')
Other
Year/Fishery 2004_Triennial
Groups
Pred Absorbance # Absorbance above 1.05 = 'High'; Absorbance below or equal to 1.05 = 'Low'
Age High Low High Low
0 0 641 0 3 1
1 0 1300 1 103 180
2 0 661 2 6 21
3 0 331 3 7 5
4 0 337 4 6 18
5 0 488 5 32 64
6 0 522 6 33 71
7 0 524 7 7 23
8 0 485 8 2 7
9 0 503 9 2 12
10 0 483 10 0 5
11 1 431 11 0 7
12 4 299 12 1 7
13 7 200 13 1 3
14 9 127 14 0 1
15 13 110 15 0 1
16 12 72 16 1 2
17 7 37 17 0 3
18 7 33 18 0 2
19 13 21 19 0 0
20 11 8 20 0 0
21 18 16 21 0 1
22 21 16 22 0 0
23 17 4 23 0 0
24 5 1 24 0 0
25 1 0 25 0 0
# -------------------------
Ages seem fine, the first-derivative transformation appears to have taken care of the issue, see Fig. 2 here:
https://cdnsciencepub.com/doi/pdf/10.1139/cjfas-2023-0045
However, ....
# ============ With 2023-24 CA Rec - NOT Bias Correction 22 year/fishery groups ====================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
John-R-Wallace-NOAA/FishNIRS documentation built on April 12, 2025, 12:59 a.m.
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sourceFunctionURL <- function (URL, type = c("function", "script")[1]) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
if (!any(installed.packages()[, 1] %in% "httr")) install.packages("httr")
File.ASCII <- tempfile()
if(type == "function")
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(gsub(' ', '%20', URL))
if(type == "function") {
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
if(type == "script") {
fileName <- strsplit(URL, "/")[[1]]
fileName <- rev(fileName)[1]
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), fileName)
}
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/panel.xyplot.loess.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/agreementFigure.R")
# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Predict_NN_Age_Wrapper.R")
# source("C:/SIDT/Train_NN_Model/agreementFigure.R")
# source("C:/SIDT/Predict_NN_Ages/Predict_NN_Age.R")
source("C:/SIDT/Predict_NN_Ages/Predict_NN_Age_Wrapper.R")
# Spectra_Set needs to be in position '1' as in: assign("Spectra_Set", "Sable_Combo_2022", pos = 1)
# Spectra_Set <- "CLPR_Combo_1985__2024"
Spectra_Set <- "CLPR_SWFSC_1985__2024"
# =============================== NOT Bias Correction ========================================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
# !!! After moving C:\SIDT\Predict_NN_Ages\Predicted_Ages run "C:\SIDT\Chilipepper\Fit Von Bert & Double Reads\Add Double Reads to New Ages.R" for double read figures !!!
Estimate Std. Error t value Pr(>|t|)
Linf 48.7030996 0.156198260 311.80309 0
K 0.2037147 0.002702931 75.36808 0
t0 -0.2244905 0.003609395 62.19617 0
Males
Estimate Std. Error t value Pr(>|t|)
Linf 35.6674871 0.112558762 316.87881 0
K 0.3165704 0.005529543 57.25074 0
t0 -0.3335626 0.005954828 56.01549 0
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_Combo_1985__2024_Str_Wgt_Sex\\Predicted_Ages NO BIAS Corr\\NN Predicted Ages, 26 Mar 2025.RData")
Table(!New_Ages$Used_NN_Model)
New_Ages$Used_NN_Model[is.na(New_Ages$Used_NN_Model)] <- FALSE
save(New_Ages, file = "C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_Combo_1985__2024_Str_Wgt_Sex\\Predicted_Ages NO BIAS Corr\\NN Predicted Ages, 26 Mar 2025.RData")
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_Combo_1985__2024_Str_Wgt_Sex\\Predicted_Ages NO BIAS Corr\\NN Predicted Ages, 26 Mar 2025.RData")
Table(!New_Ages$Used_NN_Model)
change(New_Ages[New_Ages$Used_NN_Model, ])
Table(New_Ages$TMA, New_Ages$Pred_Age_Bias_Corr_plus_Delta_rounded)
sum(Table(New_Ages$Used_NN_Model))
Table(!is.na(New_Ages$TMA))
Table(New_Ages$Used_NN_Model, !is.na(New_Ages$TMA))
change(New_Ages[New_Ages$Used_NN_Model, ])
Spectra in NN Model 4,618 # Red circles in Metadata_Length_cm_vs_NN_Pred_Median.png and Metadata_structure_weight_g_vs_NN_Pred_Median.png
Spectra with TMA but not in the NN model 198 # Purple circles in the same figures
Spectra without TMA predicted by NN model 2,980 # Red circles in the same figures
Total Spectra 7,796
Percent predicted ages without TMA: 38.2% (100 * 2,980/7,796)
# =============================== Bias Correction ========================================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = c(12, 13:15), Bias_Reduction_Factor = 1.25, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.4386, k = 0.2064, t0 = 0.2800), M_vonBert = list(Linf = 35.6959, k = 0.3156, t0 = 0.3796),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
# !!! After moving C:\SIDT\Predict_NN_Ages\Predicted_Ages run "C:\SIDT\Chilipepper\Fit Von Bert & Double Reads\Add Double Reads to New Ages.R" for double read figures !!!
# ============ 2004 Triennial Only - NOT Bias Correction ====================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = FALSE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData", Corr_Calc = FALSE, # All TMA are missing so need Corr_Calc = FALSE
Model_Spectra_Meta_Path = "C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
# ============ With 2004 Triennial - Low scans only - NOT Bias Correction ====================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = FALSE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA_Low_Tri.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA_Low_Tri.RData", main = "Metadata: Str Wgt, Sex")
# ============ With 2004 Triennial - All Tri scans - NOT Bias Correction ====================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
# ----------- Look at high/low absorbance by tray
load("C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
plotly.Spec(Model_Spectra_Meta, N_Samp = 'All', colorGroup = 'Tray', htmlPlotFolder = paste0(Predicted_Ages_Path, '/Spectra Figure Triennial by Tray'), Debug = TRUE)
# metadata <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_2004_Triennial_Otolith_Weights_SWFSC.xlsx", sheet = 1, detectDates = TRUE) # Load in ancillary data
New_Ages <- match.f(New_Ages, Model_Spectra_Meta, 'specimen_id', 'specimen_id', 'Tray')
Table(New_Ages$Scan_Pos, New_Ages$Tray)
1 2 3 4 5 6 7 <NA>
High 41 15 79 69 0 0 0 146
Low 59 80 20 30 98 97 50 7650
# ------------ Predicted age by high/low absorbance by 2004 Triennial vs all other year/fishery groups -----------------
Table(New_Ages$Pred_Age_Bias_Corr_plus_Delta_rounded, New_Ages$Scan_Pos, New_Ages$Year %in% '2004_Triennial')
Other
Year/Fishery 2004_Triennial
Groups
Pred Absorbance # Absorbance above 1.05 = 'High'; Absorbance below or equal to 1.05 = 'Low'
Age High Low High Low
0 0 641 0 3 1
1 0 1300 1 103 180
2 0 661 2 6 21
3 0 331 3 7 5
4 0 337 4 6 18
5 0 488 5 32 64
6 0 522 6 33 71
7 0 524 7 7 23
8 0 485 8 2 7
9 0 503 9 2 12
10 0 483 10 0 5
11 1 431 11 0 7
12 4 299 12 1 7
13 7 200 13 1 3
14 9 127 14 0 1
15 13 110 15 0 1
16 12 72 16 1 2
17 7 37 17 0 3
18 7 33 18 0 2
19 13 21 19 0 0
20 11 8 20 0 0
21 18 16 21 0 1
22 21 16 22 0 0
23 17 4 23 0 0
24 5 1 24 0 0
25 1 0 25 0 0
# -------------------------
Ages seem fine, the first-derivative transformation appears to have taken care of the issue, see Fig. 2 here:
https://cdnsciencepub.com/doi/pdf/10.1139/cjfas-2023-0045
However, ....
# ============ With 2023-24 CA Rec - NOT Bias Correction 22 year/fishery groups ====================
# CLPR_Combo_1985__2024_CA_OR_Comm with Str_Wgt and Sex !!! No bias correction !!!
Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_Combo_1985__2024_Str_Wgt_Sex", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE,
Bias_Adj_Factor_Ages = NULL, Bias_Reduction_Factor = NULL, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
# Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"),
Metadata_Extra = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), Meta_Data_Factors = c("Sex", "Month"), scanUniqueName = 'filenames', Debug_plotly.Spec = TRUE,
Graph_Metadata = c("structure_weight_g", "Length_cm", "Sex", "Month", "Latitude_dd"), F_vonBert = list(Linf = 48.7031, k = 0.2037, t0 = 0.2245), M_vonBert = list(Linf = 35.6675, k = 0.3166, t0 = 0.3336),
Metadata_Extra_File = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData",
Model_Spectra_Meta_Path = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData", main = "Metadata: Str Wgt, Sex")
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