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ui.r
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require(shiny)
require(shinyjs)
require(shinyWidgets)
require(shinyFiles)
require(shinyBS)
linebreaks <- function(n){HTML(strrep(br(), n))}
ui<-function(request){
shinyUI(fluidPage(theme = "bootstrap.css",
useShinyjs(),
titlePanel("Welcome to the Stock Assessment Continuum Tool, powered by Stock Synthesis"),
h4(p(strong("(Formerly known as the Stock Synthesis Data-Limited (SS-DL) tool), this tool uses the",tags$a(href="https://github.com/nmfs-stock-synthesis/stock-synthesis", "Stock Synthesis"),"framework to implement a ",tags$a(href="javascript:window.open('SS-DL-approaches.html', '_blank','width=600,height=400')", "variety of types"), "of models."))),
h5(p("Any suggested changes or requests? Please submit an issue with the recommendation" ,tags$a(href="https://github.com/shcaba/SS-DL-tool/issues", "here"))),
br(),
sidebarLayout(
sidebarPanel(
style = "position:fixed;width:30%;height: 90vh; overflow-y: scroll;",
shinyjs::hidden(wellPanel(id="Data_panel",
h4(strong("Choose data file")),
fluidRow(column(width=12,fileInput('file2', 'Catch time series',
accept = c(
'text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv'
)
))),
fluidRow(column(width=12,fileInput('file1', 'Length composition',
accept = c(
'text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv'
)
))),
fluidRow(column(width=12,fileInput('file3', 'Age composition',
accept = c(
'text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv'
)
))),
#Mute for now, pull back in when index methods are ready
fileInput('file4', 'Abundance index',
accept = c(
'text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv'
)
),
h4(strong("Clear data files")),
fluidRow(column(width=3,actionButton("reset_ct", "Catches")),
column(width=3,actionButton("reset_lt", "Length")),
column(width=3,actionButton("reset_age", "Ages")),
column(width=3,actionButton("reset_index", "Index"))),
)
),
shinyjs::hidden(wellPanel(id="Existing_files",
fluidRow(column(width=10,checkboxInput("user_model","Use existing model files?",FALSE))),
h5(em("Do not use this option with catch only models.")),
h5(em("Make sure the model run is in the Scenarios folder. Put that folder name in the Scenario name input and run the model.")),
h5(em("Using an existing model allows you to do run complex and custom model runs outside the options of the SS-DL tool, but still use some of the quick features.")),
h5(em("Examples are applying jitters or any of the additional SS options that do not modify the data or control files.")),
h5(em("To do sensitivity runs for pre-existing models, make a copy of the folder and re-name it, then make desired changes in the data and/or control files.")),
)
),
shinyjs::hidden(wellPanel(id="panel_Ct_F_LO",
h4(strong("Use constant catch or estimate fishing mortality directly?")),
h5(em("Using constant catch assumes the same catch in all years in order to fit the length composition data (similar to LBSPR, but the model integrates the fit of each year, not each year separately)")),
h5(em("It provides a long-term average response (F) to estimating stock status, and thus useful with non-continous years of sampling.")),
h5(em("Fishing rate can also be directly estimated from the length composition with no assumption in catches (similar to the LIME approach).")),
h5(em("This approach is a more variable reponse to estimating stock status, and is best used with continuous years of contemporary data. Recruitment can also be estimated.")),
h5(em("Recruitment can also be estimated in both approaches.")),
fluidRow(column(width=10,selectInput("Ct_F_LO_select","Approach",c("Choose an option","Constant Catch","Estimate F")))),
)
),
shinyjs::hidden(wellPanel(id="panel_ct_wt_LO",
h4(strong("Weight fleet lengths by relative catch")),
h5(em("The relative catch contribution needs specification with multiple length-only fleets")),
h5(em("Example: Two fleets, with fleet 2 catching 2 times the amount as fleet 1, the entry would be 1,2.")),
h5(em("Each entry will be relative to the highest value.")),
fluidRow(column(width=10,textInput("Wt_fleet_Ct","Relative catch values",value=""))),
)
),
shinyjs::hidden(wellPanel(id="panel_data_wt_lt",
h4(strong("Data-weighting")),
h5(em("Data weighting balances information content of biological data with model structure")),
h5(em("Data weighting balances across factors (e.g, fleets, sex, etc.)")),
h5(em("The default value is equally weighting among fleets based on input effective sample size inputs")),
h5(em("If using an existing model, chose 'None' to maintain the same weighting as the existing model")),
# fluidRow(column(width=10,prettyCheckbox(
# inputId = "dirichlet",
# label = "Use Dirichlet multinomial weighting?",
# value=FALSE,
# shape="curve",
# icon = icon("check"),
# animation="smooth"),
# bigger=TRUE),
# fill=TRUE),
awesomeRadio(
inputId = "Data_wt",
label = "Choose data-weighting option",
choices = c("None","Dirichlet-multinomial", "Francis", "McAllister-Ianelli"),
selected = "None",
status = "warning"
)
# fluidRow(column(width=6, prettyCheckbox(
# inputId = "dirichlet",
# label = "Use Dirichlet weighting?",
# shape = "round",
# outline = TRUE,
# status = "info"))),
# fluidRow(column(width=6, prettyCheckbox(
# inputId = "Francis_wt",
# label = "Use Francis weighting?",
# shape = "round",
# outline = TRUE,
# status = "info"))),
# fluidRow(column(width=6, prettyCheckbox(
# inputId = "MI_wt",
# label = "Use McAllister and Ianelli?",
# shape = "round",
# outline = TRUE,
# status = "info"))),
# h5(em("After the first run, you can check the Francis or harmonic mean methods for suggested weightings")),
# fluidRow(column(width=6,textInput("Lt_datawts", "Lengths weights by fleet", value=""))),
)
),
shinyjs::hidden(wellPanel(id="panel_SSLO_LH",
h4(strong("Life history inputs")),
wellPanel(id="panel_SSLO_fixed",
h4(strong(em("Female"))),
fluidRow(column(width=6,numericInput("M_f", "Natural mortality", value=NA,min=0, max=10000, step=0.00001))),
fluidRow(column(width=6,numericInput("Linf_f", "Asymptotic size (Linf)", value=NA,min=0, max=10000, step=0.001)),
column(width=6,numericInput("k_f","Growth coefficient k", value=NA,min=0, max=10000, step=0.00001))),
fluidRow(column(width=6,numericInput("t0_f","Age at length 0 (t0)", value=NA,min=-100, max=10000, step=0.001)),
column(width=6,textInput("CV_lt_f","CV at length (young then old)", value="0.1,0.1"))),
fluidRow(column(width=6,numericInput("L50_f", "Length at 50% maturity", value=NA,min=0, max=10000, step=0.001)),
column(width=6,numericInput("L95_f","Length at 95% maturity", value=NA,min=0, max=10000, step=0.001))),
#h5("Fecundity-length relationship F=aL^b. Set equal to the W-L relationship to get fecundity equivalent to spawning biomass"),
#fluidRow(column(width=6,numericInput("Fec_a_f", "Coefficient a", value=0.00001,min=0, max=10000, step=-0.01)),
# column(width=6,numericInput("Fec_b_f","Exponent b", value=3,min=0, max=10000, step=0.01))),
# fluidRow(column(width=6,numericInput("WLa_f", "Weight-Length alpha", value=0.00001,min=0, max=10000, step=0.000000001)),
# column(width=6,numericInput("WLb_f","Weight-length beta", value=3,min=0, max=10000, step=0.01))),
),
h4(strong(em("Male"))),
h5("Enter male specific values. Otherwise, males are assumed equal to females"),
h5("If estimating any female life history parameters and you want males to equal the females estimated values, use the offset option and pre-specify the male parameter(s) to 0 to ensure males = females. If you don't do this, the males values will stay at the female starting values."),
fluidRow(column(width=6,div(checkboxInput("male_parms","Males specific values?",FALSE),style = "font-size: 16px !important;")),
column(width=6,checkboxInput("male_offset","Males offset from females (log(m/f))?",FALSE))),
#tags$style("#male_parms {font-size: 16px !important;}"),
# fluidRow(column(width=10,prettyCheckbox("male_parms","Males specific values?",
# value=FALSE,
# shape="curve",
# icon = icon("check"),
# animation="smooth"),
# bigger=TRUE),
# fill=TRUE,
# status="default"),
#fluidRow(column(width=7, h3("Males specific values?")),column(width=2,checkboxInput("male_parms","Males specific values?",FALSE,width="150%"))),
wellPanel(
#uiOutput("Male_parms_inputs_label"),
uiOutput("Male_parms_inputs1"),
uiOutput("Male_parms_inputs2"),
uiOutput("Male_parms_inputs3")
# uiOutput("Male_parms_inputs4")
),
)
),
shinyjs::hidden(wellPanel(id="panel_SS_LH_fixed_est_tog",
fluidRow(column(width=10,switchInput("est_parms","Estimate parameters?",
value=FALSE,
onLabel = "YES",
offLabel = "NO",
onStatus = "success",
offStatus = "danger"))),
h5("Parameters can either be pre-specified (i.e., set to specific value) or estimated."),
h5("Estimating parameters is a data-based approach to determining life history values"),
h5("Estimating parameters can also propagate parameter uncertainty into derived model outputs"),
br(),
h5("When estimating parameters with catch and length (SS-CL) models consider:"),
tags$ul(tags$li(h5(p("It is recommended to run the model first by pre-specifying parameters.")))),
tags$ul(tags$li(h5(p("Then run likelihood profiles to see if the data contain any information on parameters.")))),
tags$ul(tags$li(h5(p("Parameters that seem informed by the data (i.e., result in realistic values) are good candidates for estimation.")))),
tags$ul(tags$li(h5(p("The most likely parameters to have information from fishery-based lengths are Linf and M.")))),
h5("Not all parameters need be estimated. Fix parameters by turning the phase negative (e.g., -1)."),
h5("The concept of phasing allows the user to say when to start parameter estimate, thus spreading out parameter estimation instead of doing all estimation at once. For example, parameters in phase 1 will begin estimation immediately. Parameters in phase 2 will stay at their input value until phase 1 estimation is complete. All parameters in any earlier phases continue to be re-estimated in subsequent phases. Have questions on what phase to put parameters? Click", tags$a(href="javascript:window.open('Phasing_AEP.pdf', '_blank','width=600,height=400')", "here"), "for some suggestions."),
h5(p("Natural mortality is an often difficult value to obtain. Consider using",tags$a(href="https://connect.fisheries.noaa.gov/natural-mortality-tool/", "The Natural Mortality Tool"), " (click ctl+left on the link to open the tool) to either obtain natural mortality values or developing a prior for use in estimating natural mortality. The Github repository for it can be found",tags$a(href="https://github.com/shcaba/Natural-Mortality-Tool", "here")," (click ctl+left on the link to open the repo).")),
br(),
h5("Load life history values instead of inputting them?"),
uiOutput("LH_load_file"),
)),
shinyjs::hidden(wellPanel(id="panel_SS_LH_fixed",
h4(strong("Life history inputs")),
wellPanel(id="panel_SS_fixed",
h4(strong(em("Female"))),
fluidRow(column(width=6,numericInput("M_f_fix", "Natural mortality", value=NA,min=0, max=10000, step=0.00001))),
fluidRow(column(width=6,numericInput("Linf_f_fix", "Asymptotic size (Linf)", value=NA,min=0, max=10000, step=0.001)),
column(width=6,numericInput("k_f_fix","Growth coefficient k", value=NA,min=0, max=10000, step=0.00001))),
fluidRow(column(width=6,numericInput("t0_f_fix","Age at length 0 (t0)", value=NA,min=0, max=10000, step=0.001)),
column(width=6,textInput("CV_lt_f_fix","CV at length (young then old)", value="0.1,0.1"))),
fluidRow(column(width=6,numericInput("L50_f_fix", "Length at 50% maturity", value=NA,min=0, max=10000, step=0.001)),
column(width=6,numericInput("L95_f_fix","Length at 95% maturity", value=NA,min=0, max=10000, step=0.001))),
h5("Length-weight relationship W=aL^b. Weight is in kg and length in cm."),
fluidRow(column(width=6,numericInput("WLa_f_fix", "a in W=aL^b", value=0.00001,min=0, max=10000, step=0.000000001)),
column(width=6,numericInput("WLb_f_fix","b in W=aL^b", value=3,min=0, max=10000, step=0.0001))),
h5("Length-fecundity relationship F=aL^b. Fecundity is measured in number of eggs or pups. Set equal to the length-weight relationship to get fecundity equivalent to spawning biomass."),
fluidRow(column(width=6,numericInput("Fec_a_f_fix", "a in F=aL^b", value=0.00001,min=0, max=10000, step=0.000000001)),
column(width=6,numericInput("Fec_b_f_fix","b in F=aL^b", value=3,min=0, max=10000, step=0.0001))),
),
h4(strong(em("Male"))),
h5("Enter male specific values. Otherwise, males are assumed equal to females"),
h5(em("If estimating females and wanting to match males to estimated female parameters, use the male offset option and set offsets to 0. This will ensure male values are equal to estimated female males. Otherwise, males will retain the initial female values.")),
fluidRow(column(width=6,checkboxInput("male_parms_fix","Males specific values?",FALSE)),
column(width=6,checkboxInput("male_offset_fix","Males offset from females (log(m/f)?",FALSE))),
#fluidRow(column(width=7, h3("Males specific values?")),column(width=2,checkboxInput("male_parms","Males specific values?",FALSE,width="150%"))),
wellPanel(
# uiOutput("Male_parms_inputs_label_fix"),
uiOutput("Male_parms_inputs1_fix"),
uiOutput("Male_parms_inputs2_fix"),
uiOutput("Male_parms_inputs3_fix"),
uiOutput("Male_parms_inputs4_fix")
),
)
),
shinyjs::hidden(wellPanel(id="panel_SS_LH_est",
h4(strong("Life history inputs")),
# fluidRow(column(width=10,switchInput("est_parms2","Estimate parameters?",value=TRUE))),
wellPanel(id="panel_SS_est",
h4(strong(em("Female"))),
dropdownButton(
selectInput("M_f_prior","Prior type for M",c("no prior","symmetric beta", "beta","lognormal","gamma","normal")),
numericInput("M_f_mean", "Mean", value=NA,min=0, max=10000, step=0.00001),
numericInput("M_f_SD", "SD", value=0,min=0, max=10000, step=0.00001),
numericInput("M_f_phase", "Phase", value=-1,min=-999, max=10, step=1),
circle = FALSE, right=TRUE, status = "danger", icon = icon("skull-crossbones"), width = "300px",label="Natural mortality"
),
br(),
h5(strong("Growth")),
dropdownButton(
selectInput("Linf_f_prior","Prior type for Linf",c("no prior","symmetric beta", "beta","lognormal","gamma","normal")),
numericInput("Linf_f_mean", "Mean", value=NA,min=0, max=10000, step=0.001),
numericInput("Linf_f_SD", "SD", value=0,min=0, max=10000, step=0.0001),
numericInput("Linf_f_phase", "Phase", value=-1,min=-999, max=10, step=1),
circle = FALSE, right=TRUE, status = "danger", icon = icon("infinity"), width = "300px",label="Linf: Asymptotic size"
),
br(),
dropdownButton(
selectInput("k_f_prior","Prior type for k",c("no prior","symmetric beta", "beta","lognormal","gamma","normal")),
numericInput("k_f_mean", "Mean", value=NA,min=0, max=10000, step=0.00001),
numericInput("k_f_SD", "SD", value=0,min=0, max=10000, step=0.00001),
numericInput("k_f_phase", "Phase", value=-1,min=-999, max=10, step=1),
circle = FALSE, right=TRUE, status = "danger", icon = icon("ruler-horizontal"), width = "300px",label="k: VB growth coefficient"
),
br(),
dropdownButton(
selectInput("t0_f_prior","Prior type for t0",c("no prior","symmetric beta", "beta","lognormal","gamma","normal")),
numericInput("t0_f_mean", "Mean", value=NA,min=-100, max=10000, step=0.001),
numericInput("t0_f_SD", "SD", value=0,min=0, max=10000, step=0.001),
numericInput("t0_f_phase", "Phase", value=-1,min=-999, max=10, step=1),
circle = FALSE, right=TRUE, status = "danger", icon = icon("baby-carriage"), width = "300px",label="t0: Age at size 0"
),
br(),
dropdownButton(
selectInput("CV_lt_f_young_prior","Prior type for CV_lt young",c("no prior","symmetric beta", "beta","lognormal","gamma","normal")),
numericInput("CV_lt_f_young_mean", "Mean", value=0.1,min=0, max=10000, step=0.0001),
numericInput("CV_lt_f_young_SD", "SD", value=0,min=0, max=10000, step=0.0001),
numericInput("CV_lt_f_young_phase", "Phase", value=-1,min=-999, max=10, step=1),
circle = FALSE, right=TRUE, status = "danger", icon = icon("dice"), width = "300px",label="CV at length (young)"
),
br(),
dropdownButton(
selectInput("CV_lt_f_old_prior","Prior type for CV_lt old",c("no prior","symmetric beta", "beta","lognormal","gamma","normal")),
numericInput("CV_lt_f_old_mean", "Mean", value=0.1,min=0, max=10000, step=0.0001),
numericInput("CV_lt_f_old_SD", "SD", value=0,min=0, max=10000, step=0.0001),
numericInput("CV_lt_f_old_phase", "Phase", value=-1,min=-999, max=10, step=1),
circle = FALSE, right=TRUE, status = "danger", icon = icon("dice"), width = "300px",label="CV at length (old)"
),
h4(strong("Maturity and weight-length relationships")),
fluidRow(column(width=6,numericInput("L50_f_est", "Length at 50% maturity", value=NA,min=0, max=10000, step=0.001)),
column(width=6,numericInput("L95_f_est","Length at 95% maturity", value=NA,min=0, max=10000, step=0.001))),
h5("Length-weight relationship W=aL^b. Weight is in kg and length in cm."),
fluidRow(column(width=6,numericInput("WLa_f_est", "a in W=aL^b", value=0.00001,min=0, max=10000, step=0.000000001)),
column(width=6,numericInput("WLb_f_est","b in W=aL^b", value=3,min=0, max=10000, step=0.001))),
h5("Length-fecundity relationship F=aL^b. Fecundity is measured in number of eggs or pups. Set equal to the length-weight relationship to get fecundity equivalent to spawning biomass."),
fluidRow(column(width=6,numericInput("Fec_a_f_est", "a in F=aL^b", value=0.00001,min=0, max=10000, step=0.000000001)),
column(width=6,numericInput("Fec_b_f_est","b in F=aL^b", value=3,min=0, max=10000, step=0.001))),
),
h4(strong(em("Male"))),
h5("Enter male specific values. Otherwise, males are assumed equal to females"),
h5(em("If estimating females and wanting to match males to estimated female parameters, use the male offset option and set offsets to 0. This will ensure male values are equal to estimated female males. Otherwise, males will retain the initial female values.")),
fluidRow(column(width=6,checkboxInput("male_parms_est","Males specific values?",FALSE)),
column(width=6,checkboxInput("male_offset_est","Males offset from females (log(m/f))?",FALSE))),
#fluidRow(column(width=7, h3("Males specific values?")),column(width=2,checkboxInput("male_parms","Males specific values?",FALSE,width="150%"))),
wellPanel(
# uiOutput("Male_parms_inputs_label_est"),
uiOutput("Male_parms_inputs_M_est"),
uiOutput("Male_parms_inputs_space1"),
uiOutput("Male_parms_inputs_Growth_label"),
uiOutput("Male_parms_inputs_Linf_est"),
uiOutput("Male_parms_inputs_space2"),
uiOutput("Male_parms_inputs_k_est"),
uiOutput("Male_parms_inputs_space3"),
uiOutput("Male_parms_inputs_t0_est"),
uiOutput("Male_parms_inputs_space4"),
uiOutput("Male_parms_inputs_CV_est_young"),
br(),
uiOutput("Male_parms_inputs_CV_est_old"),
uiOutput("Male_parms_inputs_space5"),
uiOutput("Male_parms_inputs_WL_est")
),
)
),
# shinyjs::hidden(wellPanel(id="panel_SS_est",
# h3("Life history inputs"),
# fluidRow(column(width=10,switchInput("est_parms2","Fix parameters?"))),
# wellPanel(
# h4(em("Female")),
# fluidRow(column(width=6,numericInput("Nages","Max. age", value=NA,min=1, max=1000, step=1))),
# h5(strong("Natural mortality")),
# fluidRow(column(width=4,style='padding:1px;',align="center", selectInput("M_prior","Prior type",c("lognormal","truncated normal","uniform","beta"))),
# column(width=3,style='padding:2px;',align="center",numericInput("M_f_mean", "Mean", value=NA,min=0, max=10000, step=0.001)),
# column(width=3,style='padding:2px;',align="center",numericInput("M_f_SD", "SD", value=0,min=0, max=10000, step=0.001)),
# column(width=2,style='padding:2px;',align="center",numericInput("M_f_phase", "Phase", value=-1,min=-999, max=10, step=0.001))),
# h5(strong("Growth")),
# h5(strong("Linf")),
# fluidRow(column(width=4,style='padding:1px;',align="center",selectInput("Linf_f_prior","Prior type",c("lognormal","truncated normal","uniform","beta"))),
# column(width=3,style='padding:2px;',align="center",numericInput("Linf_f_mean", "Mean", value=NA,min=0, max=10000, step=0.001)),
# column(width=3,style='padding:2px;',align="center",numericInput("Linf_f_SD", "SD", value=0,min=0, max=10000, step=0.001)),
# column(width=2,style='padding:2px;',align="center",numericInput("Linf_f_phase", "Phase", value=-1,min=-999, max=10, step=0.001))),
# h5(strong("k")),
# fluidRow(column(width=4,style='padding:2px;',selectInput("k_f_prior","Prior type",c("lognormal","truncated normal","uniform","beta"))),
# column(width=3,style='padding:2px;',numericInput("k_f_mean", "Mean", value=NA,min=0, max=10000, step=0.001)),
# column(width=3,style='padding:2px;',numericInput("k_f_SD", "SD", value=0,min=0, max=10000, step=0.001)),
# column(width=2,style='padding:2px;',align="center",numericInput("k_f_phase", "Phase", value=-1,min=-999, max=10, step=0.001))),
# h5(strong("t0")),
# fluidRow(column(width=4,style='padding:2px;',selectInput("t0_f_prior","Prior type",c("lognormal","truncated normal","uniform","beta"))),
# column(width=3,style='padding:2px;',numericInput("t0_f_mean", "Mean", value=NA,min=0, max=10000, step=0.001)),
# column(width=3,style='padding:2px;',numericInput("t0_f_SD", "SD", value=0,min=0, max=10000, step=0.001)),
# column(width=2,style='padding:2px;',align="center",numericInput("t0_f_phase", "Phase", value=-1,min=-999, max=10, step=0.001))),
# h5(strong("Length CV")),
# fluidRow(column(width=4,style='padding:2px;',selectInput("CV_f_prior","Prior type",c("lognormal","truncated normal","uniform","beta"))),
# column(width=3,style='padding:2px;',numericInput("CV_f_mean", "Mean", value=0.1,min=0, max=10000, step=0.001)),
# column(width=3,style='padding:2px;',numericInput("CV_f_SD", "SD", value=0,min=0, max=10000, step=0.001)),
# column(width=2,style='padding:2px;',align="center",numericInput("CV_f_phase", "Phase", value=-1,min=-999, max=10, step=0.001))),
# h5(strong("Maturity and weight-length relationships")),
# fluidRow(column(width=6,numericInput("L50_f", "Length at 50% maturity", value=NA,min=0, max=10000, step=0.01)),
# column(width=6,numericInput("L95_f","Length at 95% maturity", value=NA,min=0, max=10000, step=0.01))),
# fluidRow(column(width=6,numericInput("Fec_a", "Wt-based fec coeff", value=1,min=0, max=10000, step=-0.01)),
# column(width=6,numericInput("Fec_b","Wt-based fec exp", value=1,min=0, max=10000, step=0.01))),
# fluidRow(column(width=6,numericInput("WLa_f", "Weight-Length alpha", value=0.00001,min=0, max=10000, step=0.000000001)),
# column(width=6,numericInput("WLb_f","Weight-length beta", value=3,min=0, max=10000, step=0.01))),
# ),
# fluidRow(column(width=10,checkboxInput("male_parms_est","Males specific values?",FALSE))),
# #fluidRow(column(width=7, h3("Males specific values?")),column(width=2,checkboxInput("male_parms","Males specific values?",FALSE,width="150%"))),
# wellPanel(
# uiOutput("Male_parms_inputs_label_est"),
# uiOutput("Male_parms_inputs1_est"),
# uiOutput("Male_parms_inputs2_est"),
# uiOutput("Male_parms_inputs3_est"),
# uiOutput("Male_parms_inputs4_est")
# ),
# )
# ),
shinyjs::hidden(wellPanel(id="panel_SSS",
h4(strong("Life history inputs")),
h5(em("If using the uniform prior, low and high range go in the mean and SD input, respectively.")),
wellPanel(
h4(em("Female")),
h5(strong("Natural mortality")),
dropdownButton(
selectInput("M_prior_sss","Prior type for M",c("lognormal","normal","uniform","no prior")),
numericInput("M_f_mean_sss", "Mean", value=NA,min=0, max=10000, step=0.00001),
numericInput("M_f_SD_sss", "SD", value=0.44,min=0, max=10000, step=0.00001),
circle = FALSE, right=TRUE, status = "danger", icon = icon("skull-crossbones"), width = "300px",label="Natural mortality"
),
h5(strong("Growth")),
dropdownButton(
selectInput("Linf_f_prior_sss","Prior type for Linf",c("no prior","normal")),
numericInput("Linf_f_mean_sss", "Mean", value=NA,min=0, max=10000, step=0.001),
numericInput("Linf_f_SD_sss", "SD", value=0,min=0, max=10000, step=0.001),
circle = FALSE, right=TRUE, status = "danger", icon = icon("infinity"), width = "300px",label="Linf: Asymptotic size"
),
br(),
dropdownButton(
selectInput("k_f_prior_sss","Prior type for k",c("no prior","normal")),
numericInput("k_f_mean_sss", "Mean", value=NA,min=0, max=10000, step=0.00001),
numericInput("k_f_SD_sss", "SD", value=0,min=0, max=10000, step=0.00001),
circle = FALSE, right=TRUE, status = "danger", icon = icon("ruler-horizontal"), width = "300px",label="k: VB growth coefficient"
),
br(),
fluidRow(column(width=6,numericInput("Linf_k_cor_sss", "Correlation between Linf and k", value=-0.9,min=-1, max=1, step=0.001))),
br(),
dropdownButton(
selectInput("t0_f_prior_sss","Prior type for t0",c("no prior","normal")),
numericInput("t0_f_mean_sss", "Mean", value=NA,min=-100, max=10000, step=0.001),
numericInput("t0_f_SD_sss", "SD", value=0,min=0, max=1000, step=0.001),
circle = FALSE, right=TRUE, status = "danger", icon = icon("baby-carriage"), width = "300px",label="t0: Age at size 0"
),
h5(em("Length CV")),
dropdownButton(
selectInput("CV_lt_f_young_prior_sss","Prior type for CV_lt young",c("no prior")),
numericInput("CV_lt_f_young_mean_sss", "Mean", value=0.1,min=0, max=10000, step=0.0001),
numericInput("CV_lt_f_young_SD_sss", "SD", value=0,min=0, max=10000, step=0.0001),
circle = FALSE, right=TRUE, status = "danger", icon = icon("dice"), width = "300px",label="CV at length"
),
dropdownButton(
selectInput("CV_lt_f_old_prior_sss","Prior type for CV_lt old",c("no prior")),
numericInput("CV_lt_f_old_mean_sss", "Mean", value=0.1,min=0, max=10000, step=0.0001),
numericInput("CV_lt_f_old_SD_sss", "SD", value=0,min=0, max=10000, step=0.0001),
circle = FALSE, right=TRUE, status = "danger", icon = icon("dice"), width = "300px",label="CV at length"
),
h5(strong("Maturity and weight-length relationships")),
fluidRow(column(width=6,numericInput("L50_f_sss", "Length at 50% maturity", value=NA,min=0, max=10000, step=0.001)),
column(width=6,numericInput("L95_f_sss","Length at 95% maturity", value=NA,min=0, max=10000, step=0.001))),
h5("Length-weight relationship W=aL^b. Weight is in kg and length in cm."),
fluidRow(column(width=6,numericInput("WLa_f_sss", "a in W=aL^b", value=0.00001,min=0, max=10000, step=0.000000001)),
column(width=6,numericInput("WLb_f_sss","b in W=aL^b", value=3,min=0, max=10000, step=0.001))),
h5("Length-fecundity relationship F=aL^b. Fecundity is measured in number of eggs or pups. Set equal to the length-weight relationship to get fecundity equivalent to spawning biomass."),
fluidRow(column(width=6,numericInput("Fec_a_f_sss", "a in F=aL^b", value=0.00001,min=0, max=10000, step=0.000000001)),
column(width=6,numericInput("Fec_b_f_sss","b in F=aL^b", value=3,min=0, max=10000, step=0.001))),
),
fluidRow(column(width=10,checkboxInput("male_parms_SSS","Males specific values?",FALSE)),
column(width=10,checkboxInput("male_offset_SSS","Males offset to females (log(m/f)?",FALSE))),
#fluidRow(column(width=7, h3("Males specific values?")),column(width=2,checkboxInput("male_parms_SSS","Males specific values?",FALSE,width="150%"))),
wellPanel(
uiOutput("Male_parms_inputs_label_SSS"),
uiOutput("Male_parms_inputs_M_SSS"),
uiOutput("Male_parms_inputs_space1_SSS"),
uiOutput("Male_parms_inputs_Growth_label_SSS"),
uiOutput("Male_parms_inputs_Linf_SSS"),
uiOutput("Male_parms_inputs_space2_SSS"),
uiOutput("Male_parms_inputs_k_SSS"),
uiOutput("Male_parms_inputs_space3_SSS"),
uiOutput("Male_parms_inputs_t0_SSS"),
uiOutput("Male_parms_inputs_space4_SSS"),
uiOutput("Male_parms_inputs_CV_young_SSS"),
uiOutput("Male_parms_inputs_CV_old_SSS"),
uiOutput("Male_parms_inputs_WL_SSS")
),
)
),
#SSS Stock status input
shinyjs::hidden(wellPanel(id="panel_SS_stock_status",
h4(strong("Relative stock status")),
#wellPanel(
fluidRow(column(width=6,numericInput("status_year", "Relative stock status year", value=NA,min=1000, max=3000, step=1))),
dropdownButton(
selectInput("Depl_prior_sss","Prior type for relative stock status",c("beta","lognormal","truncated normal","uniform","no prior")),
numericInput("Depl_mean_sss", "Mean", value=NA,min=0.001, max=1, step=0.001),
numericInput("Depl_SD_sss", "SD", value=0.2,min=0, max=1000, step=0.001),
circle = FALSE, status = "danger", icon = icon("battery-half"), width = "300px",label="Relative Stock Status"
)
)
),
################################
#Stock-recruitment/Productivity#
################################
shinyjs::hidden(wellPanel(id="panel_SSS_prod",
h4(strong("Stock-recruitment parameters")),
br(),
fluidRow(column(width=6,numericInput("lnR0_sss", "Log initial recruitment (ln(R0))", value=7,min=0.01, max=20, step=0.01))),
dropdownButton(
selectInput("h_prior_sss","Steepness",c("symmetric beta","beta","truncated normal","truncated lognormal","uniform","no prior")),
numericInput("h_mean_sss", "Mean", value=0.7,min=0.2, max=1, step=0.001),
numericInput("h_SD_sss", "SD", value=0.15,min=0, max=10000, step=0.001),
circle = FALSE, status = "danger", icon = icon("recycle"), width = "300px",label="Steepness"
),
)
),
shinyjs::hidden(wellPanel(id="panel_SS_LO_prod",
h4(strong("Stock-recruitment parameters")),
# wellPanel(
fluidRow(column(width=6,numericInput("h_LO","Steepness", value=0.7,min=0.2, max=1, step=0.01))),
# ),
)
),
shinyjs::hidden(wellPanel(id="panel_SS_prod_fixed",
h4(strong("Stock-recruitment parameters")),
# wellPanel(
fluidRow(column(width=4,numericInput("h","Beverton-Holt Steepness", value=0.7,min=0.2, max=1, step=0.01)),
column(width=4,numericInput("lnR0", "Log initial recruitment", value=7,min=0.01, max=20, step=0.01)),
column(width=4,numericInput("rec_month", "Recruitment month", value=1,min=1, max=12, step=0.01))),
# ),
)
),
shinyjs::hidden(wellPanel(id="panel_SS_prod_est",
h4(strong("Stock-recruitment parameters")),
# wellPanel(
dropdownButton(
selectInput("h_ss_prior","Prior type for steepness",c("no prior","symmetric beta", "beta","lognormal","gamma","normal")),
numericInput("h_mean_ss", "Mean", value=0.7,min=0.2, max=1, step=0.001),
numericInput("h_SD_ss", "SD", value=0.15,min=0, max=10000, step=0.001),
numericInput("h_phase", "Phase", value=-1,min=-999, max=10, step=0.001),
circle = FALSE, status = "danger", icon = icon("recycle"), width = "300px",label="Steepness"
),
fluidRow(column(width=6,numericInput("lnR0_est", "Log initial recruitment", value=7,min=0, max=20, step=0.01)),
column(width=6,numericInput("rec_month", "Recruitment month", value=1,min=1, max=12, step=0.01))),
)),
# fluidRow(column(width=4,style='padding:1px;',align="center", selectInput("h_ss_prior","Steepness",c("beta","symmetric beta","truncated normal","trunc lognormal","uniform"))),
# column(width=3,style='padding:2px;',align="center",numericInput("h_mean_ss", "Mean", value=0.7,min=0, max=10000, step=0.001)),
# column(width=3,style='padding:2px;',align="center",numericInput("h_SD_ss", "SD", value=0.15,min=0, max=10000, step=0.001)),
# column(width=2,style='padding:2px;',align="center",numericInput("h_phase", "Phase", value=-1,min=-999, max=10, step=0.001))),
# fluidRow(column(width=6,numericInput("lnR0_est", "Initial recruitment (lnR0)", value=9,min=0, max=20, step=0.01))),
# # ),
# )
# ),
#Recruitment estimation
shinyjs::hidden(wellPanel(id="panel_SS_recdevs",
fluidRow(column(width=10,checkboxInput("rec_choice","Estimate recruitment?",FALSE))),
wellPanel(
# fluidRow(column(width=8,offset=-10, h3("Estimate recruitment?")),column(width=6,checkboxInput("rec_choice","",FALSE))),
# fluidRow(column(width=8, h3("Estimate recruitment?")),column(width=4,radioButtons("rec_choice","",FALSE))),
tags$style("
.checkbox { /* checkbox is a div class*/
# line-height: 10px;
margin-bottom: 0px;
margin-left: 0px;
font-size: 20px;
}
input[type='checkbox']{ /* style for checkboxes */
width: 20px; /*Desired width*/
height: 20px; /*Desired height*/
line-height: 100px;
# span {
# margin-left: 30px; /*set the margin, so boxes don't overlap labels*/
# line-height: 30px;
}
}"),
uiOutput("Rec_options1"),
uiOutput("Rec_options6"),
uiOutput("Rec_options2"),
fluidRow(column(width=10,checkboxInput("biasC_choice","Bias correct recruitments?",FALSE))),
h5("Years of no bias correction"),
uiOutput("Rec_options3"),
h5("Years of bias correction"),
uiOutput("Rec_options4"),
uiOutput("Rec_options5")
),
)
),
#Selectivity
shinyjs::hidden(wellPanel(id="panel_selectivity_sss",
# wellPanel(
h4(strong("Selectivity")),
h5("Enter parameter values for each fleet and survey."),
h5("Example using 50% selectivity with two fleets: Inputs could be 35,40 for starting values."),
p("If using a mix of logistic and dome-shaped selectivities, select", strong("dome-shaped"),"and use the default values (10000,0.0001,0.9999 for the additonal parameters, respectively) to achieve a logistic shape for any given fleet."),
br(),
fluidRow(selectInput("Sel_choice_sss","Length selectivity type",c("Logistic","Dome-shaped"))),
uiOutput("Sel_parms1_sss"),
uiOutput("Sel_parms2_sss"),
uiOutput("Sel_parms3_sss")
)
),
shinyjs::hidden(wellPanel(id="panel_selectivity",
# wellPanel(
h4(strong("Selectivity")),
h5("Enter parameter and phase values for each fleet and survey."),
h5("Example using 50% selectivity with two fleets: Inputs could be 35,40 and 2,2 for starting values and phases respectively."),
h5("The phase input indicates estimated parameters. To pre-specify the parameter, set the phase value to a negative number."),
p("If using a mix of logistic and dome-shaped selectivities, select", strong("dome-shaped"),"and pre-specify (i.e., use a negative phase) the provided default values (10000,0.0001,0.9999 for the additonal parameters, respectively) to achieve a logistic shape for any given fleet."),
br(),
fluidRow(selectInput("Sel_choice","Length selectivity type",c("Logistic","Dome-shaped"))),
# fluidRow(column(width=6,numericInput("Sel50", "Length at 50% Selectivity", value=NA,min=0, max=10000, step=0.01)),
# column(width=6,numericInput("Sel50_phase","Estimation phase", value=1,min=-1000, max=10, step=1))),
# fluidRow(column(width=6,numericInput("Selpeak", "Length at Peak Selectvity", value=NA,min=0, max=10000, step=0.01)),
# column(width=6,numericInput("Selpeak_phase","Estimation phase", value=1,min=-1000, max=10, step=1))),
#if(input$Sel_choice=="Logistic")
# {uiOutput("Sel_logistic")},
#if(input$Sel_choice=="Dome-shaped")
# {uiOutput("Sel_domed")}
uiOutput("Sel_parms1"),
uiOutput("Sel_parms2"),
uiOutput("Sel_parms3"),
uiOutput("Sel_parms4"),
uiOutput("Sel_parms5")
# fluidRow(checkboxInput("Sex_lt_sel","Sex-specific selectivity?",FALSE)),
# fluidRow(checkboxInput("age_sel_choice","Age-based selectivity?",FALSE))
# ),
)
),
#Jitter inputs
# shinyjs::hidden(wellPanel(id="panel_SS_jitter1",
shinyjs::hidden(wellPanel(id="panel_SS_jitter",
fluidRow(column(width=10,checkboxInput("jitter_choice","Jitter starting values?",FALSE))),
uiOutput("Jitter_value"),
h5("Jittering refers to changing the input starting values."),
h5("Jittering provides a quick way to adjust starting values for two main purposes:"),
tags$ul(tags$li(h5(p("Start the model at different values to assist model convergence.")))),
tags$ul(tags$li(h5(p("Validate global versus local model convergence. This requires running many models at different jittered starting values to make sure a lower minimized likelihood value is not found. If a lower likelihood value is found, that would be considered the best fit model.")))),
h5("Run just 1 jitter value to find a converged model. Then run multiple jittered models to confrim that model is the best fit model."),
)
),
#Reference Points
shinyjs::hidden(wellPanel(id="panel_RPs",
fluidRow(column(width=10,checkboxInput("RP_choices","Define reference points?",FALSE))),
uiOutput("RP_selection1"),
uiOutput("RP_selection2")
)
),
#Forecast options
shinyjs::hidden(wellPanel(id="panel_Forecasts",
fluidRow(column(width=10,checkboxInput("Forecast_choice","Define forecasts?",FALSE))),
uiOutput("Forecasts")
)
),
shinyjs::hidden(wellPanel(id="panel_Mod_dims",
h4(strong("Model dimensions: years and ages")),
h5(p(em("Starting year values based on first year of data inputs"))),
# tags$ul(tags$li(h5(p(em("If catch data is used, starting and ending model years are based on the time series of catch"))))),
# tags$ul(tags$li(h5(p(em("If using only length or age data, starting model year is based on earliest year minus age at 95% Linf"))))),
# h5(p(em("Start year recommendations are:"))),
# tags$ul(tags$li(h5(p(em("If length data only, count the year back from the first year of length data based on maximum age likely contained in length data"))))),
# tags$ul(tags$li(h5(p(em("If using catch data, use the first year of catches"))))),
# h5(p(em(""))),
uiOutput("Model_dims1"),
# uiOutput("Model_dims2"),
)
),
shinyjs::hidden(wellPanel(id="panel_advanced_SS",
h4(strong("Additional SS options")),
h5(p(strong("Additional SS commands can be found ",tags$a(href="javascript:window.open('SS_commands.pdf', '_blank','width=600,height=400')", " here")))),
#fluidRow(column(width=10,checkboxInput("advance_ss_click","Advanced SS options",FALSE))),
popify(uiOutput("AdvancedSS_nohess"),"Run -nohess option","Turning off the Hessian option skips over asymptotic variance and speeds the model up. Use this option to more quickly explore models. Estimate variance once you are done exploring."),
popify(uiOutput("AdvancedSS_addcomms"),"Custom SS run commands","Click the advanced SS commands link above to get options. One interesting option is -hess_step which attempts to make the model gradient 0. This should be run once a final model is found."),
popify(uiOutput("AdvancedSS_addcomms_comms"),"Custom SS run commands"),
fluidRow(column(width=4,popify(uiOutput("AdvancedSS_noplots"),"Output plots","Diagnostic and results plots are produced by default. This switch turns those plots off in case you want to speed up the model run.")),
#popify(uiOutput("AdvancedSS_plots_RP"),"Target and limit reference points specification","Reference points are used to interpret the relative stock size plot compared to management objectives"),
column(width=8,popify(uiOutput("AdvancedSS_plots_RP_inputs"),"Relative Stock Size target and limit reference points","Enter a value between 0 and 1 for both."))),
popify(uiOutput("AdvancedSS_noestabs"),"No Executive Summary tables","Executive Summary tables take time to make, but provide summary tables of the model. When exploring models, it is better to turn this off to speed up the model run."),
#popify(uiOutput("AdvancedSS_par"),"Switch to use the ss.par file","The ss.par file contains all parameter values used in the previous model run. It can be handy to run models from the par file to confirm you have reached the best fit model. The par file can also be used to expedite forecasts by turning the maximum phase to zero (see next option) and using the par file."),
popify(uiOutput("AdvancedSS_phase0"),"Maximum phase = 0","Setting maximum phase to 0 turns off all parameter estimation and is useful when forecasting catch into the future. Couple with using the ss.par file (see above option)."),
#popify(uiOutput("AdvancedSS_datanew"),"Switch starter file to use the data_echo.ss_new file.","This file is a copy of the data file used in the last run. It has additional notes for inputs, and can be modified to run new scenarios, if desired."),
#popify(uiOutput("AdvancedSS_controlnew"),"Switch starter file to use the control.ss_new file.","This file is a copy of the control file used in the previous run, but starting values are the ending values of the previous model. Like the ss.par file, it can be used to run from where the last model finished, but also provides a convenient way to change other parameter specifications."),
#popify(uiOutput("AdvancedSS_forecastnew"),"Overwrite the forecast.ss file with the forecast.ss_new file.","This file is a copy of the forecast file used in the previous run. In the event you want to use this file, this switch will overwrite the forecast file content with what is in the forecast.ss_new."),
popify(uiOutput("AdvancedSS_Ctunits"),"Catch units","The default assumption is that catch is in metric tons (option 1), but if they are in numbers, option 2 should be used."),
popify(uiOutput("AdvancedSS_Ctunitsfleets"),"Enter units","1=biomass; 2=numbers. Enter one of these numbers for each removal fleet."),
popify(uiOutput("AdvancedSS_GT1"),"Growth morphs","Stock Synthesis can track multiple growth morphs distributed around a central morph. The default here is tracking 5 morphs, but using only 1 is also common. This switch moves from 5 to 1 morph."),
popify(uiOutput("AdvancedSS_Indexvar"),"Added index variance.","The input index variance is often underestimated. This option allows more variance to be added to each index in order to improve model fit. This is a type of weighting option for indices. The added variance is the same for each year of a particular index, but can be different across indices. Beware large index outliers that may overinflate added variances in order to get the model to fit that one data point."),
popify(uiOutput("AdvancedSS_Sex3options"),"Sex=3 option for biological compositions","This switch changes the per sex biological compositions (sex = 1 for females and 2 for males) into a two sex length composition that retains the overall biological composition of the sample. This may add additional information on the underlying sex ratio of the population, but should be tested against using the biological compositions by sex. Choose to apply this method to lengths and/or ages."),
fluidRow(column(width=6,uiOutput("AdvancedSS_Sex3")),
column(width=6,uiOutput("AdvancedSS_AgeSex3"))),
#popify(uiOutput("AdvancedSS_Sex3"),"Sex=3 option for lengths","This switch changes the per sex length compositions (sex = 1 for females and 2 for males) into a two sex length composition that retains the overall length composition of the sample. This may add additional information on the underlying sex ratio of the population, but should be tested against using the length compositions by sex."),
#popify(uiOutput("AdvancedSS_AgeSex3"),"Sex=3 option for ages","This switch changes the per sex age compositions (sex = 1 for females and 2 for males) into a two sex length composition that retains the overall length composition of the sample. This may add additional information on the underlying sex ratio of the population, but should be tested against using the age compositions by sex."),
popify(uiOutput("AdvancedSS_ageerror"),"Ageing error matrices.","Add as many custom ageing error matrices as needed. See the folders Example data files --> ageing error matrices for examples of the ageing error input."),
uiOutput("AdvancedSS_ageerror_in"),
fluidRow(style = "padding-right:50px;padding-left:50px;padding-top:-1000px; padding-bottom:0px;",h6("Software to calculate ageing error matrices from multiple age reads is available ",tags$a(href="https://github.com/pfmc-assessments/nwfscAgeingError", "here"),"(click ctl+left on the link to open the repo).")),
#uiOutput("AdvancedSS_retro_choice"),
#uiOutput("AdvancedSS_retro_years"),
br(),
h5(strong(p("Define modelled length bins"))),
h5("Default values are by 2 cm bin ranging from 4 to 25% above the Linf value. If using conditional age at lengths, length bins must be consistent with these population bins, not the length data bins."),
h5(p(em("Inputs must be smaller and larger than the length composition bins. Input values will be overridden to meet this requirement"))),
uiOutput("AdvancedSS_Ltbin"),
br(),
h5(strong(p("Define plus group age"))),
h5("Default value is based on the female natural mortality value or the maximum age found in the age data file."),
h5(p(em("If you change this value here AND you are using age data, you will need to update the age data (and ageing error) to match the dimensions expressed here."))),
uiOutput("AdvancedSS_Nages")
# prettyCheckbox(
# inputId = "no_hess", label = "Turn off Hessian",
# shape = "round", outline = TRUE, status = "info"),
# prettyCheckbox(
# inputId = "no_plots_tables", label = "Turn off plots and tables",
# shape = "round", outline = TRUE, status = "info"),
# prettyCheckbox(
# inputId = "GT1", label = "Use only one growth type",
# shape = "round", outline = TRUE, status = "info"),
# prettyCheckbox(
# inputId = "Sex3", label = "Retain sex ratio in length compositions (Sex = 3)",
# shape = "round", outline = TRUE, status = "info"),
)
),
shinyjs::hidden(wellPanel(id="panel_advanced_user_SS",
h4(strong("Additional SS options")),
#fluidRow(column(width=10,checkboxInput("advance_ss_click","Advanced SS options",FALSE))),
popify(uiOutput("AdvancedSS_nohess_user"),"Run -nohess option","Turning off the Hessian option skips over asymptotic variance and speeds the model up. Use this option to more quickly explore models. Estimate variance once you are done exploring."),
popify(uiOutput("AdvancedSS_addcomms_user"),"Custom SS run commands","Click the advanced SS commands link above to get options. One interesting option is -hess_step which attempts to make the model gradient 0. This should be run once a final model is found."),
popify(uiOutput("AdvancedSS_addcomms_comms_user"),"Custom SS run commands","Click the advanced SS commands link above to get options. One interesting option is -hess_step which attempts to make the model gradient 0. This should be run once a final model is found."),
fluidRow(column(width=4,popify(uiOutput("AdvancedSS_noplots_user"),"Output plots","Diagnostic and results plots are produced by default. This switch turns those plots off in case you want to speed up the model run.")),
#popify(uiOutput("AdvancedSS_plots_RP_user"),"Target and limit reference points specification","Reference points are used to interpret the relative stock size plot compared to management objectives"),
column(width=8,popify(uiOutput("AdvancedSS_plots_RP_inputs_user"),"Relative Stock Size target and limit reference points","Enter a value between 0 and 1 for both."))),
popify(uiOutput("AdvancedSS_noestabs_user"),"No Executive Summary tables","Executive Summary tables take time to make, but provide summary tables of the model. When exploring models, it is better to turn this off to speed up the model run."),
popify(uiOutput("AdvancedSS_par_user"),"Switch to use the ss.par file","The ss.par file contains all parameter values used in the previous model run. It can be handy to run models from the par file to confirm you have reached the best fit model. The par file can also be used to expedite forecasts by turning the maximum phase to zero (see next option) and using the par file."),
popify(uiOutput("AdvancedSS_phase0_user"),"Maximum phase = 0","Setting maximum phase to 0 turns off all parameter estimation and is useful when forecasting catch into the future. Couple with using the ss.par file (see above option)."),
popify(uiOutput("AdvancedSS_datanew_user"),"Switch starter file to use the data_echo.ss_new file.","This file is a copy of the data file used in the last run. It has additional notes for inputs, and can be modified to run new scenarios, if desired."),
popify(uiOutput("AdvancedSS_controlnew_user"),"Switch starter file to use the control.ss_new file.","This file is a copy of the control file used in the previous run, but starting values are the ending values of the previous model. Like the ss.par file, it can be used to run from where the last model finished, but also provides a convenient way to change other parameter specifications."),
popify(uiOutput("AdvancedSS_forecastnew_user"),"Overwrite the forecast.ss file with the forecast.ss_new file.","This file is a copy of the forecast file used in the previous run. In the event you want to use this file, this switch will overwite the forecast file content with what is in the forecast.ss_new."),
#uiOutput("AdvancedSS_retro_choice_user"),
#uiOutput("AdvancedSS_retro_years_user")
)
),
shinyjs::hidden(wellPanel(id="panel_advanced_SSS",
h4(strong("Additional SS options")),
h5(strong("Choosing catch units")),
h6(strong("Default is biomass (in MT), but click below button to specify for each fleet.")),
#fluidRow(column(width=10,checkboxInput("advance_ss_click","Advanced SS options",FALSE))),
uiOutput("AdvancedSS_Ctunits_SSS"),
uiOutput("AdvancedSS_Ctunitsfleets_SSS"),
h5(strong("Add additional growth platoons?")),
uiOutput("AdvancedSS_GT5_SSS"),
h5(strong(p("Define plus group age"))),
h5("Default value is based on the female natural mortality value or the maximum age found in the age data file."),
uiOutput("AdvancedSSS_Nages")
)
),
#SSS iterations
shinyjs::hidden(wellPanel(id="panel_SSS_reps",
h4(strong("SSS run specifications")),
fluidRow(column(width=10,numericInput("SSS_reps", "Number of SSS iterations", value=1000,min=1, max=1000000, step=1))),
fluidRow(column(width=10,numericInput("SSS_seed", "Seed number for draws", value=19,min=1, max=1000000, step=1)))
)
),
#wellPanel(
shinyjs::hidden(awesomeRadio(
inputId = "OS_choice",
label = "Which OS?",
choices = c("Windows","Mac","Linux"),
selected = "Windows",
inline=TRUE,
status = "warning")),
#),
shinyjs::hidden(wellPanel(id="Scenario_panel",
h4(strong("Scenario name")),
fluidRow(column(width=8,textInput("Scenario_name", strong("Choose the name of your scenario"), value="Scenario_1"))),
h5(p(em("Each scenario folder is saved. Changing the scenario name therefore creates a new folder of results."))),
h5(p(em("Using different scenario names when changing data or parameter values allows easy sensitivity exploration."))),
h5(p(strong("For Mac and Linux users, do not leave spaces in the Scenario name."))),
br(),
# h4(strong("Select a folder to copy results")),
# h5(p(em("Results are copied from the 'Scenarios' folder"))),
# h5(p(em("Required to access results if using the online version"))),
# shinyDirButton(
# id="Modelout_dir",
# label="Select model folder",
# title="Choose folder to copy model scenario"
# ),
)
),
shinyjs::hidden(wellPanel(id="SaveSession_panel",
h4(p(strong("Save session inputs before model run"))),
h5(p("Worried about losing your inputs from a crashed model? Click the 'save session inputs' button BEFORE running the model to save your inputs.")),
h5(p("To recovery those inputs, you can paste the link into another webpage. The SS-DL tool needs to be running to recovery another session. Note you will also need to reload data files to see the saved inputs.")),
h5(p("You can also retain the link to share or report inputs. Consider adding a text file with the link into any model run folders for future explorations.")),
br(),
bookmarkButton(label="Save session inputs",
width="50%",
icon("copy"),
style="font-size:120%;border:2px solid;color:#FFFFFF;background:#005595"),
br(),
br(),
)),
shinyjs::hidden(actionButton("run_SS",strong("Run Model"),
width="100%",
icon("circle-play"),
style="font-size:120%;border:2px solid;color:#FFFFFF;background:#5D9741")),
shinyjs::hidden(actionButton("run_SSS",strong("Run SSS"),
width="100%",
icon("circle-play"),
style="font-size:120%;border:2px solid;color:#FFFFFF; background:#5D9741")),
br(),
br(),
####################
### Other panels ###
####################
########################
### Model efficiency ###
########################
shinyjs::hidden(wellPanel(id="Modeff_panel",
h4(strong("Model efficiency and Bayesian analysis")),
h5(em("Using ",tags$a(href="https://github.com/Cole-Monnahan-NOAA/adnuts", "AD NUTS"),"to improve model efficiency and performance.")),
h5(em("This can also offer speedier Bayesian approaches.")),
br(),
h5(strong("Choose folder of model to evaluate")),
h5(em("")),
shinyDirButton(
"ModEff_dir",
label="Select model folder",
title="Choose folder of model to evaluate"
),
br(),
#uiOutput("ModEff_model_pick"),
br(),
h5(strong("Choose method to use")),
h5(("There are two main Bayesian methods to choose from:")),
tags$ul(tags$li(h5(p(em("Random walk Metropolis (RWM). This method is useful to quickly explore parameter behavior."))))),
tags$ul(tags$li(h5(p(em("No u-turn (Nuts). This method can quickly run Bayesian models once an efficient model is established. No thinning necessary, as that is part of the algorithm"))))),
h5(("The following is a recommended work flow to find and run an efficient Bayesian model:")),
tags$ul(tags$li(h5(p(em("Optimize you model with a short Bayesian run. Click the 'optimize model' button."))))),
tags$ul(tags$li(h5(p(em("Run your optimized model using RWM with 2000 iterations and thin = 10, then use the pairs plots to look for parameters that don't change value across kept draws."))))),
tags$ul(tags$li(h5(p(em("Evaluate the produced pairs plots to look for parameters that don't change value across kept draws."))))),
tags$ul(tags$li(h5(p(em("Parameters that don't move should be pre-specified in the model, and the model re-optimized."))))),
tags$ul(tags$li(h5(p(em("Run the RWM model again and continue looking for and pre-specifying any non-moving parameters."))))),
br(),
h5(("Once the model specification is finalized, you can")),
tags$ul(tags$li(h5(p(em("Re-run with Hessian (go back to the first tab to re-run model) to get asymptotic variance estimates. "))))),
tags$ul(tags$li(h5(p(em("Re-optimize, then consider using the NUTS option with 1000 iterations or keep running the RWM option until convergence criteria are reached by increasing the thinning value and/or number of iterations. This produces a Bayesian representation of uncertainty."))))),
br(),
h5(("Good model convergence is indicated when:")),
tags$ul(tags$li(h5(p(em("Minimum effective sample size (ESS) > 200"))))),
tags$ul(tags$li(h5(p(em("Rhat <1.1, This measures the ratio of overestimated to underestimated variance."))))),
awesomeCheckbox(
inputId = "Opt_mod",
label = "Optimize model?",
value = TRUE,
status = "danger"
),
h5(("One should include model optimization before running the evaluation methods below if this is the first run of a given model specification, including if parameters have been pre-specified since the last exploration.")),
br(),
# awesomeCheckbox(
# inputId = "run_stanout",
# label = "Run Stan GUI?",
# value = TRUE,
# status = "danger"
# ),
# br(),
h5(strong("Choose method and evaluation inputs")),
awesomeRadio(
inputId = "ModEff_choice",
label = "",
choices = c("RWM", "Nuts"),
selected="RWM",
inline = TRUE,
status = "success"
),
fluidRow(column(width=5,numericInput("iter", "How many iterations to run?", value=2000,min=1, max=1000000000000, step=1)),
column(width=5,numericInput("thin","Thinning (RWM only): # of iterations to keep?", value=10,min=1, max=1000000000, step=10))),
actionButton("run_adnuts",strong("Run model"),
width="100%",
icon("circle-play"),
style="font-size:120%;border:2px solid;color:#FFFFFF; background:#236192"),
br(),
br(),
h5(strong("Once the model is finished, further model diagnostics are available by loading the fit_model.RData object created (use the load() function in R) and running launch_shinyadmb(fit_model).")),
h5(strong("This cannot be done while the SS-DL tool is open, so either use another R terminal or close the SS-DL app.")),
)),
###########################
### Likelihood profiles ###
###########################
shinyjs::hidden(wellPanel(id="Profile_panel",
h4(strong("Run likelihood profiles")),
h5(em("Likelihood profiles are a powerful way to understand the information content of data and sensitivity of models to parameter uncertainty.")),
h5(em("A likelihood profile pre-specifies a chosen parameter to a specified set of values in a reference model. The reference model will maintain estimation of any other parameters estimated in the reference model.")),
h5(em("For example, natural mortality (M) could be profiled over the value 0.1 to 0.3 at steps of 0.01. This creates 21 model runs that pre-specify M to different values while keeping all other specifications the same as the reference model.")),
h5(em("For each model run, the likelihood value and derived outputs are retained for analysis.")),
h5(em("Any likelihood values >1.96 units from the minimum value are identify as models statistically less supported by the data. ")),
h5(em("Plots with the profiled parameter values compared to the likelihood values and derived model outputs indicate how much information is contained in the model for the parameter and how sensitive the model is to parameters values resulting non-statistically different models.")),
br(),
h5(strong("Choose folder of scenario to run profile")),
#shinyFilesButton("LikeProf_dir", "Select scenario", "Choose folder containing model scenarios", multiple = F),
shinyDirButton(
id="LP_dir",
label="Select scenario",
title="Choose folder containing model scenarios"
),
br(),
br(),
h4(("Individual likelihood profiles- each parameter run independently.")),
h5(em("If choosing multiple parameters to individually profile over, entries should be done in order of the parameters shown and separated by a comma (e.g., 0.1, 0.3).")),
h5(em("The range of values must also include the value of the model being used. If not, the profile will not run.")),
uiOutput("LikeProf_model_picks"),
# fluidRow(selectInput("Profile_choice_choice","Parameter to profile",c("Steepness","lnR0","Natural mortality","Linf","k"))),
fluidRow(column(width=4,textInput("Prof_Low_val", "Low value", value="")),
column(width=4,textInput("Prof_Hi_val", "High value", value="")),
column(width=4,textInput("Prof_step","Sequence step", value=""))),
#br(),
actionButton("run_Profiles",strong("Run Likelihood Profile"),
width="100%",
icon("circle-play"),
style="font-size:120%;border:2px solid;color:#FFFFFF; background:#236192"),
br(),
br(),
br(),
h4(("Another option is to run simultaneous likelihood profiles.")),
h5(em("For example, Linf and k are negatively correlated and should be changing together in a profile.")),
h5(em("Read in .csv file with vectors of values for each parameter.")),
h5(em("See the 'Multi_profile_headers.csv' file for parameter header names. Erase columns not used and fill in rows with values for each likelihood run.")),
# fluidRow(column(width=10,checkboxInput("multi_profile","Apply all vectors in one profile?",FALSE))),
uiOutput("Profile_multi_values"),
fluidRow(column(width=5,numericInput("TRP_multi_like", "Target reference point (max=1; 0= no plot)? ", value=0,min=0, max=1, step=0.001)),
column(width=5,numericInput("LRP_multi_like","Limit reference point (max=1; 0= no plot)?", value=0,min=0, max=1, step=0.001))),
fluidRow(column(width=10,checkboxInput("Hess_multi_like","Include uncertainty estimation?",TRUE))),
# fluidRow(column(width=4,numericInput("Prof_Low_val", "Low value", value=NA,min=0, max=10000, step=0.001)),
# column(width=4,numericInput("Prof_Hi_val", "High value", value=NA,min=0, max=10000, step=0.001)),
# column(width=4,numericInput("Prof_step","Sequence step", value=NA,min=0, max=10000, step=0.001))),
#fluidRow(column(width=8,textInput("Profile_plot_file", strong("Label plot file"), value="Profile X"))),
#h5(strong("Choose folder of scenario to run profile")),
#uiOutput("LikeProf_dir_out"),
actionButton("run_MultiProfiles",strong("Run Likelihood Multi-Profile"),
width="100%",
icon("circle-play"),
style="font-size:120%;border:2px solid;color:#FFFFFF; background:#236192"),
)),
###############################
######## Retrospectives #######
###############################
shinyjs::hidden(wellPanel(id="Retro_panel",
h4(strong("Retrospective comparisons and plots")),
h5(em("Retrospecitive modelling means sequentially removing one year of data up to a specified number of years (e.g., -10 years).")),
h5(em("To make these comparisons, choose first the directory containing models, then the models to compare.")),
h5(em("A time series plot of comparisons are shown in the main panel to the right for the follwing model outputs:")),
tags$ul(tags$li(h5(p(em("Spawning output"))))),
tags$ul(tags$li(h5(p(em("Relative spawning output"))))),
tags$ul(tags$li(h5(p(em("Recruitment"))))),
h5(em("A complete compliment of comparison plots (along with the plot on the right) are saved in the chosen folder labeled 'retro'")),
#h5(strong(em("Retrospective Comparison Plots"))),
br(),
h5(strong("Choose folder containing model for retrospective analysis")),