inst/shinyApp/ui_layers/ui_binary_map.R
In luismurao/nichetoolbox: From getting biodiversity data to evaluating species distribution models in a friendly GUI environment
ui_binary <- sidebarLayout(position = "left",
sidebarPanel(
tags$head(
tags$script(src = 'https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML', type = 'text/javascript')
),
titlePanel("NicheToolBox"),
h3("Binary map conversion"),
wellPanel(
radioButtons("valdata_type",label = "Select a method to convert your map",
choices = c("Minimum training presence"= "min_traing_pres",
"Confusion matrix optimization"="pres_abs_data",
"User threshold selection" = "user_threshold"
),selected ="pres_abs_data" )
),
#p("In this section contains a set of functions that will
# help you to find the best threshold cut-off."),
p("Upload the model prediction raster in ASCII format"),
radioButtons('formatBin', 'Format', c('.asc')),
fileInput('fileBin', 'Data file'),
conditionalPanel("input.valdata_type=='pres_abs_data'",
h4("Real Presences and Absences"),
p("Upload your validation data. The data must be in the following format:"),
HTML('<table style="width:80%" border="1" align="center">
<tr>
<th> long</th>
<th> lat</th>
<th> PrecenseAbsence</th>
</tr>
<tr>
<td> -65.38</td>
<td> -10.38</td>
<td> 1</td>
</tr>
<tr>
<td> -62.11</td>
<td> -11.22</td>
<td> 0</td>
</tr>
</table>'),
radioButtons('formatVal', 'Format', c('CSV')),
fileInput('fileVal', 'Data file'),
sliderInput("thRange","Select a threshold range to look for the best cut off",
min = 0,max = 1,step = 0.1,value = c(0,0.8)),
numericInput("partStep",label = "Select a partition step for your range",
value = 0.01,min = 0.001,max = 0.02),
radioButtons("optim_by1","Optimization by",choices = c("Kappa"= "kappa",
"TSS" = "tss")),
shinysky::busyIndicator("Computation In progress",wait = 0),
actionButton("searchTh","Search threshold",styleclass="primary"),
h3("Confusion matrix"),
HTML("Enter values for: a, b, c y d"),
numericInput(inputId="a",label="Correctly predicted\npresences (a)",value=28,min=0,max=1000000),
numericInput(inputId="b",label="Predicted as present but\nactually absent(b)",value=7,min=0,max=1000000),
numericInput(inputId="c",label="Predicted as absent but\nactually present (c)",value=11,min=0,max=1000000),
numericInput(inputId="d",label="Correctly predicted\nabsences (d)",value=62,min=0,max=1000000),
h5("Download binary map"),
downloadButton("downloadBinary_mc_opt",label = "Download Binary",class = "primary"),
h5("Download binary metadata"),
downloadButton("downloadBinary_metadata_mc_opt",label = "Download metadata",class = "primary"),
radioButtons(inputId = "elecc1",label = h4("Select a metric"),choices =
list(
"Show all metrics" = "all1",
"Prevalence" = "prev1",
"Specificity" = "espe1",
"Sensitivity" = "sens1",
"TSS"="tss1","Kappa"="kappa1",
"Correct classification rate"= "tcc1",
"Misclassification rate"= "tcin1",
"Negative predictive power" = "npp1",
"Positive predictive power" = "ppp1",
"Omission error fraction" = "feom1",
"Commission error fraction" = "fecom1",
"False negative rate" = "tfn1",
"False positive rate" = "tfp1"),selected = "all1"),
#checkboxInput("binomial_test_1",label = "Binomial test (in development)",value = FALSE),
conditionalPanel(condition = "input.binomial_test_1 == true",
numericInput(inputId = "prop_area1",label = "Enter the proportion of the predicted area",
value = "0.5",min = 0.0,max = 1,step=0.01)),
downloadButton(outputId = "todoCM",label = "Download results")
),
conditionalPanel("input.valdata_type=='user_threshold'",
h4("User threshold cut-off"),
wellPanel(
p("Please select a threshold:"),
numericInput("thresholdBin","Threshold",value = 0.08,step = 0.01),
actionButton("compBin",label = "Compute Binary",styleclass="primary"),
h5("Download binary map"),
downloadButton("downloadBinary_user",
label = "Download Binary",class = "primary"),
h5("Download binary metadata"),
downloadButton("downloadBinary_metadata_user",
label = "Download metadata",class = "primary")
)),
conditionalPanel("input.valdata_type=='min_traing_pres'",
h4("Presence data"),
p("Upload your traning data. The data must be in the following format:"),
br(),
HTML('<table style="width:100%">
<tr>
<th>Species</th>
<th>long</th>
<th>lat</th>
</tr>
<tr>
<td>sp_name</td>
<td>-65.38</td>
<td>-10.38</td>
</tr>
</table>'
),
fileInput("val_data_mtp","Data file"),
radioButtons('formatVal_mtp', 'Format', c('CSV')),
h5("Download binary map"),
downloadButton("downloadBinary_mtp",
label = "Download Binary",class = "primary"),
h5("Download binary metadata"),
downloadButton("downloadBinary_metadata_mtp",
label = "Download metadata",class = "primary")
)
),
mainPanel(
conditionalPanel("input.valdata_type=='pres_abs_data'",
dataTableOutput("threshold_sims"),
uiOutput("area_cm_method"),
plotOutput("binary_CM_optim"),
tags$style(type='text/css', '#metric {background-color: rgba(255,255,0,0.40); color: green;}'),
br(),
h3("Confusion matrix"),
br(),
tableOutput('con_tableBin'),
conditionalPanel(condition = "input.elecc1=='kappa1'",
h3("Kappa"),
HTML("<p align = 'justify'>
$$Kappa = \\frac{((a+d)-(((a+c)*(a+b)+(b+d)*(c+d))/N))}{(N-(((a+c)*(a+b)+(b+d)*(c+d))/N))}$$</p>")),
conditionalPanel(condition = "input.elecc1 =='tss1'",h3("TSS"),
HTML("<p align = 'justify'>
$$TSS = \\frac{a}{(a+c)} + \\frac{d}{(b+d)} - 1$$</p>")),
conditionalPanel(condition = "input.elecc1=='prev1'",h3("Prevalence"),
HTML('<p align="center"><img src="prevalencia.png" alt="Prevalencia" width="800" height="400" align="middle"></p>'),
br(),
HTML("<p align = 'justify'>
$$Prevalencia = \\frac{(a + c)}{(a+b+c+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='tcc1'",h3("Correct classification rate"),
HTML('<p align="center"><img src="posi_class_rate.png" alt="PCRate" width="800" height="400" align="middle"></p>'),
HTML("<p align= 'justify'>
$$T.C.C = \\frac{(a+d)}{(a+b+c+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='tcin1'",h3("Misclassification rate"),
HTML('<img src="miss_rate.png" alt="MissClassRate" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$T.C.I = \\frac{(b+c)}{(a+b+c+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='npp1'",h3("Negative predictive power"),
HTML('<img src="nega_predi_pw.png" alt="NegativePredRate" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$N.P.P = \\frac{d}{(c+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='ppp1'",h3("Positive predictive power"),
HTML('<img src="posi_pred_pw.png" alt="PosiPredRate" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$P.P.P = \\frac{a}{(a+b)}$$</p>")),
conditionalPanel(condition = "input.elecc1 =='tfn1'",h3("False negative rate"),
HTML("<p align = 'justify'>
$$T.F.N =\\frac{c}{(a+c)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='tfp1'", h3("False positive rate"),
HTML("<p align = 'justify'>
$$T.F.P = \\frac{b}{(b+d)}$$ </p>")),
conditionalPanel(condition = "input.elecc1=='espe1'",h3("Specificity"),
HTML('<img src="especifi.png" alt="Especificidad" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$Especificidad = \\frac{d}{(b+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='sens1'",h3("Sensibility"),
HTML('<img src="sensi.png" alt="Sensibilidad" width="600" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$Sensibilidad = \\frac{a}{(a+c)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='feom1'",h3("Omission error"),
HTML('<img src="feom.png" alt="FEOM" width="800" height="400" align="middle">'),
HTML("<p align ='justify'>
$$F.E.O=\\frac{c}{(a+c)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='fecom1'",h3("Comission error"),
HTML('<img src="fecom.png" alt="FECOM" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$F.E.C=\\frac{b}{(b+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='all1'",h3("Evaluation metrics for SDMs")),
br(),
verbatimTextOutput(outputId = "cm_method_metrics"),
tags$style(type='text/css', '#cm_method_metrics {background-color: rgba(255,255,0,0.40); color: green;}')
#conditionalPanel(condition = "input.binomial_test_1 == true",
# br(),
# h3("Binomial Test"),
# tags$style(type='text/css', '#cm_method_metrics {background-color: rgba(255,255,0,0.40); color: green;}'),
# verbatimTextOutput("binomial1"))
),
conditionalPanel("input.valdata_type=='min_traing_pres'",
uiOutput("area_mtp_method"),
plotOutput("binary_mtp")
),
conditionalPanel("input.valdata_type=='user_threshold'",
uiOutput("area_user_method"),
plotOutput("binary_user")
)
)
)
luismurao/nichetoolbox documentation built on May 21, 2019, 8:56 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
ui_binary <- sidebarLayout(position = "left",
sidebarPanel(
tags$head(
tags$script(src = 'https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML', type = 'text/javascript')
),
titlePanel("NicheToolBox"),
h3("Binary map conversion"),
wellPanel(
radioButtons("valdata_type",label = "Select a method to convert your map",
choices = c("Minimum training presence"= "min_traing_pres",
"Confusion matrix optimization"="pres_abs_data",
"User threshold selection" = "user_threshold"
),selected ="pres_abs_data" )
),
#p("In this section contains a set of functions that will
# help you to find the best threshold cut-off."),
p("Upload the model prediction raster in ASCII format"),
radioButtons('formatBin', 'Format', c('.asc')),
fileInput('fileBin', 'Data file'),
conditionalPanel("input.valdata_type=='pres_abs_data'",
h4("Real Presences and Absences"),
p("Upload your validation data. The data must be in the following format:"),
HTML('<table style="width:80%" border="1" align="center">
<tr>
<th> long</th>
<th> lat</th>
<th> PrecenseAbsence</th>
</tr>
<tr>
<td> -65.38</td>
<td> -10.38</td>
<td> 1</td>
</tr>
<tr>
<td> -62.11</td>
<td> -11.22</td>
<td> 0</td>
</tr>
</table>'),
radioButtons('formatVal', 'Format', c('CSV')),
fileInput('fileVal', 'Data file'),
sliderInput("thRange","Select a threshold range to look for the best cut off",
min = 0,max = 1,step = 0.1,value = c(0,0.8)),
numericInput("partStep",label = "Select a partition step for your range",
value = 0.01,min = 0.001,max = 0.02),
radioButtons("optim_by1","Optimization by",choices = c("Kappa"= "kappa",
"TSS" = "tss")),
shinysky::busyIndicator("Computation In progress",wait = 0),
actionButton("searchTh","Search threshold",styleclass="primary"),
h3("Confusion matrix"),
HTML("Enter values for: a, b, c y d"),
numericInput(inputId="a",label="Correctly predicted\npresences (a)",value=28,min=0,max=1000000),
numericInput(inputId="b",label="Predicted as present but\nactually absent(b)",value=7,min=0,max=1000000),
numericInput(inputId="c",label="Predicted as absent but\nactually present (c)",value=11,min=0,max=1000000),
numericInput(inputId="d",label="Correctly predicted\nabsences (d)",value=62,min=0,max=1000000),
h5("Download binary map"),
downloadButton("downloadBinary_mc_opt",label = "Download Binary",class = "primary"),
h5("Download binary metadata"),
downloadButton("downloadBinary_metadata_mc_opt",label = "Download metadata",class = "primary"),
radioButtons(inputId = "elecc1",label = h4("Select a metric"),choices =
list(
"Show all metrics" = "all1",
"Prevalence" = "prev1",
"Specificity" = "espe1",
"Sensitivity" = "sens1",
"TSS"="tss1","Kappa"="kappa1",
"Correct classification rate"= "tcc1",
"Misclassification rate"= "tcin1",
"Negative predictive power" = "npp1",
"Positive predictive power" = "ppp1",
"Omission error fraction" = "feom1",
"Commission error fraction" = "fecom1",
"False negative rate" = "tfn1",
"False positive rate" = "tfp1"),selected = "all1"),
#checkboxInput("binomial_test_1",label = "Binomial test (in development)",value = FALSE),
conditionalPanel(condition = "input.binomial_test_1 == true",
numericInput(inputId = "prop_area1",label = "Enter the proportion of the predicted area",
value = "0.5",min = 0.0,max = 1,step=0.01)),
downloadButton(outputId = "todoCM",label = "Download results")
),
conditionalPanel("input.valdata_type=='user_threshold'",
h4("User threshold cut-off"),
wellPanel(
p("Please select a threshold:"),
numericInput("thresholdBin","Threshold",value = 0.08,step = 0.01),
actionButton("compBin",label = "Compute Binary",styleclass="primary"),
h5("Download binary map"),
downloadButton("downloadBinary_user",
label = "Download Binary",class = "primary"),
h5("Download binary metadata"),
downloadButton("downloadBinary_metadata_user",
label = "Download metadata",class = "primary")
)),
conditionalPanel("input.valdata_type=='min_traing_pres'",
h4("Presence data"),
p("Upload your traning data. The data must be in the following format:"),
br(),
HTML('<table style="width:100%">
<tr>
<th>Species</th>
<th>long</th>
<th>lat</th>
</tr>
<tr>
<td>sp_name</td>
<td>-65.38</td>
<td>-10.38</td>
</tr>
</table>'
),
fileInput("val_data_mtp","Data file"),
radioButtons('formatVal_mtp', 'Format', c('CSV')),
h5("Download binary map"),
downloadButton("downloadBinary_mtp",
label = "Download Binary",class = "primary"),
h5("Download binary metadata"),
downloadButton("downloadBinary_metadata_mtp",
label = "Download metadata",class = "primary")
)
),
mainPanel(
conditionalPanel("input.valdata_type=='pres_abs_data'",
dataTableOutput("threshold_sims"),
uiOutput("area_cm_method"),
plotOutput("binary_CM_optim"),
tags$style(type='text/css', '#metric {background-color: rgba(255,255,0,0.40); color: green;}'),
br(),
h3("Confusion matrix"),
br(),
tableOutput('con_tableBin'),
conditionalPanel(condition = "input.elecc1=='kappa1'",
h3("Kappa"),
HTML("<p align = 'justify'>
$$Kappa = \\frac{((a+d)-(((a+c)*(a+b)+(b+d)*(c+d))/N))}{(N-(((a+c)*(a+b)+(b+d)*(c+d))/N))}$$</p>")),
conditionalPanel(condition = "input.elecc1 =='tss1'",h3("TSS"),
HTML("<p align = 'justify'>
$$TSS = \\frac{a}{(a+c)} + \\frac{d}{(b+d)} - 1$$</p>")),
conditionalPanel(condition = "input.elecc1=='prev1'",h3("Prevalence"),
HTML('<p align="center"><img src="prevalencia.png" alt="Prevalencia" width="800" height="400" align="middle"></p>'),
br(),
HTML("<p align = 'justify'>
$$Prevalencia = \\frac{(a + c)}{(a+b+c+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='tcc1'",h3("Correct classification rate"),
HTML('<p align="center"><img src="posi_class_rate.png" alt="PCRate" width="800" height="400" align="middle"></p>'),
HTML("<p align= 'justify'>
$$T.C.C = \\frac{(a+d)}{(a+b+c+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='tcin1'",h3("Misclassification rate"),
HTML('<img src="miss_rate.png" alt="MissClassRate" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$T.C.I = \\frac{(b+c)}{(a+b+c+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='npp1'",h3("Negative predictive power"),
HTML('<img src="nega_predi_pw.png" alt="NegativePredRate" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$N.P.P = \\frac{d}{(c+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='ppp1'",h3("Positive predictive power"),
HTML('<img src="posi_pred_pw.png" alt="PosiPredRate" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$P.P.P = \\frac{a}{(a+b)}$$</p>")),
conditionalPanel(condition = "input.elecc1 =='tfn1'",h3("False negative rate"),
HTML("<p align = 'justify'>
$$T.F.N =\\frac{c}{(a+c)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='tfp1'", h3("False positive rate"),
HTML("<p align = 'justify'>
$$T.F.P = \\frac{b}{(b+d)}$$ </p>")),
conditionalPanel(condition = "input.elecc1=='espe1'",h3("Specificity"),
HTML('<img src="especifi.png" alt="Especificidad" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$Especificidad = \\frac{d}{(b+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='sens1'",h3("Sensibility"),
HTML('<img src="sensi.png" alt="Sensibilidad" width="600" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$Sensibilidad = \\frac{a}{(a+c)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='feom1'",h3("Omission error"),
HTML('<img src="feom.png" alt="FEOM" width="800" height="400" align="middle">'),
HTML("<p align ='justify'>
$$F.E.O=\\frac{c}{(a+c)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='fecom1'",h3("Comission error"),
HTML('<img src="fecom.png" alt="FECOM" width="800" height="400" align="middle">'),
HTML("<p align = 'justify'>
$$F.E.C=\\frac{b}{(b+d)}$$</p>")),
conditionalPanel(condition = "input.elecc1=='all1'",h3("Evaluation metrics for SDMs")),
br(),
verbatimTextOutput(outputId = "cm_method_metrics"),
tags$style(type='text/css', '#cm_method_metrics {background-color: rgba(255,255,0,0.40); color: green;}')
#conditionalPanel(condition = "input.binomial_test_1 == true",
# br(),
# h3("Binomial Test"),
# tags$style(type='text/css', '#cm_method_metrics {background-color: rgba(255,255,0,0.40); color: green;}'),
# verbatimTextOutput("binomial1"))
),
conditionalPanel("input.valdata_type=='min_traing_pres'",
uiOutput("area_mtp_method"),
plotOutput("binary_mtp")
),
conditionalPanel("input.valdata_type=='user_threshold'",
uiOutput("area_user_method"),
plotOutput("binary_user")
)
)
)
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