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server.R
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server.R
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# Define server logic
server <- function(input, output, session) {
output$priors <- renderUI({
if (input$cal.bayesian) {
conditionalPanel(
condition = "input$cal.bayesian",
style = "margin-left: 20px;",
checkboxInput("priors", label = "Weak priors (Recommended)", value = TRUE)
)
}
})
options(shiny.maxRequestSize=800*1024^2)
# Show package citations
get_path <- reactive({
path <- file.path(paste0(getwd()), paste("Rpackages", ".bib", sep=""))
return(path)
})
get_bib <- reactive({
## Bib logic here
pkgbib <- bibtex::read.bib("Rpackages.bib")
df <- bib2df(get_path()) %>% dplyr::select(BIBTEXKEY, NOTE, AUTHOR, TITLE, YEAR, JOURNAL, VOLUME, PAGES, URL)
df$AUTHOR <- unlist(lapply(df$AUTHOR, paste, collapse = ", "))
df <- df %>% arrange(AUTHOR)
return(df)
})
output$bibTable <- DT::renderDataTable({
bib <- get_bib()
bib
}, caption = "This is an automatically generated list of R packages used to render and run BayClump. Citation information is provided by package authors.", options = list(pageLength = 20, info = FALSE))
# Calibration tab
output$BayClump_cal_temp <- downloadHandler(
filename = "BayClump_calibration_template.csv",
content = function(file) {
write.csv(BayClump_calibration_template, file, row.names = FALSE)
}
)
calibrationData = reactive({
switch(input$calset,
"model1" = return(Petersen),
"model2" = return(Anderson),
"model1and2" = return(PetersenAnderson),
"mycal" = reactiveValues({
req(input$calibrationdata)
n_rows = length(count.fields(input$calibrationdata$datapath))
df_out = read.csv(input$calibrationdata$datapath)
return(df_out)
}),
"all" = reactiveValues({
req(input$calibrationdata)
n_rows = length(count.fields(input$calibrationdata$datapath))
df_out = read.csv(input$calibrationdata$datapath)
alldat <- rbind(df_out, PetersenAnderson)
return(alldat)
})
)
})
#For parameter estimates
if(exists("wb")) rm(wb) # Delete any existing workbook in preparation for new results
wb <- createWorkbook("calibration output") # Prepare a workbook for calibration outputs
#For convergence
if(exists("wb3")) rm(wb3) # Delete any existing workbook in preparation for new results
wb3 <- createWorkbook("Bayesian output") # Prepare a workbook for calibration outputs
if(exists("wb4")) rm(wb4) # Delete any existing workbook in preparation for new results
wb4 <- createWorkbook("Bayesian posterior output") # Prepare a workbook for calibration outputs
#For convergence
if(exists("wb5")) rm(wb5) # Delete any existing workbook in preparation for new results
wb5 <- createWorkbook("Bayesian reconstruction posterior output") # Prepare a workbook for calibration outputs
observeEvent(input$calset,{
req(input$calset == "mycal")
showModal(modalDialog(
fileInput("calibrationdata", "Select calibration data file", accept = ".csv" )
))}
)
observe({
output$contents <- renderTable({
calsummary <- calibrationData() %>%
summarize(
"Samples" = length(calibrationData()$Sample.Name),
#"Unique samples" = length(unique(calibrationData()$Sample.Name)),
#"Total replicates" = sum(calibrationData()$N),
"Materials" = length(unique(calibrationData()$Material))
)
return(calsummary)
},
rownames=FALSE, options = list(pageLength = 1, info = FALSE)
)
})
toListen <- reactive({
list(input$runmods)
})
modresult <- eventReactive(toListen() , {
if ("all" %in% input$calibrationdata) {
print(noquote("Please upload calibration data first"))
}
priors <<- input$priors
priors <<- ifelse(isTRUE(priors), "Weak", "Informative")
replicates <<- input$replication
ngenerationsBayes <<- input$generations
ngenerationsrec.bayesian <<- input$generations
##Download priors
if(exists("wb6")) rm(wb6)
wb6 <- createWorkbook("Priors - Calibration")
if("Settings" %in% names(wb6) == TRUE)
{removeWorksheet(wb6, "Settings")}
if("Distributions" %in% names(wb6) == TRUE)
{removeWorksheet(wb6, "Distributions") }
pd <- cal.prior(prior = priors)
addWorksheet(wb6, "Settings") # Add a blank sheet
writeData(wb6, sheet = "Settings", attr(pd, "params")) # Write regression data
addWorksheet(wb6, "Distributions") # Add a blank sheet
writeData(wb6, sheet = "Distributions", pd) # Write regression data
output$downloadPriorsCalibration <- downloadHandler(
filename = function() {
paste("Priors_calibration_", Sys.time(), ".xlsx", sep="")
},
content = function(file) {
saveWorkbook(wb6, file, overwrite = TRUE)
}
)
##Download priors (reconstructions)
if(exists("wb7")) rm(wb7)
wb7 <- createWorkbook("Priors - Reconstruction")
if("Settings" %in% names(wb7) == TRUE)
{removeWorksheet(wb7, "Settings")}
if("Distributions" %in% names(wb7) == TRUE)
{removeWorksheet(wb7, "Distributions") }
pd <- rec.prior(prior = priors)
addWorksheet(wb7, "Settings") # Add a blank sheet
writeData(wb7, sheet = "Settings", attr(pd, "params")) # Write regression data
addWorksheet(wb7, "Distributions") # Add a blank sheet
writeData(wb7, sheet = "Distributions", pd) # Write regression data
output$downloadPriorsReconstruction <- downloadHandler(
filename = function() {
paste("Priors_reconstruction_", Sys.time(), ".xlsx", sep="")
},
content = function(file) {
saveWorkbook(wb7, file, overwrite = TRUE)
}
)
# Remove existing worksheets from wb on "run" click, if any
if("Linear regression" %in% names(wb) == TRUE)
{removeWorksheet(wb, "Linear regression") & removeWorksheet(wb, "Linear regression CI")}
if("Inverse linear regression" %in% names(wb) == TRUE)
{removeWorksheet(wb, "Inverse linear regression") & removeWorksheet(wb, "Inverse linear regression CI")}
if("York regression" %in% names(wb) == TRUE)
{removeWorksheet(wb, "York regression") & removeWorksheet(wb, "York regression CI")}
if("Deming regression" %in% names(wb) == TRUE)
{removeWorksheet(wb, "Deming regression") & removeWorksheet(wb, "Deming regression CI")}
if("Bayesian model no errors" %in% names(wb) == TRUE)
{removeWorksheet(wb, "Bayesian model no errors") & removeWorksheet(wb, "Bayesian model no errors CI")}
if("Bayesian model with errors" %in% names(wb) == TRUE)
{removeWorksheet(wb, "Bayesian model with errors") & removeWorksheet(wb, "Bayesian model with errors CI")}
if("Bayesian mixed w errors" %in% names(wb) == TRUE)
{removeWorksheet(wb, "Bayesian mixed w errors") & removeWorksheet(wb, "Bayesian mixed w errors CI")}
##Also for the Bayesian sheet
if("Bayesian model no errors" %in% names(wb3) == TRUE)
{removeWorksheet(wb3, "Bayesian model no errors")}
if("Bayesian model with errors" %in% names(wb3) == TRUE)
{removeWorksheet(wb3, "Bayesian model with errors") }
if("Bayesian mixed w errors" %in% names(wb3) == TRUE)
{removeWorksheet(wb3, "Bayesian mixed w errors")}
##Also for the Bayesian posterior sheet
if("Bayesian model no errors" %in% names(wb4) == TRUE)
{removeWorksheet(wb4, "Bayesian model no errors")}
if("Bayesian model with errors" %in% names(wb4) == TRUE)
{removeWorksheet(wb4, "Bayesian model with errors") }
if("Bayesian mixed w errors" %in% names(wb4) == TRUE)
{removeWorksheet(wb4, "Bayesian mixed w errors")}
lmcals <<- NULL
lminversecals <<- NULL
yorkcals <<- NULL
demingcals <<- NULL
bayeslincals <<- NULL
# Calibration data
calData <<- NULL
calData <<- calibrationData()
# Recode NA or 0 error values to dummy value
calData$D47error[calData$D47error == 0] <<- 0.000001
calData$TempError[calData$TempError == 0] <<- 0.000001
calData$D47error[is.na(calData$D47error)] <<- 0.000001
calData$TempError[is.na(calData$TempError)] <<- 0.000001
calData$Material[is.na(calData$Material)] <<- 1
calData$D47error <<- abs(calData$D47error)
calData$TempError <<- abs(calData$TempError)
calData$Material <<- factor(calData$Material, labels = seq(1:length(unique(calData$Material))))
if(min(as.numeric(calData$Material)) != 1){
print(noquote("The sequence of Materials now start from 1"))
}
##Limits of the CI
minLim <- min(calData$Temperature)
maxLim <- max(calData$Temperature)
observeEvent(input$max, {
updateSliderInput(inputId = "n", max = input$max)
})
if(input$cal.ols != FALSE |
input$cal.wols != FALSE |
input$cal.york != FALSE |
input$cal.deming != FALSE |
input$cal.bayesian != FALSE) {
withProgress(message = "Running selected models, please wait", {
if(input$cal.ols == FALSE) {
}
if(input$cal.wols == FALSE) {
}
if(input$cal.york == FALSE) {
}
if(input$cal.deming == FALSE) {
}
if(input$cal.bayesian == FALSE) {
}
totalModels <- c(input$cal.ols, input$cal.wols, input$cal.york,
input$cal.deming, input$cal.bayesian)
TotProgress <- length(which(totalModels == TRUE))
if(input$cal.ols != FALSE) {
#sink(file = "out/linmodtext.txt", type = "output")
lmcals <<- cal.ols(calData, replicates = replicates)
#sink()
incProgress(1/TotProgress, detail="...Done fitting the OLS...")
lmci <<- cal.ci(data = lmcals, from = minLim, to = maxLim)
lmcalci <- as.data.frame(lmci)
output$lmcalibration <- renderPlotly({
lmfig <- plot_ly(calibrationData()
)
lmfig <- lmfig %>%
add_trace(x = ~calibrationData()$Temperature,
y = ~D47,
type = "scatter",
mode = "markers",
marker = list(color = "black"),
opacity = 0.5,
name = "Raw data",
text = as.character(calibrationData()$Sample.Name),
hovertemplate = paste(
"<b>Sample: %{text}</b><br><br>",
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>",
"Mineralogy: ", as.character(calibrationData()$Mineralogy),"<br>",
"Material: ", as.character(calibrationData()$Material),
"<extra></extra>"))
lmfig <- lmfig %>%
add_ribbons(data = lmcalci,
x = ~x,
y = ~median_est,
ymin = ~ci_lower_est,
ymax = ~ci_upper_est,
line = list(color = "#ffd166"),
fillcolor = "#ffd166",
opacity = 0.5,
name = "95% CI",
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>")) %>%
add_lines(data = lmcalci,
x = ~x,
y = ~median_est,
name = "Mean estimate",
line = list(color = "black", dash = "dash"),
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>"))
lmfig <- lmfig %>% layout(title = "<b> Linear calibration model </b>",
legend=list(title=list(text="Legend")),
xaxis = list(title = "Temperature (10<sup>6</sup>/T<sup>2</sup>)", hoverformat = ".1f"),
yaxis = list(title = "Δ<sub>47</sub> (‰)", hoverformat = ".3f"))
return(lmfig)
})
addWorksheet(wb, "Linear regression") # Add a blank sheet
addWorksheet(wb, "Linear regression CI") # Add a blank sheet
lmcalci2 <- lmcalci
names(lmcalci2) <- c("10^6/T^2", "D47_median_est", "D47_ci_lower_est", "D47_ci_upper_est")
writeData(wb, sheet = "Linear regression", lmcals) # Write regression data
writeData(wb, sheet = "Linear regression CI", lmcalci2)
output$lmcal <- renderPrint({
do.call(rbind.data.frame,apply(lmcals, 2, function(x){
cbind.data.frame(Mean= round(mean(x), 4), `SD`=round(sd(x),7))
}))
})
}
if(input$cal.wols != FALSE) {
#sink(file = "out/inverselinmodtext.txt", type = "output")
lminversecals <<- cal.wols(calData, replicates = replicates)
#sink()
incProgress(1/TotProgress, detail="...Done fitting weighted OLS...")
lminverseci <- cal.ci(data = lminversecals, from = minLim, to = maxLim)
lminversecalci <- as.data.frame(lminverseci)
output$lminversecalibration <- renderPlotly({
lminversefig <- plot_ly(data = calibrationData()
)
lminversefig <- lminversefig %>%
add_trace(x = ~calibrationData()$Temperature,
y = ~D47,
type = "scatter",
mode = "markers",
marker = list(color = "black"),
opacity = 0.5,
name = "Raw data",
text = as.character(calibrationData()$Sample.Name),
hovertemplate = paste(
"<b>Sample: %{text}</b><br><br>",
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>",
"Mineralogy: ", as.character(calibrationData()$Mineralogy),"<br>",
"Material: ", as.character(calibrationData()$Material),
"<extra></extra>")) %>%
add_ribbons(data = lminversecalci,
x = ~x,
y = ~median_est,
ymin = ~ci_lower_est,
ymax = ~ci_upper_est,
line = list(color = "#ffd166"),
fillcolor = "#ffd166",
opacity = 0.5,
name = "95% CI",
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>")) %>%
add_lines(data = lminversecalci,
x = ~x,
y = ~median_est,
name = "Mean estimate",
line = list(color = "black", dash = "dash"),
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>"))
lminversefig <- lminversefig %>% layout(title = "<b> Inverse linear calibration model </b>",
legend=list(title=list(text="Legend")),
xaxis = list(title = "Temperature (10<sup>6</sup>/T<sup>2</sup>)", hoverformat = ".1f"),
yaxis = list(title = "Δ<sub>47</sub> (‰)", hoverformat = ".3f"))
return(lminversefig)
})
addWorksheet(wb, "Inverse linear regression") # Add a blank sheet
addWorksheet(wb, "Inverse linear regression CI") # Add a blank sheet
lminversecalci2 <- lminversecalci
names(lminversecalci2) <- c("10^6/T^2", "D47_median_est", "D47_ci_lower_est", "D47_ci_upper_est")
writeData(wb, sheet = "Inverse linear regression", lminversecals) # Write regression data
writeData(wb, sheet = "Inverse linear regression CI", lminversecalci2)
# cat("\nInverse regression complete \n *R^2=", round(unlist(attributes(lminversecals)$R2[1],4)),
# " (95% CI, ",round(unlist(attributes(lminversecals)$R2[2],4)),"-",round(unlist(attributes(lminversecals)$R2[3],4)),")"
# )
output$lminversecal <- renderPrint({
do.call(rbind.data.frame,apply(lminversecals, 2, function(x){
cbind.data.frame(Mean= round(mean(x), 4),`SD` = round(sd(x),7))
}))
})
}
if(input$cal.york != FALSE) {
#sink(file = "out/yorkmodtext.txt", type = "output")
yorkcals <<- cal.york(calData, replicates = replicates)
#sink()
incProgress(1/TotProgress, detail="...Done fitting York regression...")
yorkci <- cal.ci(data = yorkcals, from = minLim, to = maxLim)
yorkcalci <- as.data.frame(yorkci)
output$yorkcalibration <- renderPlotly({
yorkfig <- plot_ly(data = calibrationData()
)
yorkfig <- yorkfig %>%
add_trace(x = ~calibrationData()$Temperature,
y = ~D47,
type = "scatter",
mode = "markers",
marker = list(color = "black"),
opacity = 0.5,
name = "Raw data",
text = as.character(calibrationData()$Sample.Name),
hovertemplate = paste(
"<b>Sample: %{text}</b><br><br>",
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>",
"Mineralogy: ", as.character(calibrationData()$Mineralogy),"<br>",
"Material: ", as.character(calibrationData()$Material),
"<extra></extra>")) %>%
add_ribbons(data = yorkcalci,
x = ~x,
y = ~median_est,
ymin = ~ci_lower_est,
ymax = ~ci_upper_est,
line = list(color = "#ffd166"),
fillcolor = "#ffd166",
opacity = 0.5,
name = "95% CI",
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>")) %>%
add_lines(data = yorkcalci,
x = ~x,
y = ~median_est,
name = "Mean estimate",
line = list(color = "black", dash = "dash"),
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>"))
yorkfig <- yorkfig %>% layout(title = "<b> York calibration model </b>",
legend=list(title=list(text="Legend")),
xaxis = list(title = "Temperature (10<sup>6</sup>/T<sup>2</sup>)", hoverformat = ".1f"),
yaxis = list(title = "Δ<sub>47</sub> (‰)", hoverformat = ".3f"))
return(yorkfig)
})
addWorksheet(wb, "York regression") # Add a blank sheet
addWorksheet(wb, "York regression CI") # Add a blank sheet
yorkcalci2 <- yorkcalci
names(yorkcalci2) <- c("10^6/T^2", "D47_median_est", "D47_ci_lower_est", "D47_ci_upper_est")
writeData(wb, sheet = "York regression", yorkcals) # Write regression data
writeData(wb, sheet = "York regression CI", yorkcalci2)
output$york <- renderPrint({
do.call(rbind.data.frame,apply(yorkcals, 2, function(x){
cbind.data.frame(Mean= round(mean(x), 4),`SD`=round(sd(x),7))
}))
})
}
if(input$cal.deming != FALSE) {
#sink(file = "out/demingmodtext.txt", type = "output")
demingcals <<- cal.deming(calData, replicates = replicates)
#sink()
incProgress(1/TotProgress, detail="...Done fitting Deming regression model...")
demingci <- cal.ci(data = demingcals, from = minLim, to = maxLim)
demingcalci <- as.data.frame(demingci)
output$demingcalibration <- renderPlotly({
demingfig <- plot_ly(data = calibrationData()
)
demingfig <- demingfig %>%
add_trace(x = ~calibrationData()$Temperature,
y = ~D47,
type = "scatter",
mode = "markers",
marker = list(color = "black"),
opacity = 0.5,
name = "Raw data",
text = as.character(calibrationData()$Sample.Name),
hovertemplate = paste(
"<b>Sample: %{text}</b><br><br>",
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>",
"Mineralogy: ", as.character(calibrationData()$Mineralogy),"<br>",
"Material: ", as.character(calibrationData()$Material),
"<extra></extra>")) %>%
add_ribbons(data = demingcalci,
x = ~x,
y = ~median_est,
ymin = ~ci_lower_est,
ymax = ~ci_upper_est,
line = list(color = "#ffd166"),
fillcolor = "#ffd166",
opacity = 0.5,
name = "95% CI",
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>")) %>%
add_lines(data = demingcalci,
x = ~x,
y = ~median_est,
name = "Mean estimate",
line = list(color = "black", dash = "dash"),
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>"))
demingfig <- demingfig %>% layout(title = "<b> Deming calibration model </b>",
legend=list(title=list(text="Legend")),
xaxis = list(title = "Temperature (10<sup>6</sup>/T<sup>2</sup>)", hoverformat = ".1f"),
yaxis = list(title = "Δ<sub>47</sub> (‰)", hoverformat = ".3f"))
return(demingfig)
})
addWorksheet(wb, "Deming regression") # Add a blank sheet
addWorksheet(wb, "Deming regression CI") # Add a blank sheet
demingcalci2 <- demingcalci
names(demingcalci2) <- c("10^6/T^2", "D47_median_est", "D47_ci_lower_est", "D47_ci_upper_est")
writeData(wb, sheet = "Deming regression", demingcals) # Write regression data
writeData(wb, sheet = "Deming regression CI", demingcalci2)
output$deming <- renderPrint({
do.call(rbind.data.frame,apply(demingcals, 2, function(x){
cbind.data.frame(Mean= round(mean(x), 4),`SD`=round(sd(x),7))
}))
})
}
if(input$cal.bayesian != FALSE) {
#sink(file = "out/Bayeslinmodtext.txt", type = "output")
bayeslincals <<- cal.bayesian(calibrationData = calData,
priors = priors,
numSavedSteps = ngenerationsBayes)
PostBLM1_fit_NoErrors <-do.call(rbind, mcmc.list(
lapply(1:ncol(bayeslincals$BLM1_fit_NoErrors), function(x) {
mcmc(as.array(bayeslincals$BLM1_fit_NoErrors)[,x,])
})))
PostBLM1_fit_NoErrors <- PostBLM1_fit_NoErrors[,-grep("log_lik", colnames(PostBLM1_fit_NoErrors))]
PostBLM1_fit <- do.call(rbind, mcmc.list(
lapply(1:ncol(bayeslincals$BLM1_fit), function(x) {
mcmc(as.array(bayeslincals$BLM1_fit)[,x,])
})))
PostBLM1_fit <- PostBLM1_fit[,-grep("log_lik", colnames(PostBLM1_fit))]
PostBLM3_fit <- do.call(rbind, mcmc.list(
lapply(1:ncol(bayeslincals$BLM3_fit), function(x) {
mcmc(as.array(bayeslincals$BLM3_fit)[,x,])
})))
PostBLM3_fit <- PostBLM3_fit[,-grep("log_lik", colnames(PostBLM3_fit))]
#sink()
incProgress(1/TotProgress, detail="...Done fitting the Bayesian linear regression model...")
bayeslincinoerror <- cal.ci(data = PostBLM1_fit_NoErrors, from = minLim, to = maxLim)
bayeslincalcinoerror <- as.data.frame(bayeslincinoerror)
bayeslinciwitherror <- cal.ci(data = PostBLM1_fit, from = minLim, to = maxLim)
bayeslincalciwitherror <- as.data.frame(bayeslinciwitherror)
output$bayeslincalibration <- renderPlotly({
bayeslinfig <- plot_ly(data = calibrationData()
)
bayeslinfig <- bayeslinfig %>%
add_trace(x = ~calibrationData()$Temperature,
y = ~D47,
type = "scatter",
mode = "markers",
marker = list(color = "black"),
opacity = 0.5,
name = "Raw data",
text = as.character(calibrationData()$Sample.Name),
hovertemplate = paste(
"<b>Sample: %{text}</b><br><br>",
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>",
"Mineralogy: ", as.character(calibrationData()$Mineralogy),"<br>",
"Material: ", as.character(calibrationData()$Material),
"<extra></extra>")) %>%
add_ribbons(data = bayeslincalcinoerror,
x = ~x,
y = ~median_est,
ymin = ~ci_lower_est,
ymax = ~ci_upper_est,
line = list(color = "#ffd166"),
fillcolor = "#ffd166",
opacity = 0.5,
name = "95% CI - no error",
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>")) %>%
add_lines(data = bayeslincalcinoerror,
x = ~x,
y = ~median_est,
name = "Mean estimate - no error",
line = list(color = "black", dash = "dash"),
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>")) %>%
add_ribbons(data = bayeslincalciwitherror,
x = ~x,
y = ~median_est,
ymin = ~ci_lower_est,
ymax = ~ci_upper_est,
line = list(color = "#446455"),
fillcolor = "#446455",
opacity = 0.5,
name = "95% CI - with error",
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>")) %>%
add_lines(data = bayeslincalciwitherror,
x = ~x,
y = ~median_est,
name = "Mean estimate - with error",
line = list(color = "#446455", dash = "dash"),
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>"))
bayeslinfig <- bayeslinfig %>% layout(title = "<b> Bayesian linear calibration model </b>",
legend=list(title=list(text="Legend")),
xaxis = list(title = "Temperature (10<sup>6</sup>/T<sup>2</sup>)", hoverformat = ".1f"),
yaxis = list(title = "Δ<sub>47</sub> (‰)", hoverformat = ".3f"))
return(bayeslinfig)
})
addWorksheet(wb, "Bayesian model no errors") # Add a blank sheet
addWorksheet(wb, "Bayesian model no errors CI") # Add a blank sheet
addWorksheet(wb, "Bayesian model with errors") # Add a blank sheet
addWorksheet(wb, "Bayesian model with errors CI") # Add a blank sheet
bayeslincalcinoerror2 <- bayeslincalcinoerror
names(bayeslincalcinoerror2) <- c("10^6/T^2", "D47_median_est", "D47_ci_lower_est", "D47_ci_upper_est")
bayeslincalciwitherror2 <- bayeslincalciwitherror
names(bayeslincalciwitherror2) <- c("10^6/T^2", "D47_median_est", "D47_ci_lower_est", "D47_ci_upper_est")
writeData(wb, sheet = "Bayesian model no errors", PostBLM1_fit_NoErrors) # Write regression data
writeData(wb, sheet = "Bayesian model no errors CI", bayeslincalcinoerror2)
writeData(wb, sheet = "Bayesian model with errors", PostBLM1_fit) # Write regression data
writeData(wb, sheet = "Bayesian model with errors CI", bayeslincalciwitherror2)
##For the Bayesian sheet
conv_BLM <- summary(bayeslincals$BLM1_fit_NoErrors)$summary
conv_BLM_errors <- summary(bayeslincals$BLM1_fit)$summary
conv_BLM <- cbind.data.frame(parameter=row.names(conv_BLM),conv_BLM)
conv_BLM_errors <- cbind.data.frame(parameter=row.names(conv_BLM_errors),conv_BLM_errors)
addWorksheet(wb3, "Bayesian model no errors") # Add a blank sheet
addWorksheet(wb3, "Bayesian model with errors") # Add a blank sheet
writeData(wb3, sheet = "Bayesian model no errors", conv_BLM) # Write regression data
writeData(wb3, sheet = "Bayesian model with errors", conv_BLM_errors) # Write regression data
##For the posterior sheet
addWorksheet(wb4, "Bayesian model no errors") # Add a blank sheet
addWorksheet(wb4, "Bayesian model with errors") # Add a blank sheet
writeData(wb4, sheet = "Bayesian model no errors", PostBLM1_fit_NoErrors) # Write regression data
writeData(wb4, sheet = "Bayesian model with errors", PostBLM1_fit) # Write regression data
outBLM <- summary(bayeslincals$BLM1_fit_NoErrors)$summary
output$blinnoerr <- renderPrint({
ret <- outBLM[c(1:2),c(1,3)]
cbind.data.frame(Mean= round(ret[,1], 4),`SD`=round(ret[,2],7))
})
outBLMerrors <- summary(bayeslincals$BLM1_fit)$summary
output$blinwerr <- renderPrint({
ret <- outBLMerrors[c(1:2),c(1,3)]
cbind.data.frame(Mean= round(ret[,1], 4),`SD`=round(ret[,2],7))
})
outBLMM <- summary(bayeslincals$BLM3_fit)$summary
outBLMM <- as.data.frame(outBLMM[grep("alpha|beta", row.names(outBLMM)),] )
output$blinmwerr <- renderPrint({
ret <- outBLMM[,c(1,3)]
ret2 <- cbind.data.frame(Mean= round(ret[,1], 4),`SD`=round(ret[,2],7))
row.names(ret2) <- row.names(ret)
ret2
}
)
outBLMM2 <- PostBLM3_fit
outBLMM2 <- as.data.frame(outBLMM2[,grep("alpha|beta", colnames(outBLMM2))] )
nmat <- ncol(outBLMM2)/2
outBLMM2_alpha <- as.data.frame(outBLMM2[,grep("alpha", colnames(outBLMM2))])
outBLMM2_beta <- as.data.frame(outBLMM2[,grep("beta", colnames(outBLMM2))])
bayeslmminciwitherror <- lapply(1:nmat, function(x){
subset <- cbind.data.frame("alpha" = outBLMM2_alpha[,x], "beta" = outBLMM2_beta[,x])
cal.ci(data = subset, from = minLim, to = maxLim)[[1]]
})
bayeslmminciwitherror <- rbindlist(bayeslmminciwitherror, idcol="Material")
bayeslmmincalciwitherror <- as.data.frame(bayeslmminciwitherror)
addWorksheet(wb, "Bayesian mixed w errors") # Add a blank sheet
addWorksheet(wb, "Bayesian mixed w errors CI") # Add a blank sheet
bayeslmmincalciwitherror2 <- bayeslmmincalciwitherror
names(bayeslmmincalciwitherror2) <- c("material", "10^6/T^2", "D47_median_est", "D47_ci_lower_est", "D47_ci_upper_est")
writeData(wb, sheet = "Bayesian mixed w errors", PostBLM3_fit) # Write regression data
writeData(wb, sheet = "Bayesian mixed w errors CI", bayeslmmincalciwitherror2)
##For the Bayesian sheet
conv_BLMM <- summary(bayeslincals$BLM3_fit)$summary
conv_BLMM <- cbind.data.frame(parameter=row.names(conv_BLMM),conv_BLMM)
addWorksheet(wb3, "Bayesian mixed w errors") # Add a blank sheet
writeData(wb3, sheet = "Bayesian mixed w errors", conv_BLMM) # Write regression data
addWorksheet(wb4, "Bayesian mixed w errors") # Add a blank sheet
writeData(wb4, sheet = "Bayesian mixed w errors", PostBLM3_fit) # Write regression data
output$bayesmixedcalibration <- renderPlotly({
bayesmixedfig <- plot_ly(data = calibrationData()
)
bayesmixedfig <- bayesmixedfig %>%
add_trace(x = ~calibrationData()$Temperature,
y = ~D47,
type = "scatter",
mode = "markers",
marker = list(color = "black"),
opacity = 0.5,
name = "Raw data",
text = as.character(calibrationData()$Sample.Name),
hovertemplate = paste(
"<b>Sample: %{text}</b><br><br>",
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>",
"Mineralogy: ", as.character(calibrationData()$Mineralogy),"<br>",
"Material: ", as.character(calibrationData()$Material),
"<extra></extra>"))
colsTarget <- viridis(nmat, option = "D", end = 0.9)
materials <- unique(bayeslmmincalciwitherror$Material)
for(y in 1:nmat){
bayesmixedfig <- bayesmixedfig %>%
add_ribbons(data = bayeslmmincalciwitherror[bayeslmmincalciwitherror$Material==materials[y],],
x = ~x,
y = ~median_est,
ymin = ~ci_lower_est,
ymax = ~ci_upper_est,
line = list(color = colsTarget[y]),
fillcolor = colsTarget[y],
opacity = 0.5,
name = paste("95% CI - with error, Material ", materials[y]),
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>")) %>%
add_lines(data = bayeslmmincalciwitherror[bayeslmmincalciwitherror$Material==materials[y],],
x = ~x,
y = ~median_est,
name = paste("Mean estimate - with error, Material ", materials[y]),
line = list(color = colsTarget[y], dash = "dash"),
hovertemplate = paste(
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>"))
}
bayesmixedfig <- bayesmixedfig %>% layout(title = "<b> Bayesian mixed model </b>",
legend=list(title=list(text="Legend")),
xaxis = list(title = "Temperature (10<sup>6</sup>/T<sup>2</sup>)", hoverformat = ".1f"),
yaxis = list(title = "Δ<sub>47</sub> (‰)", hoverformat = ".3f"))
return(bayesmixedfig)
})
lootab <- attr(bayeslincals,"loo")
cat("Model comparison in loo\n\n")
cat("")
print(lootab)
}
})
}
})
output$modresults <- renderPrint({
modresult()
})
output$downloadcalibrations <- downloadHandler(
filename = function() {
paste("Calibration_output_", Sys.time(), ".xlsx", sep="")
},
content = function(file) {
saveWorkbook(wb, file, overwrite = TRUE)
}
)
output$downloadBayesian <- downloadHandler(
filename = function() {
paste("Bayesian_output_", Sys.time(), ".xlsx", sep="")
},
content = function(file) {
saveWorkbook(wb3, file, overwrite = TRUE)
}
)
output$downloadPosteriorCalibration <- downloadHandler(
filename = function() {
paste("Bayesian_posterior_output_", Sys.time(), ".xlsx", sep="")
},
content = function(file) {
saveWorkbook(wb4, file, overwrite = TRUE)
}
)
# Calibration plots tab
observe({
minlength <- length(unique(calibrationData()$Mineralogy))
if( !all(is.na(calibrationData()$Mineralogy)) == TRUE ){
output$rawcaldata <- renderPlotly({
rawcalfig <- plot_ly(calibrationData(),
x = ~Temperature,
y = ~D47,
type = "scatter",
mode = "markers",
#linetype = ~as.factor(Material),
color = ~as.factor(Mineralogy),
colors = viridis_pal(option = "D", end = 0.9)(minlength),
opacity = 0.6,
error_y = ~list(array = ~D47error, color = "#000000"),
error_x = ~list(array = ~TempError, color = "#000000"),
text = as.character(calibrationData()$Sample.Name),
hovertemplate = paste(
"<b>Sample: %{text}</b><br><br>",
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>",
"Mineralogy: ", as.character(calibrationData()$Mineralogy),"<br>",
"Material: ", as.character(calibrationData()$Material),
"<extra></extra>"))
rawcalfig <- rawcalfig %>% layout(title = "<b> Raw calibration data from user input </b>",
legend=list(title=list(text="Material and mineralogy")),
xaxis = list(title = "Temperature (10<sup>6</sup>/T<sup>2</sup>)"),
yaxis = list(title = "Δ<sub>47</sub> (‰)", hoverformat = ".3f"))
return(rawcalfig)
})
}else{
output$rawcaldata <- renderPlotly({
rawcalfig <- plot_ly(calibrationData(),
x = ~Temperature,
y = ~D47,
type = "scatter",
mode = "lines+markers",
linetype = ~as.factor(Material),
color = ~as.factor(Material),
colors = viridis_pal(option = "D", end = 0.9)(length(unique(calibrationData()$Material))),
opacity = 0.6,
error_y = ~list(array = ~D47error, color = "#000000"),
error_x = ~list(array = ~TempError, color = "#000000"),
text = as.character(calibrationData()$Sample.Name),
hovertemplate = paste(
"<b>Sample: %{text}</b><br><br>",
"Temperature (10<sup>6</sup>/T<sup>2</sup>): %{x}<br>",
"Δ<sub>47</sub> (‰): %{y}<br>",
"Material: ", as.character(calibrationData()$Material),
"<extra></extra>"))
rawcalfig <- rawcalfig %>% layout(title = "<b> Raw calibration data from user input </b>",
legend=list(title=list(text="Material")),
xaxis = list(title = "Temperature (10<sup>6</sup>/T<sup>2</sup>)"),
yaxis = list(title = "Δ<sub>47</sub> (‰)", hoverformat = ".3f"))
return(rawcalfig)
})
}
})
# Reconstruction tab
output$BayClump_reconstruction_template.csv <- downloadHandler(
filename = "BayClump_reconstruction_template.csv",
content = function(file) {
write.csv(BayClump_reconstruction_template, file, row.names = FALSE)
}
)
reconstructionData = reactive({
req(input$reconstructiondata)
n_rows = length(count.fields(input$reconstructiondata$datapath))
df_out = read.csv(input$reconstructiondata$datapath)
return(df_out)
})
if(exists("wb2")) rm(wb2) # Delete any existing workbook in preparation for new results
wb2 <- createWorkbook("reconstruction output") # Prepare a workbook for reconstruction outputs
observe({
output$contents2 <- renderTable({
recsummary <- reconstructionData() %>%
summarize(
"Unique samples" = length(unique(reconstructionData()$Sample)),
"Total replicates" = sum(reconstructionData()$N),
"Materials" = length(unique(reconstructionData()$Material))
)
return(recsummary)
},
rownames=FALSE, options = list(pageLength = 1, info = FALSE)
)
})
recresult <- eventReactive(input$runrec, {
if(is.null(input$reconstructiondata)) {print(noquote("Please upload reconstruction data first"))}
if(!is.null(input$reconstructiondata)) {
recData <- NULL
recData <- reconstructionData()
# Remove existing worksheets from wb2 on "run" click, if any
if("Linear" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Linear")}
if("Linear w no uncertainty" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Linear w no uncertainty")}
if("Inverse linear" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Inverse linear")}
if("Inverse linear w no uncertainty" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Inverse linear w no uncertainty")}
if("York" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "York")}
if("York w no uncertainty" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "York w no uncertainty")}
if("Deming" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Deming")}
if("Deming w no uncertainty" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Deming w no uncertainty")}
if("Bayes and error" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Bayes and error") }
if("Bayes w error no uncertainty" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Bayes w error no uncertainty") }
if("Bayesian predictions" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Bayesian predictions")}
if("Bayesian mixed model" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Bayesian mixed model")}
if("Bayesian linear model, errors" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Bayesian linear model, errors")}
if("Bayesian linear model" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Bayesian linear model")}
if("Bayesian linear mixed model" %in% names(wb2) == TRUE)
{removeWorksheet(wb2, "Bayesian linear mixed model")}
##Also for the Bayesian posterior sheet