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server.R
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server.R
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options(shiny.maxRequestSize=30*1024^2) ## increase file upload to 30MB
shinyServer(function(input, output,session) {
## initiate trap type
trapType <- "single"
## read in input data
traps <- reactive({
if(input$example == TRUE){
if(input$trapType_ex== "single"){
load("shiny_example_traps.RData")
traps <- shiny_example_traps
traps
}else{
if(input$trapType_ex== "multi"){
load("shiny_multi_traps.RData")
traps <- shiny_multi_traps
traps
}
}
}else{
req(input$file1)
traps <- read.csv(input$file1$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
validate(need("x" %in% names(traps) & "y" %in% names(traps) & "post" %in% names(traps),
"Trap file must contain columns named x, y, and post"))
validate(need(class(traps$post)=="integer", "Please give post ID as a whole number"))
validate(need(class(traps$x)%in%c("integer","numeric"), "Please ensure x coordinate is numeric"))
validate(need(class(traps$y)%in%c("integer","numeric"), "Please ensure y coordinate is numeric"))
return(traps)
}
})
detections <- reactive({
if("simple" %in% input$which_example & input$example == TRUE & input$trapType_ex== "single"){
load("shiny_example_detections.RData")
detections <- shiny_example_detections[,1:3]
disable("bearing_range")
hide("bearing_range")
return(detections)
}else{
if("bearings" %in% input$which_example & input$example == TRUE & input$trapType_ex== "single"){
load("shiny_example_detections.RData")
detections <- shiny_example_detections[,1:4]
enable("bearing_range")
shinyjs::show("bearing_range")
return(detections)
}else{
if("distance" %in% input$which_example & input$example == TRUE & input$trapType_ex == "single"){
load("shiny_example_detections.RData")
detections <- shiny_example_detections[,-4]
disable("bearing_range")
hide("bearing_range")
return(detections)
}else{
if("bd" %in% input$which_example & input$example == TRUE & input$trapType_ex == "single"){
load("shiny_example_detections.RData")
detections <- shiny_example_detections
enable("bearing_range")
shinyjs::show("bearing_range")
return(detections)
}else{
if("simple" %in% input$which_example_multi& input$example == TRUE & input$trapType_ex== "multi"){
load("shiny_multi_detections.RData")
detections <- shiny_multi_detections[,1:4]
disable("bearing_range")
hide("bearing_range")
return(detections)
}else{
if("bearings" %in% input$which_example_multi & input$example == TRUE & input$trapType_ex== "multi"){
load("shiny_multi_detections.RData")
detections <- shiny_multi_detections[,1:5]
enable("bearing_range")
shinyjs::show("bearing_range")
return(detections)
}else{
req(input$file2)
detections <- read.csv(input$file2$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
validate(need("occasion" %in% names(detections) & "group" %in% names(detections) &
"post" %in% names(detections),
"Detections file must contain columns named occasion, group, and post"))
validate(need(class(detections$group) == "integer", "Please give group ID as a whole number"))
validate(need(class(detections$occasion) == "integer", "Please give occasion ID as a whole number"))
validate(need(class(detections$post) == "integer", "Please give post ID as a whole number"))
if("bearing" %in% names(detections)){
validate(need(class(detections$bearing) == "numeric" | class(detections$bearing) == "integer", "Please make sure all bearings are numeric"))
enable("bearing_range")
shinyjs::show("bearing_range")
}else{
disable("bearing_range")
hide("bearing_range")
}
if("distance" %in% names(detections)){
validate(need(class(detections$distance) == "numeric" | class(detections$distance) == "integer" , "Please make sure all distances are numeric"))
}
return(detections)
}
}
}
}
}
}
})
## single/multi variable
trapType <- reactive({
if("array"%in%names(detections()) & "array"%in%names(traps()) & input$trapType_ex == "multi"){
trapType <- "multi"
}else{
if( "array"%in%names(detections()) & "array"%in%names(traps()) & input$trapType_us == "multi"){
trapType <- "multi"
}else{
trapType <- "single"
}
}
})
## covariates
covariates <- reactive({
if("yes"%in%input$example_covariates){
lst <- list(covariate = raster::raster("example_raster.tif"))
}else{
req(input$covs)
files <- input$covs
lst <- list()
for(i in 1:length(files[,1])){
lst[[i]] <- raster::raster(files[[i,"datapath"]])
}
names(lst) <- unlist(strsplit(files[,1],".tif"))
}
lst
})
output$cov.list <- renderPlot({
req(covariates())
covariates <- covariates()
covs <- lapply(covariates,function(x) gplot(x) +
geom_tile(aes(fill = value)) +
xlab("x-axis") + ylab("y-axis") +
theme(panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1)))
for(i in 1:length(covariates)){covs[[i]] <- covs[[i]] + ggtitle(names(covariates)[i])}
do.call(grid.arrange,covs)
})
## covariate buttons
output$covariate_controls <- renderUI({
req(covariates())
covariates <- covariates()
checkboxGroupInput("covariate.choose", "Choose covariates to include in your model",
names(covariates))
})
## covariate factor buttons
output$cov_factor <- renderUI({
req(covariates())
req(input$covariate.choose)
which.covariates <- input$covariate.choose
covariates <- covariates()
covariates <- covariates[which.covariates]
checkboxGroupInput("covariate.factor", "Which covariates are factor covariates (tick for yes)",
names(covariates))
})
cov.use <- reactive({
req(covariates())
req(input$covariate.choose)
req(mask())
mask <- mask()
if(class(mask) == "list"){
msk.locs <- lapply(mask, function(x) cbind(x[,1],x[,2]))
which.covariates <- input$covariate.choose
covariates <- covariates()
covariates <- covariates[which.covariates]
if(names(covariates) %in% input$covariate.factor){
covariates[[input$covariate.factor]] <- as.factor(covariates[[input$covariate.factor]])
levels(covariates[[input$covariate.factor]])[[1]]$category <- as.factor(levels(covariates[[input$covariate.factor]])[[1]]$ID)
}
covariates.use <- list()
for(i in 1:length(msk.locs)){
tmp <- lapply(covariates,function(x) if(is.factor(x)){
factorValues(x,extract(x,msk.locs[[i]]))
}else{
data.frame(extract(x,msk.locs[[i]]))
}
)
covariates.use[[i]] <- as.data.frame(tmp)
names(covariates.use[[i]]) <- names(covariates)
}
return(covariates.use)
}else{
msk.locs <- cbind(mask[,1],mask[,2])
which.covariates <- input$covariate.choose
covariates <- covariates()
covariates <- covariates[which.covariates]
covariates.use <- lapply(covariates, function(x) if(is.factor(x)){
factorValues(x,extract(x,msk.locs))
}else{
extract(x,msk.locs)
}
)
names(covariates.use) <- names(covariates)
return(as.data.frame(covariates.use))
}
})
## which array raw
output$which_array_raw <- renderUI({
detections <- detections()
traps <- traps()
validate(need(!is.null(traps),""))
validate(need(!is.null(detections),""))
validate(need("array"%in%names(detections),""))
validate(need("array"%in%names(traps),""))
validate(need(trapType() == "multi",""))
arrs <- as.numeric(names(table(traps$array)))
mn <- min(arrs)
mx <- max(arrs)
numericInput("choose_trap_raw", "Choose trap array for plot",
min = mn, max = mx,
value = mn)
})
## which array capt
output$which_array_capt <- renderUI({
detections <- detections()
traps <- traps()
validate(need(!is.null(traps),""))
validate(need(!is.null(detections),""))
validate(need("array"%in%names(detections),""))
validate(need("array"%in%names(traps),""))
validate(need(trapType() == "multi",""))
arrs <- as.numeric(names(table(traps$array)))
mn <- min(arrs)
mx <- max(arrs)
numericInput("choose_trap_capt", "Choose trap array for capture history matrix",
min = mn, max = mx,
value = mn)
})
## which array output plots
output$which_array <- renderUI({
detections <- detections()
traps <- traps()
validate(need(!is.null(traps),""))
validate(need(!is.null(detections),""))
validate(need("array"%in%names(detections),""))
validate(need("array"%in%names(traps),""))
validate(need(trapType() == "multi",""))
## validate(need(length(table(traps$array))==length(table(detections$array)),"Need equal number of arrays in detection file as in trap file"))
arrs <- as.numeric(names(table(traps$array)))
mn <- min(arrs)
mx <- max(arrs)
numericInput("choose_trap", "Choose trap array for output plots",
min = mn, max = mx,
value = mn)
})
## Clunky way of enabling/disabling buttons
observe({
if(input$example == TRUE){
disable("file1")
disable("file2")
disable("header")
disable("sep")
disable("quote")
hide("file1")
hide("file2")
hide("header")
hide("sep")
hide("quote")
enable("example_covariates")
shinyjs::show("example_covariates")
}else{
enable("file1")
enable("file2")
enable("header")
enable("sep")
enable("quote")
shinyjs::show("file1")
shinyjs::show("file2")
shinyjs::show("header")
shinyjs::show("sep")
shinyjs::show("quote")
disable("which_example_multi")
shinyjs::hide("which_example_multi")
disable("which_example")
shinyjs::hide("which_example")
disable("example_covariates")
shinyjs::hide("example_covariates")
}
if(trapType() == "single" & input$example == TRUE){
enable("which_example")
enable("example_covariates")
shinyjs::show("example_covariates")
shinyjs::show("which_example")
disable("which_example_multi")
shinyjs::hide("which_example_multi")
}
if(trapType() == "multi" & input$example == TRUE){
disable("example_covariates")
shinyjs::hide("example_covariates")
disable("which_example")
shinyjs::hide("which_example")
enable("which_example_multi")
shinyjs::show("which_example_multi")
}
## initislly disable some options if no model fitted
observeEvent(!input$fit,{
disable("downloadSurfPlot")
disable("downloadContPlot")
disable("downloadDetPlot")
disable("call.num")
disable("reset_locplot")
disable("distD")
disable("downloadbearingPlot")
disable("downloaddistancePlot")
disable("anispeed")
disable("report")
})
observeEvent(input$fit,{
enable("downloadSurfPlot")
enable("downloadContPlot")
enable("downloadDetPlot")
enable("call.num")
enable("reset_locplot")
enable("distD")
enable("downloadbearingPlot")
enable("downloaddistancePlot")
enable("anispeed")
enable("report")
})
if(input$example == FALSE | isTruthy(input$file1) == FALSE){
disable("downloadMask")
disable("buffer")
disable("spacing")
}
if(isTruthy(input$file1) == TRUE | input$example == TRUE){
enable("downloadMask")
enable("buffer")
enable("spacing")
}
if(input$example == FALSE | isTruthy(input$file1) == FALSE & isTruthy(input$file2) == FALSE){
disable("select")
disable("fixedParamSelection")
disable("fit")
hide("bearing_range")
}
if(input$example == TRUE | isTruthy(input$file1) == TRUE & isTruthy(input$file2) == TRUE){
enable("select")
enable("fixedParamSelection")
enable("fit")
shinyjs::show("bearing_range")
}
})
## output trap locations
output$traps <- renderTable({
traps <- traps()
if(input$disp == "head") {
return(head(traps))
}else{
return(traps)
}
},
striped = TRUE)
## code to plot trap locations
output$trapsPlot <- renderPlot({
traps <- traps()
if(!is.null(traps$post)){
plot(traps$x,traps$y,asp = 1,type = "n",xlab = "x-axis",ylab = "y-axis")
text(traps$x,traps$y,traps$post,lwd = 2)
}else{
plot(traps$x,traps$y,asp = 1,pch = 4,cex = 2,lwd = 3,xlab = "x-axis",ylab = "y-axis")
}
})
output$detections <- renderTable({
detections <- detections()
if(input$disp == "head") {
return(head(detections))
}else{
return(detections)
}
},
striped = TRUE)
capthist <- reactive({
detections <- detections()
traps <- traps()
validate(need(!is.null(traps),""))
validate(need(!is.null(detections),""))
if("bearing" %in% names(detections)){
if(input$bearing_range == "rad"){
validate(need(min(detections$bearing) >= 0 & max(detections$bearing) <= 2*pi,
"Your bearing measurements are outside the range of radians,
please indicate correct bearing measurement in the sidebar."))
}else{
if(input$bearing_range == "bd"){
validate(need(max(detections$bearing) > 2*pi,"Looks like your bearing measurements
are already in radians, please indicate correct bearing measurement in the sidebar. "))
detections$bearing <- (pi/180)*detections$bearing
}
}
}
if(trapType() == "multi"){
traps <- split(traps, traps$array)
capt.hist <- get.capt.hist(detections, traps = traps)
for(i in 1:length(capt.hist)){
colnames(capt.hist[[i]][[1]]) <- paste(1:ncol(capt.hist[[i]][[1]]))
n.row <- nrow(capt.hist[[i]][[1]])
if(n.row != 0 ){
oc <- detections$occasion[detections$array == i]
grp <- detections$group[detections$array == i]
rownames(capt.hist[[i]][[1]]) <- unique(paste("occasion",oc, "group",
grp))
}
}
}else{
validate(need(length(table(traps$array))==length(table(detections$array)),
"Need equal number of arrays in detection file as in trap file"))
validate(need(trapType() == "single",""))
capt.hist <- get.capt.hist(detections, traps = traps)
colnames(capt.hist[[1]]) <- paste(1:ncol(capt.hist[[1]]))
rownames(capt.hist[[1]]) <- unique(paste("occasion",detections$occasion, "group", detections$group))
}
return(capt.hist)
})
output$capt.hist <- renderTable({
capthist <- capthist()
traps <- traps()
validate(need(!is.null(capthist),""))
if(trapType() == "single"){
if(input$disp == "head") {
return(head(capthist[[1]]))
}else{
return(capthist[[1]])
}
}else{
if(trapType() == "multi"){
if(input$disp == "head") {
validate(need(input$choose_trap <= max(as.numeric(names(table(traps$array)))),"Please provide valid array"))
return(head(capthist[[input$choose_trap_capt]][[1]]))
}else{
validate(need(input$choose_trap <= max(as.numeric(names(table(traps$array)))),"Please provide valid array"))
try(capthist[[input$choose_trap_capt]][[1]],silent = TRUE)
}
}
}
},striped = TRUE,rownames = TRUE,colnames = TRUE,digits = 0)
## chage buffer slider based on trap range
## chage spacing slider based on trap range
observe({
if(!is.null(traps())) {
traps <- traps()
detections <- detections()
if(trapType() == "single"){
maxdistance <- diff(range(traps$x,traps$y))/4
updateNumericInput(session, "spacing", max = maxdistance, value = maxdistance/2)
maxdistance <- 4*diff(range(traps$x,traps$y))
updateNumericInput(session, "buffer", max = maxdistance,value = maxdistance/2)
}else{
validate(need("array"%in%names(detections),""))
validate(need("array"%in%names(traps),""))
traps <- split(traps, traps$array)
maxdistance <- diff(range(traps[[1]]$x,traps[[1]]$y))/4
updateNumericInput(session, "spacing", max = maxdistance, value = maxdistance/2)
maxdistance <- 4*diff(range(traps[[1]]$x,traps[[1]]$y))
updateNumericInput(session, "buffer", max = maxdistance,value = maxdistance/2)
}
}
})
## show all plot for raw data
output$show <- renderPlot({
traps <- traps()
capt.hist <- capthist()
if(trapType() == "single"){
validate(need(input$show.call.num,"Please provide a call number"))
validate(need(input$show.call.num <= nrow(capt.hist$bincapt),"Please provide a valid call number"))
show.data(traps, capt.hist,id = input$show.call.num)
legend("top",legend = paste("call",input$show.call.num,sep = " "),bty = "n")
}else{
validate(need(trapType() == "multi",""))
traps <- split(traps, traps$array)
validate(need(input$show.call.num,"Please provide a call number"))
try(show.data(traps[[input$choose_trap_raw]], capt.hist[[input$choose_trap_raw]],id = input$show.call.num),silent = TRUE)
legend("top",legend = paste("array", input$choose_trap_raw, " call",input$show.call.num,sep = " "),bty = "n")
}
})
## change buffer sliding in advanced increase buffer option chosen
observe({
if("inc" %in% input$advancedOptions) {
maxdistance <- input$incmaskbuffer
updateSliderInput(session, "buffer", max = maxdistance,value = maxdistance/2)
}
})
## plot of mask
mask <- eventReactive(input$msk,{
shinyjs::show("processing_msk") ## stuff to disable fitting button
traps <- traps()
validate(need(!is.null(traps),""))
validate(need(input$buffer > input$spacing,"The mask buffer cannot be less than the spacing"))
validate(need(input$buffer/input$spacing < 80, "Infeasibly fine mask"))
if(trapType() == "single"){
traps <- as.matrix(cbind(traps$x,traps$y))
mask <- create.mask(traps,input$buffer,input$spacing)
}else{
traps <- split(traps, traps$array)
traps <- lapply(traps,function(x) cbind(x$x,x$y))
mask <- lapply(traps,create.mask,buffer = input$buffer,spacing = input$spacing)
}
hide("processing_msk")
enable("fit")
return(mask)
})
output$maskPlot <- renderPlot({
traps <- traps()
mask <- mask()
if(trapType() == "single"){
grid.arrange(show.mask(mask,traps))
}else{
traps <- split(traps, traps$array)
validate(need(is.list(mask), ""))
m.lst <- list()
for(i in 1:length(traps)){ m.lst[[i]] <- show.mask(mask[[i]], traps = traps[[i]])}
do.call(grid.arrange, m.lst)
}
})
## print out mask buffer info
output$maskinfo <- renderText({
validate(need(!is.null(mask()),""))
paste("This mask is assuming that a distance of ",input$buffer,
"meters is the maximum distance at which a detection is feasibly possible")
})
## choose which parameters of which detection function to fit, conditional numeric input for fixing param values
output$fixedParamSelection <- renderUI({
params.fix <- cbind(c("g0","sigma","g0","sigma","z","lambda0","sigma","shape","scale"),
c("hn","hn","hr","hr","hr","hhn","hhn","th","th"))
checkboxGroupInput("parameter", "Fix which parameters:",
choices = as.character(params.fix[params.fix[,2] == input$select,1]),inline = TRUE)
})
output$fixedg0 <- renderUI({
conditionalPanel(condition = "input.parameter.includes('g0')",
numericInput("g0","fix g0 to:",value = 1,min = 1,max = 100,step = 1)
)
})
output$fixedsigma <- renderUI({
conditionalPanel(condition = "input.parameter.includes('sigma')",
numericInput("sigma","fix sigma to:",value = 1,min = 1,max = 100,step = 1)
)
})
output$fixedz <- renderUI({
conditionalPanel(condition = "input.parameter.includes('z')",
numericInput("z","fix z to:",value = 1,min = 1,max = 100,step = 1)
)
})
output$fixedlambda0 <- renderUI({
conditionalPanel(condition = "input.parameter.includes('lambda0')",
numericInput("lambda0","fix lambda0 to:",value = 1,min = 1,max = 100,step = 1)
)
})
output$fixedshape <- renderUI({
conditionalPanel(condition = "input.parameter.includes('shape')",
numericInput("shape","fix shape to:",value = 1,min = 1,max = 100,step = 1)
)
})
output$startParamSelection <- renderUI({
params.fix <- cbind(c("g0","sigma","g0","sigma","z","lambda0","sigma","shape","scale"),
c("hn","hn","hr","hr","hr","hhn","hhn","th","th"))
checkboxGroupInput("parset", "Set starting values for which parameters:",
choices = as.character(params.fix[params.fix[,2] == input$select,1]),inline = TRUE)
})
output$svg0 <- renderUI({
conditionalPanel(condition = "input.parset.includes('g0') && !input.parameter.includes('g0')",
numericInput("svg0","g0 start value:",value = 1,min = 1,max = 100,step = 1)
)
}) ## set starting value of g0 ensure it isn't already fixed
output$svsigma <- renderUI({
conditionalPanel(condition = "input.parset.includes('sigma') && !input.parameter.includes('sigma')",
numericInput("svsigma","sigma start value:",value = 1,min = 1,max = 100,step = 1)
)
}) ## set starting value of sigma ensure it isn't already fixed
output$svz <- renderUI({
conditionalPanel(condition = "input.parset.includes('z') && !input.parameter.includes('z')",
numericInput("svz","z start value:",value = 1,min = 1,max = 100,step = 1)
)
}) ## set starting value of z ensure it isn't already fixed
output$svlambda0 <- renderUI({
conditionalPanel(condition = "input.parset.includes('lambda0') && !input.parameter.includes('lambda0')",
numericInput("svlambda0","lambda0 start value:",value = 1,min = 1,max = 100,step = 1)
)
}) ## set starting value of lambda0 ensure it isn't already fixed
output$svshape <- renderUI({
conditionalPanel(condition = "input.parset.includes('shape') && !input.parameter.includes('shape')",
numericInput("svshape","shape start value:",value = 1,min = 1,max = 100,step = 1)
)
}) ## set starting value of shape ensure it isn't already fixed
output$svscale <- renderUI({
conditionalPanel(condition = "input.parset.includes('scale') && !input.parameter.includes('scale')",
numericInput("svscale","scale start value:",value = 1,min = 1,max = 100,step = 1)
)
}) ## set starting value of scale ensure it isn't already fixed
output$svshape.1 <- renderUI({
conditionalPanel(condition = "input.parset.includes('shape.1') && !input.parameter.includes('shape.1')",
numericInput("svshape.1","shape.1 start value:",value = 1,min = 1,max = 100,step = 1)
)
}) ## set starting value of shape.1 ensure it isn't already fixed
output$svshape.2 <- renderUI({
conditionalPanel(condition = "input.parset.includes('shape.2') && !input.parameter.includes('shape.2')",
numericInput("svshape.2","shape.2 start value:",value = 1,min = 1,max = 100,step = 1)
)
}) ## set starting value of shape.2 ensure it isn't already fixed
## Fit model based on inputs of user and output parameter estimates and plots
fit <- eventReactive(input$fit,{
detections <- detections()
traps <- traps()
if(trapType() == "single"){
traps <- as.matrix(cbind(traps$x,traps$y))
}else{
traps <- split(traps, traps$array)
traps <- lapply(traps,function(x) cbind(x$x,x$y))
}
mask <- mask()
validate(need(!is.null(mask), "Please construct mask"))
nms <- names(detections)
capt.hist <- capthist()
validate(need(!is.null(capt.hist), "No capture hstory information"))
if(trapType() == "multi"){
validate(need(length(capt.hist) == length(mask), "Please construct mask for your loaded traps"))
}
## fixed values
param.fix <- input$parameter
param.fix.value <- list(g0 = input$g0,sigma = input$sigma,z = input$z,lambda0 = input$lambda0,shape = input$shape,
scale = input$scale, shape.1 = input$shape.1,shape.2 = input$shape.2)
idx <- match(param.fix,names(param.fix.value))
fix <- param.fix.value[idx]
## starting values
param.sv <- input$parset
param.sv.value <- list(g0 = input$svg0,sigma = input$svsigma,z = input$svz,lambda0 = input$svlambda0,
svshape = input$svshape,
scale = input$svscale, shape.1 = input$svshape.1,shape.2 = input$svshape.2)
idsv <- match(param.sv,names(param.sv.value))
sv <- param.sv.value[idsv]
fit <- NULL
disable("downloadSurfPlot")
disable("downloadContPlot")
disable("downloadDetPlot")
disable("downloadbearingPlot")
disable("downloaddistancePlot")
disable("downloadMask")
disable("downloadModel")
disable("anispeed")
disable("report")
disable("fit")
disable("side-panel")
shinyjs::show("processing") ## stuff to disable fitting button
if(!is.null(input$covariate.choose)){
ihd <- list(model = as.formula(paste("~",
paste(input$covariate.choose,
collapse = " + ", sep = " "))),
covariates = cov.use())
fit <- fit.ascr(capt = capt.hist,traps = traps,mask = mask,detfn = input$select,
fix = fix, sv = sv,trace = TRUE, ihd.opts = ihd)
}else{
fit <- fit.ascr(capt = capt.hist,traps = traps,mask = mask,detfn = input$select,
fix = fix, sv = sv,trace = TRUE)
}
enable("fit")
enable("side-panel")
enable("downloadSurfPlot")
enable("downloadContPlot")
enable("downloadDetPlot")
enable("downloadbearingPlot")
enable("downloaddistancePlot")
enable("anispeed")
enable("report")
enable("downloadMask")
enable("downloadModel")
hide("processing")
return(fit)
})
## coefficients
output$coefs <- renderTable({
fit <- fit()
if(class(fit)[1]=="ascr"){
ci <- confint(fit)
res <- data.frame(Estimate = summary(fit)$coefs,Std.Error = summary(fit)$coefs.se, "2.5%" = ci[,1], "97.5%" = ci[,2] )
if(res$Estimate[1] < 0.01){res <- matrix(apply(res,1,formatC, format = "e",digits = 2),ncol = 4, byrow = TRUE)}
rownames(res) <- names(coef(fit))
colnames(res) <- c("Estimate", "Std.Error", "2.5% Cl", "97.5% Cl")
return(res)
}
},rownames = TRUE,digits = 3)
## AIC and log Likelihood
output$AIClL <- renderTable({
fit <- fit()
if(class(fit)[1]=="ascr"){
tab <- rbind(AIC = AIC(fit),logLik = fit$loglik)
colnames(tab) <- "value"
return(tab)
}
},rownames = TRUE)
## Detection function plots and location estimate plots
output$detectionsurf <- renderPlot({
fit <- fit()
if(class(fit)[1] == "ascr"){
par(mfrow = c(1,2))
show.detsurf(fit,session = input$choose_trap)
show.detsurf(fit,session = input$choose_trap, surface = FALSE)
}else{
plot(1,1,col="white",axes = FALSE,xlab = "",ylab = "")
text(1,1,paste("convergence issues try advanced options"),col = "grey")
}
})
output$detfn <- renderPlot({
fit <- fit()
if(class(fit)[1]=="ascr"){
detfn <- fit$args$detfn
pars <- get.par(fit, pars = fit$detpars, cutoff = fit$fit.types["ss"],as.list = TRUE)
buffer <- attr(get.mask(fit,session = input$choose_trap), "buffer")
probs <- ascr:::calc.detfn(buffer, detfn = detfn, pars = pars,ss.link =fit$args$ss.opts$ss.link)
ascr:::show.detfn(fit)
if(probs >= 0.1){
legend("center", bty = "n",paste("The detection probability at the mask buffer of ", buffer, "m is", round(probs,3), "(i.e., non-zero), perhaps increase mask buffer."),cex = 0.7,text.col = "red")
}
}else{
plot(1,1,col="white",axes = FALSE,xlab = "",ylab = "")
text(1,1,paste("convergence issues try advanced options"),col = "grey")
}
})
## When a double-click happens, check if there's a brush on the plot.
## If so, zoom to the brush bounds; if not, reset the zoom.
ranges <- reactiveValues(x = NULL, y = NULL)
observeEvent(input$locsplot_dblclick, {
brush <- input$locsplot_brush
if (!is.null(brush)) {
ranges$x <- c(brush$xmin, brush$xmax)
ranges$y <- c(brush$ymin, brush$ymax)
} else {
ranges$x <- NULL
ranges$y <- NULL
}
})
observeEvent(input$reset_locplot,{
ranges$x <- NULL
ranges$y <- NULL
})
output$locs <- renderPlot({
fit <- fit()
mask <- mask()
if(trapType() == "single"){
msk <- mask()
}else{
msk <- mask[[input$choose_trap]]
}
if(class(fit)[1]=="ascr"){
validate(need(input$call.num,"Please provide a call number"))
validate(need(input$call.num <= nrow(fit$args$capt[[1]]$bincapt),"Please provide a valid call number"))
if("fine" %in% input$advancedOptions & is.null(ranges$x)){
validate(need(input$plotmaskspacing,
"Please provide a spacing for the (plotting) mask or uncheck this option"))
validate(need(input$plotmaskspacing > 0,
"Cannnot have a spacing of zero meters"))
validate(need(input$buffer > input$plotmaskspacing,
"The mask buffer cannot be less than the (plotting) mask spacing"))
validate(need(input$plotmaskspacing < input$spacing,
"To obtain a smooth plot the (plotting) mask spacing should be finer than the model fit mask "))
locations(fit,input$call.num,mask = msk)
legend("top",legend = paste("call",input$call.num,sep = " "),bty = "n")
}else{
if(!is.null(ranges$x) & !("fine" %in% input$advancedOptions)){
locations(fit,input$call.num,xlim = ranges$x, ylim = ranges$y)
legend("top",legend = paste("call",input$call.num,sep = " "),bty = "n")
}else{
if("fine" %in% input$advancedOptions & !is.null(ranges$x)){
validate(need(input$plotmaskspacing,
"Please provide a spacing for the (plotting) mask or uncheck this option"))
validate(need(input$plotmaskspacing > 0,
"Cannnot have a spacing of zero meters"))
validate(need(input$buffer > input$plotmaskspacing,
"The mask buffer cannot be less than the (plotting) mask spacing"))
validate(need(input$plotmaskspacing < input$spacing,
"To obtain a smooth plot the (plotting) mask spacing should be finer than the model fit mask "))
if(trapType() == "single"){
locations(fit,input$call.num,mask = msk,xlim = ranges$x, ylim = ranges$y)
}else{
locations(fit,session = input$choose_trap,input$call.num,mask = msk,xlim = ranges$x, ylim = ranges$y)
title(main = paste("array",input$choose_trap))
}
legend("top",legend = paste("call",input$call.num,sep = " "),bty = "n")
}else{
if(trapType() == "single"){
locations(fit, input$call.num)
}else{
locations(fit,session = input$choose_trap, input$call.num)
title(main = paste("array",input$choose_trap))
}
legend("top",legend = paste("call",input$call.num,sep = " "),bty = "n")
}
}
}
}else{
plot(1,1,col="white",axes = FALSE,xlab = "",ylab = "")
text(1,1,paste("Convergence issues try advanced options"),col = "grey")
}
},width = 700,height = 700)
## Measurement error plots
output$bearing_pdf <- renderPlot({
fit <- fit()
validate(need(!is.null(fit$args$capt[[1]]$bearing),"No bearing data provided"))
kappa = fit$coefficients["kappa"]
if(trapType() == "single"){
theta = sort(fit$args$capt[[1]]$bearing - pi)
theta = seq(min(theta), max(theta), length.out = 1000)
show.dvm(theta = theta, kappa = kappa)
}else{
theta = sort(fit$args$capt[[input$choose_trap]]$bearing - pi)
theta = seq(min(theta), max(theta), length.out = 1000)
show.dvm(theta = theta, kappa = kappa)
title(paste("array", input$choose_trap))
}
})
output$distance_pdf <- renderPlot({
fit <- fit()
validate(need(!is.null(fit$args$capt[[1]]$dist),"No distance data provided"))
validate(need(!(input$distD == 0), "Distance cannot be zero"))
validate(need(!is.null(input$distD), "Please provide distance for measurement error distribution"))
validate(need(input$distD < max(fit$args$capt[[1]]$dist),"Distance cannot be greater than those observed"))
d <- input$distD
shape <- fit$coefficients["alpha"]
if(trapType() == "single"){
x <- sort(fit$args$capt[[1]]$dist)
show.distgam(x = x, shape = shape, d = d)
}else{
x <- sort(fit$args$capt[[input$choose_trap]]$dist)
show.distgam(x = x, shape = shape, d = d)
title(paste("array", input$choose_trap))
}
})
## inhomogeneous density plot
output$density_surface <- renderPlot({
req(!is.null(input$covariate.choose))
show.Dsurf(fit(), session = input$choose_trap)
title(main = paste("array",input$choose_trap))
})
## Downloads
output$downloaddensity_surfPlot<- downloadHandler(
filename = "ascr_density_surface.png",
content = function(file) {
req(!is.null(input$covariate.choose))
png(file)
show.Dsurf(fit(), session = input$choose_trap)
title(main = paste("array",input$choose_trap))
dev.off()
})
output$downloadMask <- downloadHandler(
filename = "ascrMask.png",
content = function(file) {
png(file)
traps <- traps()
mask <- mask()
if(trapType() == "single"){
show.mask(mask,traps)
}else{
traps <- split(traps, traps$array)
m.lst <- list()
for(i in 1:length(traps)){ m.lst[[i]] <- show.mask(mask[[i]], traps = traps[[i]])}
do.call(grid.arrange, m.lst)
}
dev.off()
})
output$downloadSurfPlot <- downloadHandler(
filename = "ascr_detection_surface_plot.png",
content = function(file) {
png(file)
fit <- fit()
if(class(fit)[1] == "ascr"){
par(mfrow = c(1,2))
show.detsurf(fit,session = input$choose_trap)
show.detsurf(fit,,session = input$choose_trap, surface = TRUE)
}else{
plot(1,1,col="white",axes = FALSE,xlab = "",ylab = "")
text(1,1,paste("convergence issues try advanced options"),col = "grey")
}
dev.off()
})
output$downloadContPlot <- downloadHandler(
filename = "ascr_detection_contour_plot.png",
content = function(file) {
png(file)
fit <- fit()
if(class(fit)[1] == "ascr"){
par(mfrow = c(1,2))
show.detsurf(fit,session = input$choose_trap)
show.detsurf(fit,,session = input$choose_trap, surface = FALSE)
}else{
plot(1,1,col="white",axes = FALSE,xlab = "",ylab = "")
text(1,1,paste("convergence issues try advanced options"),col = "grey")
}
dev.off()
})
output$downloadDetPlot <- downloadHandler(
filename = "ascr_detection_function_plot.png",
content = function(file) {
png(file)
fit <- fit()
if(class(fit)[1]=="ascr"){
ascr:::show.detfn(fit)
}else{
NULL
}
dev.off()
})
## deal with bearing and distance plots
observe({
fit <- fit()
if(is.null(fit$args$capt[[1]]$dist)){
disable("downloaddistancePlot")
disable("distD")
}else{
enable("downloaddistancePlot")
enable("distD")
}
if(is.null(fit$args$capt[[1]]$bearing)){
disable("downloadbearingPlot")
}else{
enable("downloadbearingPlot")
}
})
output$downloadbearingPlot <- downloadHandler(
filename = "ascr_bearing_distribution_plot.png",
content = function(file) {
fit <- fit()
validate(need(!is.null(fit$args$capt[[1]]$bearing),"No bearing data provided"))
png(file)
if(class(fit)[1]=="ascr"){
kappa = fit$coefficients["kappa"]
if(trapType() == "single"){
theta = sort(fit$args$capt[[1]]$bearing - pi)
show.dvm(theta = theta, kappa = kappa)
}else{
theta = sort(fit$args$capt[[input$choose_trap]]$bearing - pi)
show.dvm(theta = theta, kappa = kappa)
title(paste("array", input$choose_trap))
}
}else{
NULL
}
dev.off()
})
output$downloaddistancePlot <- downloadHandler(
filename = "ascr_distance_distribution_plot.png",
content = function(file) {
fit <- fit()
validate(need(!is.null(fit$args$capt[[1]]$dist),"No distance data provided"))
png(file)
if(class(fit)[1]=="ascr"){
d <- input$distD
shape <- fit$coefficients["alpha"]
if(trapType() == "single"){
x <- sort(fit$args$capt[[1]]$dist)
show.distgam(x = x, shape = shape, d = d)
}else{
x <- sort(fit$args$capt[[input$choose_trap]]$dist)
show.distgam(x = x, shape = shape, d = d)
title(paste("array", input$choose_trap))
}
}else{
NULL
}
dev.off()
})
output$downloadModel <- downloadHandler(
filename = paste("ascr_",date(),".RData",sep = ""),
content = function(file){
fit <- fit()
save(fit,file = file)
}
)
output$report <- downloadHandler(
filename = "animation.html",