DynNom.ols <- function(model, data, clevel = 0.95, m.summary = c("raw", "formatted"),
covariate = c("slider", "numeric")) {
data <- data.frame(data)
model <- update(model,x=T,y=T)
if (length(dim(data)) > 2)
stop("Error in data format: dataframe format required")
if(length(class(model$y))==1){
if (class(model$y)[1] == "logical") stop("Error in model syntax: logical form for response not supported")} else{
if (class(model$y)[2] == "logical") stop("Error in model syntax: logical form for response not supported")
}
vars=c()
for(i in 1:length(model$Design$name)){
if(model$Design$assume[i]!="interaction") vars[i] <- as.character(model$Design$name[i]) else vars[i]="inter"
}
vars <- subset(vars,vars!="inter")
vars <- as.character(c(model$terms[[2]],vars))
cl.vars <- model$Design$assume[model$Design$assume!="interaction"]
cvars <- NULL
if(class(model$y)=="ts") {
cvars[1] <- class(model$y[1])
} else{
cvars[1] <- class(model$y)
}
for(i in 2:length(vars)) cvars[i]=cl.vars[i-1]
covariate <- match.arg(covariate)
m.summary <- match.arg(m.summary)
input.data <- NULL
old.d <- NULL
runApp(list(
ui = bootstrapPage(fluidPage(
titlePanel("Dynamic Nomogram"),
sidebarLayout(sidebarPanel(uiOutput("manySliders.f"),
uiOutput("manySliders.n"),
checkboxInput("limits", "Set x-axis ranges"),
conditionalPanel(condition = "input.limits == true",
numericInput("uxlim", "x-axis lower", NA),
numericInput("lxlim", "x-axis upper", NA)),
actionButton("add", "Predict"),
br(), br(),
helpText("Press Quit to exit the application"),
actionButton("quit", "Quit")
),
mainPanel(tabsetPanel(id = "tabs",
tabPanel("Graphical Summary", plotlyOutput("plot")),
tabPanel("Numerical Summary", verbatimTextOutput("data.pred")),
tabPanel("Model Summary", verbatimTextOutput("summary"))
)
)
))),
server = function(input, output){
q <- observe({
if (input$quit == 1)
stopApp()
})
limits0 <- c(mean(as.numeric(model$y)) - 3 * sd(model$y),
mean(as.numeric(model$y)) + 3 * sd(model$y))
limits <- reactive({
if (as.numeric(input$limits) == 1) {
limits <- c(input$lxlim, input$uxlim)
} else {
limits <- limits0
}
})
neededVar <- vars[-1]
data <- cbind(resp=model$y[1:length(model$y)],na.omit(data[, neededVar]))
data <- as.data.frame(data)
colnames(data) <- c("resp",neededVar)
input.data <<- data[1, ]
input.data[1, ] <<- NA
b <- 1
i.factor <- NULL
i.numeric <- NULL
for (j in 2:length(vars)) {
for (i in 1:length(data)) {
if (vars[j] == names(data)[i]) {
if (cvars[j] == "category" |
cvars[j] == "scored"|
cvars[j] == "factor"|
cvars[j] == "ordered") {
i.factor <- rbind(i.factor, c(vars[j], j, i, b))
(break)()
}
if (cvars[j] == "rcspline"|
cvars[j] == "asis"|
cvars[j] == "lspline"|
cvars[j] == "polynomial"|
cvars[j] == "numeric" |
cvars[j] == "integer"|
cvars[j] == "double"|
cvars[j] == "matrx") {
i.numeric <- rbind(i.numeric, c(vars[j], j, i))
b <- b + 1
(break)()
}
}
}
}
nn <- nrow(i.numeric)
if (is.null(nn)) {
nn <- 0
}
nf <- nrow(i.factor)
if (is.null(nf)) {
nf <- 0
}
if (nf > 0) {
output$manySliders.f <- renderUI({
slide.bars <- list(lapply(1:nf, function(j) {
selectInput(paste("factor", j, sep = ""),
vars[as.numeric(i.factor[j, 2])],
model$Design$parms[[i.factor[j,1]]], multiple = FALSE)
}))
do.call(tagList, slide.bars)
})
}
if (nn > 0) {
output$manySliders.n <- renderUI({
if (covariate == "slider") {
slide.bars <- list(lapply(1:nn, function(j) {
sliderInput(paste("numeric", j, sep = ""),
vars[as.numeric(i.numeric[j, 2])],
min = floor(min(na.omit(data[, as.numeric(i.numeric[j, 3])]))),
max = ceiling(max(na.omit(data[, as.numeric(i.numeric[j, 3])]))),
value = mean(na.omit(data[, as.numeric(i.numeric[j, 3])])) )
}))
}
if (covariate == "numeric") {
slide.bars <- list(lapply(1:nn, function(j) {
numericInput(paste("numeric", j, sep = ""),
vars[as.numeric(i.numeric[j, 2])],
value = round(mean(na.omit(data[, as.numeric(i.numeric[j, 3])]))))
}))
}
do.call(tagList, slide.bars)
})
}
a <- 0
new.d <- reactive({
input$add
if (nf > 0) {
input.f <- vector("list", nf)
for (i in 1:nf) {
input.f[[i]] <- isolate({
input[[paste("factor", i, sep = "")]]
})
names(input.f)[i] <- i.factor[i, 1]
}
}
if (nn > 0) {
input.n <- vector("list", nn)
for (i in 1:nn) {
input.n[[i]] <- isolate({
input[[paste("numeric", i, sep = "")]]
})
names(input.n)[i] <- i.numeric[i, 1]
}
}
if (nn == 0) {
out <- data.frame(do.call("cbind", input.f))
}
if (nf == 0) {
out <- data.frame(do.call("cbind", input.n))
}
if (nf > 0 & nn > 0) {
out <- data.frame(do.call("cbind", input.f), do.call("cbind", input.n))
}
if (a == 0) {
wher <- match(names(out), names(input.data)[-1])
out <- out[wher]
input.data <<- rbind(input.data[-1], out)
}
if (a > 0) {
wher <- match(names(out), names(input.data))
out <- out[wher]
if (isTRUE(compare(old.d, out)) == FALSE){
input.data <<- rbind(input.data, out)
}
}
a <<- a + 1
out
})
p1 <- NULL
old.d <- NULL
data2 <- reactive({
if (input$add == 0)
return(NULL)
if (input$add > 0) {
if (isTRUE(compare(old.d, new.d())) == FALSE) {
OUT <- isolate({
if(is.error(try(predict(model, newdata = new.d(), se.fit = TRUE)))==T){
d.p <- data.frame(Prediction = NA, Lower.bound = NA,
Upper.bound = NA)
} else{
pred <- predict(model, newdata = new.d(), se.fit = TRUE)
lwb <- pred$linear.predictors - (qt(1 - (1 - clevel)/2, model$df.residual) * pred$se.fit)
upb <- pred$linear.predictors + (qt(1 - (1 - clevel)/2, model$df.residual) * pred$se.fit)
if(is.infinite(round(pred$linear.predictors,digits = 4))){
d.p <- data.frame(Prediction = NA, Lower.bound = NA,
Upper.bound = NA)
} else{
d.p <- data.frame(Prediction = round(pred$linear.predictors,digits = 4),
Lower.bound = round(lwb,digits=4), Upper.bound = round(upb,digits = 4))
}
old.d <<- new.d()
data.p <- cbind(d.p, counter = 1)
p1 <<- rbind(p1, data.p)
p1$count <- seq(1, dim(p1)[1])
p1
}
})
} else {
p1$count <- seq(1, dim(p1)[1])
OUT <- p1
}
}
OUT
})
output$plot <- renderPlotly({
if (input$add == 0)
return(NULL)
if (is.null(new.d()))
return(NULL)
if (dim(na.omit(data2()))[1]==0 ) return(NULL)
if (is.na(input$lxlim) | is.na(input$uxlim)) {
lim <- limits0
} else {
lim <- limits()
}
PredictNO <- 0:(sum(data2()$counter) - 1)
in.d <- data.frame(input.data[-1,])
xx=matrix(paste(names(in.d), ": ",t(in.d), sep=""), ncol=dim(in.d)[1])
Covariates=apply(xx,2,paste,collapse="<br />")
yli <- c(0 - 0.5, 10 + 0.5)
if (dim(input.data)[1] > 11)
yli <- c(dim(input.data)[1] - 11.5, dim(input.data)[1] - 0.5)
p <- ggplot(data = data2()[!is.na(data2()$Prediction),],aes(x = Prediction, y = PredictNO,
text = Covariates, label = Prediction, label2 = Lower.bound, label3=Upper.bound))
p <- p + geom_point(size = 2, colour = data2()$count[!is.na(data2()$Prediction)], shape = 15)
p <- p + ylim(yli[1], yli[2]) + coord_cartesian(xlim = lim)
p <- p + geom_errorbarh(xmax = data2()$Upper.bound[!is.na(data2()$Prediction)], xmin = data2()$Lower.bound[!is.na(data2()$Prediction)],
size = 1.45, height = 0.4, colour = data2()$count[!is.na(data2()$Prediction)])
p <- p + labs(title = paste(clevel * 100, "% ", "Confidence Interval for Response", sep = ""),
x = "Response", y = NULL)
p <- p + theme_bw() + theme(axis.text.y = element_blank(), text = element_text(face = "bold", size = 10))
p
gp=ggplotly(p, tooltip = c("text","label","label2","label3"))
gp
})
output$data.pred <- renderPrint({
if (input$add > 0) {
if (nrow(data2() > 0)) {
if (dim(input.data)[2] == 1) {
in.d <- data.frame(input.data[-1, ])
names(in.d) <- vars[2]
data.p <- cbind(in.d, data2()[1:3])
data.p$Prediction[is.na(data.p$Prediction)] <- "Not"
data.p$Lower.bound[is.na(data.p$Lower.bound)] <- "IN"
data.p$Upper.bound[is.na(data.p$Upper.bound)] <- "RANGE"
}
if (dim(input.data)[2] > 1) {
data.p <- cbind(input.data[-1, ], data2()[1:3])
data.p$Prediction[is.na(data.p$Prediction)] <- "Not"
data.p$Lower.bound[is.na(data.p$Lower.bound)] <- "IN"
data.p$Upper.bound[is.na(data.p$Upper.bound)] <- "RANGE"
}
stargazer(data.p, summary = FALSE, type = "text")
}
}
})
output$summary <- renderPrint({
if (m.summary == "raw"){
print(model)
} else{
if(is.null(model$stat)==T){
stargazer(model,type = "text", omit.stat = c("LL", "ser", "f"), ci = TRUE, ci.level = clevel,
single.row = TRUE, title = paste("Linear Regression:", model$call[2], sep = " "))
} else{
stargazer(model,model$stats, type = "text", omit.stat = c("LL", "ser", "f"), ci = TRUE, ci.level = clevel,
single.row = TRUE, title = paste("Linear Regression:", model$call[2], sep = " "))
}
}
})
}
)
)
}
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