Nothing
R/shinyFSSA.R
In Rfssa: Functional Singular Spectrum Analysis
Defines functions
server.fssa
# create the shiny application user interface
ui.fssa <- fluidPage(
tags$head(tags$style(HTML("body { max-width: 1250px !important; }"))),
titlePanel("FSSA Illustration"),
sidebarLayout(
sidebarPanel(
width = 3, tags$head(tags$style(type = "text/css", ".well { max-width: 300px; }")),
tags$div(title = "Pick your functional basis", radioButtons("bs.fr", "Choose Basis:", choices = c("B-spline", "Fourier"), selected = "B-spline", inline = TRUE)),
tags$div(title = "Pick the degree of polynomial for the B-spline", uiOutput("xdeg", width = "250px")),
tags$div(title = "Pick the number of basis", uiOutput("xdf", width = "250px")),
tags$hr(style = "border-color: red;", width = "150px"),
column(6, tags$div(title = "Grouping used for the SSA algorithms", textInput("g", "Groups", value = "1:2"))),
column(6, tags$div(title = "Pick the groups fo reconstruction", uiOutput("sg"))),
column(6, tags$div(title = "The dimensions used in FPCA, SSA and FSSA", uiOutput("d"))),
column(6, tags$div(title = "See Manual", checkboxGroupInput("dmd", "Functions", choices = c("Demean" = "dmd", "Dbl Range" = "dbl")))),
tags$div(title = "Window length parameter used in SSA and FSSA", sliderInput("ssaL", HTML("Win.L. (SSA):"), min = 1, max = 50, value = 20, step = 1, width = "210px")),
column(6, uiOutput("run.ssa"))
),
mainPanel(
width = 9, tags$style(type = "text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; },"), # ".nav-tabs {font-size: 10px}"),
tabsetPanel(
id = "Panel", type = "tabs",
tabPanel(
title = "Input Data", value = "Data",
column(12, uiOutput("ts.selected", align = "center"), style = "color:red;"),
fluidRow(
column(4, radioButtons("f.choice", "Choose from:", c("Server" = "server", "Upload" = "upload", "Simulate" = "sim"), selected = "sim", inline = TRUE, width = "250px")),
column(4, uiOutput("s.choice", width = "250px")), column(2, uiOutput("noise.t", width = "125px")), column(2, uiOutput("noise.p", width = "125px")),
column(4, uiOutput("file")), column(4, uiOutput("sep"), uiOutput("header"))
),
column(4, uiOutput("model")), column(4, uiOutput("t.len")), column(2, uiOutput("a.f")), column(2, uiOutput("n.sd")),
column(8, plotOutput("data.plot", height = 600, width = 600)), column(4, tableOutput("data"))
),
tabPanel(
"Basis Functions",
column(8, plotOutput("basis.desc", height = 600, width = 600)), column(4, uiOutput("basis.n", width = "300px"))
),
tabPanel(
"Data Analysis",
column(4, uiOutput("desc", width = "250px")), column(4, uiOutput("as.choice", width = "400px"), uiOutput("run.fda.gcv", width = "200px"), uiOutput("rec.type", width = "300px")), column(2, uiOutput("freq")), column(2, uiOutput("sts.choice")),
fluidRow(
column(
8, conditionalPanel(condition = "output.flag_plot", plotOutput("res.plot", height = 600, width = 600)),
conditionalPanel(condition = "output.flag_plotly", plotlyOutput("res.ly", height = 600, width = 600))
),
column(4, uiOutput("s.plot"), fluidRow(column(8, uiOutput("b.indx")), column(4, uiOutput("s.CI"))), column(12, uiOutput("comp.obs"), verbatimTextOutput("RMSEs")))
)
),
tabPanel(
"Forecasting",
fluidRow(
column(3, checkboxGroupInput("fcast.method", "Forecasting Method:", choices = c("Recurrent" = "recurrent", "Vector" = "vector", "___Only New" = "only.new"), selected = c("recurrent", "only.new"), width = "250px")),
column(4, uiOutput("fcast.horizon")), column(2, uiOutput("run.fcast")), column(3, uiOutput("fcast.type"))
),
fluidRow(
column(
8, conditionalPanel(condition = "output.fcast_plot", plotOutput("fcast.plot", height = 600, width = 600)),
conditionalPanel(condition = "output.fcast_plotly", plotlyOutput("fcast.ly", height = 600, width = 600))
),
column(4, uiOutput("fcast.select"))
)
),
tabPanel("Manual", includeMarkdown(system.file("shiny/rmd", "report.Rmd", package = "Rfssa")))
)
)
)
)
# Define server logic required to run fssa
server.fssa <- function(input, output, clientData, session) {
iTs <- reactiveVal(list())
iTrs <- reactiveVal(list())
iXs <- reactiveVal(list())
itmp <- reactiveVal(0)
previous_s.plot <- reactiveVal(0)
df <- 100
vf <- 20
T <- 100
output$flag_plotly <- reactive(input$desc %in% c("fssa.reconst", "ssa.reconst") && input$rec.type %in% c("heatmap", "line", "3Dline", "3Dsurface"))
output$flag_plot <- reactive(!(input$desc %in% c("fssa.reconst", "ssa.reconst") && input$rec.type %in% c("heatmap", "line", "3Dline", "3Dsurface")))
output$fcast_plotly <- reactive(input$fcast.type %in% c("heatmap", "line", "3Dline", "3Dsurface"))
output$fcast_plot <- reactive(input$fcast.type == "regular")
outputOptions(output, "flag_plotly", suspendWhenHidden = FALSE)
outputOptions(output, "flag_plot", suspendWhenHidden = FALSE)
outputOptions(output, "fcast_plotly", suspendWhenHidden = FALSE)
outputOptions(output, "fcast_plot", suspendWhenHidden = FALSE)
hideTab(inputId = "Panel", target = "Forecasting")
updateTabsetPanel(session, "Panel", selected = "Manual")
rfar <- function(N, norm, psi, Eps, basis) {
# Create Corresponding matrix of an integral (kernel) operator
# Get an kernel function k(s,t) corresponds to an integral operator and
# a basis system incuding d basis functions, return corresponding d*d matrix of the operator with respect
# to basis system.
OpsMat <- function(kernel, basis) {
u <- seq(0, 1, by = 0.01)
n <- length(u)
K_mat <- outer(u, u, FUN = kernel)
K_t <- smooth.basis(u, K_mat, basis)$fd # the kernel function convert
# to fd object w.r.t the first argument. K_t is an objcect of n fd.
A <- inprod(K_t, basis) # An n*d matrix.
K <- smooth.basis(u, A, basis)$fd # d fd object.
B <- inprod(K, basis) # An d*d matrix.
return(B) # return to OpsMat.
}
Psi_mat0 <- OpsMat(psi, basis)
Gram <- inprod(basis, basis)
Psi_mat <- solve(Gram) %*% Psi_mat0
E <- Eps$coefs
X <- E
for (i in 2:N) X[, i] <- Psi_mat %*% X[, i - 1] + E[, i]
X_fd <- fd(X, basis)
return(X_fd) # return to rfar function.
}
gamma0 <- function(norm) {
f <- function(x) {
g <- function(y) psi0(x, y)^2
return(integrate(g, 0, 1)$value) # return into f.
}
f <- Vectorize(f)
A <- integrate(f, 0, 1)$value
return(norm / A) # return into gamma.
}
psi0 <- function(x, y) 2 - (2 * x - 1)^2 - (2 * y - 1)^2
fpca_proj <- function(i, U) {
harm <- U$harmonics[i]
scores <- U$scores[, i]
m <- nrow(harm$coefs)
n <- length(scores)
coef <- matrix(NA, nrow = m, ncol = n)
for (i0 in 1:m) for (j in 1:n) coef[i0, j] <- harm$coefs[i0] * scores[j]
pc <- fd(coef, harm$basis)
return(pc)
}
fpca_rec <- function(d1, d2, U) {
s <- fpca_proj(d1, U)
if (d2 > d1) for (i0 in (d1 + 1):d2) s <- s + fpca_proj(i0, U)
return(s)
}
Tr <- function(tau, t) {
ifelse(input$model %in% c("f1", "f12"), 1, 0) * exp(tau^2) * cos(2 * pi * t * input$a.f) +
ifelse(input$model %in% c("f12", "f2"), -1, 0) * cos(2 * pi * tau / 0.5) * sin(2 * pi * t * input$a.f)
}
simulate <- function() {
if (is.null(input$a.f)) {
return()
}
tau <- seq(0, 1, length = T)
t <- 1:input$t.len
Trs <- outer(tau, t, FUN = Tr)
set.seed(T * input$t.len * input$a.f * input$n.sd)
noise <- Z <- matrix(rnorm(input$t.len * T, 0, input$n.sd), nrow = T)
if (input$noise.t == "ar1") {
if (input$noise.p) {
Z[1, ] <- 0
A <- diag(1, T)
if (T > 2) for (i in 1:(T - 2)) diag(A[-(1:i), ]) <- input$noise.p^i
if (T > 1) A[T, 1] <- input$noise.p^(T - 1)
noise <- A %*% Z
}
}
if (input$noise.t == "swn") {
Z <- matrix(rnorm(input$t.len * input$xdf, 0, input$n.sd), ncol = input$t.len)
basis.Z <- fda::create.bspline.basis(c(0, 1), input$xdf)
tau <- seq(0, 1, length = T)
basis.noise <- fda::fd(Z, basis.Z)
noise <- eval.fd(tau, basis.noise)
}
if (input$noise.t == "far1") {
k0 <- gamma0(input$noise.p)
psi <- function(x, y) k0 * psi0(x, y)
Z[1, ] <- 0
noise <- apply(Z, 2, cumsum)
if (input$bs.fr == "B-spline") {
basis.Z <- fda::create.bspline.basis(c(0, 1), input$xdf)
} else {
basis.Z <- fda::create.fourier.basis(c(0, 1), input$xdf)
}
tau <- seq(0, 1, length = T)
Eps <- smooth.basis(tau, noise, basis.Z)$fd
basis.noise <- rfar(input$t.len, input$noise.p, psi, Eps, basis.Z)
noise <- eval.fd(tau, basis.noise)
}
# Trs <- scale(Trs,scale=F);
return(list(Trs = Trs, noise = noise))
}
observeEvent(input$dmd, {
updateTabsetPanel(session, "Panel", selected = "Data")
updateCheckboxGroupInput(session, "s.plot", selected = "")
})
observeEvent(input$s.plot, {
if (previous_s.plot() == 0 && (sum(c("ssa", "fssa") %in% input$s.plot))) previous_s.plot(1)
if (previous_s.plot() == 1 && !(sum(c("ssa", "fssa") %in% input$s.plot))) previous_s.plot(0)
})
observeEvent(input$run.ssa, {
showTab(inputId = "Panel", target = "Forecasting")
})
output$xdeg <- renderUI({
if (input$bs.fr == "Fourier") {
return()
}
sliderInput("xdeg", HTML("Degree of B-spline Basis:"), min = 0, max = 5, value = 3, step = 1, width = "250px")
})
output$xdf <- renderUI({
sliderInput("xdf", paste("Deg. of freedom of", input$bs.fr, "Basis:"), min = ifelse(input$bs.fr == "B-spline", input$xdeg + 1, 3), max = df, value = vf, step = ifelse(input$bs.fr == "B-spline", 1, 2), width = "250px")
})
output$sg <- renderUI({
m <- length(eval(parse(text = paste0("list(", input$g, ")"))))
sliderInput("sg", "Select G:", min = 1, max = m, value = c(1, m), step = 1)
})
output$d <- renderUI({
sliderInput("d", "d", min = 1, max = input$ssaL, value = c(1, 2), step = 1)
})
output$run.ssa <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
actionButton("run.ssa", paste("run (F)SSA"))
})
run_ssa <- eventReactive(
{
input$run.ssa
previous_s.plot()
},
{
withProgress(message = "SSA.FSSA: Running", value = 0, {
if (input$bs.fr == "B-spline") {
bas.fssa <- fda::create.bspline.basis(c(0, 1), nbasis = input$xdf, norder = input$xdeg + 1)
} else {
bas.fssa <- fda::create.fourier.basis(c(0, 1), nbasis = input$xdf)
}
tau <- seq(0, 1, length = nrow(iTs()))
Uf <- fssa(funts(X = iTs(), basisobj = bas.fssa), input$ssaL)
Us <- ssa(t(iTs()), input$ssaL, kind = "mssa")
return(list(Uf = Uf, Us = Us, tau = tau, bas.fssa = bas.fssa))
})
}
)
run_fpca <- function() {
withProgress(message = "(D)FPCA: Running", value = 0, {
if (input$bs.fr == "B-spline") {
bas.fssa <- fda::create.bspline.basis(c(0, 1), nbasis = input$xdf, norder = input$xdeg + 1)
} else {
bas.fssa <- fda::create.fourier.basis(c(0, 1), nbasis = input$xdf)
}
tau <- seq(0, 1, length = nrow(iTs()))
Y <- smooth.basis(tau, iTs(), bas.fssa)$fd
f.pca <- pca.fd(Y, nharm = min(input$d[2], input$xdf), centerfns = FALSE)
fpca.rec <- fpca_rec(min(input$d[1], input$xdf), min(input$d[2], input$xdf), f.pca)
fpca.re <- eval.fd(tau, fpca.rec)
# d.fpca <- fts.dpca(Y, Ndpc = input$d[2]); dfpca.re <- eval.fd(tau,d.fpca$Xhat)
return(list(fpca = fpca.re, dfpca = fpca.re))
})
}
output$s.choice <- renderUI({
if (input$f.choice != "server") {
return()
}
if (!length(iXs())) {
callcenter <- loadCallcenterData()
Jambi <- loadJambiData()
Xs <- list()
Xs[[1]] <- matrix(sqrt(callcenter$calls), nrow = 240)
Xs[[2]] <- Xs[[3]] <- matrix(NA, nrow = 128, ncol = dim(Jambi$NDVI)[3])
for (i in 1:dim(Jambi$NDVI)[3]) {
Xs[[2]][, i] <- density(Jambi$NDVI[, , i], from = 0, to = 1, n = 128)$y
Xs[[3]][, i] <- density(Jambi$EVI[, , i], from = 0, to = 1, n = 128)$y
}
colnames(Xs[[2]]) <- colnames(Xs[[3]]) <- Jambi$Date
names(Xs) <- c("Callcenter", "NDVI", "EVI")
iXs(Xs)
}
s.choices <- 1:length(iXs())
names(s.choices) <- names(iXs())
selectInput("s.choice", "Select a file from server: ", choices = s.choices, width = "250px")
})
output$noise.t <- renderUI({
if (input$f.choice != "sim") {
return()
}
selectInput("noise.t", "Type of noice: ", choices = c("AR(1)" = "ar1", "FAR(1)" = "far1", "Smooth WN" = "swn"), width = "125px")
})
output$noise.p <- renderUI({
if (input$f.choice != "sim") {
return()
}
if (!is.null(input$noise.t)) {
if (input$noise.t == "swn") {
return()
}
}
sliderInput("noise.p", "AR Parameter:", min = 0, max = 1, value = 0, step = 0.01, width = "125px")
})
output$model <- renderUI({
if (input$f.choice != "sim") {
return()
}
choices <- c("f(\u03C4)" = "f1", "g(\u03C4)" = "f2", "f(\u03C4) + g(\u03C4)" = "f12")
radioButtons("model", "Model:", choices = choices, selected = "f1", inline = TRUE, width = "250px")
})
output$t.len <- renderUI({
if (input$f.choice != "sim") {
return()
}
sliderInput("t.len", "Length of TS", min = 1, max = 200, value = 50, width = "250px")
})
output$n.sd <- renderUI({
if (input$f.choice != "sim") {
return()
}
sliderInput("n.sd", "Noise SD:", min = 0, max = 1, value = 0.05, width = "125px")
})
output$a.f <- renderUI({
if (input$f.choice != "sim") {
return()
}
sliderInput("a.f", HTML("ω, Ang. Freq:"), min = 0, max = 0.5, value = 0.1, step = 0.01, width = "125px")
})
output$file <- renderUI({
if (input$f.choice != "upload") {
return()
}
fileInput("file", "Choose CSV File", accept = c("text/csv", "text/comma-separated-values,text/plain", ".csv"))
})
output$sep <- renderUI({
if (input$f.choice != "upload") {
return()
}
radioButtons("sep", "Separator", c("," = ",", ":" = ":", ";" = ";", Tab = "\t"), ",", inline = TRUE)
})
output$header <- renderUI({
if (input$f.choice != "upload") {
return()
}
checkboxInput("header", "Header", TRUE)
})
output$ts.selected <- renderText({
if (input$f.choice == "upload" && is.null(input$file)) {
return("<b>Select a 'csv' file that contain the time series in its columns</b>")
}
if (input$f.choice == "upload") {
Ts <- as.matrix(read.table(input$file$datapath, header = input$header, sep = input$sep))
} else if (input$f.choice == "server") {
if (is.null(input$s.choice)) {
return()
}
i <- as.numeric(input$s.choice)
Ts <- iXs()[[i]]
} else {
simul <- simulate()
Ts <- simul$Trs + simul$noise
}
if (!length(Ts)) {
return()
}
if (is.null(colnames(Ts))) {
colnames(Ts) <- paste("fn", 1:ncol(Ts))
}
if ("dmd" %in% input$dmd) {
Ts <- Ts - mean(Ts)
if (input$f.choice == "sim") simul$Trs <- simul$Trs - mean(simul$Trs)
}
updateSliderInput(session, "ssaL", max = min(120, trunc(ncol(Ts) / 2)))
updateSelectInput(session, "desc", selected = "ts")
updateSliderInput(session, "dimn", max = min(10, ncol(Ts)), value = min(2, ncol(Ts)))
text <- mark("test")
text <- paste("<b>", ncol(Ts), "Time series of length", nrow(Ts), "</b>")
if (input$f.choice == "sim") iTrs(simul$Trs)
iTs(Ts)
return(text)
})
output$data <- renderTable({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
return(head(as.matrix(iTs()[, 1:min(9, ncol(iTs()))]), 15))
})
output$data.plot <- renderPlot({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (input$f.choice == "server") {
i <- as.numeric(input$s.choice)
fname <- names(iXs())[i]
} else if (input$f.choice == "upload") {
fname <- input$file$name
} else {
fname <- "Simulation"
}
ts.plot(iTs(), main = paste("Time Series -", fname), ylab = "", ylim = range(iTs()), gpars = list(xaxt = "n"), xlab = "tau")
if (input$f.choice == "sim") for (i in 1:ncol(iTrs())) points(iTrs()[, i], type = "l", col = 2)
})
output$basis.n <- renderUI({
sliderInput("basis.n", "Basis #:", min = 1, max = input$xdf, value = 1, step = 1, width = "400px")
})
output$basis.desc <- renderPlot({
if (is.null(input$basis.n)) {
return()
}
xs <- seq(0, 1, length.out = 1000)
if (input$bs.fr == "B-spline") {
Bx <- fda::bsplineS(xs, breaks = seq(0, 1, length.out = input$xdf - input$xdeg + 1), norder = input$xdeg + 1)
} else {
Bx <- fda::fourier(xs, nbasis = input$xdf)
}
ts.plot(Bx, col = 8, main = "B-spline Basis", xlab = "Grid Points", gpars = list(xaxt = "n"))
points(Bx[, input$basis.n], type = "l", lwd = 2, col = 2)
axis(1, trunc(summary(1:nrow(Bx))[-4]))
})
output$desc <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
choices <- list(
Summary = c("Functional Time Series" = "ts", "How many basis? (GCV)" = "gcv"),
FSSA = c("Scree" = "fssa.scree", "W.Correlation" = "fssa.wcor", "Paired" = "fssa.pair", "Singular Vectors" = "fssa.singV", "Periodogram" = "fssa.perGr", "Singular Functions" = "fssa.singF", "Reconstruction" = "fssa.reconst"),
SSA = c("Scree" = "ssa.scree", "W.Correlation" = "ssa.wcor", "Paired" = "ssa.pair", "Singular Vectors" = "ssa.vec", "Functions" = "ssa.funs", "Reconstruction" = "ssa.reconst")
)
selectInput("desc", "Select Plot Type", choices = choices, width = "250px")
})
output$as.choice <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (input$desc != "ts") {
return()
}
radioButtons("as.choice", "Plot Choices:", c("All" = "all", "Multiple" = "mult", "Single" = "single"), selected = "all", inline = TRUE, width = "400px")
})
output$sts.choice <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (((input$desc == "ts" && input$as.choice != "all")) && !("bf" %in% input$s.plot && length(input$s.plot) == 1) && length(input$s.plot)) {
if (input$as.choice == "single") {
sliderInput("sts.choice", "Choose function:", min = 1, max = ncol(iTs()), value = ifelse(is.null(input$sts.choice), 1, input$sts.choice), step = 1, width = "400px")
} else {
sliderInput("sts.choice", "Choose clusters:", min = 1, max = input$freq, value = ifelse(is.null(input$sts.choice), 0, input$sts.choice), step = 1, width = "200px")
}
}
})
output$rec.type <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (!input$desc %in% c("fssa.reconst", "fssa.singF", "ssa.reconst")) {
return()
}
if (input$desc == "fssa.singF") {
selectInput("rec.type", "Type", choices = c("Heat plot" = "lheats", "Regular Plot" = "lcurves"), width = "250px")
} else if (input$desc == "ssa.reconst") {
selectInput("rec.type", "Type", choices = c("Heat Plot" = "heatmap", "Regular Plot" = "line", "3D Plot (line)" = "3Dline", "3D Plot (surface)" = "3Dsurface"), width = "250px")
} else {
selectInput("rec.type", "Type", choices = c("Heat Plot" = "heatmap", "Regular Plot" = "line", "3D Plot (line)" = "3Dline", "3D Plot (surface)" = "3Dsurface"), width = "250px")
}
})
output$freq <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (((input$desc == "ts" && input$as.choice == "mult")) && !("bf" %in% input$s.plot && length(input$s.plot) == 1) && length(input$s.plot)) {
sliderInput("freq", "Period:", min = 1, max = trunc(ncol(iTs()) / 2), value = ifelse(is.null(input$freq), 1, input$freq), step = 1, width = "200px")
}
})
output$s.plot <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (input$desc != "ts") {
return()
}
choices <- c("Time Series (Raw Data)" = "ts", "True Functions" = "tf", "Functional SSA" = "fssa", "Multivariate SSA" = "ssa", "Functional PCA" = "fpca", "Basis Functions" = "bf", "Smoothing" = "bss") # , "Dyn. Func. PCA" = "dfpca"
if (input$f.choice != "sim") {
choices <- choices[-2]
}
checkboxGroupInput("s.plot", "Plot:", choices = choices, selected = choices[1], width = "250px")
})
output$b.indx <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || !input$desc %in% c("ts") || !length(intersect(input$s.plot, c("bf", "bss")))) {
return()
}
val <- c(1, input$xdf)
if (length(input$b.indx) == 2) val <- input$b.indx
sliderInput("b.indx", "Basis Contr.", min = 1, max = input$xdf, value = val, dragRange = TRUE, step = 1, width = "200px")
})
output$s.CI <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (input$desc == "ts" && length(intersect(input$s.plot, c("bss")))) {
if (input$as.choice == "single") checkboxInput("s.CI", "Show CI", ifelse(is.null(input$s.CI), FALSE, input$s.CI), width = "200px")
}
})
output$comp.obs <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (input$desc != "ts") {
return()
}
checkboxInput("comp.obs", "Compare fit. vs obs.", FALSE, width = "200px")
})
output$run.fda.gcv <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (input$desc != "gcv") {
return()
}
actionButton("run.fda.gcv", paste("update GCV"))
})
fda.gcv <- eventReactive(input$run.fda.gcv, {
withProgress(message = "FDA.GCV: # of Basis", value = 0, {
GCV <- NULL
df <- min(df, nrow(iTs()))
if (input$bs.fr == "B-spline") nbasis <- (input$xdeg + 1):(df - 1) else nbasis <- seq(3, df, by = 2)
for (l in 1:length(nbasis)) {
if (input$bs.fr == "B-spline") {
bas.fssa <- fda::create.bspline.basis(c(0, 1), nbasis = nbasis[l], norder = input$xdeg + 1)
} else {
bas.fssa <- fda::create.fourier.basis(c(0, 1), nbasis = nbasis[l])
}
GCV[l] <- sum(smooth.basis(seq(0, 1, length.out = nrow(iTs())), iTs(), bas.fssa)$gcv)
incProgress(1 / length(nbasis), detail = l)
}
itmp(1)
return(list(GCV = GCV, nbasis = nbasis))
})
})
output$res.plot <- renderPlot({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (input$desc %in% c("fssa.reconst", "ssa.reconst") && !is.null(input$rec.type)) {
if (input$rec.type %in% c("heatmap", "line", "3Dline", "3Dsurface")) {
return()
}
}
if (input$f.choice == "server") {
fname <- names(iXs())[as.numeric(input$s.choice)]
} else if (input$f.choice == "upload") {
fname <- input$file$name
} else {
fname <- "Simulation"
}
indx <- as.numeric(input$sts.choice)
Ts <- iTs()
name.Ts <- names(iTs())
if (length(intersect(input$s.plot, c("bf", "bss")))) {
if (is.null(input$b.indx)) {
return()
}
b.indx <- input$b.indx[1]:input$b.indx[2]
}
if (length(intersect(input$s.plot, c("bss")))) {
if (input$bs.fr == "B-spline") {
B <- fda::bsplineS(seq(0, 1, length.out = nrow(Ts)), breaks = seq(0, 1, length.out = input$xdf - input$xdeg + 1), norder = input$xdeg + 1)
} else {
B <- fda::fourier(seq(0, 1, length.out = nrow(Ts)), nbasis = input$xdf)
}
if ("bss" %in% input$s.plot) {
cB <- solve(t(B) %*% B) %*% t(B)
if (input$desc == "ts") {
S <- B %*% cB
vB <- ifelse(nrow(Ts) > ncol(B), sum((Ts - S %*% Ts)^2) / ((nrow(Ts) - ncol(B)) * ncol(Ts)) * diag(S), 0)
}
}
}
if ("bf" %in% input$s.plot || input$desc == "basis") {
if (input$bs.fr == "B-spline") {
Bs <- fda::bsplineS(seq(0, 1, length.out = 1000), breaks = seq(0, 1, length.out = input$xdf - input$xdeg + 1), norder = input$xdeg + 1)
} else {
Bs <- fda::fourier(seq(0, 1, length.out = 1000), nbasis = input$xdf)
}
Bs <- Bs * sd(Ts)
}
if (substr(input$desc, 1, 4) == "fssa" || sum(c("fssa", "ssa") %in% input$s.plot) || substr(input$desc, 1, 3) == "ssa") {
sr <- run_ssa()
input.g <- eval(parse(text = paste0("list(", input$g, ")")))
Qs <- matrix(0, nrow = nrow(Ts), ncol = ncol(Ts))
isolate(sr$Qs <- reconstruct(sr$Us, groups = input.g))
isolate(sr$Qf <- freconstruct(sr$Uf, input.g))
Qf <- sr$Qf[[1]]
Qf$coefs[[1]][, ] <- 0
for (i in input$sg[1]:input$sg[2]) {
Qs <- Qs + t(sr$Qs[[i]])
Qf$coefs[[1]] <- Qf$coefs[[1]] + sr$Qf[[i]]$coefs[[1]]
}
Q <- Qf$B_mat[[1]] %*% Qf$coefs[[1]]
}
if ("fpca" %in% input$s.plot || "dfpca" %in% input$s.plot) {
f.pca <- run_fpca()
fpca <- f.pca$fpca
dfpca <- f.pca$dfpca
}
if (substr(input$desc, 1, 4) == "fssa") {
if (input$desc == "fssa.scree") {
plot(sr$Uf, type = "values", d = input$d[2])
} else if (input$desc == "fssa.wcor") {
plot(sr$Uf, type = "wcor", groups = input$d[1]:input$d[2])
} else if (input$desc == "fssa.pair") {
plot(sr$Uf, type = "paired", idx = input$d[1]:input$d[2])
} else if (input$desc == "fssa.singV") {
plot(sr$Uf, type = "vectors", idx = input$d[1]:input$d[2])
} else if (input$desc == "fssa.perGr") {
plot(sr$Uf, type = "periodogram", idx = input$d[1]:input$d[2])
} else if (input$desc == "fssa.singF") {
plot(sr$Uf, type = ifelse(is.null(input$rec.type), "lheats", input$rec.type), idx = input$d[1]:input$d[2])
}
} else if (substr(input$desc, 1, 3) == "ssa") {
if (input$desc == "ssa.scree") {
plot(sr$Us, type = "values", numvalues = input$d[2])
} else if (input$desc == "ssa.wcor") {
plot(sr$Us, type = "wcor", groups = input$d[1]:input$d[2])
} else if (input$desc == "ssa.pair") {
plot(sr$Us, type = "paired", idx = input$d[1]:input$d[2])
} else if (input$desc == "ssa.vec") {
plot(sr$Us, type = "vectors", idx = input$d[1]:input$d[2])
} else if (input$desc == "ssa.funs") plot(sr$Us, type = "series", groups = input$d[1]:input$d[2])
} else if (input$desc == "gcv") {
res <- fda.gcv()
ind.m <- which(res$GCV == min(res$GCV))
plot(res$nbasis, res$GCV, type = "b", xlab = "n.basis", log = "y", ylab = "GCV", main = paste("Gen. Cross Validation -", fname), cex.lab = 1.5, pch = 20)
if (itmp()) {
updateSliderInput(session, "xdf", value = res$nbasis[ind.m])
itmp(0)
}
abline(v = input$xdf, col = 1, lty = 2)
} else {
m.lab <- paste(fname, "-", colnames(Ts)[indx])
if (input$as.choice == "all") {
indx <- 1:ncol(Ts)
m.lab <- fname
} else if (input$as.choice == "mult") {
indt <- 1:ncol(Ts) %% input$freq
indt[indt == 0] <- input$freq
indx <- (1:ncol(Ts))[which(indt == indx)]
}
if ("ts" %in% input$s.plot) {
clcol <- rep(1, ncol(Ts))
} else {
clcol <- rep(0, ncol(Ts))
}
if ("dbl" %in% input$dmd) rng <- range(Ts, -Ts) else rng <- range(Ts)
ts.plot(Ts[, indx], col = clcol[indx], main = paste("Time Series -", m.lab, "-", name.Ts), ylab = "", ylim = rng, gpars = list(xaxt = "n"))
if ("ssa" %in% input$s.plot) {
for (i in indx) points(Qs[, i], type = "l", col = 5)
}
if ("fssa" %in% input$s.plot) {
for (i in indx) points(Q[, i], type = "l", col = 6)
}
if ("fpca" %in% input$s.plot) {
for (i in indx) points(fpca[, i], type = "l", col = 2, lty = 2)
}
if ("dfpca" %in% input$s.plot) {
for (i in indx) points(dfpca[, i], type = "l", col = 3, lty = 2)
}
if ("bss" %in% input$s.plot) {
f.est <- matrix(0, nrow = nrow(Ts), ncol = ncol(Ts))
for (i in indx) {
f.est[, i] <- as.matrix(B[, b.indx]) %*% (cB %*% Ts[, i])[b.indx]
if (input$as.choice == "single") {
if (input$s.CI) {
polygon(c(1:nrow(Ts), nrow(Ts):1), c(f.est[, i] - 2 * sqrt(vB), rev(f.est[, i]) + 2 * rev(sqrt(vB))), border = 4, lwd = 1, col = 5)
if ("ts" %in% input$s.plot) points(Ts[, i], type = "l", col = 1)
}
}
points(f.est[, i], col = 4, type = "l")
}
}
if ("tf" %in% input$s.plot) {
for (i in indx) points(iTrs()[, i], type = "l", col = 2)
}
if ("bf" %in% input$s.plot) {
for (i in b.indx) points(seq(1, nrow(Ts), length.out = 1000), Bs[, i], col = 8, type = "l", lty = 2)
}
axis(1, trunc(summary(1:nrow(Ts))[-4]))
if (input$f.choice == "sim" || input$comp.obs) {
if (input$comp.obs) Ys <- Ts else Ys <- iTrs()
if (input$comp.obs) RMSEs <- NULL else RMSEs <- paste(" SNR =", round(sum(Ts[, indx]^2) / sum((Ts[, indx] - Ys[, indx])^2), 4), "\n")
if (input$comp.obs) RMSEs <- NULL else RMSEs <- paste(RMSEs, "RMSE.obs =", round(sqrt(mean((Ts[, indx] - Ys[, indx])^2)), 4), "\n")
if ("bss" %in% input$s.plot) RMSEs <- paste(RMSEs, "RMSE.bs.smooth =", round(sqrt(mean((f.est[, indx] - Ys[, indx])^2)), 4), "\n")
if ("fpca" %in% input$s.plot) RMSEs <- paste(RMSEs, "RMSE.fpca =", round(sqrt(mean((fpca[, indx] - Ys[, indx])^2)), 4), "\n")
if ("ssa" %in% input$s.plot) RMSEs <- paste(RMSEs, "RMSE.ssa =", round(sqrt(mean((Qs[, indx] - Ys[, indx])^2)), 4), "\n")
if ("fssa" %in% input$s.plot) RMSEs <- paste(RMSEs, "RMSE.fssa =", round(sqrt(mean((Q[, indx] - Ys[, indx])^2)), 4), "\n")
if (input$comp.obs) RMSEs <- paste(RMSEs, "\n") else RMSEs <- paste(RMSEs, "\n Ang.obs =", round(max(acos(diag(t(scale(Ts[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
if ("bss" %in% input$s.plot) RMSEs <- paste(RMSEs, "Ang.bs.smooth =", round(max(acos(diag(t(scale(f.est[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
if ("fpca" %in% input$s.plot) RMSEs <- paste(RMSEs, "Ang.fpca =", round(max(acos(diag(t(scale(fpca[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
if ("ssa" %in% input$s.plot) RMSEs <- paste(RMSEs, "Ang.ssa =", round(max(acos(diag(t(scale(Qs[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
if ("fssa" %in% input$s.plot) RMSEs <- paste(RMSEs, "Ang.fssa =", round(max(acos(diag(t(scale(Q[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
output$RMSEs <- renderText({
RMSEs
})
} else {
output$RMSEs <- renderText({
"Real Data"
})
}
}
})
output$res.ly <- renderPlotly({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (input$desc %in% c("fssa.reconst", "ssa.reconst")) {
if (!input$rec.type %in% c("heatmap", "line", "3Dline", "3Dsurface")) {
return()
}
}
sr <- run_ssa()
input.g <- eval(parse(text = paste0("list(", input$g, ")")))
if (input$desc == "fssa.reconst") {
isolate(sr$Qf <- freconstruct(sr$Uf, input.g))
Qf <- funts(X = matrix(0, nrow = nrow(iTs()), ncol = ncol(iTs())), basisobj = sr$bas.fssa, argval = sr$tau)
for (i in input$sg[1]:input$sg[2]) {
Qf <- Qf + sr$Qf[[i]]
}
myplot <- plotly_funts(Qf, types = input$rec.type)
} else {
isolate(sr$Qs <- reconstruct(sr$Us, groups = input.g))
Qs <- matrix(0, nrow = nrow(iTs()), ncol = ncol(iTs()))
for (i in input$sg[1]:input$sg[2]) {
Qs <- Qs + t(sr$Qs[[i]])
}
Qs <- funts(X = Qs, basisobj = sr$bas.fssa, argval = sr$tau)
myplot <- plotly_funts(Qs, types = input$rec.type)
}
return(myplot[[1]])
})
output$fcast.horizon <- renderUI({
if (is.null(nrow(iTs()))) {
return()
}
sliderInput("fcast.horizon", HTML("Forecasting Horizon:"), min = 0, max = ncol(iTs()), value = min(50, ncol(iTs()) / 2), step = 1, width = "250px")
})
output$run.fcast <- renderUI({
if (is.null(nrow(iTs()))) {
return()
}
actionButton("run.fcast", paste("run Forecast"))
})
output$fcast.type <- renderUI({
if (is.null(input$run.fcast)) {
return()
}
selectInput("fcast.type", "Type", choices = c("Regular Plot" = "regular", "Heat Plot" = "heatmap", "Line Plot" = "line", "3D Plot (line)" = "3Dline", "3D Plot (surface)" = "3Dsurface"), width = "250px")
})
run_fcast <- eventReactive(input$run.fcast, {
withProgress(message = "FSSA Forecast: Running", value = 0, {
sr <- run_ssa()
input.g <- eval(parse(text = paste0("list(", input$g, ")")))
fc <- list()
only.new <- ("only.new" %in% input$fcast.method)
if ("recurrent" %in% input$fcast.method) fc$rec <- fforecast(U = sr$Uf, groups = input.g, len = input$fcast.horizon, method = "recurrent", only.new = only.new)
if ("vector" %in% input$fcast.method) fc$vector <- fforecast(U = sr$Uf, groups = input.g, len = input$fcast.horizon, method = "vector", only.new = only.new)
return(fc)
})
})
output$fcast.select <- renderUI({
if (is.null(input$run.fcast) || input$run.fcast == 0) {
return()
}
if (sum(c("recurrent", "vector") %in% input$fcast.method) == 2) selectInput("fcast.select", "Select Output", choices = c("Recurrent Forecasting" = "1", "Vector Forecasting" = "2"), width = "250px")
})
output$fcast.plot <- renderPlot({
if (is.null(input$run.fcast)) {
return()
}
fc <- run_fcast()
if (length(fc) == 1) i <- 1 else i <- as.numeric(input$fcast.select)
plot(fc[[i]], group_index = input$sg[1])
})
output$fcast.ly <- renderPlotly({
if (is.null(input$run.fcast)) {
return()
}
fc <- run_fcast()
Qf <- fc[[1]][[1]]
Qf$coefs[[1]][, ] <- 0
if (length(fc) == 1) i <- 1 else i <- as.numeric(input$fcast.select)
for (j in input$sg[1]:input$sg[2]) {
Qf <- Qf + fc[[i]][[j]]
}
myplot <- plotly_funts(Qf, types = input$fcast.type)
return(myplot[[1]])
})
}
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Rfssa documentation built on May 29, 2024, 8:58 a.m.
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Defines functions server.fssa
# create the shiny application user interface
ui.fssa <- fluidPage(
tags$head(tags$style(HTML("body { max-width: 1250px !important; }"))),
titlePanel("FSSA Illustration"),
sidebarLayout(
sidebarPanel(
width = 3, tags$head(tags$style(type = "text/css", ".well { max-width: 300px; }")),
tags$div(title = "Pick your functional basis", radioButtons("bs.fr", "Choose Basis:", choices = c("B-spline", "Fourier"), selected = "B-spline", inline = TRUE)),
tags$div(title = "Pick the degree of polynomial for the B-spline", uiOutput("xdeg", width = "250px")),
tags$div(title = "Pick the number of basis", uiOutput("xdf", width = "250px")),
tags$hr(style = "border-color: red;", width = "150px"),
column(6, tags$div(title = "Grouping used for the SSA algorithms", textInput("g", "Groups", value = "1:2"))),
column(6, tags$div(title = "Pick the groups fo reconstruction", uiOutput("sg"))),
column(6, tags$div(title = "The dimensions used in FPCA, SSA and FSSA", uiOutput("d"))),
column(6, tags$div(title = "See Manual", checkboxGroupInput("dmd", "Functions", choices = c("Demean" = "dmd", "Dbl Range" = "dbl")))),
tags$div(title = "Window length parameter used in SSA and FSSA", sliderInput("ssaL", HTML("Win.L. (SSA):"), min = 1, max = 50, value = 20, step = 1, width = "210px")),
column(6, uiOutput("run.ssa"))
),
mainPanel(
width = 9, tags$style(type = "text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; },"), # ".nav-tabs {font-size: 10px}"),
tabsetPanel(
id = "Panel", type = "tabs",
tabPanel(
title = "Input Data", value = "Data",
column(12, uiOutput("ts.selected", align = "center"), style = "color:red;"),
fluidRow(
column(4, radioButtons("f.choice", "Choose from:", c("Server" = "server", "Upload" = "upload", "Simulate" = "sim"), selected = "sim", inline = TRUE, width = "250px")),
column(4, uiOutput("s.choice", width = "250px")), column(2, uiOutput("noise.t", width = "125px")), column(2, uiOutput("noise.p", width = "125px")),
column(4, uiOutput("file")), column(4, uiOutput("sep"), uiOutput("header"))
),
column(4, uiOutput("model")), column(4, uiOutput("t.len")), column(2, uiOutput("a.f")), column(2, uiOutput("n.sd")),
column(8, plotOutput("data.plot", height = 600, width = 600)), column(4, tableOutput("data"))
),
tabPanel(
"Basis Functions",
column(8, plotOutput("basis.desc", height = 600, width = 600)), column(4, uiOutput("basis.n", width = "300px"))
),
tabPanel(
"Data Analysis",
column(4, uiOutput("desc", width = "250px")), column(4, uiOutput("as.choice", width = "400px"), uiOutput("run.fda.gcv", width = "200px"), uiOutput("rec.type", width = "300px")), column(2, uiOutput("freq")), column(2, uiOutput("sts.choice")),
fluidRow(
column(
8, conditionalPanel(condition = "output.flag_plot", plotOutput("res.plot", height = 600, width = 600)),
conditionalPanel(condition = "output.flag_plotly", plotlyOutput("res.ly", height = 600, width = 600))
),
column(4, uiOutput("s.plot"), fluidRow(column(8, uiOutput("b.indx")), column(4, uiOutput("s.CI"))), column(12, uiOutput("comp.obs"), verbatimTextOutput("RMSEs")))
)
),
tabPanel(
"Forecasting",
fluidRow(
column(3, checkboxGroupInput("fcast.method", "Forecasting Method:", choices = c("Recurrent" = "recurrent", "Vector" = "vector", "___Only New" = "only.new"), selected = c("recurrent", "only.new"), width = "250px")),
column(4, uiOutput("fcast.horizon")), column(2, uiOutput("run.fcast")), column(3, uiOutput("fcast.type"))
),
fluidRow(
column(
8, conditionalPanel(condition = "output.fcast_plot", plotOutput("fcast.plot", height = 600, width = 600)),
conditionalPanel(condition = "output.fcast_plotly", plotlyOutput("fcast.ly", height = 600, width = 600))
),
column(4, uiOutput("fcast.select"))
)
),
tabPanel("Manual", includeMarkdown(system.file("shiny/rmd", "report.Rmd", package = "Rfssa")))
)
)
)
)
# Define server logic required to run fssa
server.fssa <- function(input, output, clientData, session) {
iTs <- reactiveVal(list())
iTrs <- reactiveVal(list())
iXs <- reactiveVal(list())
itmp <- reactiveVal(0)
previous_s.plot <- reactiveVal(0)
df <- 100
vf <- 20
T <- 100
output$flag_plotly <- reactive(input$desc %in% c("fssa.reconst", "ssa.reconst") && input$rec.type %in% c("heatmap", "line", "3Dline", "3Dsurface"))
output$flag_plot <- reactive(!(input$desc %in% c("fssa.reconst", "ssa.reconst") && input$rec.type %in% c("heatmap", "line", "3Dline", "3Dsurface")))
output$fcast_plotly <- reactive(input$fcast.type %in% c("heatmap", "line", "3Dline", "3Dsurface"))
output$fcast_plot <- reactive(input$fcast.type == "regular")
outputOptions(output, "flag_plotly", suspendWhenHidden = FALSE)
outputOptions(output, "flag_plot", suspendWhenHidden = FALSE)
outputOptions(output, "fcast_plotly", suspendWhenHidden = FALSE)
outputOptions(output, "fcast_plot", suspendWhenHidden = FALSE)
hideTab(inputId = "Panel", target = "Forecasting")
updateTabsetPanel(session, "Panel", selected = "Manual")
rfar <- function(N, norm, psi, Eps, basis) {
# Create Corresponding matrix of an integral (kernel) operator
# Get an kernel function k(s,t) corresponds to an integral operator and
# a basis system incuding d basis functions, return corresponding d*d matrix of the operator with respect
# to basis system.
OpsMat <- function(kernel, basis) {
u <- seq(0, 1, by = 0.01)
n <- length(u)
K_mat <- outer(u, u, FUN = kernel)
K_t <- smooth.basis(u, K_mat, basis)$fd # the kernel function convert
# to fd object w.r.t the first argument. K_t is an objcect of n fd.
A <- inprod(K_t, basis) # An n*d matrix.
K <- smooth.basis(u, A, basis)$fd # d fd object.
B <- inprod(K, basis) # An d*d matrix.
return(B) # return to OpsMat.
}
Psi_mat0 <- OpsMat(psi, basis)
Gram <- inprod(basis, basis)
Psi_mat <- solve(Gram) %*% Psi_mat0
E <- Eps$coefs
X <- E
for (i in 2:N) X[, i] <- Psi_mat %*% X[, i - 1] + E[, i]
X_fd <- fd(X, basis)
return(X_fd) # return to rfar function.
}
gamma0 <- function(norm) {
f <- function(x) {
g <- function(y) psi0(x, y)^2
return(integrate(g, 0, 1)$value) # return into f.
}
f <- Vectorize(f)
A <- integrate(f, 0, 1)$value
return(norm / A) # return into gamma.
}
psi0 <- function(x, y) 2 - (2 * x - 1)^2 - (2 * y - 1)^2
fpca_proj <- function(i, U) {
harm <- U$harmonics[i]
scores <- U$scores[, i]
m <- nrow(harm$coefs)
n <- length(scores)
coef <- matrix(NA, nrow = m, ncol = n)
for (i0 in 1:m) for (j in 1:n) coef[i0, j] <- harm$coefs[i0] * scores[j]
pc <- fd(coef, harm$basis)
return(pc)
}
fpca_rec <- function(d1, d2, U) {
s <- fpca_proj(d1, U)
if (d2 > d1) for (i0 in (d1 + 1):d2) s <- s + fpca_proj(i0, U)
return(s)
}
Tr <- function(tau, t) {
ifelse(input$model %in% c("f1", "f12"), 1, 0) * exp(tau^2) * cos(2 * pi * t * input$a.f) +
ifelse(input$model %in% c("f12", "f2"), -1, 0) * cos(2 * pi * tau / 0.5) * sin(2 * pi * t * input$a.f)
}
simulate <- function() {
if (is.null(input$a.f)) {
return()
}
tau <- seq(0, 1, length = T)
t <- 1:input$t.len
Trs <- outer(tau, t, FUN = Tr)
set.seed(T * input$t.len * input$a.f * input$n.sd)
noise <- Z <- matrix(rnorm(input$t.len * T, 0, input$n.sd), nrow = T)
if (input$noise.t == "ar1") {
if (input$noise.p) {
Z[1, ] <- 0
A <- diag(1, T)
if (T > 2) for (i in 1:(T - 2)) diag(A[-(1:i), ]) <- input$noise.p^i
if (T > 1) A[T, 1] <- input$noise.p^(T - 1)
noise <- A %*% Z
}
}
if (input$noise.t == "swn") {
Z <- matrix(rnorm(input$t.len * input$xdf, 0, input$n.sd), ncol = input$t.len)
basis.Z <- fda::create.bspline.basis(c(0, 1), input$xdf)
tau <- seq(0, 1, length = T)
basis.noise <- fda::fd(Z, basis.Z)
noise <- eval.fd(tau, basis.noise)
}
if (input$noise.t == "far1") {
k0 <- gamma0(input$noise.p)
psi <- function(x, y) k0 * psi0(x, y)
Z[1, ] <- 0
noise <- apply(Z, 2, cumsum)
if (input$bs.fr == "B-spline") {
basis.Z <- fda::create.bspline.basis(c(0, 1), input$xdf)
} else {
basis.Z <- fda::create.fourier.basis(c(0, 1), input$xdf)
}
tau <- seq(0, 1, length = T)
Eps <- smooth.basis(tau, noise, basis.Z)$fd
basis.noise <- rfar(input$t.len, input$noise.p, psi, Eps, basis.Z)
noise <- eval.fd(tau, basis.noise)
}
# Trs <- scale(Trs,scale=F);
return(list(Trs = Trs, noise = noise))
}
observeEvent(input$dmd, {
updateTabsetPanel(session, "Panel", selected = "Data")
updateCheckboxGroupInput(session, "s.plot", selected = "")
})
observeEvent(input$s.plot, {
if (previous_s.plot() == 0 && (sum(c("ssa", "fssa") %in% input$s.plot))) previous_s.plot(1)
if (previous_s.plot() == 1 && !(sum(c("ssa", "fssa") %in% input$s.plot))) previous_s.plot(0)
})
observeEvent(input$run.ssa, {
showTab(inputId = "Panel", target = "Forecasting")
})
output$xdeg <- renderUI({
if (input$bs.fr == "Fourier") {
return()
}
sliderInput("xdeg", HTML("Degree of B-spline Basis:"), min = 0, max = 5, value = 3, step = 1, width = "250px")
})
output$xdf <- renderUI({
sliderInput("xdf", paste("Deg. of freedom of", input$bs.fr, "Basis:"), min = ifelse(input$bs.fr == "B-spline", input$xdeg + 1, 3), max = df, value = vf, step = ifelse(input$bs.fr == "B-spline", 1, 2), width = "250px")
})
output$sg <- renderUI({
m <- length(eval(parse(text = paste0("list(", input$g, ")"))))
sliderInput("sg", "Select G:", min = 1, max = m, value = c(1, m), step = 1)
})
output$d <- renderUI({
sliderInput("d", "d", min = 1, max = input$ssaL, value = c(1, 2), step = 1)
})
output$run.ssa <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
actionButton("run.ssa", paste("run (F)SSA"))
})
run_ssa <- eventReactive(
{
input$run.ssa
previous_s.plot()
},
{
withProgress(message = "SSA.FSSA: Running", value = 0, {
if (input$bs.fr == "B-spline") {
bas.fssa <- fda::create.bspline.basis(c(0, 1), nbasis = input$xdf, norder = input$xdeg + 1)
} else {
bas.fssa <- fda::create.fourier.basis(c(0, 1), nbasis = input$xdf)
}
tau <- seq(0, 1, length = nrow(iTs()))
Uf <- fssa(funts(X = iTs(), basisobj = bas.fssa), input$ssaL)
Us <- ssa(t(iTs()), input$ssaL, kind = "mssa")
return(list(Uf = Uf, Us = Us, tau = tau, bas.fssa = bas.fssa))
})
}
)
run_fpca <- function() {
withProgress(message = "(D)FPCA: Running", value = 0, {
if (input$bs.fr == "B-spline") {
bas.fssa <- fda::create.bspline.basis(c(0, 1), nbasis = input$xdf, norder = input$xdeg + 1)
} else {
bas.fssa <- fda::create.fourier.basis(c(0, 1), nbasis = input$xdf)
}
tau <- seq(0, 1, length = nrow(iTs()))
Y <- smooth.basis(tau, iTs(), bas.fssa)$fd
f.pca <- pca.fd(Y, nharm = min(input$d[2], input$xdf), centerfns = FALSE)
fpca.rec <- fpca_rec(min(input$d[1], input$xdf), min(input$d[2], input$xdf), f.pca)
fpca.re <- eval.fd(tau, fpca.rec)
# d.fpca <- fts.dpca(Y, Ndpc = input$d[2]); dfpca.re <- eval.fd(tau,d.fpca$Xhat)
return(list(fpca = fpca.re, dfpca = fpca.re))
})
}
output$s.choice <- renderUI({
if (input$f.choice != "server") {
return()
}
if (!length(iXs())) {
callcenter <- loadCallcenterData()
Jambi <- loadJambiData()
Xs <- list()
Xs[[1]] <- matrix(sqrt(callcenter$calls), nrow = 240)
Xs[[2]] <- Xs[[3]] <- matrix(NA, nrow = 128, ncol = dim(Jambi$NDVI)[3])
for (i in 1:dim(Jambi$NDVI)[3]) {
Xs[[2]][, i] <- density(Jambi$NDVI[, , i], from = 0, to = 1, n = 128)$y
Xs[[3]][, i] <- density(Jambi$EVI[, , i], from = 0, to = 1, n = 128)$y
}
colnames(Xs[[2]]) <- colnames(Xs[[3]]) <- Jambi$Date
names(Xs) <- c("Callcenter", "NDVI", "EVI")
iXs(Xs)
}
s.choices <- 1:length(iXs())
names(s.choices) <- names(iXs())
selectInput("s.choice", "Select a file from server: ", choices = s.choices, width = "250px")
})
output$noise.t <- renderUI({
if (input$f.choice != "sim") {
return()
}
selectInput("noise.t", "Type of noice: ", choices = c("AR(1)" = "ar1", "FAR(1)" = "far1", "Smooth WN" = "swn"), width = "125px")
})
output$noise.p <- renderUI({
if (input$f.choice != "sim") {
return()
}
if (!is.null(input$noise.t)) {
if (input$noise.t == "swn") {
return()
}
}
sliderInput("noise.p", "AR Parameter:", min = 0, max = 1, value = 0, step = 0.01, width = "125px")
})
output$model <- renderUI({
if (input$f.choice != "sim") {
return()
}
choices <- c("f(\u03C4)" = "f1", "g(\u03C4)" = "f2", "f(\u03C4) + g(\u03C4)" = "f12")
radioButtons("model", "Model:", choices = choices, selected = "f1", inline = TRUE, width = "250px")
})
output$t.len <- renderUI({
if (input$f.choice != "sim") {
return()
}
sliderInput("t.len", "Length of TS", min = 1, max = 200, value = 50, width = "250px")
})
output$n.sd <- renderUI({
if (input$f.choice != "sim") {
return()
}
sliderInput("n.sd", "Noise SD:", min = 0, max = 1, value = 0.05, width = "125px")
})
output$a.f <- renderUI({
if (input$f.choice != "sim") {
return()
}
sliderInput("a.f", HTML("ω, Ang. Freq:"), min = 0, max = 0.5, value = 0.1, step = 0.01, width = "125px")
})
output$file <- renderUI({
if (input$f.choice != "upload") {
return()
}
fileInput("file", "Choose CSV File", accept = c("text/csv", "text/comma-separated-values,text/plain", ".csv"))
})
output$sep <- renderUI({
if (input$f.choice != "upload") {
return()
}
radioButtons("sep", "Separator", c("," = ",", ":" = ":", ";" = ";", Tab = "\t"), ",", inline = TRUE)
})
output$header <- renderUI({
if (input$f.choice != "upload") {
return()
}
checkboxInput("header", "Header", TRUE)
})
output$ts.selected <- renderText({
if (input$f.choice == "upload" && is.null(input$file)) {
return("<b>Select a 'csv' file that contain the time series in its columns</b>")
}
if (input$f.choice == "upload") {
Ts <- as.matrix(read.table(input$file$datapath, header = input$header, sep = input$sep))
} else if (input$f.choice == "server") {
if (is.null(input$s.choice)) {
return()
}
i <- as.numeric(input$s.choice)
Ts <- iXs()[[i]]
} else {
simul <- simulate()
Ts <- simul$Trs + simul$noise
}
if (!length(Ts)) {
return()
}
if (is.null(colnames(Ts))) {
colnames(Ts) <- paste("fn", 1:ncol(Ts))
}
if ("dmd" %in% input$dmd) {
Ts <- Ts - mean(Ts)
if (input$f.choice == "sim") simul$Trs <- simul$Trs - mean(simul$Trs)
}
updateSliderInput(session, "ssaL", max = min(120, trunc(ncol(Ts) / 2)))
updateSelectInput(session, "desc", selected = "ts")
updateSliderInput(session, "dimn", max = min(10, ncol(Ts)), value = min(2, ncol(Ts)))
text <- mark("test")
text <- paste("<b>", ncol(Ts), "Time series of length", nrow(Ts), "</b>")
if (input$f.choice == "sim") iTrs(simul$Trs)
iTs(Ts)
return(text)
})
output$data <- renderTable({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
return(head(as.matrix(iTs()[, 1:min(9, ncol(iTs()))]), 15))
})
output$data.plot <- renderPlot({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (input$f.choice == "server") {
i <- as.numeric(input$s.choice)
fname <- names(iXs())[i]
} else if (input$f.choice == "upload") {
fname <- input$file$name
} else {
fname <- "Simulation"
}
ts.plot(iTs(), main = paste("Time Series -", fname), ylab = "", ylim = range(iTs()), gpars = list(xaxt = "n"), xlab = "tau")
if (input$f.choice == "sim") for (i in 1:ncol(iTrs())) points(iTrs()[, i], type = "l", col = 2)
})
output$basis.n <- renderUI({
sliderInput("basis.n", "Basis #:", min = 1, max = input$xdf, value = 1, step = 1, width = "400px")
})
output$basis.desc <- renderPlot({
if (is.null(input$basis.n)) {
return()
}
xs <- seq(0, 1, length.out = 1000)
if (input$bs.fr == "B-spline") {
Bx <- fda::bsplineS(xs, breaks = seq(0, 1, length.out = input$xdf - input$xdeg + 1), norder = input$xdeg + 1)
} else {
Bx <- fda::fourier(xs, nbasis = input$xdf)
}
ts.plot(Bx, col = 8, main = "B-spline Basis", xlab = "Grid Points", gpars = list(xaxt = "n"))
points(Bx[, input$basis.n], type = "l", lwd = 2, col = 2)
axis(1, trunc(summary(1:nrow(Bx))[-4]))
})
output$desc <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
choices <- list(
Summary = c("Functional Time Series" = "ts", "How many basis? (GCV)" = "gcv"),
FSSA = c("Scree" = "fssa.scree", "W.Correlation" = "fssa.wcor", "Paired" = "fssa.pair", "Singular Vectors" = "fssa.singV", "Periodogram" = "fssa.perGr", "Singular Functions" = "fssa.singF", "Reconstruction" = "fssa.reconst"),
SSA = c("Scree" = "ssa.scree", "W.Correlation" = "ssa.wcor", "Paired" = "ssa.pair", "Singular Vectors" = "ssa.vec", "Functions" = "ssa.funs", "Reconstruction" = "ssa.reconst")
)
selectInput("desc", "Select Plot Type", choices = choices, width = "250px")
})
output$as.choice <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (input$desc != "ts") {
return()
}
radioButtons("as.choice", "Plot Choices:", c("All" = "all", "Multiple" = "mult", "Single" = "single"), selected = "all", inline = TRUE, width = "400px")
})
output$sts.choice <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (((input$desc == "ts" && input$as.choice != "all")) && !("bf" %in% input$s.plot && length(input$s.plot) == 1) && length(input$s.plot)) {
if (input$as.choice == "single") {
sliderInput("sts.choice", "Choose function:", min = 1, max = ncol(iTs()), value = ifelse(is.null(input$sts.choice), 1, input$sts.choice), step = 1, width = "400px")
} else {
sliderInput("sts.choice", "Choose clusters:", min = 1, max = input$freq, value = ifelse(is.null(input$sts.choice), 0, input$sts.choice), step = 1, width = "200px")
}
}
})
output$rec.type <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (!input$desc %in% c("fssa.reconst", "fssa.singF", "ssa.reconst")) {
return()
}
if (input$desc == "fssa.singF") {
selectInput("rec.type", "Type", choices = c("Heat plot" = "lheats", "Regular Plot" = "lcurves"), width = "250px")
} else if (input$desc == "ssa.reconst") {
selectInput("rec.type", "Type", choices = c("Heat Plot" = "heatmap", "Regular Plot" = "line", "3D Plot (line)" = "3Dline", "3D Plot (surface)" = "3Dsurface"), width = "250px")
} else {
selectInput("rec.type", "Type", choices = c("Heat Plot" = "heatmap", "Regular Plot" = "line", "3D Plot (line)" = "3Dline", "3D Plot (surface)" = "3Dsurface"), width = "250px")
}
})
output$freq <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (((input$desc == "ts" && input$as.choice == "mult")) && !("bf" %in% input$s.plot && length(input$s.plot) == 1) && length(input$s.plot)) {
sliderInput("freq", "Period:", min = 1, max = trunc(ncol(iTs()) / 2), value = ifelse(is.null(input$freq), 1, input$freq), step = 1, width = "200px")
}
})
output$s.plot <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (input$desc != "ts") {
return()
}
choices <- c("Time Series (Raw Data)" = "ts", "True Functions" = "tf", "Functional SSA" = "fssa", "Multivariate SSA" = "ssa", "Functional PCA" = "fpca", "Basis Functions" = "bf", "Smoothing" = "bss") # , "Dyn. Func. PCA" = "dfpca"
if (input$f.choice != "sim") {
choices <- choices[-2]
}
checkboxGroupInput("s.plot", "Plot:", choices = choices, selected = choices[1], width = "250px")
})
output$b.indx <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || !input$desc %in% c("ts") || !length(intersect(input$s.plot, c("bf", "bss")))) {
return()
}
val <- c(1, input$xdf)
if (length(input$b.indx) == 2) val <- input$b.indx
sliderInput("b.indx", "Basis Contr.", min = 1, max = input$xdf, value = val, dragRange = TRUE, step = 1, width = "200px")
})
output$s.CI <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (input$desc == "ts" && length(intersect(input$s.plot, c("bss")))) {
if (input$as.choice == "single") checkboxInput("s.CI", "Show CI", ifelse(is.null(input$s.CI), FALSE, input$s.CI), width = "200px")
}
})
output$comp.obs <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (input$desc != "ts") {
return()
}
checkboxInput("comp.obs", "Compare fit. vs obs.", FALSE, width = "200px")
})
output$run.fda.gcv <- renderUI({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (is.null(input$desc)) {
return()
} else if (input$desc != "gcv") {
return()
}
actionButton("run.fda.gcv", paste("update GCV"))
})
fda.gcv <- eventReactive(input$run.fda.gcv, {
withProgress(message = "FDA.GCV: # of Basis", value = 0, {
GCV <- NULL
df <- min(df, nrow(iTs()))
if (input$bs.fr == "B-spline") nbasis <- (input$xdeg + 1):(df - 1) else nbasis <- seq(3, df, by = 2)
for (l in 1:length(nbasis)) {
if (input$bs.fr == "B-spline") {
bas.fssa <- fda::create.bspline.basis(c(0, 1), nbasis = nbasis[l], norder = input$xdeg + 1)
} else {
bas.fssa <- fda::create.fourier.basis(c(0, 1), nbasis = nbasis[l])
}
GCV[l] <- sum(smooth.basis(seq(0, 1, length.out = nrow(iTs())), iTs(), bas.fssa)$gcv)
incProgress(1 / length(nbasis), detail = l)
}
itmp(1)
return(list(GCV = GCV, nbasis = nbasis))
})
})
output$res.plot <- renderPlot({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (input$desc %in% c("fssa.reconst", "ssa.reconst") && !is.null(input$rec.type)) {
if (input$rec.type %in% c("heatmap", "line", "3Dline", "3Dsurface")) {
return()
}
}
if (input$f.choice == "server") {
fname <- names(iXs())[as.numeric(input$s.choice)]
} else if (input$f.choice == "upload") {
fname <- input$file$name
} else {
fname <- "Simulation"
}
indx <- as.numeric(input$sts.choice)
Ts <- iTs()
name.Ts <- names(iTs())
if (length(intersect(input$s.plot, c("bf", "bss")))) {
if (is.null(input$b.indx)) {
return()
}
b.indx <- input$b.indx[1]:input$b.indx[2]
}
if (length(intersect(input$s.plot, c("bss")))) {
if (input$bs.fr == "B-spline") {
B <- fda::bsplineS(seq(0, 1, length.out = nrow(Ts)), breaks = seq(0, 1, length.out = input$xdf - input$xdeg + 1), norder = input$xdeg + 1)
} else {
B <- fda::fourier(seq(0, 1, length.out = nrow(Ts)), nbasis = input$xdf)
}
if ("bss" %in% input$s.plot) {
cB <- solve(t(B) %*% B) %*% t(B)
if (input$desc == "ts") {
S <- B %*% cB
vB <- ifelse(nrow(Ts) > ncol(B), sum((Ts - S %*% Ts)^2) / ((nrow(Ts) - ncol(B)) * ncol(Ts)) * diag(S), 0)
}
}
}
if ("bf" %in% input$s.plot || input$desc == "basis") {
if (input$bs.fr == "B-spline") {
Bs <- fda::bsplineS(seq(0, 1, length.out = 1000), breaks = seq(0, 1, length.out = input$xdf - input$xdeg + 1), norder = input$xdeg + 1)
} else {
Bs <- fda::fourier(seq(0, 1, length.out = 1000), nbasis = input$xdf)
}
Bs <- Bs * sd(Ts)
}
if (substr(input$desc, 1, 4) == "fssa" || sum(c("fssa", "ssa") %in% input$s.plot) || substr(input$desc, 1, 3) == "ssa") {
sr <- run_ssa()
input.g <- eval(parse(text = paste0("list(", input$g, ")")))
Qs <- matrix(0, nrow = nrow(Ts), ncol = ncol(Ts))
isolate(sr$Qs <- reconstruct(sr$Us, groups = input.g))
isolate(sr$Qf <- freconstruct(sr$Uf, input.g))
Qf <- sr$Qf[[1]]
Qf$coefs[[1]][, ] <- 0
for (i in input$sg[1]:input$sg[2]) {
Qs <- Qs + t(sr$Qs[[i]])
Qf$coefs[[1]] <- Qf$coefs[[1]] + sr$Qf[[i]]$coefs[[1]]
}
Q <- Qf$B_mat[[1]] %*% Qf$coefs[[1]]
}
if ("fpca" %in% input$s.plot || "dfpca" %in% input$s.plot) {
f.pca <- run_fpca()
fpca <- f.pca$fpca
dfpca <- f.pca$dfpca
}
if (substr(input$desc, 1, 4) == "fssa") {
if (input$desc == "fssa.scree") {
plot(sr$Uf, type = "values", d = input$d[2])
} else if (input$desc == "fssa.wcor") {
plot(sr$Uf, type = "wcor", groups = input$d[1]:input$d[2])
} else if (input$desc == "fssa.pair") {
plot(sr$Uf, type = "paired", idx = input$d[1]:input$d[2])
} else if (input$desc == "fssa.singV") {
plot(sr$Uf, type = "vectors", idx = input$d[1]:input$d[2])
} else if (input$desc == "fssa.perGr") {
plot(sr$Uf, type = "periodogram", idx = input$d[1]:input$d[2])
} else if (input$desc == "fssa.singF") {
plot(sr$Uf, type = ifelse(is.null(input$rec.type), "lheats", input$rec.type), idx = input$d[1]:input$d[2])
}
} else if (substr(input$desc, 1, 3) == "ssa") {
if (input$desc == "ssa.scree") {
plot(sr$Us, type = "values", numvalues = input$d[2])
} else if (input$desc == "ssa.wcor") {
plot(sr$Us, type = "wcor", groups = input$d[1]:input$d[2])
} else if (input$desc == "ssa.pair") {
plot(sr$Us, type = "paired", idx = input$d[1]:input$d[2])
} else if (input$desc == "ssa.vec") {
plot(sr$Us, type = "vectors", idx = input$d[1]:input$d[2])
} else if (input$desc == "ssa.funs") plot(sr$Us, type = "series", groups = input$d[1]:input$d[2])
} else if (input$desc == "gcv") {
res <- fda.gcv()
ind.m <- which(res$GCV == min(res$GCV))
plot(res$nbasis, res$GCV, type = "b", xlab = "n.basis", log = "y", ylab = "GCV", main = paste("Gen. Cross Validation -", fname), cex.lab = 1.5, pch = 20)
if (itmp()) {
updateSliderInput(session, "xdf", value = res$nbasis[ind.m])
itmp(0)
}
abline(v = input$xdf, col = 1, lty = 2)
} else {
m.lab <- paste(fname, "-", colnames(Ts)[indx])
if (input$as.choice == "all") {
indx <- 1:ncol(Ts)
m.lab <- fname
} else if (input$as.choice == "mult") {
indt <- 1:ncol(Ts) %% input$freq
indt[indt == 0] <- input$freq
indx <- (1:ncol(Ts))[which(indt == indx)]
}
if ("ts" %in% input$s.plot) {
clcol <- rep(1, ncol(Ts))
} else {
clcol <- rep(0, ncol(Ts))
}
if ("dbl" %in% input$dmd) rng <- range(Ts, -Ts) else rng <- range(Ts)
ts.plot(Ts[, indx], col = clcol[indx], main = paste("Time Series -", m.lab, "-", name.Ts), ylab = "", ylim = rng, gpars = list(xaxt = "n"))
if ("ssa" %in% input$s.plot) {
for (i in indx) points(Qs[, i], type = "l", col = 5)
}
if ("fssa" %in% input$s.plot) {
for (i in indx) points(Q[, i], type = "l", col = 6)
}
if ("fpca" %in% input$s.plot) {
for (i in indx) points(fpca[, i], type = "l", col = 2, lty = 2)
}
if ("dfpca" %in% input$s.plot) {
for (i in indx) points(dfpca[, i], type = "l", col = 3, lty = 2)
}
if ("bss" %in% input$s.plot) {
f.est <- matrix(0, nrow = nrow(Ts), ncol = ncol(Ts))
for (i in indx) {
f.est[, i] <- as.matrix(B[, b.indx]) %*% (cB %*% Ts[, i])[b.indx]
if (input$as.choice == "single") {
if (input$s.CI) {
polygon(c(1:nrow(Ts), nrow(Ts):1), c(f.est[, i] - 2 * sqrt(vB), rev(f.est[, i]) + 2 * rev(sqrt(vB))), border = 4, lwd = 1, col = 5)
if ("ts" %in% input$s.plot) points(Ts[, i], type = "l", col = 1)
}
}
points(f.est[, i], col = 4, type = "l")
}
}
if ("tf" %in% input$s.plot) {
for (i in indx) points(iTrs()[, i], type = "l", col = 2)
}
if ("bf" %in% input$s.plot) {
for (i in b.indx) points(seq(1, nrow(Ts), length.out = 1000), Bs[, i], col = 8, type = "l", lty = 2)
}
axis(1, trunc(summary(1:nrow(Ts))[-4]))
if (input$f.choice == "sim" || input$comp.obs) {
if (input$comp.obs) Ys <- Ts else Ys <- iTrs()
if (input$comp.obs) RMSEs <- NULL else RMSEs <- paste(" SNR =", round(sum(Ts[, indx]^2) / sum((Ts[, indx] - Ys[, indx])^2), 4), "\n")
if (input$comp.obs) RMSEs <- NULL else RMSEs <- paste(RMSEs, "RMSE.obs =", round(sqrt(mean((Ts[, indx] - Ys[, indx])^2)), 4), "\n")
if ("bss" %in% input$s.plot) RMSEs <- paste(RMSEs, "RMSE.bs.smooth =", round(sqrt(mean((f.est[, indx] - Ys[, indx])^2)), 4), "\n")
if ("fpca" %in% input$s.plot) RMSEs <- paste(RMSEs, "RMSE.fpca =", round(sqrt(mean((fpca[, indx] - Ys[, indx])^2)), 4), "\n")
if ("ssa" %in% input$s.plot) RMSEs <- paste(RMSEs, "RMSE.ssa =", round(sqrt(mean((Qs[, indx] - Ys[, indx])^2)), 4), "\n")
if ("fssa" %in% input$s.plot) RMSEs <- paste(RMSEs, "RMSE.fssa =", round(sqrt(mean((Q[, indx] - Ys[, indx])^2)), 4), "\n")
if (input$comp.obs) RMSEs <- paste(RMSEs, "\n") else RMSEs <- paste(RMSEs, "\n Ang.obs =", round(max(acos(diag(t(scale(Ts[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
if ("bss" %in% input$s.plot) RMSEs <- paste(RMSEs, "Ang.bs.smooth =", round(max(acos(diag(t(scale(f.est[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
if ("fpca" %in% input$s.plot) RMSEs <- paste(RMSEs, "Ang.fpca =", round(max(acos(diag(t(scale(fpca[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
if ("ssa" %in% input$s.plot) RMSEs <- paste(RMSEs, "Ang.ssa =", round(max(acos(diag(t(scale(Qs[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
if ("fssa" %in% input$s.plot) RMSEs <- paste(RMSEs, "Ang.fssa =", round(max(acos(diag(t(scale(Q[, indx], center = F)) %*% scale(Ys[, indx], center = F)) / (nrow(Ts) - 1)) * 180 / pi), 4), "\n")
output$RMSEs <- renderText({
RMSEs
})
} else {
output$RMSEs <- renderText({
"Real Data"
})
}
}
})
output$res.ly <- renderPlotly({
if ((input$f.choice == "upload" && is.null(input$file)) || (input$f.choice == "sim" && !length(input$model))) {
return()
}
if (input$desc %in% c("fssa.reconst", "ssa.reconst")) {
if (!input$rec.type %in% c("heatmap", "line", "3Dline", "3Dsurface")) {
return()
}
}
sr <- run_ssa()
input.g <- eval(parse(text = paste0("list(", input$g, ")")))
if (input$desc == "fssa.reconst") {
isolate(sr$Qf <- freconstruct(sr$Uf, input.g))
Qf <- funts(X = matrix(0, nrow = nrow(iTs()), ncol = ncol(iTs())), basisobj = sr$bas.fssa, argval = sr$tau)
for (i in input$sg[1]:input$sg[2]) {
Qf <- Qf + sr$Qf[[i]]
}
myplot <- plotly_funts(Qf, types = input$rec.type)
} else {
isolate(sr$Qs <- reconstruct(sr$Us, groups = input.g))
Qs <- matrix(0, nrow = nrow(iTs()), ncol = ncol(iTs()))
for (i in input$sg[1]:input$sg[2]) {
Qs <- Qs + t(sr$Qs[[i]])
}
Qs <- funts(X = Qs, basisobj = sr$bas.fssa, argval = sr$tau)
myplot <- plotly_funts(Qs, types = input$rec.type)
}
return(myplot[[1]])
})
output$fcast.horizon <- renderUI({
if (is.null(nrow(iTs()))) {
return()
}
sliderInput("fcast.horizon", HTML("Forecasting Horizon:"), min = 0, max = ncol(iTs()), value = min(50, ncol(iTs()) / 2), step = 1, width = "250px")
})
output$run.fcast <- renderUI({
if (is.null(nrow(iTs()))) {
return()
}
actionButton("run.fcast", paste("run Forecast"))
})
output$fcast.type <- renderUI({
if (is.null(input$run.fcast)) {
return()
}
selectInput("fcast.type", "Type", choices = c("Regular Plot" = "regular", "Heat Plot" = "heatmap", "Line Plot" = "line", "3D Plot (line)" = "3Dline", "3D Plot (surface)" = "3Dsurface"), width = "250px")
})
run_fcast <- eventReactive(input$run.fcast, {
withProgress(message = "FSSA Forecast: Running", value = 0, {
sr <- run_ssa()
input.g <- eval(parse(text = paste0("list(", input$g, ")")))
fc <- list()
only.new <- ("only.new" %in% input$fcast.method)
if ("recurrent" %in% input$fcast.method) fc$rec <- fforecast(U = sr$Uf, groups = input.g, len = input$fcast.horizon, method = "recurrent", only.new = only.new)
if ("vector" %in% input$fcast.method) fc$vector <- fforecast(U = sr$Uf, groups = input.g, len = input$fcast.horizon, method = "vector", only.new = only.new)
return(fc)
})
})
output$fcast.select <- renderUI({
if (is.null(input$run.fcast) || input$run.fcast == 0) {
return()
}
if (sum(c("recurrent", "vector") %in% input$fcast.method) == 2) selectInput("fcast.select", "Select Output", choices = c("Recurrent Forecasting" = "1", "Vector Forecasting" = "2"), width = "250px")
})
output$fcast.plot <- renderPlot({
if (is.null(input$run.fcast)) {
return()
}
fc <- run_fcast()
if (length(fc) == 1) i <- 1 else i <- as.numeric(input$fcast.select)
plot(fc[[i]], group_index = input$sg[1])
})
output$fcast.ly <- renderPlotly({
if (is.null(input$run.fcast)) {
return()
}
fc <- run_fcast()
Qf <- fc[[1]][[1]]
Qf$coefs[[1]][, ] <- 0
if (length(fc) == 1) i <- 1 else i <- as.numeric(input$fcast.select)
for (j in input$sg[1]:input$sg[2]) {
Qf <- Qf + fc[[i]][[j]]
}
myplot <- plotly_funts(Qf, types = input$fcast.type)
return(myplot[[1]])
})
}
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