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##----------#----------#----------#----------
##
## 8MFSpcapls UI
##
## > PLS
##
## Language: EN
##
## DT: 2019-01-08
##
##----------#----------#----------#----------
##' @title MEPHAS Shiny Application of PCA PLS Regression
##' @export
pls.ui <- function(){
sidebarLayout(
sidebarPanel(
h4("Model's configuration"),
checkboxInput("scale2", "Scale the data (X)", FALSE),
numericInput("nc.pls", "Number of Components", 4, min = 2, max = NA),
radioButtons("mtd.pls", "PLSR Algorithms",
choices = c(
"Kernel" = "kernelpls",
"Wide kernel" = "widekernelpls",
"SIMPLS" = "simpls",
"Classical orthogonal scores"="oscorespls",
"CPPLS" = "cppls"),
selected = "kernelpls"),
radioButtons("val", "Validation method",
choices = c("No validation" = 'none',
"Cross validation" = "CV",
"Leave-one-out validation" = "LOO"),
selected = "CV"),
hr(),
h4("Figure's configuration"),
numericInput("c1.pls", "Component at x-axis", 1, min = 1, max = 20),
numericInput("c2.pls", "Component at y-axis", 2, min = 1, max = 20)
),
mainPanel(
h4("Explained and cumulative variance"),
p(br()),
verbatimTextOutput("pls.sum"),
hr(),
h4("Plots"),
tabsetPanel(
tabPanel("Plot of scores and loadings", p(br()),
plotOutput("pls.pbiplot", width = "500px", height = "500px"),
radioButtons("which", "Choose the elements in the figure",
choices = c("X scores and loadings" = "x",
"Y scores and loadings" = "y",
"X and Y scores" = "scores",
"X and Y loadings"= "loadings"),
selected = "x")
),
tabPanel("Plot of X scores",p(br()),
plotOutput("pls.pscore", width = "500px", height = "500px")),
tabPanel("Plot of X loadings",p(br()),
plotOutput("pls.pload", width = "500px", height = "500px")),
tabPanel("Plot of coefficients",p(br()),
plotOutput("pls.pcoef", width = "500px", height = "500px")),
tabPanel("Plot of prediction",p(br()),
plotOutput("pls.pred", width = "500px", height = "500px"),
numericInput("snum", "Which component", 1, min = 1, max = NA)),
tabPanel("Plot of validation",p(br()),
plotOutput("pls.pval", width = "500px", height = "500px"))
),
hr(),
h4("Results"),
tabsetPanel(
tabPanel("New components", p(br()),
(tags$b("1. New PLS components from predictors (X)")), p(br()),
dataTableOutput("comp.x"),
downloadButton("downloadData.pls.x", "Download1"),
p(br()),
(tags$b("2. New PLS components from responses (Y)")), p(br()),
dataTableOutput("comp.y"),
downloadButton("downloadData.pls.y", "Download2")
),
tabPanel("Loadings", p(br()),
(tags$b("1. New PLS loadings from predictors (X)")), p(br()),
dataTableOutput("load.x"),
downloadButton("downloadData.pls.xload", "Download3"),
p(br()),
(tags$b("2. New PLS loadings from responses (Y)")), p(br()),
dataTableOutput("load.y"),
downloadButton("downloadData.pls.yload", "Download4")
),
tabPanel("Coefficients and projects", p(br()),
(tags$b("1. Coefficients")), p(br()),
dataTableOutput("coef"),
downloadButton("downloadData.pls.coef", "Download5"),
p(br()),
(tags$b("2. Projects")), p(br()),
dataTableOutput("proj"),
downloadButton("downloadData.pls.proj", "Download6")
),
tabPanel("Fittings and residuals", p(br()),
(tags$b("1. Fittings")), p(br()),
dataTableOutput("fit.pls"),
downloadButton("downloadData.pls.fit", "Download7"),
p(br()),
(tags$b("2. Residuals")), p(br()),
dataTableOutput("res.pls"),
downloadButton("downloadData.pls.res", "Download8")
)
)
)
)
}
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