inst/shiny/beanz_ui.R

##
##  UI definitions for BEANZ project
##

DATA.FORMAT <- c("Summary treatment effect data", "Subject level raw data");
DISPLAY.BY  <- c('By results', 'By model');
ALL.MODELS  <- c("No subgroup effect",
                 "Full stratification",
                 "Simple regression",
                 "Basic shrinkage",
                 "Regression and shrinkage",
                 "Dixon and Simon",
                 "Extended Dixon and Simon"
                 );
STAN.NAME   <- c("nse", "fs", "sr", "bs", "srs", "ds", "eds");
RESP.TYPE   <- c("Continuous",
                 "Binary with treatment effect measured by log odds ratio",
                 "Time to event with treatment effect measured by log hazard ratio");
SUB.HEAD    <- c("Estimate", "Variance");

##-------------------------------------------------------------
##           UI DEFINITIONS
##-------------------------------------------------------------

msg.warnings <- reactive({
    msg.box("Subgroup analysis are only exploratory unless such analyses
             were pre-specified in a study protocol at the design stage.
             The users should be cautious with the choices of the priors
             in Bayesian subgroup analysis. The users are encouraged to
             conduct sensitivity analysis by trying different parameter
             values in the prior distributions and examining the robustness
             of the results." ,
            type = "warning")
})

##show different type of messages
msg.box <- function(contents, type="info") {
    switch(type,
           info    = cls <- "cinfo",
           warning = cls <- "cwarning",
           success = cls <- "csuccess",
           error   = cls <- "cerror");
    rst <- '<div class="';
    rst <- paste(rst, cls, '">');
    rst <- paste(rst, contents);
    rst <- paste(rst, "</div>");
    HTML(rst);
}

##chk number of subgroups to determine plot or not
gen.chk.nsub <- function(x, max.s, A) {
    if (x > max.s) {
        msg.box("There are too many subgroup comparisons.
                     Density plots are not generated.", "warning")
    } else if (x < 2) {
        msg.box("There is only one subgroup selected.
                     Density plots are not generated.", "warning")
    } else {
        A
    }
}

##generate analysis result panels
gen.rst.tabs <- reactive({
    mrst      <- ana.rst();
    n.sel     <- length(get.subgrp.selected());
    max.s     <- input$displaynsub;

    if (is.null(mrst))
        return(NULL);

    rst <- list(widths=c(3,9));
    for (i in 1:length(mrst)) {
        rst[[length(rst)+1]] <- names(mrst)[i];
        rst[[length(rst)+1]] <- tabPanel("Effects",
                                         tabsetPanel(type="pills",
                                                     tabPanel("Effects Table",
                                                              DT::dataTableOutput(paste("tblrst",i,sep=""))),
                                                     tabPanel("Density",
                                                              plotOutput(paste("plotrst",i,sep=""))),
                                                     tabPanel("Forest plot",
                                                              plotOutput(paste("plotforest",i,sep=""))),
                                                     tabPanel("Predictive plot",
                                                              plotOutput(paste("plotpred",i,sep="")))
                                                     )
                                         );
        rst[[length(rst)+1]] <- tabPanel("Comparisons",
                                         tabsetPanel(type="pills",
                                                     tabPanel("Effects Comparison Table",
                                                              DT::dataTableOutput(paste("tblcomp",i,sep=""))),
                                                     tabPanel("Density",
                                                              lapply(n.sel,
                                                                     gen.chk.nsub,
                                                                     max.s,
                                                                     plotOutput(paste("plotcomp",i,sep="")))
                                                              ),
                                                     tabPanel("Forest plot",
                                                              lapply(n.sel,
                                                                     gen.chk.nsub,
                                                                     max.s,
                                                                     plotOutput(paste("plotforestcomp",i,sep=""))
                                                                     )))
                                         );
        rst[[length(rst)+1]] <- tabPanel("STAN Diagnosis",
                                         tabsetPanel(type="pills",
                                                     tabPanel("Raw Output",
                                                              verbatimTextOutput(paste("txtrst",i,sep=""))),
                                                     tabPanel("Information Crieria",
                                                              htmlOutput(paste("txtdic",i,sep=""))),
                                                     tabPanel("Trace Plot",
                                                              plotOutput(paste("plottrace",i,sep=""))),
                                                     tabPanel("Rhat",
                                                              plotOutput(paste("plotrhat",i,sep="")),
                                                              htmlOutput(paste("txtrhat",i,sep="")))
                                                     ##,tabPanel("Gelman-Rubin Plot",
                                                     ##         plotOutput(paste("plotgr",i,sep="")))
                                                     )
                                         );
    }
    rst
})

##genereate individual tabpanel for each model
get.stab <- function(mrst, lab, f) {
    do.call(tabsetPanel,
            c(list(type="pills"),
              lapply(1:length(mrst),
                     function(i) {tabPanel(names(mrst)[i],
                                           f(paste(lab,i,sep="")))
                     })
              ));
}

##across by models
gen.rst.tabs.mdl <- reactive({
    mrst  <- ana.rst();
    n.sel <- length(get.subgrp.selected());
    max.s <- input$displaynsub;

    if (is.null(mrst))
        return(NULL);

    pan.table      <- get.stab(mrst, "tblrst", DT::dataTableOutput);
    pan.den        <- get.stab(mrst, "plotrst", plotOutput);
    pan.forest     <- get.stab(mrst, "plotforest", plotOutput);
    pan.pred       <- get.stab(mrst, "plotpred", plotOutput);
    pan.tablecomp  <- get.stab(mrst, "tblcomp", DT::dataTableOutput);
    pan.stan       <- get.stab(mrst, "txtrst", verbatimTextOutput);
    pan.trace      <- get.stab(mrst, "plottrace", plotOutput);
    pan.dic        <- get.stab(mrst, "txtdic", htmlOutput);
    pan.gr         <- get.stab(mrst, "plotgr", plotOutput);
    pan.rhat       <- do.call(tabsetPanel,
                              c(list(type="pills"),
                                lapply(1:length(mrst),
                                       function(i) {tabPanel(names(mrst)[i],
                                                             plotOutput(paste("plotrhat",i,sep="")),
                                                             htmlOutput(paste("txtrhat", i,sep="")))
                                })
                                ));

    if (n.sel > max.s) {
        pan.dencomp <- pan.forestcomp <- msg.box("There are too many subgroup comparisons.
                                                   Plots are not generated.", "warning")
    } else if (n.sel < 2) {
        pan.dencomp <- pan.forestcomp <- msg.box("There is only one subgroup selected.
                                                  Plot is not generated.", "warning")

    } else {
        pan.dencomp    <- get.stab(mrst, "plotcomp", plotOutput);
        pan.forestcomp <- get.stab(mrst, "plotforestcomp", plotOutput);
    }

    rst <- list(widths=c(2,10),
                "Effect",
                tabPanel("Table", pan.table),
                tabPanel("Density", pan.den),
                tabPanel("Forest plot", pan.forest),
                tabPanel("Predict plot", pan.pred),
                "Comparison",
                tabPanel("Table", pan.tablecomp),
                tabPanel("Density", pan.dencomp),
                tabPanel("Forest plot", pan.forestcomp),
                "STAN Diagnosis",
                tabPanel("Raw Output", pan.stan),
                tabPanel("Information Criteria", pan.dic),
                tabPanel("Trace Plot", pan.trace),
                tabPanel("Rhat", pan.rhat)
                ##,tabPanel("Gelman-Rubin Plot", pan.gr)
                );
})


##subgroup panel
panel.subgroup <- function() {
    list(
        msg.warnings(),
        wellPanel(
            h4(textOutput("txtSel")),
            msg.box("Specify variables for defining subgroups.
                     If a covariate has more than two levels in the data,
                     please specify whether the covariate is nomial or ordinal.
                     Ordinal covariates are assumed to have a linear trend across
                     levels in its effect on the outcome."),
            fluidRow(
                conditionalPanel(paste("input.dataformat=='", DATA.FORMAT[2], "'",sep=""),
                                 column(3,
                                        h6("Treatment"),
                                        uiOutput("uiTrt"),
                                        h6("Response"),
                                        uiOutput("uiResp"),
                                        conditionalPanel(paste("input.resptype=='", RESP.TYPE[3], "'",sep=""),
                                                         h6("Censoring"),
                                                         uiOutput("uiCensor"))
                                        ),
                                 column(2,
                                        h6("Covariates"),
                                        uiOutput("uiCov")
                                        ),
                                 column(3,
                                        h6("Type of response"),
                                        radioButtons('resptype', '', RESP.TYPE)
                                        )
                                 ),
                conditionalPanel(paste("input.dataformat=='", DATA.FORMAT[1], "'",sep=""),
                                 column(3,
                                        h6("Treatment Effect Estimates"),
                                        uiOutput("uisubE"),
                                        h6("Treatment Effect Variance"),
                                        uiOutput("uisubVar")),
                                 column(2,
                                        h6("Covariates"),
                                        uiOutput("uisubCov"))),
                column(3, uiOutput("uiNominal"))
            ),
            fluidRow(
                column(9,
                       actionButton("btnSub", "Get Subgroups", styleclass = "success"),
                       align="left")
            ),

            htmlOutput("uiValid")
        ),
        ##subgroup
        uiOutput("uiSubgrp")
    )
}

panel.priors <- function() {

    ##no subgroup effect label
    n.lab1 <- sprintf("<span class='hline2'>
                           %s
                          </span>", ALL.MODELS[1]);


    f.each <- function(inx, ...) {

        n.chk <- paste("chkM", inx, sep="");
        n.par <- paste("parM", inx, sep="");
        n.lab <- sprintf("<span class='h4'>
                           %s
                          </span>", ALL.MODELS[inx])

        withTags(div(class="content",
                     checkboxInput(inputId = n.chk,
                                   label = HTML(n.lab),
                                   value=TRUE),
                     div(class='intro', id=n.par, ...))
                 )
    }

    fluidRow(column(4,
                    withTags(div(class="content",
                                 HTML(n.lab1),
                                 div(class='intro', id="parM1",
                                     h6("Prior variance of the group mean (B)"),
                                     numericInput(inputId = "m1vtau", label = "",
                                                 value = 1000, min = 100, max = 10000, step=100)
                                     ))
                             ),
                    f.each(2,
                          h6("Prior variance of the group mean (B)"),
                          numericInput(inputId = "m2vtau", label = "", value = 1000,
                                       min = 100, max = 10000, step=100)
                           ),
                    f.each(3,
                           h6("Prior variance of the intercept (B)"),
                           numericInput(inputId = "m3vtau", label = "", value = 1000,
                                       min = 100, max = 10000, step=100),
                           h6("Prior variance of regression coefficients (C)"),
                           numericInput(inputId = "m3vgamma", label = "", value = 1000,
                                       min = 100, max = 10000, step=100)
                           )
                    ),
             column(4,
                    f.each(4,
                           h6("Prior variance of the group mean (B)"),
                           numericInput(inputId = "m4vtau", label = "", value = 1000,
                                       min = 100, max = 10000, step=100),
                           h6("Prior variance of the shrinkage parameter (D)"),
                           numericInput(inputId = "m4vw", label = "", value = 1,
                                       min = 0, max = 500, step=0.5)
                           ),
                    f.each(5,
                           h6("Prior variance of the intercept (B)"),
                           numericInput(inputId = "m5vtau", label = "", value = 1000,
                                       min = 100, max = 10000, step=100),
                           h6("Prior variance of regression coefficients (C)"),
                           numericInput(inputId = "m5vgamma", label = "", value = 1000,
                                       min = 100, max = 10000, step=100),
                           h6("Prior variance of the shrinkage parameter (D)"),
                           numericInput(inputId = "m5vw", label = "", value = 1,
                                       min = 0, max = 500, step=0.5)
                          )
                    ),
             column(4,
                    f.each(6,
                           h6("Prior variance of the group mean (B)"),
                           numericInput(inputId = "m6vtau", label = "", value = 1000, min = 100,
                                       max = 10000, step=100),
                           h6("Prior variance of the shrinkage parameter (C)"),
                           numericInput(inputId = "m6vw", label = "", value = 1,
                                        min = 0.1, max = 500, step=0.5)
                           ),
                    f.each(7,
                           h6("Prior variance of the group mean"),
                           numericInput(inputId = "m7vtau", label = "", value = 1000,
                                       min = 100, max = 10000, step=100),
                           h6("Prior variance of the shrinkage parameter (D)"),
                           numericInput(inputId = "m7vw", label = "", value = 1,
                                       min = 0, max = 500, step=0.5)
                           )
                    )
             );
}

##tabset for start page
tab.start <- function() {
    tabPanel("Start",
             navlistPanel(widths=c(2,10),
                          tabPanel("What does BEANZ do?",
                                   wellPanel(includeHTML("www/beanz_do.html"))),
                          tabPanel("What does BEANZ need?",
                                   wellPanel(includeHTML("www/beanz_need.html"))),
                          tabPanel("What does BEANZ provide?",
                                   wellPanel(includeHTML("www/beanz_rst.html"))),
                          tabPanel("Warnings",
                                   wellPanel(includeHTML("www/beanz_warn.html")))
                          )
             );
}

##tabset for data uploading
tab.upload <- function(){
    tabPanel("Upload Data",
             msg.warnings(),
             fluidPage(
                 ##msg.box('Please upload data file on this page.
                 ##         Note at this stage, the software only considers discrete baseline covariates.
                 ##         Click to download a <a href="example_rawdata.txt" download>raw data</a>
                 ##         or <a href="example_subgroupinfo.txt" download>subgroup treatment effect
                 ##         </a> example file.'),
                 wellPanel(
                     h4("Upload data"),
                     msg.box("Please upload data file."),
                     fluidRow(
                         column(3,
                                h6("Choose File"),
                                fileInput(inputId = 'userdata', label = '',
                                          accept=c('text/csv','text/comma-separated-values,text/plain'))),
                         column(3,
                                h6("Data Format"),
                                radioButtons('dataformat', '', DATA.FORMAT, selected = DATA.FORMAT[2])
                                ),
                         column(2, h6("Separator"),
                                radioButtons('sep', '',
                                             c(Comma=',',Semicolon=';',Tab='\t',Space=' '),
                                             ' ')),
                         column(2, h6("Quote"),
                                radioButtons('quote', '',
                                             c(None='','Double Quote'='"','Single Quote'="'"),
                                             selected = '')),
                         column(2, h6("Other"),
                                checkboxInput(inputId='header', label='Header', value=TRUE),
                                checkboxInput(inputId="displaydata", label = "Show Data", value = TRUE))
                     )),
                 wellPanel(
                     h4("Try An Example"),
                     includeHTML("www/beanz_solvd.html"),
                     actionButton("btnExample", "Try it", styleclass = "success")
                 ),
                 wellPanel(h4("Review data"), DT::dataTableOutput("uiData"))
                 )
             )
}

##mcmc configuration
tab.mcmc <- function() {
    tabPanel("Configuration",
             msg.warnings(),
             wellPanel(
                 h4("Statistical Models and Priors"),
                 msg.box("<p>Select statistical models and specify priors for model parameters.
                             Details of the models can be found
                             in the <a href='paper_beanz.pdf'>software manual</a>.</p>"),
                 panel.priors()
             ),
             wellPanel(
                 h4("Prior of Variance"),
                 msg.box("Specify prior distribution and uncertainties parameters for SD.
                          Details can be found
                          in the <a href='paper_beanz.pdf'>software manual</a>."),
                 fluidRow(
                     column(3,
                            h6("Prior of log SD"),
                            radioButtons(inputId = "plogsig", label="",
                                         c("Normal Distribution" = 1, "Uniform Distribution" =0))
                            ),
                     column(3,
                            h6("Uncertainty of log SD"),
                            numericInput(inputId = "deltalogsig", label = "",
                                        min = 0, max = 100, value = 0, step = 0.1)
                            )
                 )
             ),
             wellPanel(
                 h4("MCMC Paramters"),
                 msg.box("Specify parameters for Bayesian posterior sampling. The target metropolis
                          acceptance rate and initial step-size are options for advanced users to
                          control STAN sampler's behavior. "),
                 fluidRow(
                     column(3,
                            h6("Number of iterations"),
                            sliderInput(inputId = "mcmciter", label = "",
                                        value = 4000, min = 100, max = 20000, step=100),
                            h6("Number of burn-in"),
                            sliderInput("mcmcburnin", label = "", value=2000, min=100, max=20000, step=100)
                            ),
                     column(3,
                            h6("Number of thinning"),
                            sliderInput(inputId = "mcmcthin", label = "", value=2, min=1, max=50, step=1),
                            h6("Number of chains"),
                            sliderInput("mcmcchain", label = "", value=4, min=4, max=10, step=1)
                            ),
                     column(3,
                            h6("Target Metropolis Acceptance Rate"),
                            sliderInput("mcmcdelta", label = "", value=0.95, min=0.05, max=1, step=0.05),
                            h6("Initial Step-size"),
                            sliderInput("mcmcstepsize", label = "", value=1, min=0.05, max=5, step=0.05)
                            ##h6("Algorithm"),
                            ##radioButtons('mcmcalg', '', c('NUTS', 'HMC', "Fixed_param"))
                            ),
                     column(3,
                            h6("Random seed"),
                            numericInput(inputId="mcmcseed", label="", value=0, min=0),
                            h6("Rhat Warning"),
                            numericInput(inputId="mcmcrhat", label="", value=1.1, min=1.05),
                            h6("Number of cores"),
                            sliderInput(inputId = "mcmccore", label="",
                                        value = 1, min = 1,
                                        max = (parallel::detectCores()-1), step = 1)
                            )
                 )),
             wellPanel(
                 h4("Display Parameters"),
                 fluidRow(
                     column(3,
                            h6("Cut off value for treatment effects"),
                            numericInput(inputId="displaycut", label="", value=0),
                            h6("Cut off value for subgroup comparison"),
                            numericInput(inputId="displaycutcomp", label="", value=0)
                            ),
                     column(3,
                            h6("Digits"),
                            numericInput(inputId="displaydigit", label="", value=3, min=1, step=1),
                            h6("Maximum subgroups for comparison plots"),
                            numericInput(inputId="displaynsub", label="", value=5, step=1)
                            ),
                     column(3,
                            h6("Transformation"),
                            checkboxInput(inputId='displayexp',
                                          label='Take exponential transformation', value=FALSE),
                            h6("Reference"),
                            checkboxInput(inputId='displayref', label='Display no subgroup effect outcome',
                                          value=TRUE)
                            ),
                     column(3,
                            h6("Organize results"),
                            radioButtons('displayby', '', DISPLAY.BY))
                 ),
                 h6("Select subgroups to display in analysis results"),
                 DT::dataTableOutput('subgrpsel')
             ))
}

##download report
panel.download <- function() {
    rst <- list(
        msg.warnings(),
        wellPanel(h4("Summary"),
                   htmlOutput("rstsummary"),
                   DT::dataTableOutput('rsttbl')));

    has.rd <- requireNamespace("rmarkdown", quietly = TRUE);
    has.pd <- requireNamespace("pander", quietly = TRUE);

    if (has.rd & has.pd) {
        rst <- c(rst,
                 list(wellPanel(h4('Download the analysis report'),
                           radioButtons('format', '', c('PDF', 'HTML', 'Word')),
                           downloadButton('btnDload')))
                 );
    } else {
        tmp.msg <- NULL;
        if (!has.rd)
            tmp.msg <- "rmarkdown";
        if (!has.pd)
            tmp.msg <- c(tmp.msg,  "pander");
        msg <- paste('R package ',
                     paste(tmp.msg, collapse=" and "),
                     ' needed for generating the analysis report. Please install.');
        rst <- c(rst,
                 list(wellPanel(msg.box(msg, "warning")))
                 );
    }
    rst
}

##define the main tabset for beans
tab.main <- function() {
    panels <- list(type = "pills",
                   id="mainpanel",
                   tab.start()
                  ,tab.upload()
                  ,tabPanel("Subgroup Specification", uiOutput("uiSubgroup"))
                  ,tab.mcmc()
                  ,tabPanel("Bayesian Analysis", uiOutput("uiAnalysis"))
                  ,tabPanel("Toolbox", uiOutput("uiTool"))
                  ,tabPanel("Report", uiOutput("uiReport"))
                   );

    ##generate
    do.call(tabsetPanel, panels);
}


## call stan for mcmc sampling
ana.rst <- reactive({

    if (is.null(input$btnAna))
        return(NULL);

    if (0 == input$btnAna)
        return(NULL);

    isolate({

        ##current subgroup data
        dat.sub <- get.subgrp();
        if (any(is.null(dat.sub),
                is.null(get.ana.models()))) return(NULL);

        mdls     <- get.ana.models();
        par.pri  <- get.par.prior();
        mcmc.par <- get.mcmc.par();

        ##Create a Progress object
        progress <- shiny::Progress$new(session, min=0, max=1);
        progress$set(message = "Analysis in progress...", value=0);

        ##Close the progress when this reactive exits (even if there's an error)
        on.exit(progress$close());

        mcmc.rst <- NULL;
        for(i in 1:length(mdls)) {
            cur.m <- which(mdls[i] == ALL.MODELS);

            ##model 7 reduced to model 6 when there is
            ##only one covariate
            if (7 == cur.m & 1==length(get.sub.cov()))
                next;

            progress$set(value=i/length(mdls), detail=mdls[i]);
            mcmc.rst[[length(mcmc.rst)+1]] <- bzCallStan(mdls=STAN.NAME[cur.m],
                                                        dat.sub=dat.sub,
                                                        var.estvar=SUB.HEAD,
                                                        var.cov=get.sub.cov(),
                                                        var.nom=input$selnomcov,
                                                        par.pri=par.pri[[cur.m]],
                                                        prior.sig=as.numeric(input$plogsig),
                                                        delta=input$deltalogsig,
                                                        chains=mcmc.par$chains,
                                                        cores=mcmc.par$cores,
                                                        control=mcmc.par$control,
                                                        iter=mcmc.par$iter,
                                                        warmup=mcmc.par$warmup,
                                                        thin=mcmc.par$thin,
                                                        seed=mcmc.par$seed);

            names(mcmc.rst)[length(mcmc.rst)] <- mdls[i];
        }
    })
    mcmc.rst
})


##-------------------------------------------------------------
##           UI FUNCTIONS
##-------------------------------------------------------------

##check subgroup specification for raw data
chk.raw.subgrp <- function(dset, resp.type, trt, resp, censor, cov, levels) {

    msg <- NULL;

    if (trt == resp)
        msg <- paste(msg, "<li>Column used multiple times </li>");

    if (any(is.na(dset[,c(trt, resp, censor, cov)])))
        msg <- paste(msg, "<li>Missing value found </li>");

    if (is.null(cov)) {
        msg <- paste(msg, "<li>No covariates selected</li>");
    }

    if (!is.null(levels)) {
        if (any(1 >= levels))
            msg <- paste(msg, "<li>Some covariate has less than two levels</li>");
    };

    if (length(unique(dset[, trt])) != 2)
        msg <- paste(msg, "<li>There are not two arms of treatment </li>");

    if (resp.type == RESP.TYPE[3]) {
        if (trt == censor | resp == censor)
            msg <- paste(msg, "<li>Column used multiple times </li>");

        if (length(unique(dset[, censor])) != 2)
            msg <- paste(msg, "<li>Censoring indicator has invalid value </li>");
    }

    if (resp.type == RESP.TYPE[2]) {
        if (length(unique(dset[, resp])) != 2)
            msg <- paste(msg, "<li>Response is not binary </li>");
    }

    ##return
    if (is.null(msg)) {
        rst <- NULL;
    } else {
        rst <- "Subgroup specification is not valid. Please check the following:";
        rst <- paste(rst, "<ul>", msg, "</ul>");
    }

    rst
}

##check subgroup specification for summary trt effect data
chk.summary.subgrp <- function(dset, est, var, cov, levels) {

    ##print(unique(dset[, cov]));
    msg <- NULL;
    if (any(is.na(dset[,c(est, var, cov)])))
        msg <- paste(msg, "<li>Missing value found </li>");

    if (is.factor(dset[,var])|is.factor(dset[,est]))
        msg <- paste(msg, "<li>Estimate and variance columns need to be numerical values </li>");

    if (any(as.numeric(dset[,var]) <= 0))
        msg <- paste(msg, "<li>Negative variance value found </li>");

    if (is.null(cov)) {
        msg <- paste(msg, "<li>No covariates selected</li>");
    };

    if (!is.null(levels)) {
        if (any(1 >= levels))
            msg <- paste(msg, "<li>Some covariate has less than two levels</li>");
    };

    ##return
    if (is.null(msg)) {
        rst <- NULL;
    } else {
        rst <- "Subgroup specification is not valid. Please check the following:";
        rst <- paste(rst, "<ul>", msg, "</ul>");
    }

    rst
}


observe ({
    inFile <- input$userdata;
    if (!is.null(inFile)){
        userLog$data <- read.csv(inFile$datapath,
                                    header=input$header,
                                    sep=input$sep,
                                    quote=input$quote)
    }
})

observe ({
    if (is.null(input$btnExample))
        return(NULL);

    if (0 == input$btnExample)
        return(NULL);

    userLog$data <- solvd.sub;
})

##get subgroup covariates
get.sub.cov <- reactive({
    ##if (-1 == userLog$SubgroupValid)
    ##    return(NULL);

    if (DATA.FORMAT[1] == input$dataformat) {
        rst <- input$selsubcov;
    } else {
        rst <- input$selcov;
    }

    rst
})

##get levels of covariates
get.sub.cov.levels <- reactive({
    c.covs <- get.sub.cov();
    c.data <- get.data();

    if (is.null(c.covs) | is.null(c.data))
        return(NULL);

    rst <- NULL;
    for (i in 1:length(c.covs)) {
        tmp.l <- length(unique(c.data[, c.covs[i]]));
        rst   <- c(rst, tmp.l);
    }

    rst
})

##read data from user input
get.data <- reactive({
    userLog$data;
})

##rhat warning
get.rhat.warn <- reactive({
    input$mcmcrhat;
})

##get subgroup effects
get.subgrp.raw <- function() {
    c.data <- get.data();
    if (is.null(c.data)        |
        is.null(input$selcov)  |
        is.null(input$selresp) |
        is.null(input$seltrt)  |
        is.null(input$resptype)
        )
        return(NULL);

    resptype <- c("continuous", "binary", "survival")[which(RESP.TYPE == input$resptype)];
    rst <- bzGetSubgrpRaw(c.data, input$selresp, input$seltrt,
                            input$selcov, input$selcensor, resptype);
}

##get sub group effects from subgroup summary file
get.subgrp.sub <- function() {
    c.data <- get.data();
    if (is.null(c.data) |
        is.null(input$selsubcov) |
        is.null(input$selsube)|
        is.null(input$selsubvar)
        )
        return();

    rst <- bzGetSubgrp(c.data, input$selsube, input$selsubvar, input$selsubcov);
}

##get subgroup data
get.subgrp <- reactive({
    if (is.null(input$btnSub))
        return(NULL);

    if (0 == input$btnSub)
        return(NULL);

    ##if (-1 == userLog$SubgroupValid)
    ##    return(NULL);
    isolate({
        rst <- tryCatch(
            {
                if (input$dataformat == DATA.FORMAT[2]) {
                    ##Create a Progress object
                    progress <- shiny::Progress$new(session, min=0, max=1);
                    progress$set(message = "Get subgroup treatment effects...", value=1);
                    on.exit(progress$close());
                    rst <- get.subgrp.raw();
                } else {
                    rst <- get.subgrp.sub();
                }
            },
            error=function(cond) {
                print(cond);
                return(NULL);
            }
        );

        rst});
})

##get selected subgroups
get.subgrp.selected <- reactive({

    input$btnAna;
    input$subgrpsel_rows_selected;

    isolate({
        dat.sub <- get.subgrp();
        if (is.null(dat.sub))
            return(NULL);

        if (is.null(input$subgrpsel_rows_selected)) {
            rst <- 1:nrow(dat.sub);
        } else {
            rst <- sort(input$subgrpsel_rows_selected);
        }
    })
    rst
})

##get model selected for analysis
##no subgroup effect model is always selected
get.ana.models <- reactive({
    rst <- ALL.MODELS[1];
    for (i in 2:length(ALL.MODELS)) {
        cur.in <- paste("chkM", i, sep="");
        if (input[[cur.in]]) {
            rst <- c(rst, ALL.MODELS[i]);
        }
    }

    rst
})

##get mcmc setup
get.mcmc.par <- reactive({
    rst <- list(iter=input$mcmciter,
                warmup=input$mcmcburnin,
                thin=input$mcmcthin,
                seed=input$mcmcseed,
                chains=input$mcmcchain,
                cores=input$mcmccore,
                control=list(adapt_delta = input$mcmcdelta,
                             stepsize    = input$mcmcstepsize),
                algorithm=input$mcmcalg);
    rst
})

##get prior parameters
get.par.prior <- reactive({
    rst <- list(mdl1=c(B=input$m1vtau),
                mdl2=c(B=input$m2vtau),
                mdl3=c(B=input$m3vtau, C=input$m3vgamma),
                mdl4=c(B=input$m4vtau, D=input$m4vw),
                mdl5=c(B=input$m5vtau, D=input$m5vw, C=input$m5vgamma),
                mdl6=c(B=input$m6vtau, D=input$m6vw),
                mdl7=c(B=input$m7vtau, D=input$m7vw));

    ##rst <- mapply(function(x) {c(x, list(vrange=input$rangelogvar))},
    ##              rst, SIMPLIFY=FALSE);
    rst
})

##trace plot height
get.traceplot.height <- function(smps, n.eachrow=2, row.height=100) {
    n   <- dim(smps)[3];
    rst <- n/n.eachrow*row.height;
}


##get dic for all models
get.all.dic <- reactive({
    ##model results
    mrst <- ana.rst();

    if (is.null(mrst))
        return(NULL);

    rst <- NULL;
    for (i in 1:length(mrst)) {
        rst <- rbind(rst,
                     c(mrst[[i]]$dic, mrst[[i]]$looic$looic));
    }
    rst
})


##get anoint analysis results
get.anoint <- reactive({
    c.data <- get.data();
    if (is.null(c.data)        |
        is.null(input$selcov)  |
        is.null(input$selresp) |
        is.null(input$seltrt)  |
        is.null(input$resptype)
        )
        return(NULL);

    print(input$selcensor);
    rst <- .get.anoint(c.data, input$selresp, input$seltrt,
                      input$selcov, input$selcensor, input$resptype);
})


pred.rst <- reactive({

    arst <- ana.rst();

    if (is.null(arst))
        return(NULL);

    isolate({
        dat.sub    <- get.subgrp();
        var.estvar <- SUB.HEAD;

        rst <- NULL;
        for(i in 1:length(arst)) {
            rst[[i]] <- bzPredSubgrp(arst[[i]],
                                     dat.sub=dat.sub,
                                     var.estvar = var.estvar);
        }
        names(rst) <- names(arst);
    })
    rst;
})

##apply anoint to subject level data
.get.anoint <- function(data.all, var.resp, var.trt, var.cov, var.censor, resptype) {

    if (resptype == RESP.TYPE[3]) {
        ##survival
        resp       <- paste("survival::Surv(", var.resp, ",", var.censor, ")", sep="");
        mdl.family <- "coxph";
    } else {
        ##continuous or binary
        resp <- var.resp;
        if (resptype == RESP.TYPE[1]) {
            mdl.family <- "gaussian";
        } else if (resptype == RESP.TYPE[2]) {
            mdl.family <- "binomial";
        }
    }

    ## interaction models
    mdl.formula <- as.formula(paste(resp, "~ (",
                                    paste(var.cov, collapse="+"), ")*",
                                    var.trt, sep=""));
    ano.rst <- do.call(anoint::anoint,
                       list(formula=mdl.formula,
                            data=data.all,
                            family=mdl.family));

    ## proportional interaction models
    mdl.fml.2 <- as.formula(paste(resp, "~ ", paste(var.cov, collapse="+"), sep=""));
    pim.rst   <- do.call(anoint::pim.fit,
                         list(formula=mdl.fml.2,
                              data=data.all,
                              family=mdl.family, trt=var.trt));

    ##return
    list(obo=anoint::obo(ano.rst),
         uim=anoint::uim(ano.rst),
         pim=pim.rst);
}

plot.pred <- function(aprst, dat.sub, var.estvar) {
    par(mfrow=c(2,2))
    funs   <- list(median, sd, min, max);
    titles <- c("Median", "Standard Deviation", "Minimum", "Maximum");

    for (i in 1:length(funs)) {
        cur.sum <- apply(aprst, 2, funs[[i]]);
        cur.obs <- funs[[i]](dat.sub[,var.estvar[1]]);
        plot(density(cur.sum), lwd=2,
             xlab="Subgroup Treatment Effect",
             ylab="Density",
             main=titles[i]);
        lines(c(cur.obs,cur.obs), c(0, 1e10), lwd=2, col="red");
        text(cur.obs, 0, "Observed", col="red");
    }
}

##get relevant parameters
get.pars <- function(pars, pattern="^mu+|^b0+|^omega+|^phi+|^bgamma+") {
    pars[grep(pattern, pars)]
}

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beanz documentation built on May 2, 2019, 4:01 a.m.