Nothing
inst/elrshiny/ui.R
In EffectLiteR: Average and Conditional Effects
shinyUI(fluidPage(
titlePanel(title="EffectLiteR"),
sidebarLayout(
sidebarPanel(
tabsetPanel(
######### Data ############
tabPanel('Data',
img(src='effectliter_logo.png', align = "right"),
br(),
actionButton("newanalysis","Start a New Analysis"),
uiOutput("reload"),
hr(),
selectizeInput(inputId="method", label="Statistical Model", selected="sem",
choices= c("sem","lm"),
width='50%'),
# hr(),
selectizeInput(inputId="exdata", label="Example Data", selected="",
choices= c("","nonortho","example01","example02lv",
"example_multilevel", "MDRS2016",
"sophonet_data_simulated",
"elrdata_categorical_items"),
options = list(placeholder = 'choose example data'),
width='50%'),
# hr(),
tryCatch(
fileInput("file1", "Data File",
accept=c(".csv", ".txt", ".sav", ".xpt",
".CSV", ".TXT", ".SAV", ".XPT",
".DAT", ".dat", ".RDS", ".rds", ".Rds"))
),
helpText('Select either a .csv, .dat, .txt, .sav, .xpt or a .rds file to be uploaded. The corresponding R function (read.csv, read.table, read.spss, read.xport, or readRDS) will be chosen automatically with the default settings for arguments. Some default arguments can be overwritten (see additional options below).'),
br(),
h5(strong("Additional Options to Read Data")),
br(),
selectizeInput(inputId="vallabels",
label=h5("Use value labels (SPSS data)"),
selected="default",
choices= c("default","yes","no"),
width='90%'),
selectizeInput(inputId="header",
label=h5("File contains variable names (csv, dat, and txt data)"),
selected="default",
choices= c("default","yes","no"),
width='90%'),
selectizeInput(inputId="sep",
label=h5("Character separating columns (csv, dat, and txt data)"),
selected="default",
choices= c("default","semicolon","white space"),
width='90%'),
selectizeInput(inputId="dec",
label=h5("Decimal character (csv, dat, and txt data)"),
selected="default",
choices= c("default","decimal point","decimal comma"),
width='90%'),
textInput(inputId="na.strings",
label=h5("Missing value code (csv, dat, and txt data)"),
value = "NA",
width='90%'),
br()
),
########## Manifest Variables ############
tabPanel('Manifest Variables',
img(src='effectliter_logo.png', align = "right"),
br(),
conditionalPanel(
condition = "!input.latenty",
selectizeInput(
inputId="variabley",
label="Dependent Variable Y",
choices="",
options = list(placeholder = 'select dependent variable'),
width='60%')
),
selectizeInput(
inputId="variablex",
label="Treatment Variable X",
choices="",
options = list(placeholder = 'select treatment variable'),
width='60%'),
selectizeInput("variablek", "Categorical Covariates K", "",
multiple=TRUE, selected="",
options = list(placeholder = 'select categorical covariates')),
selectizeInput("variablez", "Continuous Covariates Z", "",
multiple=TRUE, selected="",
options = list(placeholder = 'select continuous covariates')),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br()
),
############ Latent Variables ###########
tabPanel('Latent Variables',
img(src='effectliter_logo.png', align = "right"),
br(),
checkboxInput("latenty", "Latent dependent variable", FALSE),
conditionalPanel(
condition = "input.latenty",
selectizeInput("indicatorsy",
"Indicators of Latent Dependent Variable",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
checkboxInput("latentz", "Add latent covariates", FALSE),
conditionalPanel(
condition = "input.latentz",
###
### Indicators for latent covariates
column(8, p("Number of Latent Covariates")),
column(4, numericInput("nlatentz", label=NULL, 0,
min = 0, max = 10, width='60%')),
### Cov 1
conditionalPanel(
condition = "input.nlatentz > 0",
selectizeInput("indicatorsz1",
"Indicators of Latent Covariate 1",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 2
conditionalPanel(
condition = "input.nlatentz > 1",
selectizeInput("indicatorsz2",
"Indicators of Latent Covariate 2",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 3
conditionalPanel(
condition = "input.nlatentz > 2",
selectizeInput("indicatorsz3",
"Indicators of Latent Covariate 3",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 4
conditionalPanel(
condition = "input.nlatentz > 3",
selectizeInput("indicatorsz4",
"Indicators of Latent Covariate 4",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 5
conditionalPanel(
condition = "input.nlatentz > 4",
selectizeInput("indicatorsz5",
"Indicators of Latent Covariate 5",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 6
conditionalPanel(
condition = "input.nlatentz > 5",
selectizeInput("indicatorsz6",
"Indicators of Latent Covariate 6",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 7
conditionalPanel(
condition = "input.nlatentz > 6",
selectizeInput("indicatorsz7",
"Indicators of Latent Covariate 7",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 8
conditionalPanel(
condition = "input.nlatentz > 7",
selectizeInput("indicatorsz8",
"Indicators of Latent Covariate 8",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 9
conditionalPanel(
condition = "input.nlatentz > 8",
selectizeInput("indicatorsz9",
"Indicators of Latent Covariate 9",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 10
conditionalPanel(
condition = "input.nlatentz > 9",
selectizeInput("indicatorsz10",
"Indicators of Latent Covariate 10",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
helpText("Latent covariates will be added to the list of continuous manifest covariates (if specified).")
),
hr(),
p("Additional Options"),
###
### Names of latent variables
checkboxInput("latnames", "Names of latent variables", FALSE),
conditionalPanel(
condition = "input.latnames",
### Dependent Variable
conditionalPanel(
condition = "input.latenty",
textInput("name.etay", "Latent Dependent Variable",
value = "eta",
width='60%')
),
### Cov 1
conditionalPanel(
condition = "input.nlatentz > 0",
textInput("name.etaz1", "Latent Covariate 1",
value = "xi1",
width='60%')
),
### Cov 2
conditionalPanel(
condition = "input.nlatentz > 1",
textInput("name.etaz2", "Latent Covariate 2",
value = "xi2",
width='60%')
),
### Cov 3
conditionalPanel(
condition = "input.nlatentz > 2",
textInput("name.etaz3", "Latent Covariate 3",
value = "xi3",
width='60%')
),
### Cov 4
conditionalPanel(
condition = "input.nlatentz > 3",
textInput("name.etaz4", "Latent Covariate 4",
value = "xi4",
width='60%')
),
### Cov 5
conditionalPanel(
condition = "input.nlatentz > 4",
textInput("name.etaz5", "Latent Covariate 5",
value = "xi5",
width='60%')
),
### Cov 6
conditionalPanel(
condition = "input.nlatentz > 5",
textInput("name.etaz6", "Latent Covariate 6",
value = "xi6",
width='60%')
),
### Cov 7
conditionalPanel(
condition = "input.nlatentz > 6",
textInput("name.etaz7", "Latent Covariate 7",
value = "xi7",
width='60%')
),
### Cov 8
conditionalPanel(
condition = "input.nlatentz > 7",
textInput("name.etaz8", "Latent Covariate 8",
value = "xi8",
width='60%')
),
### Cov 9
conditionalPanel(
condition = "input.nlatentz > 8",
textInput("name.etaz9", "Latent Covariate 9",
value = "xi9",
width='60%')
),
### Cov 10
conditionalPanel(
condition = "input.nlatentz > 9",
textInput("name.etaz10", "Latent Covariate 10",
value = "xi10",
width='60%')
)
),
###
### Customized Measurement Models
checkboxInput("custommeasmodels", "Customize measurement models", FALSE),
conditionalPanel(
condition = "input.custommeasmodels",
### Dependent Variable
conditionalPanel(
condition = "input.latenty",
selectizeInput(
inputId="mm.etay",
label="Measurement Model for Latent Dependent Variable",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 1
conditionalPanel(
condition = "input.nlatentz > 0",
selectizeInput(
inputId="mm.etaz1",
label="Measurement Model for Latent Covariate 1",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 2
conditionalPanel(
condition = "input.nlatentz > 1",
selectizeInput(
inputId="mm.etaz2",
label="Measurement Model for Latent Covariate 2",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 3
conditionalPanel(
condition = "input.nlatentz > 2",
selectizeInput(
inputId="mm.etaz3",
label="Measurement Model for Latent Covariate 3",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 4
conditionalPanel(
condition = "input.nlatentz > 3",
selectizeInput(
inputId="mm.etaz4",
label="Measurement Model for Latent Covariate 4",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 5
conditionalPanel(
condition = "input.nlatentz > 4",
selectizeInput(
inputId="mm.etaz5",
label="Measurement Model for Latent Covariate 5",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 6
conditionalPanel(
condition = "input.nlatentz > 5",
selectizeInput(
inputId="mm.etaz6",
label="Measurement Model for Latent Covariate 6",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 7
conditionalPanel(
condition = "input.nlatentz > 6",
selectizeInput(
inputId="mm.etaz7",
label="Measurement Model for Latent Covariate 7",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 8
conditionalPanel(
condition = "input.nlatentz > 7",
selectizeInput(
inputId="mm.etaz8",
label="Measurement Model for Latent Covariate 8",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 9
conditionalPanel(
condition = "input.nlatentz > 8",
selectizeInput(
inputId="mm.etaz9",
label="Measurement Model for Latent Covariate 9",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 10
conditionalPanel(
condition = "input.nlatentz > 9",
selectizeInput(
inputId="mm.etaz10",
label="Measurement Model for Latent Covariate 10",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
)
),
helpText("Names of latent variables and customize measurement models are optional. If not specified, EffectLiteR picks default names and tries to guess a reasonable measurement model: Congeneric for latent variables with three or more indicators, essentially tau-equivalent for latent variables with less than three indicators and for latent variables with cross-loadings (e.g., method factors), and parallel for single-indicator latent variables.")
),
########## Options #############
tabPanel('Options',
img(src='effectliter_logo.png', align = "right"),
br(),
selectInput("control", "Reference Group", "", width='60%'),
radioButtons("missing", "Missing Data",
choices=c("default","listwise","fiml","pairwise"),
selected = "default"),
radioButtons("fixed.cell", "Sampling Model",
choices=c("default"="default",
"stochastic"="stochastic",
"fixed cell sizes"="fixed",
"fixed cell sizes and fixed means of Z"="fixed+e"),
selected = "default"),
radioButtons("estimator", "Estimator",
choices=c("default"="default",
"ML"="ML",
"DWLS"="DWLS"),
selected = "default"),
radioButtons("se", "Standard Errors",
choices=c("default","standard","boot",
"robust.sem","robust.huber.white"),
selected = "default"),
conditionalPanel(
condition = "input.se == 'boot'",
numericInput("bootstrap", "Number of bootstrap draws",
min=1, max=5000, value=5)
),
radioButtons("homoscedasticity", "Residual variances",
choices=c("default","homoscedastic", "heteroscedastic"),
selected = "default"),
radioButtons("test.stat", "Test statistic for hypotheses",
choices=c("default","Chisq", "Ftest"),
selected = "default")
),
########## Interactions #############
tabPanel('Interactions',
img(src='effectliter_logo.png', align = "right"),
br(),
radioButtons("interactions", "Interactions",
choices=c("Full model"="all",
"Only two-way interactions"="2-way",
"Only X:K and X:Z interactions"="X:K,X:Z",
"Only X:K interactions"="X:K",
"Only X:Z interactions"="X:Z",
"No treatment*covariate interactions"="none",
"No interactions"="no"),
selected = "all",
width='60%'),
br(),
helpText('These are some pre-defined sets of constraints on interactions. You can consult the regression model in the main output to see which coefficients are fixed to zero. In addition, you can use the additional options input to specify customized constraints.')
),
########## Propensity Scores #############
tabPanel('Propensity Scores',
img(src='effectliter_logo.png', align = "right"),
br(),
checkboxInput(
inputId="propscoreformula",
label="Specify formula for propensity score model yourself",
value=FALSE,
width='60%'),
conditionalPanel(
condition = "!input.propscoreformula",
br(),
selectizeInput("propscore", "Covariates in Propensity Score Model", "",
multiple=TRUE, selected="",
options = list(placeholder = 'select covariates'))
),
conditionalPanel(
condition = "input.propscoreformula",
br(),
textInput("prop.formula", "Specify formula",
value = "")
),
br(),
helpText('Propensity scores are predicted probabilities from a multinomial regression of the treatment variable on the above selected covariates. The logit transformed propensity score(s) are included as continuous covariates in the EffectLiteR analysis. For more flexibility, you can specify the R formula yourself.')
),
########## Complex Survey #############
tabPanel('Complex Survey',
img(src='effectliter_logo.png', align = "right"),
br(),
selectizeInput("ids", "Cluster Variable", "",
multiple=FALSE, selected="",
options = list(placeholder = 'select cluster ID'),
width='60%'),
selectizeInput("weights", "Sampling Weights", "",
multiple=FALSE, selected="",
options = list(placeholder = 'select sampling weights'),
width='60%'),
helpText('The lavaan.survey package is called to account for cluster variables and sampling weights. This is an experimental feature.'),
helpText('Note: Only use weights if you know what you are doing. For example, some conditional treatment effects may require different weights than average effects.')
),
########## User Specified Tests #############
tabPanel('User-Specified Tests',
img(src='effectliter_logo.png', align = "right"),
br(),
h5("Test Continuous Covariates"),
helpText('This joint test can be used to check significance of regression coefficients (main effects and interactions) related to the selected subset of continuous covariates in the model'),
selectizeInput("subconcov", "", "",
multiple=TRUE, selected="",
options = list(placeholder = 'select subset of covariates')),
br(),
h5("User-Specified Parameters"),
helpText('The text will be appended to the lavaan syntax generated by EffectLiteR. It can for example be used to compute user-specified effects.'),
helpText('Example: newparameter := g000 + g100'),
tags$textarea(id="add.syntax", rows=5, cols=40, ""),
br(),
br(),
h5("User-Specified Wald Test"),
helpText('This text can be used to specify an additional (user-specified) Wald test based on names of model parameters.'),
helpText('Example: g000 == 0 ; g100 == 0'),
tags$textarea(id="add.syntax.wald", rows=5, cols=40, "")
)
)),
mainPanel(
tabsetPanel(
######### Data Table ##########
tabPanel('Data', dataTableOutput("mytable1")),
######### EffectLiteR ##########
tabPanel("EffectLiteR", verbatimTextOutput("summary")),
######### lavaan or lm Syntax ##########
tabPanel("Syntax",
br(),
downloadLink('downloadLavData', 'Download Data'),
br(),
br(),
verbatimTextOutput("elrcall"),
conditionalPanel(
condition="method == 'sem'",
verbatimTextOutput("lavcall")
),
verbatimTextOutput("lavsyntax")),
######### lavaan or lm Results ##########
tabPanel("Results", verbatimTextOutput("lavresults")),
######### Conditional Effects I ##########
tabPanel('Conditional Effects I',
verbatimTextOutput("helptextcondeffects"),
downloadLink('downloadConditionalEffects', 'Download Conditional Effects Data'),
br(),
br(),
dataTableOutput("condeffs")),
######### Conditional Effects II ##########
tabPanel("Conditional Effects II",
verbatimTextOutput("helptextcondeffects2"),
br(),
br(),
column(3, wellPanel(
h5("Values of Covariates"),
uiOutput("ui")
)) ,
column(9, wellPanel(
h5("Conditional Effects"),
verbatimTextOutput("outputcondeffect2"),
br(),
br(),
h5("Descriptive Statistics for Continuous Covariates"),
verbatimTextOutput("descriptivestats")
))
),
######### Conditional Effects III ##########
tabPanel("Conditional Effects III",
verbatimTextOutput("helptextaggeff"),
br(),
br(),
column(3, wellPanel(
h5("Values of Covariates and Treatment"),
uiOutput("uiaggeff"),
uiOutput("uiaggeff2")
)) ,
column(9, wellPanel(
h5("Aggregated Effects"),
verbatimTextOutput("outputaggeff")
)) ,
br(),
br(),
br(),
br(),
column(12, wellPanel(
h5("Subset used to compute aggregated effects"),
uiOutput("uiaggeff3"),
dataTableOutput("aggeffstable")
))
),
######### Conditional Effects IV ##########
tabPanel("Conditional Effects IV",
verbatimTextOutput("helptextcondeff4"),
br(),
br(),
column(5, wellPanel(
selectInput("gxselectce4",
"Effect function",
"g1(K,Z)",
multiple=FALSE,
selectize=TRUE),
selectizeInput("variablece4", "Variable W", "",
multiple=FALSE, selected="",
options = list(placeholder = 'select variable')),
numericInput("bootstrapce4", "Number of bootstrap draws",
min=1, max=5000, value=NA)
)) ,
column(7, wellPanel(
h5("Regression Coefficients E(gx | W)"),
verbatimTextOutput("outputcondeff4")
))
),
######### Output User Specified Tests ##########
tabPanel("User-Specified Tests",
verbatimTextOutput("covtests"),
verbatimTextOutput("addeffects"),
verbatimTextOutput("waldtest")
),
######### Plot 1 ##########
tabPanel("Plot 1",
verbatimTextOutput("helptextplot1"),
plotOutput("plot1")
),
######### Plot 2 ##########
tabPanel("Plot 2",
verbatimTextOutput("helptextplot2"),
selectizeInput(
inputId="zselect",
label="Continuous Covariate",
choices="",
options = list(placeholder = 'select continuous covariate'),
width='30%'),
plotOutput("plot2")),
######### Plot 3 ##########
tabPanel("Plot 3",
verbatimTextOutput("helptextplot3"),
column(3, wellPanel(
selectInput("gxselect",
"Effect function",
"g1(K,Z)",
multiple=FALSE,
selectize=TRUE),
selectInput("zselect2",
"Regressor",
"",
multiple=FALSE,
selectize=TRUE),
selectInput("zselect3",
"Colour variable",
"",
multiple=FALSE,
selectize=TRUE),
selectInput("regline",
"Regression Line",
c("default","smooth","linear","none"),
multiple=FALSE,
selectize=TRUE),
br(),
h5("Confidence Intervals"),
checkboxInput("show.ci", "Show CIs", value=FALSE),
checkboxInput("show.cir", "Show Regression CI", value=FALSE)
)) ,
column(9,
plotOutput("plot3"))
),
######### Plot 4 ##########
tabPanel("Plot 4",
verbatimTextOutput("helptextplot4"),
column(3, wellPanel(
selectInput("gxselect2",
"Effect function",
"g1(K,Z)",
multiple=FALSE,
selectize=TRUE),
selectInput("zselect4",
"Colour variable",
"",
multiple=FALSE,
selectize=TRUE),
br(),
h5("Confidence Intervals"),
checkboxInput("show.ci2", "Show CIs", value=FALSE)
)) ,
column(9,
plotOutput("plot4"))
)
)
))
))
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shinyUI(fluidPage(
titlePanel(title="EffectLiteR"),
sidebarLayout(
sidebarPanel(
tabsetPanel(
######### Data ############
tabPanel('Data',
img(src='effectliter_logo.png', align = "right"),
br(),
actionButton("newanalysis","Start a New Analysis"),
uiOutput("reload"),
hr(),
selectizeInput(inputId="method", label="Statistical Model", selected="sem",
choices= c("sem","lm"),
width='50%'),
# hr(),
selectizeInput(inputId="exdata", label="Example Data", selected="",
choices= c("","nonortho","example01","example02lv",
"example_multilevel", "MDRS2016",
"sophonet_data_simulated",
"elrdata_categorical_items"),
options = list(placeholder = 'choose example data'),
width='50%'),
# hr(),
tryCatch(
fileInput("file1", "Data File",
accept=c(".csv", ".txt", ".sav", ".xpt",
".CSV", ".TXT", ".SAV", ".XPT",
".DAT", ".dat", ".RDS", ".rds", ".Rds"))
),
helpText('Select either a .csv, .dat, .txt, .sav, .xpt or a .rds file to be uploaded. The corresponding R function (read.csv, read.table, read.spss, read.xport, or readRDS) will be chosen automatically with the default settings for arguments. Some default arguments can be overwritten (see additional options below).'),
br(),
h5(strong("Additional Options to Read Data")),
br(),
selectizeInput(inputId="vallabels",
label=h5("Use value labels (SPSS data)"),
selected="default",
choices= c("default","yes","no"),
width='90%'),
selectizeInput(inputId="header",
label=h5("File contains variable names (csv, dat, and txt data)"),
selected="default",
choices= c("default","yes","no"),
width='90%'),
selectizeInput(inputId="sep",
label=h5("Character separating columns (csv, dat, and txt data)"),
selected="default",
choices= c("default","semicolon","white space"),
width='90%'),
selectizeInput(inputId="dec",
label=h5("Decimal character (csv, dat, and txt data)"),
selected="default",
choices= c("default","decimal point","decimal comma"),
width='90%'),
textInput(inputId="na.strings",
label=h5("Missing value code (csv, dat, and txt data)"),
value = "NA",
width='90%'),
br()
),
########## Manifest Variables ############
tabPanel('Manifest Variables',
img(src='effectliter_logo.png', align = "right"),
br(),
conditionalPanel(
condition = "!input.latenty",
selectizeInput(
inputId="variabley",
label="Dependent Variable Y",
choices="",
options = list(placeholder = 'select dependent variable'),
width='60%')
),
selectizeInput(
inputId="variablex",
label="Treatment Variable X",
choices="",
options = list(placeholder = 'select treatment variable'),
width='60%'),
selectizeInput("variablek", "Categorical Covariates K", "",
multiple=TRUE, selected="",
options = list(placeholder = 'select categorical covariates')),
selectizeInput("variablez", "Continuous Covariates Z", "",
multiple=TRUE, selected="",
options = list(placeholder = 'select continuous covariates')),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br()
),
############ Latent Variables ###########
tabPanel('Latent Variables',
img(src='effectliter_logo.png', align = "right"),
br(),
checkboxInput("latenty", "Latent dependent variable", FALSE),
conditionalPanel(
condition = "input.latenty",
selectizeInput("indicatorsy",
"Indicators of Latent Dependent Variable",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
checkboxInput("latentz", "Add latent covariates", FALSE),
conditionalPanel(
condition = "input.latentz",
###
### Indicators for latent covariates
column(8, p("Number of Latent Covariates")),
column(4, numericInput("nlatentz", label=NULL, 0,
min = 0, max = 10, width='60%')),
### Cov 1
conditionalPanel(
condition = "input.nlatentz > 0",
selectizeInput("indicatorsz1",
"Indicators of Latent Covariate 1",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 2
conditionalPanel(
condition = "input.nlatentz > 1",
selectizeInput("indicatorsz2",
"Indicators of Latent Covariate 2",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 3
conditionalPanel(
condition = "input.nlatentz > 2",
selectizeInput("indicatorsz3",
"Indicators of Latent Covariate 3",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 4
conditionalPanel(
condition = "input.nlatentz > 3",
selectizeInput("indicatorsz4",
"Indicators of Latent Covariate 4",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 5
conditionalPanel(
condition = "input.nlatentz > 4",
selectizeInput("indicatorsz5",
"Indicators of Latent Covariate 5",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 6
conditionalPanel(
condition = "input.nlatentz > 5",
selectizeInput("indicatorsz6",
"Indicators of Latent Covariate 6",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 7
conditionalPanel(
condition = "input.nlatentz > 6",
selectizeInput("indicatorsz7",
"Indicators of Latent Covariate 7",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 8
conditionalPanel(
condition = "input.nlatentz > 7",
selectizeInput("indicatorsz8",
"Indicators of Latent Covariate 8",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 9
conditionalPanel(
condition = "input.nlatentz > 8",
selectizeInput("indicatorsz9",
"Indicators of Latent Covariate 9",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
### Cov 10
conditionalPanel(
condition = "input.nlatentz > 9",
selectizeInput("indicatorsz10",
"Indicators of Latent Covariate 10",
"", multiple=TRUE,
options = list(placeholder = 'select indicators'))
),
helpText("Latent covariates will be added to the list of continuous manifest covariates (if specified).")
),
hr(),
p("Additional Options"),
###
### Names of latent variables
checkboxInput("latnames", "Names of latent variables", FALSE),
conditionalPanel(
condition = "input.latnames",
### Dependent Variable
conditionalPanel(
condition = "input.latenty",
textInput("name.etay", "Latent Dependent Variable",
value = "eta",
width='60%')
),
### Cov 1
conditionalPanel(
condition = "input.nlatentz > 0",
textInput("name.etaz1", "Latent Covariate 1",
value = "xi1",
width='60%')
),
### Cov 2
conditionalPanel(
condition = "input.nlatentz > 1",
textInput("name.etaz2", "Latent Covariate 2",
value = "xi2",
width='60%')
),
### Cov 3
conditionalPanel(
condition = "input.nlatentz > 2",
textInput("name.etaz3", "Latent Covariate 3",
value = "xi3",
width='60%')
),
### Cov 4
conditionalPanel(
condition = "input.nlatentz > 3",
textInput("name.etaz4", "Latent Covariate 4",
value = "xi4",
width='60%')
),
### Cov 5
conditionalPanel(
condition = "input.nlatentz > 4",
textInput("name.etaz5", "Latent Covariate 5",
value = "xi5",
width='60%')
),
### Cov 6
conditionalPanel(
condition = "input.nlatentz > 5",
textInput("name.etaz6", "Latent Covariate 6",
value = "xi6",
width='60%')
),
### Cov 7
conditionalPanel(
condition = "input.nlatentz > 6",
textInput("name.etaz7", "Latent Covariate 7",
value = "xi7",
width='60%')
),
### Cov 8
conditionalPanel(
condition = "input.nlatentz > 7",
textInput("name.etaz8", "Latent Covariate 8",
value = "xi8",
width='60%')
),
### Cov 9
conditionalPanel(
condition = "input.nlatentz > 8",
textInput("name.etaz9", "Latent Covariate 9",
value = "xi9",
width='60%')
),
### Cov 10
conditionalPanel(
condition = "input.nlatentz > 9",
textInput("name.etaz10", "Latent Covariate 10",
value = "xi10",
width='60%')
)
),
###
### Customized Measurement Models
checkboxInput("custommeasmodels", "Customize measurement models", FALSE),
conditionalPanel(
condition = "input.custommeasmodels",
### Dependent Variable
conditionalPanel(
condition = "input.latenty",
selectizeInput(
inputId="mm.etay",
label="Measurement Model for Latent Dependent Variable",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 1
conditionalPanel(
condition = "input.nlatentz > 0",
selectizeInput(
inputId="mm.etaz1",
label="Measurement Model for Latent Covariate 1",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 2
conditionalPanel(
condition = "input.nlatentz > 1",
selectizeInput(
inputId="mm.etaz2",
label="Measurement Model for Latent Covariate 2",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 3
conditionalPanel(
condition = "input.nlatentz > 2",
selectizeInput(
inputId="mm.etaz3",
label="Measurement Model for Latent Covariate 3",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 4
conditionalPanel(
condition = "input.nlatentz > 3",
selectizeInput(
inputId="mm.etaz4",
label="Measurement Model for Latent Covariate 4",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 5
conditionalPanel(
condition = "input.nlatentz > 4",
selectizeInput(
inputId="mm.etaz5",
label="Measurement Model for Latent Covariate 5",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 6
conditionalPanel(
condition = "input.nlatentz > 5",
selectizeInput(
inputId="mm.etaz6",
label="Measurement Model for Latent Covariate 6",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 7
conditionalPanel(
condition = "input.nlatentz > 6",
selectizeInput(
inputId="mm.etaz7",
label="Measurement Model for Latent Covariate 7",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 8
conditionalPanel(
condition = "input.nlatentz > 7",
selectizeInput(
inputId="mm.etaz8",
label="Measurement Model for Latent Covariate 8",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 9
conditionalPanel(
condition = "input.nlatentz > 8",
selectizeInput(
inputId="mm.etaz9",
label="Measurement Model for Latent Covariate 9",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
),
### Cov 10
conditionalPanel(
condition = "input.nlatentz > 9",
selectizeInput(
inputId="mm.etaz10",
label="Measurement Model for Latent Covariate 10",
choices = c("default" = "default",
"equivalent measures" = "parallel",
"essentially equivalent measures" = "tau-equi",
"congeneric measures" = "tau-cong",
"essentially equivalent measures categorical" = "tau-equi-categorical",
"congeneric measures categorical" = "tau-cong-categorical"),
width='100%')
)
),
helpText("Names of latent variables and customize measurement models are optional. If not specified, EffectLiteR picks default names and tries to guess a reasonable measurement model: Congeneric for latent variables with three or more indicators, essentially tau-equivalent for latent variables with less than three indicators and for latent variables with cross-loadings (e.g., method factors), and parallel for single-indicator latent variables.")
),
########## Options #############
tabPanel('Options',
img(src='effectliter_logo.png', align = "right"),
br(),
selectInput("control", "Reference Group", "", width='60%'),
radioButtons("missing", "Missing Data",
choices=c("default","listwise","fiml","pairwise"),
selected = "default"),
radioButtons("fixed.cell", "Sampling Model",
choices=c("default"="default",
"stochastic"="stochastic",
"fixed cell sizes"="fixed",
"fixed cell sizes and fixed means of Z"="fixed+e"),
selected = "default"),
radioButtons("estimator", "Estimator",
choices=c("default"="default",
"ML"="ML",
"DWLS"="DWLS"),
selected = "default"),
radioButtons("se", "Standard Errors",
choices=c("default","standard","boot",
"robust.sem","robust.huber.white"),
selected = "default"),
conditionalPanel(
condition = "input.se == 'boot'",
numericInput("bootstrap", "Number of bootstrap draws",
min=1, max=5000, value=5)
),
radioButtons("homoscedasticity", "Residual variances",
choices=c("default","homoscedastic", "heteroscedastic"),
selected = "default"),
radioButtons("test.stat", "Test statistic for hypotheses",
choices=c("default","Chisq", "Ftest"),
selected = "default")
),
########## Interactions #############
tabPanel('Interactions',
img(src='effectliter_logo.png', align = "right"),
br(),
radioButtons("interactions", "Interactions",
choices=c("Full model"="all",
"Only two-way interactions"="2-way",
"Only X:K and X:Z interactions"="X:K,X:Z",
"Only X:K interactions"="X:K",
"Only X:Z interactions"="X:Z",
"No treatment*covariate interactions"="none",
"No interactions"="no"),
selected = "all",
width='60%'),
br(),
helpText('These are some pre-defined sets of constraints on interactions. You can consult the regression model in the main output to see which coefficients are fixed to zero. In addition, you can use the additional options input to specify customized constraints.')
),
########## Propensity Scores #############
tabPanel('Propensity Scores',
img(src='effectliter_logo.png', align = "right"),
br(),
checkboxInput(
inputId="propscoreformula",
label="Specify formula for propensity score model yourself",
value=FALSE,
width='60%'),
conditionalPanel(
condition = "!input.propscoreformula",
br(),
selectizeInput("propscore", "Covariates in Propensity Score Model", "",
multiple=TRUE, selected="",
options = list(placeholder = 'select covariates'))
),
conditionalPanel(
condition = "input.propscoreformula",
br(),
textInput("prop.formula", "Specify formula",
value = "")
),
br(),
helpText('Propensity scores are predicted probabilities from a multinomial regression of the treatment variable on the above selected covariates. The logit transformed propensity score(s) are included as continuous covariates in the EffectLiteR analysis. For more flexibility, you can specify the R formula yourself.')
),
########## Complex Survey #############
tabPanel('Complex Survey',
img(src='effectliter_logo.png', align = "right"),
br(),
selectizeInput("ids", "Cluster Variable", "",
multiple=FALSE, selected="",
options = list(placeholder = 'select cluster ID'),
width='60%'),
selectizeInput("weights", "Sampling Weights", "",
multiple=FALSE, selected="",
options = list(placeholder = 'select sampling weights'),
width='60%'),
helpText('The lavaan.survey package is called to account for cluster variables and sampling weights. This is an experimental feature.'),
helpText('Note: Only use weights if you know what you are doing. For example, some conditional treatment effects may require different weights than average effects.')
),
########## User Specified Tests #############
tabPanel('User-Specified Tests',
img(src='effectliter_logo.png', align = "right"),
br(),
h5("Test Continuous Covariates"),
helpText('This joint test can be used to check significance of regression coefficients (main effects and interactions) related to the selected subset of continuous covariates in the model'),
selectizeInput("subconcov", "", "",
multiple=TRUE, selected="",
options = list(placeholder = 'select subset of covariates')),
br(),
h5("User-Specified Parameters"),
helpText('The text will be appended to the lavaan syntax generated by EffectLiteR. It can for example be used to compute user-specified effects.'),
helpText('Example: newparameter := g000 + g100'),
tags$textarea(id="add.syntax", rows=5, cols=40, ""),
br(),
br(),
h5("User-Specified Wald Test"),
helpText('This text can be used to specify an additional (user-specified) Wald test based on names of model parameters.'),
helpText('Example: g000 == 0 ; g100 == 0'),
tags$textarea(id="add.syntax.wald", rows=5, cols=40, "")
)
)),
mainPanel(
tabsetPanel(
######### Data Table ##########
tabPanel('Data', dataTableOutput("mytable1")),
######### EffectLiteR ##########
tabPanel("EffectLiteR", verbatimTextOutput("summary")),
######### lavaan or lm Syntax ##########
tabPanel("Syntax",
br(),
downloadLink('downloadLavData', 'Download Data'),
br(),
br(),
verbatimTextOutput("elrcall"),
conditionalPanel(
condition="method == 'sem'",
verbatimTextOutput("lavcall")
),
verbatimTextOutput("lavsyntax")),
######### lavaan or lm Results ##########
tabPanel("Results", verbatimTextOutput("lavresults")),
######### Conditional Effects I ##########
tabPanel('Conditional Effects I',
verbatimTextOutput("helptextcondeffects"),
downloadLink('downloadConditionalEffects', 'Download Conditional Effects Data'),
br(),
br(),
dataTableOutput("condeffs")),
######### Conditional Effects II ##########
tabPanel("Conditional Effects II",
verbatimTextOutput("helptextcondeffects2"),
br(),
br(),
column(3, wellPanel(
h5("Values of Covariates"),
uiOutput("ui")
)) ,
column(9, wellPanel(
h5("Conditional Effects"),
verbatimTextOutput("outputcondeffect2"),
br(),
br(),
h5("Descriptive Statistics for Continuous Covariates"),
verbatimTextOutput("descriptivestats")
))
),
######### Conditional Effects III ##########
tabPanel("Conditional Effects III",
verbatimTextOutput("helptextaggeff"),
br(),
br(),
column(3, wellPanel(
h5("Values of Covariates and Treatment"),
uiOutput("uiaggeff"),
uiOutput("uiaggeff2")
)) ,
column(9, wellPanel(
h5("Aggregated Effects"),
verbatimTextOutput("outputaggeff")
)) ,
br(),
br(),
br(),
br(),
column(12, wellPanel(
h5("Subset used to compute aggregated effects"),
uiOutput("uiaggeff3"),
dataTableOutput("aggeffstable")
))
),
######### Conditional Effects IV ##########
tabPanel("Conditional Effects IV",
verbatimTextOutput("helptextcondeff4"),
br(),
br(),
column(5, wellPanel(
selectInput("gxselectce4",
"Effect function",
"g1(K,Z)",
multiple=FALSE,
selectize=TRUE),
selectizeInput("variablece4", "Variable W", "",
multiple=FALSE, selected="",
options = list(placeholder = 'select variable')),
numericInput("bootstrapce4", "Number of bootstrap draws",
min=1, max=5000, value=NA)
)) ,
column(7, wellPanel(
h5("Regression Coefficients E(gx | W)"),
verbatimTextOutput("outputcondeff4")
))
),
######### Output User Specified Tests ##########
tabPanel("User-Specified Tests",
verbatimTextOutput("covtests"),
verbatimTextOutput("addeffects"),
verbatimTextOutput("waldtest")
),
######### Plot 1 ##########
tabPanel("Plot 1",
verbatimTextOutput("helptextplot1"),
plotOutput("plot1")
),
######### Plot 2 ##########
tabPanel("Plot 2",
verbatimTextOutput("helptextplot2"),
selectizeInput(
inputId="zselect",
label="Continuous Covariate",
choices="",
options = list(placeholder = 'select continuous covariate'),
width='30%'),
plotOutput("plot2")),
######### Plot 3 ##########
tabPanel("Plot 3",
verbatimTextOutput("helptextplot3"),
column(3, wellPanel(
selectInput("gxselect",
"Effect function",
"g1(K,Z)",
multiple=FALSE,
selectize=TRUE),
selectInput("zselect2",
"Regressor",
"",
multiple=FALSE,
selectize=TRUE),
selectInput("zselect3",
"Colour variable",
"",
multiple=FALSE,
selectize=TRUE),
selectInput("regline",
"Regression Line",
c("default","smooth","linear","none"),
multiple=FALSE,
selectize=TRUE),
br(),
h5("Confidence Intervals"),
checkboxInput("show.ci", "Show CIs", value=FALSE),
checkboxInput("show.cir", "Show Regression CI", value=FALSE)
)) ,
column(9,
plotOutput("plot3"))
),
######### Plot 4 ##########
tabPanel("Plot 4",
verbatimTextOutput("helptextplot4"),
column(3, wellPanel(
selectInput("gxselect2",
"Effect function",
"g1(K,Z)",
multiple=FALSE,
selectize=TRUE),
selectInput("zselect4",
"Colour variable",
"",
multiple=FALSE,
selectize=TRUE),
br(),
h5("Confidence Intervals"),
checkboxInput("show.ci2", "Show CIs", value=FALSE)
)) ,
column(9,
plotOutput("plot4"))
)
)
))
))
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