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inst/shiny-squid/source/server/modules/module4/svr_mod4_step_5.R
In squid: Statistical Quantification of Individual Differences
#Server functions for module 4 step 5
c(
######### Set variables #########
# Set hidden variables
Mod4Step5updateVind <- function(input, nb.IS){
m <- matrix(rep(0,(nb.IS*2)^2),(nb.IS*2))
diag(m)[1] <- input$Mod4Step5_Vi1
diag(m)[1 + nb.IS] <- input$Mod4Step5_Vi2
m[nb.IS + 1,1] <- input$Mod4Step5_Corr_I
return(m)
},
Mod4Step5updateB <- function(input, nb.IS){
m <- matrix(rep(0,(nb.IS*2)), nrow = 1)
m[1,2] <- input$Mod4Step5_B11
m[1,nb.IS + 2] <- input$Mod4Step5_B12
return(m)
},
output$Mod4Step5_hidden <- renderUI({
list(
numericInput("Mod4Step5_Tmax", "", Modules_VAR$Tmax$max),
numericInput("Mod4Step5_NI", "", 100),
numericInput("Mod4Step5_NT", "", 2),
numericInput("Mod4Step5_NR", "", 10),
shinyMatrix::matrixInput("Mod4Step5_B", value = Mod4Step5updateB(input, nb.IS), class = "numeric"),
shinyMatrix::matrixInput("Mod4Step5_Vind", value = Mod4Step5updateVind(input, nb.IS), class = "numeric"),
shinyMatrix::matrixInput("Mod4Step5_Ve", value = matrix(c(input$Mod4Step5_Ve1, input$Mod4Step5_Corr_e,
input$Mod4Step5_Corr_e, input$Mod4Step5_Ve2), 2), class = "numeric"),
checkboxInput("Mod4Step5_X1_state", "", value = TRUE),
checkboxInput("Mod4Step5_X1_sto_state", "", value = TRUE)
)
}),
outputOptions(output, "Mod4Step5_hidden", suspendWhenHidden = FALSE),
# get data from squidR()
get_data <- function(){
# Call app main function
data <- squid::squidR(input, module = "Mod4Step5")
dt <- as.data.table(data$sampled_data)
dt <- dt[ , .(Time, Individual, Trait, Phenotype, X1)]
dt[ , Trait := paste0("Phenotype_", Trait)]
dt <- dcast(dt, Time + Individual + X1 ~ Trait, value.var = "Phenotype")
return(list("sampled" = dt, "full_data" = data$full_data))
},
######### Run simulation #########
# Run simulation and return results
Mod4Step5_output <- reactive({
if (input$Mod4Step5_Run == 0) # if Run button is pressed
return(NULL)
isolate({
updateCheckboxInput(session, "isRunning", value = TRUE)
dt <- get_data()
updateCheckboxInput(session, "isRunning", value = FALSE)
return(dt)
})
}),
observe({
data <- Mod4Step5_output()
if(is.null(data)){
disableActionButton("Mod4Step5_Run_1", session, "true")
disableActionButton("Mod4Step5_Run_2", session, "true")
}else{
disableActionButton("Mod4Step5_Run_1", session, "false")
disableActionButton("Mod4Step5_Run_2", session, "false")
}
}),
Mod4Step5_output_model1 <- reactive({
if (input$Mod4Step5_Run_1 == 0) # if Run button is pressed
return(NULL)
isolate({
updateCheckboxInput(session, "isRunning", value = TRUE)
data <- get_data()$sampled
library(brms)
fit1 <- readRDS("./source/server/modules/module4/stanFiles/module4_step5_brms_model1.rds")
fit1 <- update(fit1, newdata = data,
iter = 200, warmup = 100, chains = 1)
updateCheckboxInput(session, "isRunning", value = FALSE)
return(fit1)
})
}),
Mod4Step5_output_model2 <- reactive({
if (input$Mod4Step5_Run_2 == 0) # if Run button is pressed
return(NULL)
isolate({
updateCheckboxInput(session, "isRunning", value = TRUE)
data <- get_data()$sampled
library(brms)
fit2 <- readRDS("./source/server/modules/module4/stanFiles/module4_step5_brms_model2.rds")
fit2 <- update(fit2, newdata = data,
iter = 200, warmup = 100, chains = 1)
updateCheckboxInput(session, "isRunning", value = FALSE)
return(fit2)
})
}),
output$Mod4Step5_correlationplot <- renderPlot({
data <- Mod4Step5_output()
if (!is.null(data)) {
dt <- copy(data[["sampled"]])
ggplot2::ggplot(dt, ggplot2::aes(x = Phenotype_1, Phenotype_2, fill = as.factor(Individual), colour = as.factor(Individual))) +
ggplot2::geom_point() +
ggplot2::xlab("Phenotype of trait y") +
ggplot2::ylab("Phenotype of trait z") +
ggplotCustomTheme()
}else{
sim_msg()
# defaultPlot()
}
}),
output$Mod4Step5_correlationplot2 <- renderPlot({
data <- Mod4Step5_output()
if (!is.null(data)) {
dt <- copy(data[["sampled"]])
dt <- dt[ , .(Phenotype_1 = mean(Phenotype_1),
Phenotype_2 = mean(Phenotype_2)), by = Individual]
ggplot2::ggplot(dt, ggplot2::aes(x = Phenotype_1, Phenotype_2, fill = as.factor(Individual), colour = as.factor(Individual))) +
ggplot2::geom_point() +
ggplot2::xlab("Mean phenotype of trait y") +
ggplot2::ylab("Mean phenotype of trait z") +
ggplotCustomTheme()
}else{
defaultPlot()
}
}),
output$Mod4Step5_correlationplot3 <- renderPlot({
data <- Mod4Step5_output()
if (!is.null(data)) {
dt <- copy(data[["sampled"]])
dt[ , ':='(Phenotype_1 = Phenotype_1 - mean(Phenotype_1),
Phenotype_2 = Phenotype_2 - mean(Phenotype_2)), by = Individual]
ggplot2::ggplot(dt, ggplot2::aes(x = Phenotype_1, Phenotype_2, fill = as.factor(Individual), colour = as.factor(Individual))) +
ggplot2::geom_point() +
ggplot2::xlab("Deviation from individual phenotype mean of trait y") +
ggplot2::ylab("Deviation from individual phenotype mean of trait z") +
ggplotCustomTheme()
}else{
defaultPlot()
}
}),
output$Mod4Step5_environment <- renderPlot({
data <- Mod4Step5_output()
if (!is.null(data)) {
dt <- copy(data[["full_data"]])
dt <- dt[1:(input$Mod4Step5_Tmax),]
ggplot2::ggplot(dt, ggplot2::aes(x = Time, y = X1, fill = as.factor(Individual), colour = as.factor(Individual))) +
ggplot2::geom_point() +
ggplot2::xlab("Time") +
ggplot2::ylab("Environment value") +
ggplotCustomTheme()
}else{
defaultPlot()
}
}),
output$Mod4Step5_Phenotypic_Equation <- renderUI({
eq <- paste0("$$",
NOT$trait.1,"_{",NOT$time,NOT$ind,"} =
(0 + ",EQ$dev0.1,") +
",input$Mod4Step5_B11,NOT$env,"_{",NOT$time,NOT$ind,"} +
",NOT$error,"_{",NOT$trait.1,NOT$time,NOT$ind,"}$$
$$",
NOT$trait.2,"_{",NOT$time,NOT$ind,"} =
(0 + ",EQ$dev0.2,") +
",input$Mod4Step5_B12,NOT$env,"_{",NOT$time,NOT$ind,"} +
",NOT$error,"_{",NOT$trait.2,NOT$time,NOT$ind,"}
$$")
return(withMathJax(eq))
}),
Matrices <- reactive({
cov1 <- round(input$Mod4Step5_Corr_I * sqrt(input$Mod4Step5_Vi1 * input$Mod4Step5_Vi2),3)
eq1 <- paste0(
"$$ \\Omega_{",NOT$devI,"}=
\\begin{pmatrix}
V_{",NOT$devI,"_",NOT$trait.1,"} & Cov_{",NOT$devI,"_",NOT$trait.1,",",NOT$devI,"_",NOT$trait.2,"} \\\\
Cov_{",NOT$devI,"_",NOT$trait.1,",",NOT$devI,"_",NOT$trait.2,"} & V_{", NOT$devI,"_",NOT$trait.2,"} \\\\
\\end{pmatrix}
=
\\begin{pmatrix}
",input$Mod4Step5_Vi1," & ",cov1," \\\\
",cov1," & ",input$Mod4Step5_Vi2,"\\\\
\\end{pmatrix}
$$")
cov2 <- round(input$Mod4Step5_Corr_e * sqrt(input$Mod4Step5_Ve1 * input$Mod4Step5_Ve2),3)
eq2 <- paste0(
"$$ \\Omega_{",NOT$error,"}=
\\begin{pmatrix}
V_{",NOT$error,"_",NOT$trait.1,"} & Cov_{",NOT$error,"_",NOT$trait.1,",",NOT$error,"_",NOT$trait.2,"} \\\\
Cov_{",NOT$error,"_",NOT$trait.1,",",NOT$error,"_",NOT$trait.2,"} & V_{", NOT$error,"_",NOT$trait.2,"} \\\\
\\end{pmatrix}
=
\\begin{pmatrix}
",input$Mod4Step5_Ve1," & ",cov2," \\\\
",cov2," & ",input$Mod4Step5_Ve2,"\\\\
\\end{pmatrix}
$$")
return(withMathJax(paste0(eq1, eq2)))
}),
output$Mod4Step5_Matrices_1 <- renderUI({Matrices()}),
output$Mod4Step5_Matrices_2 <- renderUI({Matrices()}),
output$Mod4Step5_Matrices_3 <- renderUI({Matrices()}),
show_model_variance_results <- function(fit){
RanCoef <- VarCorr(fit)
varI1 <- round(RanCoef[["Individual"]][["sd"]]["Phenotype1_Intercept", "Estimate"]^2, 2)
varI2 <- round(RanCoef[["Individual"]][["sd"]]["Phenotype2_Intercept", "Estimate"]^2, 2)
covI12 <- round(RanCoef[["Individual"]][["cov"]]["Phenotype1_Intercept", "Estimate", "Phenotype2_Intercept"], 2)
varE1 <- round(RanCoef[["residual__"]][["sd"]]["Phenotype1", "Estimate"]^2, 2)
varE2 <- round(RanCoef[["residual__"]][["sd"]]["Phenotype2", "Estimate"]^2, 2)
covE12 <- round(RanCoef[["residual__"]][["cov"]]["Phenotype1", "Estimate", "Phenotype2"], 2)
cov1 <- round(input$Mod4Step5_Corr_I * sqrt(input$Mod4Step5_Vi1 * input$Mod4Step5_Vi2),3)
cov2 <- round(input$Mod4Step5_Corr_e * sqrt(input$Mod4Step5_Ve1 * input$Mod4Step5_Ve2),3)
eq1 <- paste0(
"$$\\Omega_{",NOT$devI,"'}=
\\begin{pmatrix}
",varI1,"\\,[",input$Mod4Step5_Vi1,"] & ",covI12,"\\,[",cov1,"] \\\\
",covI12,"\\,[",cov1,"] & ",varI2,"\\,[",input$Mod4Step5_Vi2,"]\\\\
\\end{pmatrix}\\quad")
eq2 <- paste0(
"\\Omega_{",NOT$error,"'}=
\\begin{pmatrix}
",varE1,"\\,[",input$Mod4Step5_Ve1,"] & ",covE12,"\\,[",cov2,"] \\\\
",covE12,"\\,[",cov2,"] & ",varE2,"\\,[",input$Mod4Step5_Ve1,"]\\\\
\\end{pmatrix}
$$")
return(paste0(eq1, eq2))
},
show_model_correlation_results <- function(fit){
RanCoef <- VarCorr(fit)
corI12 <- round(RanCoef[["Individual"]][["cor"]]["Phenotype1_Intercept", "Estimate", "Phenotype2_Intercept"],2)
corE12 <- round(RanCoef[["residual__"]][["cor"]]["Phenotype1", "Estimate", "Phenotype2"],2)
cor <- paste0("$$\\text{Among-individual correlation: }",corI12,"\\,[",input$Mod4Step5_Corr_I,"]$$
$$\\text{Residual within-individual correlation: }",corE12,"\\,[",input$Mod4Step5_Corr_e,"]$$")
return(cor)
},
output$Mod4Step5_Result_Matrices_Model1 <- renderUI({
fit1 <- Mod4Step5_output_model1()
if (!is.null(fit1)) {
isolate({res <- show_model_variance_results(fit1)})
return(withMathJax(res))
}else{return(withMathJax("$$...$$"))}
}),
output$Mod4Step5_Result_Matrices_Model1_corr <- renderUI({
fit1 <- Mod4Step5_output_model1()
if (!is.null(fit1)) {
isolate({cor <- show_model_correlation_results(fit1)})
return(withMathJax(cor))
}else{return(withMathJax("$$...$$"))}
}),
output$Mod4Step5_Result_Matrices_Model2 <- renderUI({
fit2 <- Mod4Step5_output_model2()
if (!is.null(fit2)) {
isolate({res <- show_model_variance_results(fit2)})
return(withMathJax(res))
}else{return(withMathJax("$$...$$"))}
}),
output$Mod4Step5_Result_Matrices_Model2_corr <- renderUI({
fit2 <- Mod4Step5_output_model2()
if (!is.null(fit2)) {
isolate({cor <- show_model_correlation_results(fit2)})
return(withMathJax(cor))
}else{return(withMathJax("$$...$$"))}
})
) # End return
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#Server functions for module 4 step 5
c(
######### Set variables #########
# Set hidden variables
Mod4Step5updateVind <- function(input, nb.IS){
m <- matrix(rep(0,(nb.IS*2)^2),(nb.IS*2))
diag(m)[1] <- input$Mod4Step5_Vi1
diag(m)[1 + nb.IS] <- input$Mod4Step5_Vi2
m[nb.IS + 1,1] <- input$Mod4Step5_Corr_I
return(m)
},
Mod4Step5updateB <- function(input, nb.IS){
m <- matrix(rep(0,(nb.IS*2)), nrow = 1)
m[1,2] <- input$Mod4Step5_B11
m[1,nb.IS + 2] <- input$Mod4Step5_B12
return(m)
},
output$Mod4Step5_hidden <- renderUI({
list(
numericInput("Mod4Step5_Tmax", "", Modules_VAR$Tmax$max),
numericInput("Mod4Step5_NI", "", 100),
numericInput("Mod4Step5_NT", "", 2),
numericInput("Mod4Step5_NR", "", 10),
shinyMatrix::matrixInput("Mod4Step5_B", value = Mod4Step5updateB(input, nb.IS), class = "numeric"),
shinyMatrix::matrixInput("Mod4Step5_Vind", value = Mod4Step5updateVind(input, nb.IS), class = "numeric"),
shinyMatrix::matrixInput("Mod4Step5_Ve", value = matrix(c(input$Mod4Step5_Ve1, input$Mod4Step5_Corr_e,
input$Mod4Step5_Corr_e, input$Mod4Step5_Ve2), 2), class = "numeric"),
checkboxInput("Mod4Step5_X1_state", "", value = TRUE),
checkboxInput("Mod4Step5_X1_sto_state", "", value = TRUE)
)
}),
outputOptions(output, "Mod4Step5_hidden", suspendWhenHidden = FALSE),
# get data from squidR()
get_data <- function(){
# Call app main function
data <- squid::squidR(input, module = "Mod4Step5")
dt <- as.data.table(data$sampled_data)
dt <- dt[ , .(Time, Individual, Trait, Phenotype, X1)]
dt[ , Trait := paste0("Phenotype_", Trait)]
dt <- dcast(dt, Time + Individual + X1 ~ Trait, value.var = "Phenotype")
return(list("sampled" = dt, "full_data" = data$full_data))
},
######### Run simulation #########
# Run simulation and return results
Mod4Step5_output <- reactive({
if (input$Mod4Step5_Run == 0) # if Run button is pressed
return(NULL)
isolate({
updateCheckboxInput(session, "isRunning", value = TRUE)
dt <- get_data()
updateCheckboxInput(session, "isRunning", value = FALSE)
return(dt)
})
}),
observe({
data <- Mod4Step5_output()
if(is.null(data)){
disableActionButton("Mod4Step5_Run_1", session, "true")
disableActionButton("Mod4Step5_Run_2", session, "true")
}else{
disableActionButton("Mod4Step5_Run_1", session, "false")
disableActionButton("Mod4Step5_Run_2", session, "false")
}
}),
Mod4Step5_output_model1 <- reactive({
if (input$Mod4Step5_Run_1 == 0) # if Run button is pressed
return(NULL)
isolate({
updateCheckboxInput(session, "isRunning", value = TRUE)
data <- get_data()$sampled
library(brms)
fit1 <- readRDS("./source/server/modules/module4/stanFiles/module4_step5_brms_model1.rds")
fit1 <- update(fit1, newdata = data,
iter = 200, warmup = 100, chains = 1)
updateCheckboxInput(session, "isRunning", value = FALSE)
return(fit1)
})
}),
Mod4Step5_output_model2 <- reactive({
if (input$Mod4Step5_Run_2 == 0) # if Run button is pressed
return(NULL)
isolate({
updateCheckboxInput(session, "isRunning", value = TRUE)
data <- get_data()$sampled
library(brms)
fit2 <- readRDS("./source/server/modules/module4/stanFiles/module4_step5_brms_model2.rds")
fit2 <- update(fit2, newdata = data,
iter = 200, warmup = 100, chains = 1)
updateCheckboxInput(session, "isRunning", value = FALSE)
return(fit2)
})
}),
output$Mod4Step5_correlationplot <- renderPlot({
data <- Mod4Step5_output()
if (!is.null(data)) {
dt <- copy(data[["sampled"]])
ggplot2::ggplot(dt, ggplot2::aes(x = Phenotype_1, Phenotype_2, fill = as.factor(Individual), colour = as.factor(Individual))) +
ggplot2::geom_point() +
ggplot2::xlab("Phenotype of trait y") +
ggplot2::ylab("Phenotype of trait z") +
ggplotCustomTheme()
}else{
sim_msg()
# defaultPlot()
}
}),
output$Mod4Step5_correlationplot2 <- renderPlot({
data <- Mod4Step5_output()
if (!is.null(data)) {
dt <- copy(data[["sampled"]])
dt <- dt[ , .(Phenotype_1 = mean(Phenotype_1),
Phenotype_2 = mean(Phenotype_2)), by = Individual]
ggplot2::ggplot(dt, ggplot2::aes(x = Phenotype_1, Phenotype_2, fill = as.factor(Individual), colour = as.factor(Individual))) +
ggplot2::geom_point() +
ggplot2::xlab("Mean phenotype of trait y") +
ggplot2::ylab("Mean phenotype of trait z") +
ggplotCustomTheme()
}else{
defaultPlot()
}
}),
output$Mod4Step5_correlationplot3 <- renderPlot({
data <- Mod4Step5_output()
if (!is.null(data)) {
dt <- copy(data[["sampled"]])
dt[ , ':='(Phenotype_1 = Phenotype_1 - mean(Phenotype_1),
Phenotype_2 = Phenotype_2 - mean(Phenotype_2)), by = Individual]
ggplot2::ggplot(dt, ggplot2::aes(x = Phenotype_1, Phenotype_2, fill = as.factor(Individual), colour = as.factor(Individual))) +
ggplot2::geom_point() +
ggplot2::xlab("Deviation from individual phenotype mean of trait y") +
ggplot2::ylab("Deviation from individual phenotype mean of trait z") +
ggplotCustomTheme()
}else{
defaultPlot()
}
}),
output$Mod4Step5_environment <- renderPlot({
data <- Mod4Step5_output()
if (!is.null(data)) {
dt <- copy(data[["full_data"]])
dt <- dt[1:(input$Mod4Step5_Tmax),]
ggplot2::ggplot(dt, ggplot2::aes(x = Time, y = X1, fill = as.factor(Individual), colour = as.factor(Individual))) +
ggplot2::geom_point() +
ggplot2::xlab("Time") +
ggplot2::ylab("Environment value") +
ggplotCustomTheme()
}else{
defaultPlot()
}
}),
output$Mod4Step5_Phenotypic_Equation <- renderUI({
eq <- paste0("$$",
NOT$trait.1,"_{",NOT$time,NOT$ind,"} =
(0 + ",EQ$dev0.1,") +
",input$Mod4Step5_B11,NOT$env,"_{",NOT$time,NOT$ind,"} +
",NOT$error,"_{",NOT$trait.1,NOT$time,NOT$ind,"}$$
$$",
NOT$trait.2,"_{",NOT$time,NOT$ind,"} =
(0 + ",EQ$dev0.2,") +
",input$Mod4Step5_B12,NOT$env,"_{",NOT$time,NOT$ind,"} +
",NOT$error,"_{",NOT$trait.2,NOT$time,NOT$ind,"}
$$")
return(withMathJax(eq))
}),
Matrices <- reactive({
cov1 <- round(input$Mod4Step5_Corr_I * sqrt(input$Mod4Step5_Vi1 * input$Mod4Step5_Vi2),3)
eq1 <- paste0(
"$$ \\Omega_{",NOT$devI,"}=
\\begin{pmatrix}
V_{",NOT$devI,"_",NOT$trait.1,"} & Cov_{",NOT$devI,"_",NOT$trait.1,",",NOT$devI,"_",NOT$trait.2,"} \\\\
Cov_{",NOT$devI,"_",NOT$trait.1,",",NOT$devI,"_",NOT$trait.2,"} & V_{", NOT$devI,"_",NOT$trait.2,"} \\\\
\\end{pmatrix}
=
\\begin{pmatrix}
",input$Mod4Step5_Vi1," & ",cov1," \\\\
",cov1," & ",input$Mod4Step5_Vi2,"\\\\
\\end{pmatrix}
$$")
cov2 <- round(input$Mod4Step5_Corr_e * sqrt(input$Mod4Step5_Ve1 * input$Mod4Step5_Ve2),3)
eq2 <- paste0(
"$$ \\Omega_{",NOT$error,"}=
\\begin{pmatrix}
V_{",NOT$error,"_",NOT$trait.1,"} & Cov_{",NOT$error,"_",NOT$trait.1,",",NOT$error,"_",NOT$trait.2,"} \\\\
Cov_{",NOT$error,"_",NOT$trait.1,",",NOT$error,"_",NOT$trait.2,"} & V_{", NOT$error,"_",NOT$trait.2,"} \\\\
\\end{pmatrix}
=
\\begin{pmatrix}
",input$Mod4Step5_Ve1," & ",cov2," \\\\
",cov2," & ",input$Mod4Step5_Ve2,"\\\\
\\end{pmatrix}
$$")
return(withMathJax(paste0(eq1, eq2)))
}),
output$Mod4Step5_Matrices_1 <- renderUI({Matrices()}),
output$Mod4Step5_Matrices_2 <- renderUI({Matrices()}),
output$Mod4Step5_Matrices_3 <- renderUI({Matrices()}),
show_model_variance_results <- function(fit){
RanCoef <- VarCorr(fit)
varI1 <- round(RanCoef[["Individual"]][["sd"]]["Phenotype1_Intercept", "Estimate"]^2, 2)
varI2 <- round(RanCoef[["Individual"]][["sd"]]["Phenotype2_Intercept", "Estimate"]^2, 2)
covI12 <- round(RanCoef[["Individual"]][["cov"]]["Phenotype1_Intercept", "Estimate", "Phenotype2_Intercept"], 2)
varE1 <- round(RanCoef[["residual__"]][["sd"]]["Phenotype1", "Estimate"]^2, 2)
varE2 <- round(RanCoef[["residual__"]][["sd"]]["Phenotype2", "Estimate"]^2, 2)
covE12 <- round(RanCoef[["residual__"]][["cov"]]["Phenotype1", "Estimate", "Phenotype2"], 2)
cov1 <- round(input$Mod4Step5_Corr_I * sqrt(input$Mod4Step5_Vi1 * input$Mod4Step5_Vi2),3)
cov2 <- round(input$Mod4Step5_Corr_e * sqrt(input$Mod4Step5_Ve1 * input$Mod4Step5_Ve2),3)
eq1 <- paste0(
"$$\\Omega_{",NOT$devI,"'}=
\\begin{pmatrix}
",varI1,"\\,[",input$Mod4Step5_Vi1,"] & ",covI12,"\\,[",cov1,"] \\\\
",covI12,"\\,[",cov1,"] & ",varI2,"\\,[",input$Mod4Step5_Vi2,"]\\\\
\\end{pmatrix}\\quad")
eq2 <- paste0(
"\\Omega_{",NOT$error,"'}=
\\begin{pmatrix}
",varE1,"\\,[",input$Mod4Step5_Ve1,"] & ",covE12,"\\,[",cov2,"] \\\\
",covE12,"\\,[",cov2,"] & ",varE2,"\\,[",input$Mod4Step5_Ve1,"]\\\\
\\end{pmatrix}
$$")
return(paste0(eq1, eq2))
},
show_model_correlation_results <- function(fit){
RanCoef <- VarCorr(fit)
corI12 <- round(RanCoef[["Individual"]][["cor"]]["Phenotype1_Intercept", "Estimate", "Phenotype2_Intercept"],2)
corE12 <- round(RanCoef[["residual__"]][["cor"]]["Phenotype1", "Estimate", "Phenotype2"],2)
cor <- paste0("$$\\text{Among-individual correlation: }",corI12,"\\,[",input$Mod4Step5_Corr_I,"]$$
$$\\text{Residual within-individual correlation: }",corE12,"\\,[",input$Mod4Step5_Corr_e,"]$$")
return(cor)
},
output$Mod4Step5_Result_Matrices_Model1 <- renderUI({
fit1 <- Mod4Step5_output_model1()
if (!is.null(fit1)) {
isolate({res <- show_model_variance_results(fit1)})
return(withMathJax(res))
}else{return(withMathJax("$$...$$"))}
}),
output$Mod4Step5_Result_Matrices_Model1_corr <- renderUI({
fit1 <- Mod4Step5_output_model1()
if (!is.null(fit1)) {
isolate({cor <- show_model_correlation_results(fit1)})
return(withMathJax(cor))
}else{return(withMathJax("$$...$$"))}
}),
output$Mod4Step5_Result_Matrices_Model2 <- renderUI({
fit2 <- Mod4Step5_output_model2()
if (!is.null(fit2)) {
isolate({res <- show_model_variance_results(fit2)})
return(withMathJax(res))
}else{return(withMathJax("$$...$$"))}
}),
output$Mod4Step5_Result_Matrices_Model2_corr <- renderUI({
fit2 <- Mod4Step5_output_model2()
if (!is.null(fit2)) {
isolate({cor <- show_model_correlation_results(fit2)})
return(withMathJax(cor))
}else{return(withMathJax("$$...$$"))}
})
) # End return
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