app/modules/regression_long_stats2.R
In JesseRed/dataVis: What the Package Does (One Line, Title Case)
library(shiny)
regressionLongStats2UI <- function(id){
ns <- NS(id)
# Thanks to the namespacing, we only need to make sure that the IDs
# are unique within this function, rather than unique across the entire app.
tagList(
uiOutput(ns("uiANOVA")),
#verbatimTextOutput(ns("textANOVAStats")),
# absolutePanel(
# bottom = 20, right = 20, width = 200,
# draggable = TRUE,
# wellPanel(
# htmlOutput(ns("html_text")),
# sliderInput("n", "", min=3, max=20, value=5),
# plotOutput("plot2", height="200px")
#
# ),
# style = "opacity: 0.92"
# ),
)
}
regressionLongStats2Server <- function(id, input_glob_sig, freq) {
moduleServer(
id,
function(input, output, session) {
#selectInput(NS(id, "var"), "Variable", choices = NULL)
ns<-session$ns
#f_utrial_list_all <- reactive({c("all", g_trials())})
output$uiANOVA <- renderUI({
fluidPage(
fluidRow(
column(4,
fluidRow(
column(6,
style = "background-color: #fcfcfc;",
style = 'border-right: 2px solid gray',
h4("trial comparison", align = "center"),
fluidRow(
column(6,
selectInput(ns("trial1"), h5("Select Trial 1", align = "center"),
choices = g_trials_named(), selected =g_trials_named()[1])
),
column(6,
selectInput(ns("trial2"), h5("Select Trial 2", align = "center"),
choices = g_trials_named(), selected = g_trials_named()[2])
)
)
),
column(6,
style = "background-color: #fcfcfc;",
#style = 'border-bottom: 2px solid gray',
style = "border-right: 2px solid black",
h4("group comparison", align = "center"),
fluidRow(
column(6,
selectInput(ns("group1"), h5("Select Group 1", align = "center"),
choices = g_groups(), selected = g_groups()[2])
),
column(6,
selectInput(ns("group2"), h5("Select Group 2", align = "center"),
choices = g_groups(), selected = g_groups()[2])
)
)
),
),
# fluidRow(
# style = "background-color: #fcfcfc;",
# #style = "border-top: 2px solid black",
# h4("is the analysis directed?", align = "left"),
# column(12,
# prettyRadioButtons(
# inputId = ns("causal"),
# label = "",
# choices = c("non-directed", "directed"),
# shape = "round",
# status = "danger",
# fill = TRUE,
# inline = TRUE
# ),
# ),
# ),
),
column(2,
style = "background-color: #fcfcfc;",
style = 'border-right: 2px solid gray',
h4("longitudinal data", align = "center"),
fluidRow(
column(6,
textInput(ns("ld_1"), h5("long data 1", align = "center"), value = "1")
),
column(6,
textInput(ns("ld_2"), h5("long data 2", align = "center"), value = "2, 3")
)
),
checkboxInput(ns("longtimefirst"), "estimate time first", value = TRUE),
checkboxInput(ns("averagelong"), "average same long subj(1 vs. av(2,3))", value = TRUE),
checkboxInput(ns("cb_same_subjects"), "include only reoccuring subj", value = TRUE)
),
column(2,
style = "background-color: #fcfcfc;",
style = 'border-right: 2px solid gray',
h4("Filter", align = "center"),
textInput(ns("filterg1"), h5("filter G1", align = "center"), value = "Zeichen__1>0"),
textInput(ns("filterg2"), h5("filter G2", align = "center"), value = "Zeichen__1>0"),
),
column(2,
style = "background-color: #fcfcfc;",
style = 'border-right: 2px solid gray',
h4("Visualize", align = "center"),
selectInput(ns("statsMethod"), h5("method"),
choices = c("Regression","ANOVA"), selected = 1)
),
column(2,
fluidRow(
column(6,
numericInput(ns("plot_height"),"plot height",800)
),
column(6,
numericInput(ns("plot_width"),"plot width",0)
),
),
fluidRow(
column(6,
numericInput(ns("plot_res"),"res",96),
),
column(6,
actionButton(ns("ExportData"), "export Data"),
),
),
)
),
fluidRow(
style = 'border-top: 2px solid gray',
column(9,
plotOutput(ns("plot"), width = "auto", height = "700px", click = ns("plot_click")),
),
column(3,
selectInput(ns("mainregressor"), h4("main regressor"),
choices = colnames(g_beh())),
selectInput(ns("regressors"), h4("potential regressors"),
multiple = TRUE, selectize = FALSE,
size = 35,
choices = colnames(g_beh()),
selected = 3)
)
),
fluidRow(align = "center", h4("Correlation matrix "),
column(9,
plotOutput(ns("plot_all_correlation"), width = "auto", height = "700px", click = ns("plot_click")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_correlation_matrix")),
)),
fluidRow(align = "center", h4("Correlation fit"),
column(9,
plotOutput(ns("plot_fit_correlation"), width = "auto", height = "700px", click = ns("plot_click_hist")),
)
#,
#column(3, align = "left",
# verbatimTextOutput(ns("text_stats_correlation_matrix")),
#)
),
fluidRow(align = "center", h4("comparison of Time 1 vs. Time 2"),
column(9,
plotOutput(ns("hist_compare_diffTime"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffTime")),
)),
fluidRow(align = "center", h4("comparison of Trial1 vs. Trial 2 of selected group 1"),
column(9,
plotOutput(ns("hist_compare_diffTrial_sameGroup1"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffTrial_sameGroup1")),
)),
fluidRow(align = "center", h4("comparison of Trial1 vs. Trial 2 of selected group 2"),
column(9,
plotOutput(ns("hist_compare_diffTrial_sameGroup2"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffTrial_sameGroup2")),
)),
fluidRow(align = "center", h4("comparison of Group 1 vs. Group 2 of selected trial 1"),
column(9,
plotOutput(ns("hist_compare_diffGroup_sameTrial1"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffGroup_sameTrial1")),
)),
fluidRow(align = "center", h4("comparison of Group 1 vs. Group 2 of selected trial 2"),
column(9,
plotOutput(ns("hist_compare_diffGroup_sameTrial2"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffGroup_sameTrial2")),
)),
# fluidRow(
# column(12,
# verbatimTextOutput(ns("text_stats")),
# )),
# fluidRow(
# column(12,
# verbatimTextOutput(ns("simple_correlation")),
# )
# ),
fluidRow(
column(12,
box(title = "simple correlation ..........expand for help", width = 12, collapsible = TRUE, collapsed = TRUE, htmlOutput(ns("htmlhelp_simple_correlation"))),
)
),
fluidRow(
column(12,
box(title = "simple correlation ..........expand for help", width = 12, collapsible = TRUE, collapsed = TRUE, verbatimTextOutput(ns("help_simple_correlation"))),
)
),
fluidRow(
column(12,
tableOutput(ns("tab_simple_time_correlation")),
)
),
fluidRow(
column(12,
box(title = "simple non-time non-subject-exclusion correlation ..........expand for help", width = 12, collapsible = TRUE, collapsed = TRUE, verbatimTextOutput(ns("help_simple_correlation2"))),
)
),
fluidRow(
column(12,
tableOutput(ns("tab_simple_group_correlation")),
)
),
fluidRow(
column(12,
tableOutput(ns("tab_simple_trial_correlation")),
)
),
fluidRow(
column(12,
box(title = "help for partial correlation below", width = 12, collapsible = TRUE, collapsed = TRUE, htmlOutput(ns("htmlhelp_partial_correlation"))),
)
),
fluidRow(
column(12,
box(title = "partial correlation ", width = 12, collapsible = TRUE, collapsed = FALSE,
fluidRow(
column(3,
verbatimTextOutput(ns("partial_correlationG1T1"))
),
column(3,
verbatimTextOutput(ns("partial_correlationG1T2"))
),
column(3,
verbatimTextOutput(ns("partial_correlationG2T1"))
),
column(3,
verbatimTextOutput(ns("partial_correlationG2T2"))
),
)),
)
),
fluidRow(
column(12,
box(title = "Included subjects", width = 12, collapsible = TRUE, collapsed = TRUE,
#uiOutput(ns("includedSubjects"))
actionButton(ns("testexclude"), "update"),
checkboxGroupInput(ns("Subjects"), label = h3("Subjects"), inline = T,
choices = g_D()$df_BD$ID,
selected = g_D()$df_BD$ID),
style = "background-color: #fcfcfc;",
style = 'border-bottom: 2px solid gray',
checkboxGroupInput(ns("Group1"), label = h3("Group 1"), inline = T,
choices = c()), #, #curdata()$df_data1$ID,
# selected = c()), #curdata()$df_data1$ID[my_included_subjects_g1()]),
style = "background-color: #fcfcfc;",
style = 'border-bottom: 2px solid gray',
checkboxGroupInput(ns("Group2"), label = h3("Group 2"), inline = T,
choices = c()) #, #curdata()$df_data2$ID,
),
)
),
# fluidRow(
# column(12,
# box(title = "partial correlation ", width = 12, collapsible = TRUE, collapsed = FALSE, verbatimTextOutput(ns("partial_correlation"))), )
# )
)
})
subjects_to_exclude = reactive({
# list of subjects that are not marked
to_exclude = setdiff( g_D()$df_BD$ID, input$Subjects)
#cat(file = stderr(), paste0("XXX subjects_to_exclude reactive = ", to_exclude, "\n"))
return(to_exclude)
})
my_included_subjects = reactive({get_included_subjects( g_D()$df_BD$ID, subjects_to_exclude())})
my_included_subjects_g1 = reactive({ req(input$Subjects); get_included_subjects( curdata()$df_data1$ID, subjects_to_exclude())})
my_included_subjects_g2 = reactive({get_included_subjects( curdata()$df_data2$ID, subjects_to_exclude())})
observeEvent(input$testexclude, {
cat(file = stderr(), paste0("included Subjects = \n"))
#cat(file = stderr(), paste0("included Subjects = ", input$Subjects, "\n"))
#cat(file = stderr(), paste0("class(subjects_to_exclude = ", class(subjects_to_exclude()), "\n"))
#cat(file = stderr(), paste0("length(subjects_to_exclude = ", length(subjects_to_exclude()), "\n"))
#cat(file = stderr(), paste0("my_included_subjects() = ", my_included_subjects(), "\n"))
numbered_IDs_all <- get_included_subjects_with_numbers(g_D()$df_BD$ID, my_included_subjects())
numbered_IDs_g1 <- get_included_subjects_with_numbers(curdata()$df_data1$ID, my_included_subjects_g1())
numbered_IDs_g2 <- get_included_subjects_with_numbers(curdata()$df_data2$ID, my_included_subjects_g2())
# updateCheckboxGroupInput(session, "Subjects",
# choices = numbered_IDs_all, inline = T,
# selected = numbered_IDs_all[my_included_subjects()])
updateCheckboxGroupInput(session, "Group1",
choices = numbered_IDs_g1, inline = T,
selected = numbered_IDs_g1[my_included_subjects_g1()]
)
updateCheckboxGroupInput(session, "Group2",
choices = numbered_IDs_g2, inline = T,
selected = numbered_IDs_g2[my_included_subjects_g2()]
)
updateCheckboxGroupInput(session, "Subjects",
choices = g_D()$df_BD$ID, inline = T,
selected = g_D()$df_BD$ID[my_included_subjects()])
# updateCheckboxGroupInput(session, "Group1",
# choices = curdata()$df_data1$ID, inline = T,
# selected = curdata()$df_data1$ID[my_included_subjects_g1()]
#
# )
#
# updateCheckboxGroupInput(session, "Group2",
# choices = curdata()$df_data2$ID, inline = T,
# selected = curdata()$df_data2$ID[my_included_subjects_g2()]
# )
})
# Funktion um an die ausgewaehlten Subjects Numbern zu schreiben damit
# die Auswahl in der GUI einfacher wird
get_included_subjects_with_numbers <- function(IDs, is_included){
# nummern duerfen nur die Subjects erhalten die selectiert sind
idx = 1
for (i in 1:length(IDs)){
if (is_included[i]){
IDs[i] <- paste0(idx,". ",IDs[i])
idx <- idx +1
}
}
return(IDs)
}
# filter data by group
data_freqmean <- reactive({
get_data_freqmean(g_data(), freq())
})
# data_1 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial1), freq())
# })
# data_2 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial2), freq())
# })
# data_g1_t1 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial1), freq())
# })
# data_g1_t2 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial2), freq())
# })
# data_g2_t1 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial1), freq())
# })
# data_g2_t2 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial2), freq())
# })
#
data_1 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial1), g_sel_freqs())
})
data_2 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial2), g_sel_freqs())
})
data_g1_t1 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial1), g_sel_freqs())
})
data_g1_t2 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial2), g_sel_freqs())
})
data_g2_t1 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial1), g_sel_freqs())
})
data_g2_t2 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial2), g_sel_freqs())
})
level_x <- reactive({round(input$plot_click$x)})
level_y <- reactive({abs(round(input$plot_click$y)-length(g_regions())-1)})
# curdata <- reactive({
# get_currently_selected_data_long3(g_D(), input$group1, input$group2, as.numeric(input$trial1), as.numeric(input$trial2), g_sel_freqs())
# # get_currently_selected_data(g_data(), input$group1, input$group2, as.numeric(input$trial1), as.numeric(input$trial2), freq())
# })
# get the data for the second time point
# die longitudinalen Daten sind kodiert als nummern hinter den IDs der Subjects XY001_1
# daher teilen wir hier die Subjects einfach entsprechend auf
curdata <- reactive({
cat(file = stderr(), paste0("curdata with dim(g_D()$mat)=", dim(g_D()$mat),"\n"))
req(input$group1)
req(input$group2)
req(input$trial1)
req(input$trial2)
req(input$ld_1)
req(input$ld_2)
# req(input$cb_same_subjects)
# req(input$averagelong)
# req(input$longtimefirst)
#gD1 <<- D1()
#gD2 <<- D2()
cat(file = stderr(), paste0("curdata with dim(g_D()$mat)=", dim(g_D()$mat),"\n"))
cat(file = stderr(), paste0("curdata with length(g_D())=", length(g_D()),"\n"))
M <- get_currently_selected_data_long3(g_D(),
input$group1,
input$group2,
as.numeric(input$trial1),
as.numeric(input$trial2),
g_sel_freqs(),
tbl_beh = g_D()$df_BD,
long_def1 = as.numeric(unlist(strsplit(input$ld_1, split=","))),
long_def2 = as.numeric(unlist(strsplit(input$ld_2, split=","))),
is_exclude_not_reoccuring_subj = input$cb_same_subjects,
averagelong = input$averagelong,
# datalong = D2()$mdat,
# tbl_beh_long = D2()$df_BD,
estimate_time_first = input$longtimefirst,
filter_g1 = input$filterg1,
filter_g2 = input$filterg2,
subjects_to_exclude = subjects_to_exclude()#,
#iscausal = iscausal(),
# network = network_new()
)
gM <<- M
return(M)
})
###########################################################
### RENDERPLOT
output$plot<-renderPlot({
req(input$trial1)
req(input$trial2)
req(input$group1)
req(input$group2)
d <- curdata()
mat_t <<- d$mat_t
mat_p <<- d$mat_p
###################
# CORRPLOT
generate_plot_Corrplot(d$mat_p, d$mat_t)
})
#
# output$hist <- renderPlot({
# req(input$plot_click$x)
# req(input$plot_click$y)
# region_x = g_regions()[level_x()]
# #cat(file = stderr(), region_x)
# #level_x = round(input$plot_click$x)
# #level_y = abs(round(input$plot_click$y)-length(g_regions())-1)
# region_x = g_regions()[level_x()]
# region_y = g_regions()[level_y()]
# # level_x = 1
# # level_y = 2
# #df = g_beh()
# d = data_freqmean()
#
# if (input$trial1 == input$trial2) {
# cat(file = stderr(), "trial1 == trial2\n")
# string1 = paste0(input$group1," vs ", input$group2, " in trial ", names(g_trials_named())[input$trial1], "\n") #utrial_list[input$trial1], "\n")
# d1 = get_data_group_freqmean(g_data(), input$group1, freq())
# d2 = get_data_group_freqmean(g_data(), input$group2, freq())
# x = d1[,level_x(), level_y(), as.numeric(input$trial1)]
# y = d2[,level_x(), level_y(), as.numeric(input$trial1)]
# df <- data.frame(Gruppe=c(rep(input$group1, times=length(x)),
# rep(input$group2, times=length(y))),
# val=c(x, y))
# df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# # means for geomline
# df_hline = data.frame(Gruppe = c(input$group1,input$group2), Means=c(mean(x), mean(y)))
# # df$val = d[,level_x, level_y, as.numeric(input$trial1)]
# # df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# # dummy2 = data.frame(Gruppe = c(0,1), Means=c(0.4, 0.5))
#
# }
# if (input$group1 == input$group2){
# string1 = paste0(g_trials()[input$trial1]," vs ", g_trials()[input$trial2], "in group ", input$group1, "\n")
# data1 = data_1()
# data2 = data_2()
# x = data1[,level_x(), level_y()]
# y = data2[,level_x(), level_y()]
# df <- data.frame(Gruppe=c(rep(g_trials()[as.numeric(input$trial1)], times=length(x)),
# rep(g_trials()[as.numeric(input$trial2)], times=length(y))),
# val=c(x, y))
# df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# # means for geomline
# df_hline = data.frame(Gruppe = c(g_trials()[as.numeric(input$trial1)],
# g_trials()[as.numeric(input$trial2)]),
# Means=c(mean(x), mean(y)))
#
# #p<-ggplot(df, aes(num, val, fill=Gruppe))
# #p + geom_bar(stat="identity") + facet_wrap(~Gruppe)
# }
#
# ##########later delete
# # temporary
# string1 = paste0(g_trials()[input$trial1]," vs ", g_trials()[input$trial2], "in group ", input$group1, "\n")
# data1 = data_1()
# data2 = data_2()
# x = data1[,level_x(), level_y()]
# y = data2[,level_x(), level_y()]
# df <- data.frame(Gruppe=c(rep(g_trials()[as.numeric(input$trial1)], times=length(x)),
# rep(g_trials()[as.numeric(input$trial2)], times=length(y))),
# val=c(x, y))
# df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# # means for geomline
# df_hline = data.frame(Gruppe = c(g_trials()[as.numeric(input$trial1)],
# g_trials()[as.numeric(input$trial2)]),
# Means=c(mean(x), mean(y)))
#
#
# ###################################
#
# ggplot(df, aes(num, val, fill=Gruppe)) +
# geom_bar(stat="identity") +
# facet_wrap(~Gruppe) +
# geom_hline(data = df_hline, aes(yintercept = Means))
#
# })
################################################################
# THE complete correlation matrix for one regressor
###################################################
output$plot_all_correlation<-renderPlot({
req(input$trial1)
req(input$trial2)
req(input$group1)
req(input$group2)
req(input$mainregressor)
d <- curdata()
mat_t <<- d$mat_t
mat_p <<- d$mat_p
diff <- d$data2 - d$data1
# berechne die Behavioralen Werte fuer den main regessor
b1 = get( input$mainregressor, curdata()$df_data1)
b2 = get( input$mainregressor, curdata()$df_data2)
out_b1 <<- b1
# da wir nur longitudinal anschauen sollte b1 und b2 gleich sein
# berechne nun die Korrelatioenen zwischen long_diff und b1
cormatrix <- apply(diff,2:3,cor.test,b1);
dim2 <- dim(diff)[2]
dim3 <- dim(diff)[3]
#mat_cor <- matrix(,nrow = dim2, ncol = dim3)
#mat_cor_p <- matrix(,nrow = dim2, ncol = dim3)
mat_cor <- d$mat_t
mat_cor_p <- d$mat_p
for (x1 in 1:dim2){
for (y1 in 1:dim3){
mat_cor[x1,y1]<-cormatrix[[x1,y1]]$estimate
mat_cor_p[x1,y1]<-cormatrix[[x1,y1]]$p.value
}
}
###################
# CORRPLOT
generate_plot_Corrplot(mat_cor_p, mat_cor)
})
################################################################
# THE fit of the specific correlation
###################################################
output$plot_fit_correlation<-renderPlot({
req(input$plot_click$x)
cat(file = stderr(), paste0("\n fit line \n"))
x = curdata()$data1[,level_y(),level_x()]
y = curdata()$data2[,level_y(),level_x()]
diff <- y - x
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
#string1 = paste0(input$trial1," vs ", input$trial2, "in group ", input$group1, "\n")
cat(file = stderr(), paste0("\n create fic correlation with time \n"))
cat(file = stderr(), paste0("curdata%data1 with dim(x)=", dim(x),"\n"))
cat(file = stderr(), paste0("curdata%data2 with dim(y)=", dim(y),"\n"))
# berechne die Behavioralen Werte fuer den main regessor
b1 = get( input$mainregressor, curdata()$df_data1)
names = get( "ID", curdata()$df_data1)
df <- data.frame(x=diff,
y=b1,
z =names )
#create scatter plot with line of best fit
ggplot(df, aes(x=x, y=y)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
geom_text(aes(label=z))
})
################################################################
# THE histogram plots of individual subjects
###########################################
output$hist_compare_diffTime <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot2(compare = "time", group=1, trial = 1)
})
output$hist_compare_diffTrial_sameGroup1 <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot(compare = "trials", group=1, trial = 1)
})
output$hist_compare_diffTrial_sameGroup2 <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot(compare = "trials", group=2, trial = 1)
})
output$hist_compare_diffGroup_sameTrial1 <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot(compare = "groups", group=1, trial = 1)
})
output$hist_compare_diffGroup_sameTrial2 <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot(compare = "groups", group=1, trial = 2)
})
################################################################
# THE text about t-statistics
###########################################
output$text_stats_correlation_matrix <- renderPrint({
req(input$plot_click)
#req(input$mainregressor)
z = correlation_matrix_description()
cat(z$mydescription)
})
output$text_stats_compare_diffTime <- renderPrint({
req(input$plot_click)
z = ttest_estimation2(compare = "time")
cat(z$mydescription)
})
output$text_stats_compare_diffGroup_sameTrial1 <- renderPrint({
req(input$plot_click)
z = ttest_estimation(compare = "groups", group = 1, trial = 1)
cat(z$mydescription)
})
output$text_stats_compare_diffGroup_sameTrial2 <- renderPrint({
req(input$plot_click)
z = ttest_estimation(compare = "groups", group = 1, trial = 2)
cat(z$mydescription)
})
output$text_stats_compare_diffTrial_sameGroup1 <- renderPrint({
req(input$plot_click)
z = ttest_estimation(compare = "trials", group = 1, trial = 1)
cat(z$mydescription)
})
output$text_stats_compare_diffTrial_sameGroup2 <- renderPrint({
req(input$plot_click)
z = ttest_estimation(compare = "trials", group = 2, trial = 1)
cat(z$mydescription)
})
####
#################################################################
output$htmlhelp_simple_correlation <- renderUI({
# if (showhtml()){
includeMarkdown(rmarkdown::render("./documentation/simple_correlation_markdown.md"))
# }
})
output$htmlhelp_parial_correlation <- renderUI({
# if (showhtml()){
includeMarkdown("./documentation/partial_correlation_markdown.md")
# }
})
output$help_simple_correlation <- renderPrint({
text = "in der obersten Tabelle stehen Werte unter einbeziehung der excludeten subjects\n"
text = paste0(text, " in den unteren beiden Tabellen dann die Correlationen zu allen Subjects\n")
text = paste0(text, " oberste Zeile zeigt die Zeitabhaengigkeit\n")
text = paste0(text, " unterschied zwischen den 2 Zeitpunkten (ggf. group, trial differenz)\n")
text = paste0(text, " in Correlation zu den Veraenderungen der behavioralen Daten\n")
text = paste0(text, " Expl: trial1 =1; trial2=1, group1=1, group2 =2, time1=1, time2=2\n")
text = paste0(text, " Gruppe ist verschieden ...beide Gruppen haben unterschiede zwischen den 2 Zeitpunkten .... Die Analyse testet auf signifikante Unterschiede zwischen diesen zeitbezogenen Unterschieden\n",
"Algorithmus:\n",
"1. entferne alle Subjects die nicht in den Daten beider Zeitpunkte zu finden sind\n",
"2. falls unterschiedliche trial gewaehlt wurden wird der subjectspezifische Unterschied zwischen den Trials berechnet\n",
"wenn estimate time first",
" Berechne X1 = Data_Zeitpunkt2_group1_trial1 - Data_Zeitpunkt1_group1_trial1 (Subjects x Regions x Regions)\n",
" X2 = Data_Zeitpunkt2_group2_trial1 - Data_Zeitpunkt1_group2_trial1 (Subjects x Regions x Regions)\n",
"wenn nicht estimate time first",
" Berechne X1 = Data_Zeitpunkt1_group1_task1 - Data_Zeitpunkt1_group2_task2 (Subjects x Regions x Regions)\n",
" X2 = Data_Zeitpunkt2_group2_task1 - Data_Zeitpunkt2_group2_task2 (Subjects x Regions x Regions)\n",
" In diesen beiden 3d Matrizen steht somit der gruppenspezifische Unterschied eines Trials zwischen den Messungen\n",
" Ein positiver Wert in dieser Matrix zeigt einen positiven Effekt der Zeit/Intervention an (in der 2. Messung groesser)\n",
"Die Behavioralen Daten B1 und B2 werden NICHT analog berechnet! Hier wird immer zuerst die Differenz \n",
" ueber die Zeit berechnet d.h. estimate time first ist immer Aktiv\n",
" soweit ich das sehe gibt es nicht wirklich eine sinnvolle Frage als das man estimat time first deaktiviert\n",
"Es ist weiterhin wichtig zu beachten, dass die Behavioralen Daten nach der Zeit subtrahiert werden,\n",
" in der Behavioralen Tabelle sollte ein sich nicht veraendernder Faktor wie z.B. das Alter nur zum ersten Zeitpunkt eingetragen sein\n",
" der zweite Zeitpunkt sollte auf 0 gesetzt sein\n",
"diese ueberlegung erfolgte in der Annahme, dass man zumeist nach sich durch eine Intervention veraendernde behaviorale Effekte sucht\n")
text = paste0(text, " Zeile : Corelation(B1,X1) , Correlation(B2,X2) \n")
text = paste0(text, " Bitte beachten, dass nicht jede Kombination einen Sinn ergibt... hier muss etwas nachgedacht werden!!!\n")
cat(text)
})
output$help_simple_correlation2 <- renderPrint({
text = "Berechnung der Korrelationen mit ALLEN Subjects und nur zum Zeitpunkt 1\n"
text = paste0(text, " in den unteren beiden Tabellen dann die Correlationen zu allen Subjects\n")
text = paste0(text, " \n")
cat(text)
})
output$help_simple_correlation3 <- renderPrint({
text = "Berechnung der Korrelationen mit ALLEN Subjects und nur zum Zeitpunkt 2\n"
text = paste0(text, " in den unteren beiden Tabellen dann die Correlationen zu allen Subjects\n")
text = paste0(text, " \n")
cat(text)
})
###########################################
# the newly created statistics section
output$tab_simple_time_correlation <- renderTable({
req(input$plot_click)
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
x_con = curdata()$data1[,level_y(),level_x()]
y_con = curdata()$data2[,level_y(),level_x()]
# berechne die Behavioralen Werte fuer den main regessor
b1 = get( input$mainregressor, curdata()$df_data1)
b2 = get( input$mainregressor, curdata()$df_data2)
df <- append_correlation_row(x1 = x_con, b1 = b1, x2 = y_con, b2 = b2,
method = "pearson",
t = g_trials()[as.numeric(input$trial1)],
g1 = input$group1,
g2 = input$group2,
reg_name = input$mainregressor)
for ( i in 1:length(input$regressors)){
b1 = get( input$regressors[i], curdata()$df_data1)
b2 = get( input$regressors[i], curdata()$df_data2)
df <- append_correlation_row(x1 = x_con, b1 = b1, x2 = y_con, b2 = b2,
method = "pearson",
t = g_trials()[as.numeric(input$trial1)],
g1 = input$group1,
g2 = input$group2,
reg_name = input$regressors[i], df=df)
}
return(df)
})
###########################################
# the newly created statistics section
output$tab_simple_group_correlation <- renderTable({
req(input$plot_click)
cat(file = stderr(), paste0("output$tab_simpple_group_correlation"))
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
xg1t1 = data_g1_t1()[,level_y(),level_x()]
xg1t2 = data_g1_t2()[,level_y(),level_x()]
xg2t1 = data_g2_t1()[,level_y(),level_x()]
xg2t2 = data_g2_t2()[,level_y(),level_x()]
# berechne Werte fuer den main regessor
#reg_name = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
b1 = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
b2 = get_beh_tbl_data_by_group(input$group2, input$mainregressor)
#cat(file = stderr(), "now create")
df <- append_correlation_row(x1 = xg1t1, b1 = b1, x2 = xg2t1, b2 = b2,
method = "pearson",
t = g_trials()[input$trial1],
g1 = input$group1,
g2 = input$group2,
reg_name = input$mainregressor)
df <- append_correlation_row(x1 = xg1t2, b1 = b1, x2 = xg2t2, b2 = b2,
method = "pearson",
t = g_trials()[input$trial2],
g1 = input$group1,
g2 = input$group2,
reg_name = input$mainregressor,
df = df)
#cat(file = stderr(), "now for loop")
for ( i in 1:length(input$regressors)){
b1 = get_beh_tbl_data_by_group(input$group1, input$regressors[i])
b2 = get_beh_tbl_data_by_group(input$group2, input$regressors[i])
df <- append_correlation_row(x1 = xg1t1, b1 = b1, x2 = xg2t1, b2 = b2,
method = "pearson",
t = g_trials()[input$trial1],
g1 = input$group1,
g2 = input$group2,
reg_name = input$regressors[i], df=df)
df <- append_correlation_row(x1 = xg1t2, b1 = b1, x2 = xg2t2, b2 = b2,
method = "pearson",
t = g_trials()[input$trial2],
g1 = input$group1,
g2 = input$group2,
reg_name = input$regressors[i], df=df)
}
return(df)
})
####
####
#################################################################
###########################################
# the newly created statistics section for different trials
output$tab_simple_trial_correlation <- renderTable({
req(input$plot_click)
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
xg1t1 = data_g1_t1()[,level_y(),level_x()]
xg1t2 = data_g1_t2()[,level_y(),level_x()]
xg2t1 = data_g2_t1()[,level_y(),level_x()]
xg2t2 = data_g2_t2()[,level_y(),level_x()]
# berechne Werte fuer den main regessor
#reg_name = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
b1 = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
b2 = get_beh_tbl_data_by_group(input$group2, input$mainregressor)
df <- append_correlation_row_trials(x1 = xg1t1, b1 = b1, x2 = xg1t2,
method = "pearson",
g = input$group1,
t1 = input$trial1,
t2 = input$trial2,
reg_name = input$mainregressor)
df <- append_correlation_row_trials(x1 = xg2t1, b1 = b2, x2 = xg2t2,
method = "pearson",
g = input$group2,
t1 = input$trial1,
t2 = input$trial2,
reg_name = input$mainregressor, df = df)
for ( i in 1:length(input$regressors)){
b1 = get_beh_tbl_data_by_group(input$group1, input$regressors[i])
df <- append_correlation_row_trials(x1 = xg1t1, b1 = b1, x2 = xg1t2,
method = "pearson",
g = input$group1,
t1 = input$trial1,
t2 = input$trial2,
reg_name = input$regressors[i], df = df)
b2 = get_beh_tbl_data_by_group(input$group2, input$regressors[i])
df <- append_correlation_row_trials(x1 = xg2t1, b1 = b2, x2 = xg2t2,
method = "pearson",
g = input$group2,
t1 = input$trial1,
t2 = input$trial2,
reg_name = input$regressors[i], df = df)
}
return(df)
})
###########################################
# the newly created statistics section for different trials
output$partial_correlationG1T1 <- renderPrint({
req(input$plot_click)
cat(create_partial_correlation_string(group=1,trial=1))
})
###########################################
# the newly created statistics section for different trials
output$partial_correlationG1T2 <- renderPrint({
req(input$plot_click)
cat(create_partial_correlation_string(group=1,trial=2))
})
###########################################
# the newly created statistics section for different trials
output$partial_correlationG2T1 <- renderPrint({
req(input$plot_click)
cat(create_partial_correlation_string(group=2,trial=1))
})
###########################################
# the newly created statistics section for different trials
output$partial_correlationG2T2 <- renderPrint({
req(input$plot_click)
cat(create_partial_correlation_string(group=2,trial=2))
})
####
#################################################################
## general function specific for this tab
#################################################################
create_partial_correlation_string <- function( group = 1, trial = 1){
cat(file = stderr(), paste0("levelx = ", level_x(), "levely = ", level_y(), "\n"))
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
cat(file = stderr(), paste0("region_x = ", region_x, " region_y = ", region_y, "\n"))
if (group==1){
gin = input$group1
b = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
if (trial == 1){
tin = g_trials()[input$trial1]
x_in = data_g1_t1()[,level_y(),level_x()]
}
if (trial == 2){
tin = g_trials()[input$trial2]
x_in = data_g1_t2()[,level_y(),level_x()]
}
}
if (group==2){
gin = input$group2
b = get_beh_tbl_data_by_group(input$group2, input$mainregressor)
if (trial == 1){
tin = g_trials()[input$trial1]
x_in_g2t1<<-data_g2_t1()
x_in = data_g2_t1()[,level_y(),level_x()]
}
if (trial == 2){
tin = g_trials()[input$trial2]
x_in_g2t1<<-data_g2_t2()
x_in = data_g2_t2()[,level_y(),level_x()]
}
}
df <- data.frame(x = x_in, y = b)
n = c("x", "y")
for ( i in 1:length(input$regressors)){
b = get_beh_tbl_data_by_group(gin, input$regressors[i])
df<-cbind(df, b)
#names(df)[names(df)=="V1"]<-input$regressors[i]
n <- c(n,input$regressors[i])
}
names(df)<-n
pc <-pcor(n, var(df))
tmptest <- pcor.test(pc, length(n)-2, length(b))
out <- paste0("group = ", gin, " trial = ",trial, "\n",
"r = ", pc, "\n",
"r^2 =", pc^2, "\n",
"t = ", tmptest[1],"\n",
"df = ", tmptest[2], "\n",
"p = ", tmptest[3]
)
return(out)
}
ttest_estimation <- function(compare = "groups",
group = 1, trial = 1){
xg1t1 = data_g1_t1()[,level_y(),level_x()]
xg1t2 = data_g1_t2()[,level_y(),level_x()]
xg2t1 = data_g2_t1()[,level_y(),level_x()]
xg2t2 = data_g2_t2()[,level_y(),level_x()]
mystring = ""
ispaired = FALSE
m1 = 0
m2 = 0
# vergleiche 2 Gruppen mit einem Trial
if (compare == "groups"){
mystring = paste0(mystring, input$group1, " vs. ", input$group2)
ispaired = FALSE
if (input$group1 == input$group2){
cat("no output in case of same groups")
return()
}
if (trial==1){
mystring = paste0(mystring, " of trial ", g_trials()[input$trial1], "\n")
z = t.test(xg1t1,xg2t1, paired = ispaired)
m1 = mean(xg1t1)
m2 = mean(xg2t1)
}
if (trial == 2){
mystring = paste0(mystring, " of trial ", g_trials()[input$trial2], "\n")
z = t.test(xg1t2,xg2t2, paired = ispaired)
m1 = mean(xg1t2)
m2 = mean(xg2t2)
}
}
# if comparing 2 trails of the same group
if (compare == "trials"){
mystring = paste0(mystring, g_trials()[as.numeric(input$trial1)], " vs. ", g_trials()[as.numeric(input$trial2)])
ispaired = TRUE
if (group==1){
z = t.test(xg1t1,xg1t2, paired = ispaired)
mystring = paste0(mystring, " of group ", input$group1, "\n")
m1 = mean(xg1t1)
m2 = mean(xg1t2)
}
if (group == 2){
z = t.test(xg2t1,xg2t2, paired = ispaired)
mystring = paste0(mystring, " of group ", input$group2, "\n")
m1 = mean(xg2t1)
m2 = mean(xg2t2)
}
}
z$mydescription <- paste0(mystring, create_my_ttest_string(z, paired = ispaired, mean1 = m1, mean2 = m2))
return(z)
}
correlation_matrix_description <- function(){
x_con = curdata()$data1[,level_y(),level_x()]
y_con = curdata()$data2[,level_y(),level_x()]
diff <- y_con - x_con
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
mystring = ""
mystring <- paste0("region_x = ", region_x, "\n")
mystring <- paste0(mystring, "region_y = ", region_y, "\n")
ispaired = FALSE
m1 = 0
m2 = 0
# berechne die Behavioralen Werte fuer den main regessor
b1 = get( input$mainregressor, curdata()$df_data1)
zcor <- cor.test(diff, b1)
z = t.test(x_con,y_con, paired = TRUE)
m1 = mean(x_con)
m2 = mean(y_con)
z$mydescription <- paste0(mystring, create_my_ttest_string(z, paired = TRUE, mean1 = m1, mean2 = m2))
z$mydescription <- paste0(z$mydescription, create_my_cortest_string(zcor))
return(z)
}
ttest_estimation2 <- function(compare = "groups"){
x_con = curdata()$data1[,level_y(),level_x()]
#x_beh = curdata()$df_data1[,level_y(),level_x()]
y_con = curdata()$data2[,level_y(),level_x()]
#y_beh = curdata()$df_data2[,level_y(),level_x()]
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
mystring = ""
ispaired = FALSE
m1 = 0
m2 = 0
# vergleiche 2 Gruppen mit einem Trial
mystring = paste0(mystring, "time ", input$ld_1, " vs. ", input$ld_2)
ispaired = TRUE
if (input$ld_1 == input$ld_2){
cat("no output in case of same time")
return()
}
z = t.test(x_con,y_con, paired = ispaired)
m1 = mean(x_con)
m2 = mean(y_con)
z$mydescription <- paste0(mystring, create_my_ttest_string(z, paired = ispaired, mean1 = m1, mean2 = m2))
return(z)
}
create_df_for_histplot <- function(compare = "groups",
group = 1, trial = 1){
xg1t1 = data_g1_t1()[,level_y(),level_x()]
xg1t2 = data_g1_t2()[,level_y(),level_x()]
xg2t1 = data_g2_t1()[,level_y(),level_x()]
xg2t2 = data_g2_t2()[,level_y(),level_x()]
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
#cat(file = stderr(), "create_df_for_histplot\n")
#string1 = paste0(input$trial1," vs ", input$trial2, "in group ", input$group1, "\n")
if (compare == "groups"){
if (input$group1 == input$group2){
cat("no output in case of same groups")
return()
}
if (trial==1){
x = xg1t1
y = xg2t1
}
if (trial == 2){
x = xg1t2
y = xg2t2
}
df <- data.frame(Gruppe=c(rep(input$group1, times=length(x)),
rep(input$group2, times=length(y))),
val=c(x, y))
df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# means for geomline
df_hline = data.frame(Gruppe = c(input$group1,input$group2), Means=c(mean(x), mean(y)))
}
# if comparing 2 trails of the same group
if (compare == "trials"){
if (group==1){
x = xg1t1
y = xg1t2
}
if (group == 2){
x = xg2t1
y = xg2t2
}
df <- data.frame(Gruppe=c(rep(g_trials()[as.numeric(input$trial1)], times=length(x)),
rep(g_trials()[as.numeric(input$trial2)], times=length(y))),
val=c(x, y))
df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# means for geomline
df_hline = data.frame(Gruppe = c(g_trials()[as.numeric(input$trial1)],
g_trials()[as.numeric(input$trial2)]),
Means=c(mean(xg1t1), mean(xg1t2)))
}
ggplot(df, aes(num, val, fill=Gruppe)) +
geom_bar(stat="identity") +
facet_wrap(~Gruppe) +
geom_hline(data = df_hline, aes(yintercept = Means))
}
create_df_for_histplot2 <- function(compare = "groups",
group = 1, trial = 1){
x = curdata()$data1[,level_y(),level_x()]
y = curdata()$data2[,level_y(),level_x()]
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
#string1 = paste0(input$trial1," vs ", input$trial2, "in group ", input$group1, "\n")
cat(file = stderr(), paste0("\n create_df_for_histplot2 with time \n"))
cat(file = stderr(), paste0("curdata%data1 with dim(x)=", dim(x),"\n"))
cat(file = stderr(), paste0("curdata%data2 with dim(y)=", dim(y),"\n"))
df <- data.frame(Gruppe=c(rep(input$group1, times=length(x)),
rep(input$group2, times=length(y))),
val=c(x, y))
df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# means for geomline
df_hline = data.frame(Gruppe = c(input$group1,input$group2), Means=c(mean(x), mean(y)))
ggplot(df, aes(num, val, fill=Gruppe)) +
geom_bar(stat="identity") +
facet_wrap(~Gruppe) +
geom_hline(data = df_hline, aes(yintercept = Means))
}
}
)
}
append_correlation_row <- function(x1 = NULL, b1 = NULL, x2 = NULL, b2 = NULL,
t = "not known",
method = "pearson", reg_name = "no_reg_name",
g1 = "not known", g2 = "not known",
df = NULL, description = "no desc.") {
m1 = cor.test(x1,b1, method = method)
m2 = cor.test(x2,b2, method = method)
#cat(file = stderr(), m1$estimate)
#cat(file = stderr(), m2$estimate)
r_ind = comparing_independent_rs(m1$estimate, m2$estimate, length(x1),length(x2))
df2 <- data.frame(regname = reg_name,
cor_method = method,
trial = t,
group1 = g1,
r1 = m1$estimate,
p1 = m1$p.value,
t1 = m1$statistic,
df1 = m1$parameter,
CI1_l = m1$conf.int[1],
CI1_h = m1$conf.int[2],
group2 = g2,
r2 = m2$estimate,
p2 = m2$p.value,
t2 = m2$statistic,
df2 = m2$parameter,
CI2_l = m2$conf.int[1],
CI2_h = m2$conf.int[2],
z_dif = r_ind[1],
p_dif = r_ind[2],
descri = description,
stringsAsFactors = FALSE
)
if (is.null(df)){
return(df2)
# df <- create_empty_df_for_correlation(num_groups = 2)
}
df_new <- rbind(df, df2)
return(df_new)
}
append_correlation_row_trials <- function(x1 = NULL, b1 = NULL, x2 = NULL,
method = "pearson", g = "not known", reg_name = "no_reg_name",
t1 = "not known", t2 = "not known",
df = NULL, description = "no desc.") {
x = x1
y = b1
z = x2
mxy = cor.test(x,y, method = method)
mzy = cor.test(z,y, method = method)
mxz = cor.test(x,z, method = method)
#comparing_independent_rs <-function(rxy, rxz, rzy, n)
r_dep = comparing_dependent_rs(mxy$estimate, mxz$estimate, mzy$estimate, length(x))
df2 <- data.frame(regname = reg_name,
cor_method = method,
group = g,
trial1 = t1,
r1 = mxy$estimate,
p1 = mxy$p.value,
t1 = mxy$statistic,
df1 = mxy$parameter,
CI1_l = mxy$conf.int[1],
CI1_h = mxy$conf.int[2],
trial2 = t2,
r2 = mzy$estimate,
p2 = mzy$p.value,
t2 = mzy$statistic,
df2 = mzy$parameter,
CI2_l = mzy$conf.int[1],
CI2_h = mzy$conf.int[2],
t_dif = r_dep[1],
p_dif = r_dep[2],
descri = description,
stringsAsFactors = FALSE
)
if (is.null(df)){
return(df2)
# df <- create_empty_df_for_correlation(num_groups = 2)
}
df_new <- rbind(df, df2)
return(df_new)
}
JesseRed/dataVis documentation built on July 16, 2025, 8:17 p.m.
R Package Documentation
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library(shiny)
regressionLongStats2UI <- function(id){
ns <- NS(id)
# Thanks to the namespacing, we only need to make sure that the IDs
# are unique within this function, rather than unique across the entire app.
tagList(
uiOutput(ns("uiANOVA")),
#verbatimTextOutput(ns("textANOVAStats")),
# absolutePanel(
# bottom = 20, right = 20, width = 200,
# draggable = TRUE,
# wellPanel(
# htmlOutput(ns("html_text")),
# sliderInput("n", "", min=3, max=20, value=5),
# plotOutput("plot2", height="200px")
#
# ),
# style = "opacity: 0.92"
# ),
)
}
regressionLongStats2Server <- function(id, input_glob_sig, freq) {
moduleServer(
id,
function(input, output, session) {
#selectInput(NS(id, "var"), "Variable", choices = NULL)
ns<-session$ns
#f_utrial_list_all <- reactive({c("all", g_trials())})
output$uiANOVA <- renderUI({
fluidPage(
fluidRow(
column(4,
fluidRow(
column(6,
style = "background-color: #fcfcfc;",
style = 'border-right: 2px solid gray',
h4("trial comparison", align = "center"),
fluidRow(
column(6,
selectInput(ns("trial1"), h5("Select Trial 1", align = "center"),
choices = g_trials_named(), selected =g_trials_named()[1])
),
column(6,
selectInput(ns("trial2"), h5("Select Trial 2", align = "center"),
choices = g_trials_named(), selected = g_trials_named()[2])
)
)
),
column(6,
style = "background-color: #fcfcfc;",
#style = 'border-bottom: 2px solid gray',
style = "border-right: 2px solid black",
h4("group comparison", align = "center"),
fluidRow(
column(6,
selectInput(ns("group1"), h5("Select Group 1", align = "center"),
choices = g_groups(), selected = g_groups()[2])
),
column(6,
selectInput(ns("group2"), h5("Select Group 2", align = "center"),
choices = g_groups(), selected = g_groups()[2])
)
)
),
),
# fluidRow(
# style = "background-color: #fcfcfc;",
# #style = "border-top: 2px solid black",
# h4("is the analysis directed?", align = "left"),
# column(12,
# prettyRadioButtons(
# inputId = ns("causal"),
# label = "",
# choices = c("non-directed", "directed"),
# shape = "round",
# status = "danger",
# fill = TRUE,
# inline = TRUE
# ),
# ),
# ),
),
column(2,
style = "background-color: #fcfcfc;",
style = 'border-right: 2px solid gray',
h4("longitudinal data", align = "center"),
fluidRow(
column(6,
textInput(ns("ld_1"), h5("long data 1", align = "center"), value = "1")
),
column(6,
textInput(ns("ld_2"), h5("long data 2", align = "center"), value = "2, 3")
)
),
checkboxInput(ns("longtimefirst"), "estimate time first", value = TRUE),
checkboxInput(ns("averagelong"), "average same long subj(1 vs. av(2,3))", value = TRUE),
checkboxInput(ns("cb_same_subjects"), "include only reoccuring subj", value = TRUE)
),
column(2,
style = "background-color: #fcfcfc;",
style = 'border-right: 2px solid gray',
h4("Filter", align = "center"),
textInput(ns("filterg1"), h5("filter G1", align = "center"), value = "Zeichen__1>0"),
textInput(ns("filterg2"), h5("filter G2", align = "center"), value = "Zeichen__1>0"),
),
column(2,
style = "background-color: #fcfcfc;",
style = 'border-right: 2px solid gray',
h4("Visualize", align = "center"),
selectInput(ns("statsMethod"), h5("method"),
choices = c("Regression","ANOVA"), selected = 1)
),
column(2,
fluidRow(
column(6,
numericInput(ns("plot_height"),"plot height",800)
),
column(6,
numericInput(ns("plot_width"),"plot width",0)
),
),
fluidRow(
column(6,
numericInput(ns("plot_res"),"res",96),
),
column(6,
actionButton(ns("ExportData"), "export Data"),
),
),
)
),
fluidRow(
style = 'border-top: 2px solid gray',
column(9,
plotOutput(ns("plot"), width = "auto", height = "700px", click = ns("plot_click")),
),
column(3,
selectInput(ns("mainregressor"), h4("main regressor"),
choices = colnames(g_beh())),
selectInput(ns("regressors"), h4("potential regressors"),
multiple = TRUE, selectize = FALSE,
size = 35,
choices = colnames(g_beh()),
selected = 3)
)
),
fluidRow(align = "center", h4("Correlation matrix "),
column(9,
plotOutput(ns("plot_all_correlation"), width = "auto", height = "700px", click = ns("plot_click")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_correlation_matrix")),
)),
fluidRow(align = "center", h4("Correlation fit"),
column(9,
plotOutput(ns("plot_fit_correlation"), width = "auto", height = "700px", click = ns("plot_click_hist")),
)
#,
#column(3, align = "left",
# verbatimTextOutput(ns("text_stats_correlation_matrix")),
#)
),
fluidRow(align = "center", h4("comparison of Time 1 vs. Time 2"),
column(9,
plotOutput(ns("hist_compare_diffTime"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffTime")),
)),
fluidRow(align = "center", h4("comparison of Trial1 vs. Trial 2 of selected group 1"),
column(9,
plotOutput(ns("hist_compare_diffTrial_sameGroup1"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffTrial_sameGroup1")),
)),
fluidRow(align = "center", h4("comparison of Trial1 vs. Trial 2 of selected group 2"),
column(9,
plotOutput(ns("hist_compare_diffTrial_sameGroup2"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffTrial_sameGroup2")),
)),
fluidRow(align = "center", h4("comparison of Group 1 vs. Group 2 of selected trial 1"),
column(9,
plotOutput(ns("hist_compare_diffGroup_sameTrial1"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffGroup_sameTrial1")),
)),
fluidRow(align = "center", h4("comparison of Group 1 vs. Group 2 of selected trial 2"),
column(9,
plotOutput(ns("hist_compare_diffGroup_sameTrial2"), width = "auto", height = "300px", click = ns("plot_click_hist")),
),
column(3, align = "left",
verbatimTextOutput(ns("text_stats_compare_diffGroup_sameTrial2")),
)),
# fluidRow(
# column(12,
# verbatimTextOutput(ns("text_stats")),
# )),
# fluidRow(
# column(12,
# verbatimTextOutput(ns("simple_correlation")),
# )
# ),
fluidRow(
column(12,
box(title = "simple correlation ..........expand for help", width = 12, collapsible = TRUE, collapsed = TRUE, htmlOutput(ns("htmlhelp_simple_correlation"))),
)
),
fluidRow(
column(12,
box(title = "simple correlation ..........expand for help", width = 12, collapsible = TRUE, collapsed = TRUE, verbatimTextOutput(ns("help_simple_correlation"))),
)
),
fluidRow(
column(12,
tableOutput(ns("tab_simple_time_correlation")),
)
),
fluidRow(
column(12,
box(title = "simple non-time non-subject-exclusion correlation ..........expand for help", width = 12, collapsible = TRUE, collapsed = TRUE, verbatimTextOutput(ns("help_simple_correlation2"))),
)
),
fluidRow(
column(12,
tableOutput(ns("tab_simple_group_correlation")),
)
),
fluidRow(
column(12,
tableOutput(ns("tab_simple_trial_correlation")),
)
),
fluidRow(
column(12,
box(title = "help for partial correlation below", width = 12, collapsible = TRUE, collapsed = TRUE, htmlOutput(ns("htmlhelp_partial_correlation"))),
)
),
fluidRow(
column(12,
box(title = "partial correlation ", width = 12, collapsible = TRUE, collapsed = FALSE,
fluidRow(
column(3,
verbatimTextOutput(ns("partial_correlationG1T1"))
),
column(3,
verbatimTextOutput(ns("partial_correlationG1T2"))
),
column(3,
verbatimTextOutput(ns("partial_correlationG2T1"))
),
column(3,
verbatimTextOutput(ns("partial_correlationG2T2"))
),
)),
)
),
fluidRow(
column(12,
box(title = "Included subjects", width = 12, collapsible = TRUE, collapsed = TRUE,
#uiOutput(ns("includedSubjects"))
actionButton(ns("testexclude"), "update"),
checkboxGroupInput(ns("Subjects"), label = h3("Subjects"), inline = T,
choices = g_D()$df_BD$ID,
selected = g_D()$df_BD$ID),
style = "background-color: #fcfcfc;",
style = 'border-bottom: 2px solid gray',
checkboxGroupInput(ns("Group1"), label = h3("Group 1"), inline = T,
choices = c()), #, #curdata()$df_data1$ID,
# selected = c()), #curdata()$df_data1$ID[my_included_subjects_g1()]),
style = "background-color: #fcfcfc;",
style = 'border-bottom: 2px solid gray',
checkboxGroupInput(ns("Group2"), label = h3("Group 2"), inline = T,
choices = c()) #, #curdata()$df_data2$ID,
),
)
),
# fluidRow(
# column(12,
# box(title = "partial correlation ", width = 12, collapsible = TRUE, collapsed = FALSE, verbatimTextOutput(ns("partial_correlation"))), )
# )
)
})
subjects_to_exclude = reactive({
# list of subjects that are not marked
to_exclude = setdiff( g_D()$df_BD$ID, input$Subjects)
#cat(file = stderr(), paste0("XXX subjects_to_exclude reactive = ", to_exclude, "\n"))
return(to_exclude)
})
my_included_subjects = reactive({get_included_subjects( g_D()$df_BD$ID, subjects_to_exclude())})
my_included_subjects_g1 = reactive({ req(input$Subjects); get_included_subjects( curdata()$df_data1$ID, subjects_to_exclude())})
my_included_subjects_g2 = reactive({get_included_subjects( curdata()$df_data2$ID, subjects_to_exclude())})
observeEvent(input$testexclude, {
cat(file = stderr(), paste0("included Subjects = \n"))
#cat(file = stderr(), paste0("included Subjects = ", input$Subjects, "\n"))
#cat(file = stderr(), paste0("class(subjects_to_exclude = ", class(subjects_to_exclude()), "\n"))
#cat(file = stderr(), paste0("length(subjects_to_exclude = ", length(subjects_to_exclude()), "\n"))
#cat(file = stderr(), paste0("my_included_subjects() = ", my_included_subjects(), "\n"))
numbered_IDs_all <- get_included_subjects_with_numbers(g_D()$df_BD$ID, my_included_subjects())
numbered_IDs_g1 <- get_included_subjects_with_numbers(curdata()$df_data1$ID, my_included_subjects_g1())
numbered_IDs_g2 <- get_included_subjects_with_numbers(curdata()$df_data2$ID, my_included_subjects_g2())
# updateCheckboxGroupInput(session, "Subjects",
# choices = numbered_IDs_all, inline = T,
# selected = numbered_IDs_all[my_included_subjects()])
updateCheckboxGroupInput(session, "Group1",
choices = numbered_IDs_g1, inline = T,
selected = numbered_IDs_g1[my_included_subjects_g1()]
)
updateCheckboxGroupInput(session, "Group2",
choices = numbered_IDs_g2, inline = T,
selected = numbered_IDs_g2[my_included_subjects_g2()]
)
updateCheckboxGroupInput(session, "Subjects",
choices = g_D()$df_BD$ID, inline = T,
selected = g_D()$df_BD$ID[my_included_subjects()])
# updateCheckboxGroupInput(session, "Group1",
# choices = curdata()$df_data1$ID, inline = T,
# selected = curdata()$df_data1$ID[my_included_subjects_g1()]
#
# )
#
# updateCheckboxGroupInput(session, "Group2",
# choices = curdata()$df_data2$ID, inline = T,
# selected = curdata()$df_data2$ID[my_included_subjects_g2()]
# )
})
# Funktion um an die ausgewaehlten Subjects Numbern zu schreiben damit
# die Auswahl in der GUI einfacher wird
get_included_subjects_with_numbers <- function(IDs, is_included){
# nummern duerfen nur die Subjects erhalten die selectiert sind
idx = 1
for (i in 1:length(IDs)){
if (is_included[i]){
IDs[i] <- paste0(idx,". ",IDs[i])
idx <- idx +1
}
}
return(IDs)
}
# filter data by group
data_freqmean <- reactive({
get_data_freqmean(g_data(), freq())
})
# data_1 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial1), freq())
# })
# data_2 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial2), freq())
# })
# data_g1_t1 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial1), freq())
# })
# data_g1_t2 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial2), freq())
# })
# data_g2_t1 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial1), freq())
# })
# data_g2_t2 <- reactive({
# get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial2), freq())
# })
#
data_1 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial1), g_sel_freqs())
})
data_2 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial2), g_sel_freqs())
})
data_g1_t1 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial1), g_sel_freqs())
})
data_g1_t2 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group1, as.numeric(input$trial2), g_sel_freqs())
})
data_g2_t1 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial1), g_sel_freqs())
})
data_g2_t2 <- reactive({
get_data_group_trial_freqmean(g_data(),input$group2, as.numeric(input$trial2), g_sel_freqs())
})
level_x <- reactive({round(input$plot_click$x)})
level_y <- reactive({abs(round(input$plot_click$y)-length(g_regions())-1)})
# curdata <- reactive({
# get_currently_selected_data_long3(g_D(), input$group1, input$group2, as.numeric(input$trial1), as.numeric(input$trial2), g_sel_freqs())
# # get_currently_selected_data(g_data(), input$group1, input$group2, as.numeric(input$trial1), as.numeric(input$trial2), freq())
# })
# get the data for the second time point
# die longitudinalen Daten sind kodiert als nummern hinter den IDs der Subjects XY001_1
# daher teilen wir hier die Subjects einfach entsprechend auf
curdata <- reactive({
cat(file = stderr(), paste0("curdata with dim(g_D()$mat)=", dim(g_D()$mat),"\n"))
req(input$group1)
req(input$group2)
req(input$trial1)
req(input$trial2)
req(input$ld_1)
req(input$ld_2)
# req(input$cb_same_subjects)
# req(input$averagelong)
# req(input$longtimefirst)
#gD1 <<- D1()
#gD2 <<- D2()
cat(file = stderr(), paste0("curdata with dim(g_D()$mat)=", dim(g_D()$mat),"\n"))
cat(file = stderr(), paste0("curdata with length(g_D())=", length(g_D()),"\n"))
M <- get_currently_selected_data_long3(g_D(),
input$group1,
input$group2,
as.numeric(input$trial1),
as.numeric(input$trial2),
g_sel_freqs(),
tbl_beh = g_D()$df_BD,
long_def1 = as.numeric(unlist(strsplit(input$ld_1, split=","))),
long_def2 = as.numeric(unlist(strsplit(input$ld_2, split=","))),
is_exclude_not_reoccuring_subj = input$cb_same_subjects,
averagelong = input$averagelong,
# datalong = D2()$mdat,
# tbl_beh_long = D2()$df_BD,
estimate_time_first = input$longtimefirst,
filter_g1 = input$filterg1,
filter_g2 = input$filterg2,
subjects_to_exclude = subjects_to_exclude()#,
#iscausal = iscausal(),
# network = network_new()
)
gM <<- M
return(M)
})
###########################################################
### RENDERPLOT
output$plot<-renderPlot({
req(input$trial1)
req(input$trial2)
req(input$group1)
req(input$group2)
d <- curdata()
mat_t <<- d$mat_t
mat_p <<- d$mat_p
###################
# CORRPLOT
generate_plot_Corrplot(d$mat_p, d$mat_t)
})
#
# output$hist <- renderPlot({
# req(input$plot_click$x)
# req(input$plot_click$y)
# region_x = g_regions()[level_x()]
# #cat(file = stderr(), region_x)
# #level_x = round(input$plot_click$x)
# #level_y = abs(round(input$plot_click$y)-length(g_regions())-1)
# region_x = g_regions()[level_x()]
# region_y = g_regions()[level_y()]
# # level_x = 1
# # level_y = 2
# #df = g_beh()
# d = data_freqmean()
#
# if (input$trial1 == input$trial2) {
# cat(file = stderr(), "trial1 == trial2\n")
# string1 = paste0(input$group1," vs ", input$group2, " in trial ", names(g_trials_named())[input$trial1], "\n") #utrial_list[input$trial1], "\n")
# d1 = get_data_group_freqmean(g_data(), input$group1, freq())
# d2 = get_data_group_freqmean(g_data(), input$group2, freq())
# x = d1[,level_x(), level_y(), as.numeric(input$trial1)]
# y = d2[,level_x(), level_y(), as.numeric(input$trial1)]
# df <- data.frame(Gruppe=c(rep(input$group1, times=length(x)),
# rep(input$group2, times=length(y))),
# val=c(x, y))
# df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# # means for geomline
# df_hline = data.frame(Gruppe = c(input$group1,input$group2), Means=c(mean(x), mean(y)))
# # df$val = d[,level_x, level_y, as.numeric(input$trial1)]
# # df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# # dummy2 = data.frame(Gruppe = c(0,1), Means=c(0.4, 0.5))
#
# }
# if (input$group1 == input$group2){
# string1 = paste0(g_trials()[input$trial1]," vs ", g_trials()[input$trial2], "in group ", input$group1, "\n")
# data1 = data_1()
# data2 = data_2()
# x = data1[,level_x(), level_y()]
# y = data2[,level_x(), level_y()]
# df <- data.frame(Gruppe=c(rep(g_trials()[as.numeric(input$trial1)], times=length(x)),
# rep(g_trials()[as.numeric(input$trial2)], times=length(y))),
# val=c(x, y))
# df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# # means for geomline
# df_hline = data.frame(Gruppe = c(g_trials()[as.numeric(input$trial1)],
# g_trials()[as.numeric(input$trial2)]),
# Means=c(mean(x), mean(y)))
#
# #p<-ggplot(df, aes(num, val, fill=Gruppe))
# #p + geom_bar(stat="identity") + facet_wrap(~Gruppe)
# }
#
# ##########later delete
# # temporary
# string1 = paste0(g_trials()[input$trial1]," vs ", g_trials()[input$trial2], "in group ", input$group1, "\n")
# data1 = data_1()
# data2 = data_2()
# x = data1[,level_x(), level_y()]
# y = data2[,level_x(), level_y()]
# df <- data.frame(Gruppe=c(rep(g_trials()[as.numeric(input$trial1)], times=length(x)),
# rep(g_trials()[as.numeric(input$trial2)], times=length(y))),
# val=c(x, y))
# df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# # means for geomline
# df_hline = data.frame(Gruppe = c(g_trials()[as.numeric(input$trial1)],
# g_trials()[as.numeric(input$trial2)]),
# Means=c(mean(x), mean(y)))
#
#
# ###################################
#
# ggplot(df, aes(num, val, fill=Gruppe)) +
# geom_bar(stat="identity") +
# facet_wrap(~Gruppe) +
# geom_hline(data = df_hline, aes(yintercept = Means))
#
# })
################################################################
# THE complete correlation matrix for one regressor
###################################################
output$plot_all_correlation<-renderPlot({
req(input$trial1)
req(input$trial2)
req(input$group1)
req(input$group2)
req(input$mainregressor)
d <- curdata()
mat_t <<- d$mat_t
mat_p <<- d$mat_p
diff <- d$data2 - d$data1
# berechne die Behavioralen Werte fuer den main regessor
b1 = get( input$mainregressor, curdata()$df_data1)
b2 = get( input$mainregressor, curdata()$df_data2)
out_b1 <<- b1
# da wir nur longitudinal anschauen sollte b1 und b2 gleich sein
# berechne nun die Korrelatioenen zwischen long_diff und b1
cormatrix <- apply(diff,2:3,cor.test,b1);
dim2 <- dim(diff)[2]
dim3 <- dim(diff)[3]
#mat_cor <- matrix(,nrow = dim2, ncol = dim3)
#mat_cor_p <- matrix(,nrow = dim2, ncol = dim3)
mat_cor <- d$mat_t
mat_cor_p <- d$mat_p
for (x1 in 1:dim2){
for (y1 in 1:dim3){
mat_cor[x1,y1]<-cormatrix[[x1,y1]]$estimate
mat_cor_p[x1,y1]<-cormatrix[[x1,y1]]$p.value
}
}
###################
# CORRPLOT
generate_plot_Corrplot(mat_cor_p, mat_cor)
})
################################################################
# THE fit of the specific correlation
###################################################
output$plot_fit_correlation<-renderPlot({
req(input$plot_click$x)
cat(file = stderr(), paste0("\n fit line \n"))
x = curdata()$data1[,level_y(),level_x()]
y = curdata()$data2[,level_y(),level_x()]
diff <- y - x
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
#string1 = paste0(input$trial1," vs ", input$trial2, "in group ", input$group1, "\n")
cat(file = stderr(), paste0("\n create fic correlation with time \n"))
cat(file = stderr(), paste0("curdata%data1 with dim(x)=", dim(x),"\n"))
cat(file = stderr(), paste0("curdata%data2 with dim(y)=", dim(y),"\n"))
# berechne die Behavioralen Werte fuer den main regessor
b1 = get( input$mainregressor, curdata()$df_data1)
names = get( "ID", curdata()$df_data1)
df <- data.frame(x=diff,
y=b1,
z =names )
#create scatter plot with line of best fit
ggplot(df, aes(x=x, y=y)) +
geom_point() +
geom_smooth(method=lm, se=FALSE) +
geom_text(aes(label=z))
})
################################################################
# THE histogram plots of individual subjects
###########################################
output$hist_compare_diffTime <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot2(compare = "time", group=1, trial = 1)
})
output$hist_compare_diffTrial_sameGroup1 <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot(compare = "trials", group=1, trial = 1)
})
output$hist_compare_diffTrial_sameGroup2 <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot(compare = "trials", group=2, trial = 1)
})
output$hist_compare_diffGroup_sameTrial1 <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot(compare = "groups", group=1, trial = 1)
})
output$hist_compare_diffGroup_sameTrial2 <- renderPlot({
req(input$plot_click$x)
create_df_for_histplot(compare = "groups", group=1, trial = 2)
})
################################################################
# THE text about t-statistics
###########################################
output$text_stats_correlation_matrix <- renderPrint({
req(input$plot_click)
#req(input$mainregressor)
z = correlation_matrix_description()
cat(z$mydescription)
})
output$text_stats_compare_diffTime <- renderPrint({
req(input$plot_click)
z = ttest_estimation2(compare = "time")
cat(z$mydescription)
})
output$text_stats_compare_diffGroup_sameTrial1 <- renderPrint({
req(input$plot_click)
z = ttest_estimation(compare = "groups", group = 1, trial = 1)
cat(z$mydescription)
})
output$text_stats_compare_diffGroup_sameTrial2 <- renderPrint({
req(input$plot_click)
z = ttest_estimation(compare = "groups", group = 1, trial = 2)
cat(z$mydescription)
})
output$text_stats_compare_diffTrial_sameGroup1 <- renderPrint({
req(input$plot_click)
z = ttest_estimation(compare = "trials", group = 1, trial = 1)
cat(z$mydescription)
})
output$text_stats_compare_diffTrial_sameGroup2 <- renderPrint({
req(input$plot_click)
z = ttest_estimation(compare = "trials", group = 2, trial = 1)
cat(z$mydescription)
})
####
#################################################################
output$htmlhelp_simple_correlation <- renderUI({
# if (showhtml()){
includeMarkdown(rmarkdown::render("./documentation/simple_correlation_markdown.md"))
# }
})
output$htmlhelp_parial_correlation <- renderUI({
# if (showhtml()){
includeMarkdown("./documentation/partial_correlation_markdown.md")
# }
})
output$help_simple_correlation <- renderPrint({
text = "in der obersten Tabelle stehen Werte unter einbeziehung der excludeten subjects\n"
text = paste0(text, " in den unteren beiden Tabellen dann die Correlationen zu allen Subjects\n")
text = paste0(text, " oberste Zeile zeigt die Zeitabhaengigkeit\n")
text = paste0(text, " unterschied zwischen den 2 Zeitpunkten (ggf. group, trial differenz)\n")
text = paste0(text, " in Correlation zu den Veraenderungen der behavioralen Daten\n")
text = paste0(text, " Expl: trial1 =1; trial2=1, group1=1, group2 =2, time1=1, time2=2\n")
text = paste0(text, " Gruppe ist verschieden ...beide Gruppen haben unterschiede zwischen den 2 Zeitpunkten .... Die Analyse testet auf signifikante Unterschiede zwischen diesen zeitbezogenen Unterschieden\n",
"Algorithmus:\n",
"1. entferne alle Subjects die nicht in den Daten beider Zeitpunkte zu finden sind\n",
"2. falls unterschiedliche trial gewaehlt wurden wird der subjectspezifische Unterschied zwischen den Trials berechnet\n",
"wenn estimate time first",
" Berechne X1 = Data_Zeitpunkt2_group1_trial1 - Data_Zeitpunkt1_group1_trial1 (Subjects x Regions x Regions)\n",
" X2 = Data_Zeitpunkt2_group2_trial1 - Data_Zeitpunkt1_group2_trial1 (Subjects x Regions x Regions)\n",
"wenn nicht estimate time first",
" Berechne X1 = Data_Zeitpunkt1_group1_task1 - Data_Zeitpunkt1_group2_task2 (Subjects x Regions x Regions)\n",
" X2 = Data_Zeitpunkt2_group2_task1 - Data_Zeitpunkt2_group2_task2 (Subjects x Regions x Regions)\n",
" In diesen beiden 3d Matrizen steht somit der gruppenspezifische Unterschied eines Trials zwischen den Messungen\n",
" Ein positiver Wert in dieser Matrix zeigt einen positiven Effekt der Zeit/Intervention an (in der 2. Messung groesser)\n",
"Die Behavioralen Daten B1 und B2 werden NICHT analog berechnet! Hier wird immer zuerst die Differenz \n",
" ueber die Zeit berechnet d.h. estimate time first ist immer Aktiv\n",
" soweit ich das sehe gibt es nicht wirklich eine sinnvolle Frage als das man estimat time first deaktiviert\n",
"Es ist weiterhin wichtig zu beachten, dass die Behavioralen Daten nach der Zeit subtrahiert werden,\n",
" in der Behavioralen Tabelle sollte ein sich nicht veraendernder Faktor wie z.B. das Alter nur zum ersten Zeitpunkt eingetragen sein\n",
" der zweite Zeitpunkt sollte auf 0 gesetzt sein\n",
"diese ueberlegung erfolgte in der Annahme, dass man zumeist nach sich durch eine Intervention veraendernde behaviorale Effekte sucht\n")
text = paste0(text, " Zeile : Corelation(B1,X1) , Correlation(B2,X2) \n")
text = paste0(text, " Bitte beachten, dass nicht jede Kombination einen Sinn ergibt... hier muss etwas nachgedacht werden!!!\n")
cat(text)
})
output$help_simple_correlation2 <- renderPrint({
text = "Berechnung der Korrelationen mit ALLEN Subjects und nur zum Zeitpunkt 1\n"
text = paste0(text, " in den unteren beiden Tabellen dann die Correlationen zu allen Subjects\n")
text = paste0(text, " \n")
cat(text)
})
output$help_simple_correlation3 <- renderPrint({
text = "Berechnung der Korrelationen mit ALLEN Subjects und nur zum Zeitpunkt 2\n"
text = paste0(text, " in den unteren beiden Tabellen dann die Correlationen zu allen Subjects\n")
text = paste0(text, " \n")
cat(text)
})
###########################################
# the newly created statistics section
output$tab_simple_time_correlation <- renderTable({
req(input$plot_click)
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
x_con = curdata()$data1[,level_y(),level_x()]
y_con = curdata()$data2[,level_y(),level_x()]
# berechne die Behavioralen Werte fuer den main regessor
b1 = get( input$mainregressor, curdata()$df_data1)
b2 = get( input$mainregressor, curdata()$df_data2)
df <- append_correlation_row(x1 = x_con, b1 = b1, x2 = y_con, b2 = b2,
method = "pearson",
t = g_trials()[as.numeric(input$trial1)],
g1 = input$group1,
g2 = input$group2,
reg_name = input$mainregressor)
for ( i in 1:length(input$regressors)){
b1 = get( input$regressors[i], curdata()$df_data1)
b2 = get( input$regressors[i], curdata()$df_data2)
df <- append_correlation_row(x1 = x_con, b1 = b1, x2 = y_con, b2 = b2,
method = "pearson",
t = g_trials()[as.numeric(input$trial1)],
g1 = input$group1,
g2 = input$group2,
reg_name = input$regressors[i], df=df)
}
return(df)
})
###########################################
# the newly created statistics section
output$tab_simple_group_correlation <- renderTable({
req(input$plot_click)
cat(file = stderr(), paste0("output$tab_simpple_group_correlation"))
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
xg1t1 = data_g1_t1()[,level_y(),level_x()]
xg1t2 = data_g1_t2()[,level_y(),level_x()]
xg2t1 = data_g2_t1()[,level_y(),level_x()]
xg2t2 = data_g2_t2()[,level_y(),level_x()]
# berechne Werte fuer den main regessor
#reg_name = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
b1 = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
b2 = get_beh_tbl_data_by_group(input$group2, input$mainregressor)
#cat(file = stderr(), "now create")
df <- append_correlation_row(x1 = xg1t1, b1 = b1, x2 = xg2t1, b2 = b2,
method = "pearson",
t = g_trials()[input$trial1],
g1 = input$group1,
g2 = input$group2,
reg_name = input$mainregressor)
df <- append_correlation_row(x1 = xg1t2, b1 = b1, x2 = xg2t2, b2 = b2,
method = "pearson",
t = g_trials()[input$trial2],
g1 = input$group1,
g2 = input$group2,
reg_name = input$mainregressor,
df = df)
#cat(file = stderr(), "now for loop")
for ( i in 1:length(input$regressors)){
b1 = get_beh_tbl_data_by_group(input$group1, input$regressors[i])
b2 = get_beh_tbl_data_by_group(input$group2, input$regressors[i])
df <- append_correlation_row(x1 = xg1t1, b1 = b1, x2 = xg2t1, b2 = b2,
method = "pearson",
t = g_trials()[input$trial1],
g1 = input$group1,
g2 = input$group2,
reg_name = input$regressors[i], df=df)
df <- append_correlation_row(x1 = xg1t2, b1 = b1, x2 = xg2t2, b2 = b2,
method = "pearson",
t = g_trials()[input$trial2],
g1 = input$group1,
g2 = input$group2,
reg_name = input$regressors[i], df=df)
}
return(df)
})
####
####
#################################################################
###########################################
# the newly created statistics section for different trials
output$tab_simple_trial_correlation <- renderTable({
req(input$plot_click)
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
xg1t1 = data_g1_t1()[,level_y(),level_x()]
xg1t2 = data_g1_t2()[,level_y(),level_x()]
xg2t1 = data_g2_t1()[,level_y(),level_x()]
xg2t2 = data_g2_t2()[,level_y(),level_x()]
# berechne Werte fuer den main regessor
#reg_name = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
b1 = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
b2 = get_beh_tbl_data_by_group(input$group2, input$mainregressor)
df <- append_correlation_row_trials(x1 = xg1t1, b1 = b1, x2 = xg1t2,
method = "pearson",
g = input$group1,
t1 = input$trial1,
t2 = input$trial2,
reg_name = input$mainregressor)
df <- append_correlation_row_trials(x1 = xg2t1, b1 = b2, x2 = xg2t2,
method = "pearson",
g = input$group2,
t1 = input$trial1,
t2 = input$trial2,
reg_name = input$mainregressor, df = df)
for ( i in 1:length(input$regressors)){
b1 = get_beh_tbl_data_by_group(input$group1, input$regressors[i])
df <- append_correlation_row_trials(x1 = xg1t1, b1 = b1, x2 = xg1t2,
method = "pearson",
g = input$group1,
t1 = input$trial1,
t2 = input$trial2,
reg_name = input$regressors[i], df = df)
b2 = get_beh_tbl_data_by_group(input$group2, input$regressors[i])
df <- append_correlation_row_trials(x1 = xg2t1, b1 = b2, x2 = xg2t2,
method = "pearson",
g = input$group2,
t1 = input$trial1,
t2 = input$trial2,
reg_name = input$regressors[i], df = df)
}
return(df)
})
###########################################
# the newly created statistics section for different trials
output$partial_correlationG1T1 <- renderPrint({
req(input$plot_click)
cat(create_partial_correlation_string(group=1,trial=1))
})
###########################################
# the newly created statistics section for different trials
output$partial_correlationG1T2 <- renderPrint({
req(input$plot_click)
cat(create_partial_correlation_string(group=1,trial=2))
})
###########################################
# the newly created statistics section for different trials
output$partial_correlationG2T1 <- renderPrint({
req(input$plot_click)
cat(create_partial_correlation_string(group=2,trial=1))
})
###########################################
# the newly created statistics section for different trials
output$partial_correlationG2T2 <- renderPrint({
req(input$plot_click)
cat(create_partial_correlation_string(group=2,trial=2))
})
####
#################################################################
## general function specific for this tab
#################################################################
create_partial_correlation_string <- function( group = 1, trial = 1){
cat(file = stderr(), paste0("levelx = ", level_x(), "levely = ", level_y(), "\n"))
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
cat(file = stderr(), paste0("region_x = ", region_x, " region_y = ", region_y, "\n"))
if (group==1){
gin = input$group1
b = get_beh_tbl_data_by_group(input$group1, input$mainregressor)
if (trial == 1){
tin = g_trials()[input$trial1]
x_in = data_g1_t1()[,level_y(),level_x()]
}
if (trial == 2){
tin = g_trials()[input$trial2]
x_in = data_g1_t2()[,level_y(),level_x()]
}
}
if (group==2){
gin = input$group2
b = get_beh_tbl_data_by_group(input$group2, input$mainregressor)
if (trial == 1){
tin = g_trials()[input$trial1]
x_in_g2t1<<-data_g2_t1()
x_in = data_g2_t1()[,level_y(),level_x()]
}
if (trial == 2){
tin = g_trials()[input$trial2]
x_in_g2t1<<-data_g2_t2()
x_in = data_g2_t2()[,level_y(),level_x()]
}
}
df <- data.frame(x = x_in, y = b)
n = c("x", "y")
for ( i in 1:length(input$regressors)){
b = get_beh_tbl_data_by_group(gin, input$regressors[i])
df<-cbind(df, b)
#names(df)[names(df)=="V1"]<-input$regressors[i]
n <- c(n,input$regressors[i])
}
names(df)<-n
pc <-pcor(n, var(df))
tmptest <- pcor.test(pc, length(n)-2, length(b))
out <- paste0("group = ", gin, " trial = ",trial, "\n",
"r = ", pc, "\n",
"r^2 =", pc^2, "\n",
"t = ", tmptest[1],"\n",
"df = ", tmptest[2], "\n",
"p = ", tmptest[3]
)
return(out)
}
ttest_estimation <- function(compare = "groups",
group = 1, trial = 1){
xg1t1 = data_g1_t1()[,level_y(),level_x()]
xg1t2 = data_g1_t2()[,level_y(),level_x()]
xg2t1 = data_g2_t1()[,level_y(),level_x()]
xg2t2 = data_g2_t2()[,level_y(),level_x()]
mystring = ""
ispaired = FALSE
m1 = 0
m2 = 0
# vergleiche 2 Gruppen mit einem Trial
if (compare == "groups"){
mystring = paste0(mystring, input$group1, " vs. ", input$group2)
ispaired = FALSE
if (input$group1 == input$group2){
cat("no output in case of same groups")
return()
}
if (trial==1){
mystring = paste0(mystring, " of trial ", g_trials()[input$trial1], "\n")
z = t.test(xg1t1,xg2t1, paired = ispaired)
m1 = mean(xg1t1)
m2 = mean(xg2t1)
}
if (trial == 2){
mystring = paste0(mystring, " of trial ", g_trials()[input$trial2], "\n")
z = t.test(xg1t2,xg2t2, paired = ispaired)
m1 = mean(xg1t2)
m2 = mean(xg2t2)
}
}
# if comparing 2 trails of the same group
if (compare == "trials"){
mystring = paste0(mystring, g_trials()[as.numeric(input$trial1)], " vs. ", g_trials()[as.numeric(input$trial2)])
ispaired = TRUE
if (group==1){
z = t.test(xg1t1,xg1t2, paired = ispaired)
mystring = paste0(mystring, " of group ", input$group1, "\n")
m1 = mean(xg1t1)
m2 = mean(xg1t2)
}
if (group == 2){
z = t.test(xg2t1,xg2t2, paired = ispaired)
mystring = paste0(mystring, " of group ", input$group2, "\n")
m1 = mean(xg2t1)
m2 = mean(xg2t2)
}
}
z$mydescription <- paste0(mystring, create_my_ttest_string(z, paired = ispaired, mean1 = m1, mean2 = m2))
return(z)
}
correlation_matrix_description <- function(){
x_con = curdata()$data1[,level_y(),level_x()]
y_con = curdata()$data2[,level_y(),level_x()]
diff <- y_con - x_con
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
mystring = ""
mystring <- paste0("region_x = ", region_x, "\n")
mystring <- paste0(mystring, "region_y = ", region_y, "\n")
ispaired = FALSE
m1 = 0
m2 = 0
# berechne die Behavioralen Werte fuer den main regessor
b1 = get( input$mainregressor, curdata()$df_data1)
zcor <- cor.test(diff, b1)
z = t.test(x_con,y_con, paired = TRUE)
m1 = mean(x_con)
m2 = mean(y_con)
z$mydescription <- paste0(mystring, create_my_ttest_string(z, paired = TRUE, mean1 = m1, mean2 = m2))
z$mydescription <- paste0(z$mydescription, create_my_cortest_string(zcor))
return(z)
}
ttest_estimation2 <- function(compare = "groups"){
x_con = curdata()$data1[,level_y(),level_x()]
#x_beh = curdata()$df_data1[,level_y(),level_x()]
y_con = curdata()$data2[,level_y(),level_x()]
#y_beh = curdata()$df_data2[,level_y(),level_x()]
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
mystring = ""
ispaired = FALSE
m1 = 0
m2 = 0
# vergleiche 2 Gruppen mit einem Trial
mystring = paste0(mystring, "time ", input$ld_1, " vs. ", input$ld_2)
ispaired = TRUE
if (input$ld_1 == input$ld_2){
cat("no output in case of same time")
return()
}
z = t.test(x_con,y_con, paired = ispaired)
m1 = mean(x_con)
m2 = mean(y_con)
z$mydescription <- paste0(mystring, create_my_ttest_string(z, paired = ispaired, mean1 = m1, mean2 = m2))
return(z)
}
create_df_for_histplot <- function(compare = "groups",
group = 1, trial = 1){
xg1t1 = data_g1_t1()[,level_y(),level_x()]
xg1t2 = data_g1_t2()[,level_y(),level_x()]
xg2t1 = data_g2_t1()[,level_y(),level_x()]
xg2t2 = data_g2_t2()[,level_y(),level_x()]
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
#cat(file = stderr(), "create_df_for_histplot\n")
#string1 = paste0(input$trial1," vs ", input$trial2, "in group ", input$group1, "\n")
if (compare == "groups"){
if (input$group1 == input$group2){
cat("no output in case of same groups")
return()
}
if (trial==1){
x = xg1t1
y = xg2t1
}
if (trial == 2){
x = xg1t2
y = xg2t2
}
df <- data.frame(Gruppe=c(rep(input$group1, times=length(x)),
rep(input$group2, times=length(y))),
val=c(x, y))
df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# means for geomline
df_hline = data.frame(Gruppe = c(input$group1,input$group2), Means=c(mean(x), mean(y)))
}
# if comparing 2 trails of the same group
if (compare == "trials"){
if (group==1){
x = xg1t1
y = xg1t2
}
if (group == 2){
x = xg2t1
y = xg2t2
}
df <- data.frame(Gruppe=c(rep(g_trials()[as.numeric(input$trial1)], times=length(x)),
rep(g_trials()[as.numeric(input$trial2)], times=length(y))),
val=c(x, y))
df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# means for geomline
df_hline = data.frame(Gruppe = c(g_trials()[as.numeric(input$trial1)],
g_trials()[as.numeric(input$trial2)]),
Means=c(mean(xg1t1), mean(xg1t2)))
}
ggplot(df, aes(num, val, fill=Gruppe)) +
geom_bar(stat="identity") +
facet_wrap(~Gruppe) +
geom_hline(data = df_hline, aes(yintercept = Means))
}
create_df_for_histplot2 <- function(compare = "groups",
group = 1, trial = 1){
x = curdata()$data1[,level_y(),level_x()]
y = curdata()$data2[,level_y(),level_x()]
region_x = g_regions()[level_x()]
region_y = g_regions()[level_y()]
#string1 = paste0(input$trial1," vs ", input$trial2, "in group ", input$group1, "\n")
cat(file = stderr(), paste0("\n create_df_for_histplot2 with time \n"))
cat(file = stderr(), paste0("curdata%data1 with dim(x)=", dim(x),"\n"))
cat(file = stderr(), paste0("curdata%data2 with dim(y)=", dim(y),"\n"))
df <- data.frame(Gruppe=c(rep(input$group1, times=length(x)),
rep(input$group2, times=length(y))),
val=c(x, y))
df$num <- ave(df$val, df$Gruppe, FUN = seq_along)
# means for geomline
df_hline = data.frame(Gruppe = c(input$group1,input$group2), Means=c(mean(x), mean(y)))
ggplot(df, aes(num, val, fill=Gruppe)) +
geom_bar(stat="identity") +
facet_wrap(~Gruppe) +
geom_hline(data = df_hline, aes(yintercept = Means))
}
}
)
}
append_correlation_row <- function(x1 = NULL, b1 = NULL, x2 = NULL, b2 = NULL,
t = "not known",
method = "pearson", reg_name = "no_reg_name",
g1 = "not known", g2 = "not known",
df = NULL, description = "no desc.") {
m1 = cor.test(x1,b1, method = method)
m2 = cor.test(x2,b2, method = method)
#cat(file = stderr(), m1$estimate)
#cat(file = stderr(), m2$estimate)
r_ind = comparing_independent_rs(m1$estimate, m2$estimate, length(x1),length(x2))
df2 <- data.frame(regname = reg_name,
cor_method = method,
trial = t,
group1 = g1,
r1 = m1$estimate,
p1 = m1$p.value,
t1 = m1$statistic,
df1 = m1$parameter,
CI1_l = m1$conf.int[1],
CI1_h = m1$conf.int[2],
group2 = g2,
r2 = m2$estimate,
p2 = m2$p.value,
t2 = m2$statistic,
df2 = m2$parameter,
CI2_l = m2$conf.int[1],
CI2_h = m2$conf.int[2],
z_dif = r_ind[1],
p_dif = r_ind[2],
descri = description,
stringsAsFactors = FALSE
)
if (is.null(df)){
return(df2)
# df <- create_empty_df_for_correlation(num_groups = 2)
}
df_new <- rbind(df, df2)
return(df_new)
}
append_correlation_row_trials <- function(x1 = NULL, b1 = NULL, x2 = NULL,
method = "pearson", g = "not known", reg_name = "no_reg_name",
t1 = "not known", t2 = "not known",
df = NULL, description = "no desc.") {
x = x1
y = b1
z = x2
mxy = cor.test(x,y, method = method)
mzy = cor.test(z,y, method = method)
mxz = cor.test(x,z, method = method)
#comparing_independent_rs <-function(rxy, rxz, rzy, n)
r_dep = comparing_dependent_rs(mxy$estimate, mxz$estimate, mzy$estimate, length(x))
df2 <- data.frame(regname = reg_name,
cor_method = method,
group = g,
trial1 = t1,
r1 = mxy$estimate,
p1 = mxy$p.value,
t1 = mxy$statistic,
df1 = mxy$parameter,
CI1_l = mxy$conf.int[1],
CI1_h = mxy$conf.int[2],
trial2 = t2,
r2 = mzy$estimate,
p2 = mzy$p.value,
t2 = mzy$statistic,
df2 = mzy$parameter,
CI2_l = mzy$conf.int[1],
CI2_h = mzy$conf.int[2],
t_dif = r_dep[1],
p_dif = r_dep[2],
descri = description,
stringsAsFactors = FALSE
)
if (is.null(df)){
return(df2)
# df <- create_empty_df_for_correlation(num_groups = 2)
}
df_new <- rbind(df, df2)
return(df_new)
}
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