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inst/shiny-examples/myapp/app.R
In MLRShiny2: Interactive Application for Working with Multiple Linear Regression
library(shiny)
library(shinyAce)
library(dplyr)
library(psych)
library(QuantPsyc)
library(forecast)
library(corrgram)
# Define UI for application that draws a histogram
ui <- fluidPage(
h6(" Authored by:", tags$img(src ="K.JPG", height=100, width=100)),
verbatimTextOutput("preface"),
tags$img(src = "T.png"),
br(),
br(),
br(),
sliderInput("Choice", label = "Choose 1 for reading from the Editor else Choose 0 for reading from the uploaded file",
min = 0, max = 1, value = 1, step = 1),
fileInput("file","Upload the *.csv file"),
helpText("Copy paste data with variable names from Excel"),
aceEditor("text", value=
"q1 q2 q3 q4 q5 q6 q7 q8 q9 q10
2 2 2 2 2 2 2 2 2 2
3 3 4 2 2 3 3 4 3 3
3 3 3 3 3 3 3 3 3 2
3 1 3 3 3 3 3 2 3 3
3 3 3 3 3 3 3 3 3 3
4 3 3 2 3 3 4 4 3 4
2 3 4 3 3 3 4 5 5 4
4 4 4 4 3 3 4 4 4 4
3 3 4 3 3 3 4 4 3 3
3 3 4 3 3 3 4 4 4 4
1 2 4 1 3 3 3 3 2 2
4 4 4 3 3 3 3 3 4 4
5 5 4 5 3 4 4 3 2 2
3 3 5 4 3 4 4 4 4 4
5 5 5 3 3 4 5 5 5 5
3 4 5 2 3 3 5 5 5 5
1 1 1 4 4 4 5 5 5 4
1 1 2 3 4 2 3 3 2 2
2 4 2 3 4 2 5 4 4 4
5 4 2 4 4 4 5 4 4 3
4 4 3 3 4 4 5 4 5 4
3 1 3 4 4 3 3 3 3 3
4 4 3 4 4 4 3 4 4 4
3 3 3 4 4 4 4 4 4 4
4 4 3 3 4 3 3 3 2 3
3 5 3 4 4 4 4 3 4 4
4 4 3 4 4 4 4 5 5 5
3 3 3 4 4 4 5 5 5 5
2 3 3 4 4 4 4 4 4 4
3 3 4 4 4 3 4 4 3 4
5 5 4 4 4 4 4 5 5 4
5 3 4 4 4 4 5 5 5 5
4 4 4 4 4 4 4 4 4 4
5 5 4 3 4 4 4 5 5 4
4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4
5 4 4 3 4 4 4 4 4 4
3 3 4 4 4 4 4 4 4 3
2 3 4 3 4 4 4 4 4 4
5 3 4 3 4 4 5 5 5 5
5 5 4 4 4 3 5 5 4 4
4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 3 4 4 4 4
4 4 4 4 4 4 4 4 4 4
3 4 4 4 4 4 4 4 4 4
3 3 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4
3 4 4 4 4 4 4 4 4 4
4 3 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4
3 4 4 4 4 4 5 5 5 4
4 4 4 4 4 4 3 4 4 4
5 4 4 4 4 4 4 5 5 5
2 3 4 4 4 4 5 5 5 4
3 3 4 5 4 3 4 4 4 4
4 4 4 4 4 5 4 4 4 4
4 3 4 4 4 4 4 4 4 4
2 2 4 2 4 4 3 3 3 4
4 4 4 4 4 5 4 4 4 4
3 3 4 4 4 4 5 4 4 4
4 4 4 4 4 4 4 4 4 4
5 5 4 3 4 3 3 5 4 5
3 4 4 3 4 3 4 4 4 4
2 5 5 4 4 5 3 5 5 5
5 5 5 4 4 4 4 4 4 4
3 5 5 5 4 4 5 5 5 4
4 5 5 4 4 3 4 4 4 3
5 5 5 4 4 4 5 5 5 5
4 5 5 4 4 4 4 4 2 4
5 5 5 5 4 5 5 5 5 5
4 4 5 4 4 4 4 4 4 4
5 4 5 4 4 4 5 5 5 4
3 3 5 5 4 4 5 5 5 4
5 5 5 4 4 4 5 5 4 5
5 5 5 4 4 4 4 4 3 4
5 5 5 4 4 4 5 5 4 5
5 5 5 3 4 5 4 5 5 4
4 5 5 4 4 4 4 4 4 4
3 5 5 4 4 4 5 5 5 4
5 5 5 4 4 4 5 4 4 5
1 1 2 4 5 4 4 4 4 4
5 4 2 4 5 4 5 5 5 5
5 5 2 5 5 5 5 5 4 5
5 5 3 5 5 5 5 5 5 5
5 5 3 5 5 5 5 5 5 5
4 4 3 5 5 5 5 5 5 5
3 4 3 4 5 5 4 4 4 3
1 1 3 5 5 5 5 5 4 4
4 4 3 5 5 5 5 5 4 4
3 3 3 5 5 5 5 5 5 5
4 5 3 5 5 5 5 5 5 4
5 4 4 4 5 4 5 4 3 4
1 1 4 5 5 5 5 5 5 4
4 3 4 4 5 2 4 4 3 4
3 4 4 5 5 5 5 5 5 5
3 3 4 5 5 4 4 4 5 5
5 5 4 4 5 4 5 5 5 5
5 4 4 5 5 5 2 5 5 3
4 4 4 4 5 5 4 5 5 5
4 3 4 4 5 5 4 5 5 4
4 4 4 5 5 5 5 5 5 4
4 4 4 4 5 5 4 5 4 5
4 4 4 5 5 4 4 4 4 4
5 4 4 5 5 4 5 5 5 4
5 1 4 5 5 5 5 5 4 5
4 4 4 5 5 5 5 5 4 5
4 4 4 4 5 5 3 3 4 4
4 3 4 4 5 4 4 4 3 3
4 4 4 5 5 4 5 5 5 4
3 4 4 4 5 4 4 3 3 4
5 5 4 4 5 5 5 5 5 5
4 4 4 5 5 4 5 5 5 5
4 4 4 5 5 5 4 4 3 4
4 4 4 4 5 4 5 5 5 5
4 4 4 4 5 5 5 5 5 5
4 4 4 4 5 4 4 4 4 4
5 5 4 4 5 4 5 5 5 5
4 4 4 4 5 5 4 4 3 3
3 4 4 4 5 5 4 4 4 4
5 5 4 5 5 5 4 5 5 5
4 2 4 4 5 3 4 5 5 4
4 5 4 4 5 4 4 5 5 4
4 5 4 4 5 5 4 4 4 4
4 4 4 5 5 5 5 5 5 5
4 4 4 5 5 5 5 5 4 5
3 3 4 5 5 5 4 4 4 3
4 4 4 5 5 5 4 5 5 4
5 4 4 4 5 5 4 5 5 4
4 4 4 4 5 5 5 5 5 5
4 4 4 5 5 5 4 5 5 4
3 3 4 5 5 5 5 5 5 5
5 4 4 5 5 4 5 5 5 5
4 4 4 5 5 4 4 5 4 5
4 4 4 5 5 5 4 5 5 5
4 4 4 4 5 5 4 4 4 4
4 4 4 5 5 5 5 5 5 5
5 5 4 5 5 5 5 5 5 5
4 4 4 5 5 5 5 5 5 5
4 4 4 4 5 4 4 5 4 5
4 4 4 5 5 5 5 5 5 5
4 4 4 5 5 5 5 5 5 5
4 4 4 5 5 4 5 5 5 5
4 4 4 5 5 5 5 4 5 4
4 3 5 4 5 5 5 5 4 5
4 5 5 5 5 4 5 5 5 4
5 5 5 5 5 5 5 5 5 5
5 2 5 5 5 3 4 3 3 3
4 4 5 5 5 5 5 5 5 5
3 5 5 4 5 4 5 5 5 5
4 4 5 3 5 4 5 5 5 5
5 4 5 5 5 5 5 5 5 5
4 4 5 5 5 5 4 4 4 4
5 5 5 5 5 5 4 5 5 5
5 4 5 4 5 3 5 5 4 5
2 4 5 4 5 5 4 4 5 5
5 5 5 5 5 5 5 5 5 4
2 5 5 4 5 5 5 5 5 5
5 5 5 4 5 4 5 5 5 4
5 5 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 4 3 4 3 4
4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 4 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 4 5 5 5 3 5 5 5 5
5 5 5 4 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 4 5 4 5 5 5 5
5 5 5 5 5 4 5 5 5 5
5 5 5 5 5 4 5 5 4 4
5 5 5 5 5 5 5 5 5 4
5 5 5 5 5 5 5 5 5 5
4 4 5 5 5 4 4 5 5 5
4 4 5 4 5 4 4 4 4 4
5 5 5 5 5 5 3 4 4 4
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
3 5 5 5 5 5 5 5 5 5
5 5 5 5 5 4 2 4 4 4
5 5 5 5 5 5 5 5 5 4
5 5 5 5 5 5 5 5 5 5
4 4 5 3 5 4 4 4 4 4
5 5 5 5 5 5 5 5 5 5
3 3 5 5 5 5 2 5 5 5
5 5 5 5 5 5 5 5 5 5
4 5 5 4 5 4 3 5 2 5
5 5 5 5 5 5 5 5 5 5
5 4 5 5 5 5 5 5 5 5
5 5 5 4 5 4 5 5 5 5
4 4 5 4 5 4 5 5 5 5
5 5 5 5 5 4 5 5 5 5
5 5 5 4 5 5 4 4 5 4
5 5 5 5 5 5 2 5 5 5
3 3 5 5 5 4 4 4 4 4
4 4 5 5 5 2 2 5 1 5
5 5 5 5 5 5 5 5 5 5
5 5 5 4 5 5 4 4 4 5
5 5 5 5 5 5 5 5 5 5
3 3 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 4 5 4 4 4 4 4
5 5 5 5 5 4 5 5 5 5
4 4 5 5 5 5 5 5 2 5
4 4 5 5 5 4 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 4 5 5 5 5 5 5 5 5
5 4 5 5 5 5 5 5 2 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 4 5 4 5 5 5 5 4 4
5 5 5 4 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 4 4 4 4
5 4 5 5 5 5 5 5 5 4
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 3 5
4 4 5 5 5 5 5 5 5 5
4 5 5 5 5 4 5 5 5 4
4 5 5 4 5 5 4 4 4 4
5 5 5 5 5 5 4 4 5 5
3 5 5 5 5 5 5 5 5 5
4 4 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 2 5 5 5 5 5 5 5 5
5 4 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 4 5 5 5 5 4 5 4 4
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5 5
4 4 5 5 5 5 5 5 5 5
4 5 5 5 5 4 5 5 4 5
5 5 5 5 5 4 5 5 5 5
4 4 5 5 5 5 4 5 5 5
4 5 5 5 5 4 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 4 4 5 5
5 5 5 4 5 5 5 5 2 2
3 5 5 5 5 5 4 5 5 5
4 5 5 5 5 5 4 4 5 5
", mode="r", theme="white"),
plotOutput("Pout"),
textAreaInput("model", "Regression Model Specification(Dependent var ~ Independent variables).You can put simply . on the Right hand side in case all the variables in the dataset except dependent variable are to be considered as independent variables", value = 'q10 ~ q1 + q4'),
sliderInput("train_num", label = "Enter the proportion of training dataset:",
min = 0.6, max = 1, value = 0.6, step = 0.01),
verbatimTextOutput("printpaper1")
)
# Define server logic required to draw a histogram
server <- function(input, output) {
output$preface <-renderPrint({
cat(sprintf("\nDr. Kartikeya Bolar\n"))
cat(sprintf("\nAssociate Professor and Area Co-Chair\n"))
cat(sprintf("\nOperations and Information Science\n"))
cat(sprintf("\nT A Pai Management Institute\n"))
})
output$Pout <-renderPlot({
if(input$Choice > 0)
{
get.text <- reactive({input$text})
if(is.null(get.text())){return ()}
dataframe<- read.table(text = get.text(),header = TRUE)
}
if(input$Choice < 1)
{file1 <- input$file
if(is.null(file1)){return()}
data= read.table(file=file1$datapath, sep=",", header = TRUE)
if(is.null(data())){return ()}
dataframe = data
}
corrgram(dataframe,order = TRUE,lower.panel = panel.shade,upper.panel = panel.pie,main ="Corrgram")
})
output$printpaper1 <-renderPrint({
# print(input$Var1)
modelmydata = input$model
if(input$Choice > 0)
{
get.text <- reactive({input$text})
if(is.null(get.text())){return ()}
dataframe<- read.table(text = get.text(),header = TRUE)
}
if(input$Choice < 1)
{file1 <- input$file
if(is.null(file1)){return()}
data= read.table(file=file1$datapath, sep=",", header = TRUE)
if(is.null(data())){return ()}
dataframe = data
}
prop = input$train_num
#index1 = grep(input$Var1,colnames(dataframe))
#print(index1)
set.seed(1)
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
mydatamodel= lm(modelmydata,data = data.frame(dataframet))
terms = mydatamodel$terms
temp = data.frame(attr(terms,"dataClasses"))
temp$variables = row.names(temp)
dependent = temp$variables[1]
options(scipen = 99999)
cat(sprintf("Data Analysis and Interpretation\n"))
sum = summary(mydatamodel)
d = data.frame( t(data.frame(sum$fstatistic)))
samplesizetrain = d$numdf + d$dendf + 1
samplesizevalid = nrow(dataframe) - samplesizetrain
a = sum$coefficients
b = data.frame(t(a))
b$X.Intercept.=NULL
a = data.frame(t(b))
a$Std..Error =NULL
a$IndVar =row.names(a)
betas = lm.beta(mydatamodel)
row.names(a) = 1:nrow(a)
a$StdEst = round(betas,2)
a$Estimate =NULL
a$t_stat= round(a$t.value,2)
a$t.value = NULL
a$P_value = round(a$Pr...t..,4)
a$Pr...t.. =NULL
a = a[order(a$StdEst,decreasing = TRUE),]
cat(sprintf("\n The sample size of the training data set is %g\n",samplesizetrain))
cat(sprintf("\n The sample size of the validation data set is %g\n",samplesizevalid))
cat(sprintf("\n The Dependent variable is %s",dependent))
cat(sprintf("\nThe independent variables are as follows :"))
print(a$IndVar)
cat(sprintf("\n 1. Goodness of Fit of the Model\n"))
cat(sprintf(" It is given by RSquare(Coefficient of Determination) which is %g\n",sum$r.squared))
rsqp = round(sum$r.squared *100,2)
cat(sprintf("It means %g independent variables together are able to explain %g percent of the changes in the dependent variable\n",d$numdf,rsqp))
cat(sprintf("\n 2. Hypothesis testing at 5 percent level of significance\n"))
cat(sprintf("\nResearcher's Hypothesis(Alternate): The independent variables influence(are related to) dependent variable\n"))
print(anova(mydatamodel))
cat(sprintf("\n To test for the overall existence/significance of the model(relationships) we need to check the F-statistic which is %g\n" , d$value))
cat(sprintf("\n After checking the F-statistic value we can conclude that the model is %s",ifelse(d$value > 4,"significant","insignificant")))
#cat(sprintf("\n To test for the relationship of independent variables with the dependent variable individually we need to check the individual t-statistic or p-value\n"))
cat(sprintf("\nSummary of the regression model with standardized estimates in decreasing\n"))
row.names(a)= 1:nrow(a)
print(a)
cat(sprintf("\n Summary of the regression model with unstandardized estimates\n"))
print(sum)
if(prop < 1)
{
cat(sprintf("\n 3. Accuracy(Predictive Power) of the Model\n"))
prediction = predict.lm(mydatamodel,newdata = dataframev)
terms = mydatamodel$terms
temp = data.frame(attr(terms,"dataClasses"))
temp$variables = row.names(temp)
dependent = temp$variables[1]
indexdependent= grep(dependent, colnames(dataframev))
cat(sprintf("\n The details of accuracy of the model in the validation data set are are as follows\n"))
acc = data.frame(accuracy(prediction,dataframev[,indexdependent[1]]))
psuedoRSquare = cor(dataframev[,indexdependent[1]],prediction)^2
print(acc)
cat(sprintf("\n The Psuedo RSquare indicated the accuracy of the model is %g",psuedoRSquare))
}
})
}
# Run the application
shinyApp(ui = ui, server = server)
Try the MLRShiny2 package in your browser
Any scripts or data that you put into this service are public.
MLRShiny2 documentation built on Oct. 2, 2019, 9:02 a.m.
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library(shiny)
library(shinyAce)
library(dplyr)
library(psych)
library(QuantPsyc)
library(forecast)
library(corrgram)
# Define UI for application that draws a histogram
ui <- fluidPage(
h6(" Authored by:", tags$img(src ="K.JPG", height=100, width=100)),
verbatimTextOutput("preface"),
tags$img(src = "T.png"),
br(),
br(),
br(),
sliderInput("Choice", label = "Choose 1 for reading from the Editor else Choose 0 for reading from the uploaded file",
min = 0, max = 1, value = 1, step = 1),
fileInput("file","Upload the *.csv file"),
helpText("Copy paste data with variable names from Excel"),
aceEditor("text", value=
"q1 q2 q3 q4 q5 q6 q7 q8 q9 q10
2 2 2 2 2 2 2 2 2 2
3 3 4 2 2 3 3 4 3 3
3 3 3 3 3 3 3 3 3 2
3 1 3 3 3 3 3 2 3 3
3 3 3 3 3 3 3 3 3 3
4 3 3 2 3 3 4 4 3 4
2 3 4 3 3 3 4 5 5 4
4 4 4 4 3 3 4 4 4 4
3 3 4 3 3 3 4 4 3 3
3 3 4 3 3 3 4 4 4 4
1 2 4 1 3 3 3 3 2 2
4 4 4 3 3 3 3 3 4 4
5 5 4 5 3 4 4 3 2 2
3 3 5 4 3 4 4 4 4 4
5 5 5 3 3 4 5 5 5 5
3 4 5 2 3 3 5 5 5 5
1 1 1 4 4 4 5 5 5 4
1 1 2 3 4 2 3 3 2 2
2 4 2 3 4 2 5 4 4 4
5 4 2 4 4 4 5 4 4 3
4 4 3 3 4 4 5 4 5 4
3 1 3 4 4 3 3 3 3 3
4 4 3 4 4 4 3 4 4 4
3 3 3 4 4 4 4 4 4 4
4 4 3 3 4 3 3 3 2 3
3 5 3 4 4 4 4 3 4 4
4 4 3 4 4 4 4 5 5 5
3 3 3 4 4 4 5 5 5 5
2 3 3 4 4 4 4 4 4 4
3 3 4 4 4 3 4 4 3 4
5 5 4 4 4 4 4 5 5 4
5 3 4 4 4 4 5 5 5 5
4 4 4 4 4 4 4 4 4 4
5 5 4 3 4 4 4 5 5 4
4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4
5 4 4 3 4 4 4 4 4 4
3 3 4 4 4 4 4 4 4 3
2 3 4 3 4 4 4 4 4 4
5 3 4 3 4 4 5 5 5 5
5 5 4 4 4 3 5 5 4 4
4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 3 4 4 4 4
4 4 4 4 4 4 4 4 4 4
3 4 4 4 4 4 4 4 4 4
3 3 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4
3 4 4 4 4 4 4 4 4 4
4 3 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4
3 4 4 4 4 4 5 5 5 4
4 4 4 4 4 4 3 4 4 4
5 4 4 4 4 4 4 5 5 5
2 3 4 4 4 4 5 5 5 4
3 3 4 5 4 3 4 4 4 4
4 4 4 4 4 5 4 4 4 4
4 3 4 4 4 4 4 4 4 4
2 2 4 2 4 4 3 3 3 4
4 4 4 4 4 5 4 4 4 4
3 3 4 4 4 4 5 4 4 4
4 4 4 4 4 4 4 4 4 4
5 5 4 3 4 3 3 5 4 5
3 4 4 3 4 3 4 4 4 4
2 5 5 4 4 5 3 5 5 5
5 5 5 4 4 4 4 4 4 4
3 5 5 5 4 4 5 5 5 4
4 5 5 4 4 3 4 4 4 3
5 5 5 4 4 4 5 5 5 5
4 5 5 4 4 4 4 4 2 4
5 5 5 5 4 5 5 5 5 5
4 4 5 4 4 4 4 4 4 4
5 4 5 4 4 4 5 5 5 4
3 3 5 5 4 4 5 5 5 4
5 5 5 4 4 4 5 5 4 5
5 5 5 4 4 4 4 4 3 4
5 5 5 4 4 4 5 5 4 5
5 5 5 3 4 5 4 5 5 4
4 5 5 4 4 4 4 4 4 4
3 5 5 4 4 4 5 5 5 4
5 5 5 4 4 4 5 4 4 5
1 1 2 4 5 4 4 4 4 4
5 4 2 4 5 4 5 5 5 5
5 5 2 5 5 5 5 5 4 5
5 5 3 5 5 5 5 5 5 5
5 5 3 5 5 5 5 5 5 5
4 4 3 5 5 5 5 5 5 5
3 4 3 4 5 5 4 4 4 3
1 1 3 5 5 5 5 5 4 4
4 4 3 5 5 5 5 5 4 4
3 3 3 5 5 5 5 5 5 5
4 5 3 5 5 5 5 5 5 4
5 4 4 4 5 4 5 4 3 4
1 1 4 5 5 5 5 5 5 4
4 3 4 4 5 2 4 4 3 4
3 4 4 5 5 5 5 5 5 5
3 3 4 5 5 4 4 4 5 5
5 5 4 4 5 4 5 5 5 5
5 4 4 5 5 5 2 5 5 3
4 4 4 4 5 5 4 5 5 5
4 3 4 4 5 5 4 5 5 4
4 4 4 5 5 5 5 5 5 4
4 4 4 4 5 5 4 5 4 5
4 4 4 5 5 4 4 4 4 4
5 4 4 5 5 4 5 5 5 4
5 1 4 5 5 5 5 5 4 5
4 4 4 5 5 5 5 5 4 5
4 4 4 4 5 5 3 3 4 4
4 3 4 4 5 4 4 4 3 3
4 4 4 5 5 4 5 5 5 4
3 4 4 4 5 4 4 3 3 4
5 5 4 4 5 5 5 5 5 5
4 4 4 5 5 4 5 5 5 5
4 4 4 5 5 5 4 4 3 4
4 4 4 4 5 4 5 5 5 5
4 4 4 4 5 5 5 5 5 5
4 4 4 4 5 4 4 4 4 4
5 5 4 4 5 4 5 5 5 5
4 4 4 4 5 5 4 4 3 3
3 4 4 4 5 5 4 4 4 4
5 5 4 5 5 5 4 5 5 5
4 2 4 4 5 3 4 5 5 4
4 5 4 4 5 4 4 5 5 4
4 5 4 4 5 5 4 4 4 4
4 4 4 5 5 5 5 5 5 5
4 4 4 5 5 5 5 5 4 5
3 3 4 5 5 5 4 4 4 3
4 4 4 5 5 5 4 5 5 4
5 4 4 4 5 5 4 5 5 4
4 4 4 4 5 5 5 5 5 5
4 4 4 5 5 5 4 5 5 4
3 3 4 5 5 5 5 5 5 5
5 4 4 5 5 4 5 5 5 5
4 4 4 5 5 4 4 5 4 5
4 4 4 5 5 5 4 5 5 5
4 4 4 4 5 5 4 4 4 4
4 4 4 5 5 5 5 5 5 5
5 5 4 5 5 5 5 5 5 5
4 4 4 5 5 5 5 5 5 5
4 4 4 4 5 4 4 5 4 5
4 4 4 5 5 5 5 5 5 5
4 4 4 5 5 5 5 5 5 5
4 4 4 5 5 4 5 5 5 5
4 4 4 5 5 5 5 4 5 4
4 3 5 4 5 5 5 5 4 5
4 5 5 5 5 4 5 5 5 4
5 5 5 5 5 5 5 5 5 5
5 2 5 5 5 3 4 3 3 3
4 4 5 5 5 5 5 5 5 5
3 5 5 4 5 4 5 5 5 5
4 4 5 3 5 4 5 5 5 5
5 4 5 5 5 5 5 5 5 5
4 4 5 5 5 5 4 4 4 4
5 5 5 5 5 5 4 5 5 5
5 4 5 4 5 3 5 5 4 5
2 4 5 4 5 5 4 4 5 5
5 5 5 5 5 5 5 5 5 4
2 5 5 4 5 5 5 5 5 5
5 5 5 4 5 4 5 5 5 4
5 5 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 4 3 4 3 4
4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 4 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 4 5 5 5 3 5 5 5 5
5 5 5 4 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 4 5 4 5 5 5 5
5 5 5 5 5 4 5 5 5 5
5 5 5 5 5 4 5 5 4 4
5 5 5 5 5 5 5 5 5 4
5 5 5 5 5 5 5 5 5 5
4 4 5 5 5 4 4 5 5 5
4 4 5 4 5 4 4 4 4 4
5 5 5 5 5 5 3 4 4 4
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
3 5 5 5 5 5 5 5 5 5
5 5 5 5 5 4 2 4 4 4
5 5 5 5 5 5 5 5 5 4
5 5 5 5 5 5 5 5 5 5
4 4 5 3 5 4 4 4 4 4
5 5 5 5 5 5 5 5 5 5
3 3 5 5 5 5 2 5 5 5
5 5 5 5 5 5 5 5 5 5
4 5 5 4 5 4 3 5 2 5
5 5 5 5 5 5 5 5 5 5
5 4 5 5 5 5 5 5 5 5
5 5 5 4 5 4 5 5 5 5
4 4 5 4 5 4 5 5 5 5
5 5 5 5 5 4 5 5 5 5
5 5 5 4 5 5 4 4 5 4
5 5 5 5 5 5 2 5 5 5
3 3 5 5 5 4 4 4 4 4
4 4 5 5 5 2 2 5 1 5
5 5 5 5 5 5 5 5 5 5
5 5 5 4 5 5 4 4 4 5
5 5 5 5 5 5 5 5 5 5
3 3 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 4 5 4 4 4 4 4
5 5 5 5 5 4 5 5 5 5
4 4 5 5 5 5 5 5 2 5
4 4 5 5 5 4 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 4 5 5 5 5 5 5 5 5
5 4 5 5 5 5 5 5 2 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 4 5 4 5 5 5 5 4 4
5 5 5 4 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 4 4 4 4
5 4 5 5 5 5 5 5 5 4
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 3 5
4 4 5 5 5 5 5 5 5 5
4 5 5 5 5 4 5 5 5 4
4 5 5 4 5 5 4 4 4 4
5 5 5 5 5 5 4 4 5 5
3 5 5 5 5 5 5 5 5 5
4 4 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 2 5 5 5 5 5 5 5 5
5 4 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 4 5 5 5 5 4 5 4 4
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5
4 5 5 5 5 5 5 5 5 5
4 4 5 5 5 5 5 5 5 5
4 5 5 5 5 4 5 5 4 5
5 5 5 5 5 4 5 5 5 5
4 4 5 5 5 5 4 5 5 5
4 5 5 5 5 4 5 5 5 5
5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 4 4 5 5
5 5 5 4 5 5 5 5 2 2
3 5 5 5 5 5 4 5 5 5
4 5 5 5 5 5 4 4 5 5
", mode="r", theme="white"),
plotOutput("Pout"),
textAreaInput("model", "Regression Model Specification(Dependent var ~ Independent variables).You can put simply . on the Right hand side in case all the variables in the dataset except dependent variable are to be considered as independent variables", value = 'q10 ~ q1 + q4'),
sliderInput("train_num", label = "Enter the proportion of training dataset:",
min = 0.6, max = 1, value = 0.6, step = 0.01),
verbatimTextOutput("printpaper1")
)
# Define server logic required to draw a histogram
server <- function(input, output) {
output$preface <-renderPrint({
cat(sprintf("\nDr. Kartikeya Bolar\n"))
cat(sprintf("\nAssociate Professor and Area Co-Chair\n"))
cat(sprintf("\nOperations and Information Science\n"))
cat(sprintf("\nT A Pai Management Institute\n"))
})
output$Pout <-renderPlot({
if(input$Choice > 0)
{
get.text <- reactive({input$text})
if(is.null(get.text())){return ()}
dataframe<- read.table(text = get.text(),header = TRUE)
}
if(input$Choice < 1)
{file1 <- input$file
if(is.null(file1)){return()}
data= read.table(file=file1$datapath, sep=",", header = TRUE)
if(is.null(data())){return ()}
dataframe = data
}
corrgram(dataframe,order = TRUE,lower.panel = panel.shade,upper.panel = panel.pie,main ="Corrgram")
})
output$printpaper1 <-renderPrint({
# print(input$Var1)
modelmydata = input$model
if(input$Choice > 0)
{
get.text <- reactive({input$text})
if(is.null(get.text())){return ()}
dataframe<- read.table(text = get.text(),header = TRUE)
}
if(input$Choice < 1)
{file1 <- input$file
if(is.null(file1)){return()}
data= read.table(file=file1$datapath, sep=",", header = TRUE)
if(is.null(data())){return ()}
dataframe = data
}
prop = input$train_num
#index1 = grep(input$Var1,colnames(dataframe))
#print(index1)
set.seed(1)
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
mydatamodel= lm(modelmydata,data = data.frame(dataframet))
terms = mydatamodel$terms
temp = data.frame(attr(terms,"dataClasses"))
temp$variables = row.names(temp)
dependent = temp$variables[1]
options(scipen = 99999)
cat(sprintf("Data Analysis and Interpretation\n"))
sum = summary(mydatamodel)
d = data.frame( t(data.frame(sum$fstatistic)))
samplesizetrain = d$numdf + d$dendf + 1
samplesizevalid = nrow(dataframe) - samplesizetrain
a = sum$coefficients
b = data.frame(t(a))
b$X.Intercept.=NULL
a = data.frame(t(b))
a$Std..Error =NULL
a$IndVar =row.names(a)
betas = lm.beta(mydatamodel)
row.names(a) = 1:nrow(a)
a$StdEst = round(betas,2)
a$Estimate =NULL
a$t_stat= round(a$t.value,2)
a$t.value = NULL
a$P_value = round(a$Pr...t..,4)
a$Pr...t.. =NULL
a = a[order(a$StdEst,decreasing = TRUE),]
cat(sprintf("\n The sample size of the training data set is %g\n",samplesizetrain))
cat(sprintf("\n The sample size of the validation data set is %g\n",samplesizevalid))
cat(sprintf("\n The Dependent variable is %s",dependent))
cat(sprintf("\nThe independent variables are as follows :"))
print(a$IndVar)
cat(sprintf("\n 1. Goodness of Fit of the Model\n"))
cat(sprintf(" It is given by RSquare(Coefficient of Determination) which is %g\n",sum$r.squared))
rsqp = round(sum$r.squared *100,2)
cat(sprintf("It means %g independent variables together are able to explain %g percent of the changes in the dependent variable\n",d$numdf,rsqp))
cat(sprintf("\n 2. Hypothesis testing at 5 percent level of significance\n"))
cat(sprintf("\nResearcher's Hypothesis(Alternate): The independent variables influence(are related to) dependent variable\n"))
print(anova(mydatamodel))
cat(sprintf("\n To test for the overall existence/significance of the model(relationships) we need to check the F-statistic which is %g\n" , d$value))
cat(sprintf("\n After checking the F-statistic value we can conclude that the model is %s",ifelse(d$value > 4,"significant","insignificant")))
#cat(sprintf("\n To test for the relationship of independent variables with the dependent variable individually we need to check the individual t-statistic or p-value\n"))
cat(sprintf("\nSummary of the regression model with standardized estimates in decreasing\n"))
row.names(a)= 1:nrow(a)
print(a)
cat(sprintf("\n Summary of the regression model with unstandardized estimates\n"))
print(sum)
if(prop < 1)
{
cat(sprintf("\n 3. Accuracy(Predictive Power) of the Model\n"))
prediction = predict.lm(mydatamodel,newdata = dataframev)
terms = mydatamodel$terms
temp = data.frame(attr(terms,"dataClasses"))
temp$variables = row.names(temp)
dependent = temp$variables[1]
indexdependent= grep(dependent, colnames(dataframev))
cat(sprintf("\n The details of accuracy of the model in the validation data set are are as follows\n"))
acc = data.frame(accuracy(prediction,dataframev[,indexdependent[1]]))
psuedoRSquare = cor(dataframev[,indexdependent[1]],prediction)^2
print(acc)
cat(sprintf("\n The Psuedo RSquare indicated the accuracy of the model is %g",psuedoRSquare))
}
})
}
# Run the application
shinyApp(ui = ui, server = server)
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