R/Run_Logistic.R
In 1moein/listentodata: A package for Marketing Analytics
Defines functions
Run_Logistic
Documented in
Run_Logistic
#' Logistic Regression Analysis
#'
#' This function conducts a Logistic regression analysis and provides the results in a pdf file.
#' It can also perform predictions on a new csv data file and provide the results in a csv file.
#'
#' @param mydata csv data file
#' @param myformula formula for the glm() model
#' @param prediction_data csv data file with the same set of variable names as those in the mydata data set; intended for prediction. The da
#' The default value set at 0 tells the function that no prediction data set has been provided.
#' @param resizepaper How much larger should the pdf paper size be to fit everything?
#'
#' @export
#'
#' @examples
#' \dontrun{
#' # This is the sample code to be copied and used in a new R Script:
#' library(listentodata)
#' clear_console()
#' mydata = load_csv_data()
#' head(mydata)
#' str(mydata)
#' myformula = "visited ~ ."
#' resizepaper = 1
#' Run_Logistic(mydata, myformula, resizepaper)
#' # If you have data for prediction:
#' prediction_data = load_csv_data()
#' Run_Logistic(mydata, myformula, prediction_data, resizepaper)
#' }
Run_Logistic = function(mydata,myformula, prediction_data=0, resizepaper = 1) {
# Companies constantly send out promotional emails to their customers hoping
# to get them to click a link within these emails and visit their websites.
# Such visits can engage customers with the company's website and potentially
# lead to online purchases.
#
# In this example, we analyze some characteristics of promotional emails that
# have been sent to 1000 customers. We will determine what factors increase
# the probability that customers might click the link inside these promotional
# emails to visit the company's website.
#
# The outcome of interest in this example is binary and can be coded as either
# 0 or 1; there are two possibilities: the email recipients either visit the
# website or not.
# # For testing purposes
# mydata = read.csv("T:\\MarketingAnalytics\\marketing_analytics\\Data for regression\\emailclickdata.csv")
# myformula = "visited ~ ."
# prediction_data = read.csv("T:\\MarketingAnalytics\\marketing_analytics\\Data for regression\\newemails.csv")
# if (datafortraining<=0.9 & datafortraining>=0.2) {
# set.seed(1378)
# index <- sample(1:nrow(mydata),round(datafortraining*nrow(mydata)))
# d <- mydata[index,]
# df_test <- d[-index,]
# } else {
# stop("The value of datafortraining represents \n the percentage of the data you will use for\n training your model. It must be between 0.2 and 0.9.\n Analysis was aborted; Please chage that value and try again!\n We suggest using 0.75")
# }
d = mydata
ds = summary(d)
larger = resizepaper
# showable = dim(d)[2]
# if (showable>14){
# showable = 14
# }
# dsample = d[1:10,1:showable]
options(scipen=999) # to prevent the use of scientific notation in results
# # Create a theme for formatting our tables
# if (!require(gridExtra)) install.packages("gridExtra")
# library(gridExtra)
mytablecolors = c("#e5f5e0","#fff7dc")
mytheme = gridExtra::ttheme_minimal(
core=list(bg_params = list(fill = mytablecolors, col=NA),
fg_params=list(fontface=3)),
colhead=list(fg_params=list(col="navyblue", fontface=4L)),
rowhead=list(fg_params=list(col="black", fontface=3L)))
# (Optional) In the above theme, we can use fill = hsv(0.61,seq(0.1,1,length.out = 25), 0.99,0.6) to paint the table with shades of blue
# Show the segment sizes table with the new formatting
fullcols = dim(ds)[2]
firsthalf = ceiling(fullcols/2)
secondhalf = firsthalf + 1
# gridExtra::grid.arrange(top="\n\n\n\n\n\n View the first 10 rows of the data set \n a max. of 14 columns can be shown here.", gridExtra::tableGrob(dsample, theme=mytheme))
# gridExtra::grid.arrange(top="\n\n\n\n\n\n Summary Statistics for the variables \n Page 1 of 2", gridExtra::tableGrob(ds[,1:firsthalf], theme=mytheme))
# gridExtra::grid.arrange(top="\n\n\n\n\n\n Summary Statistics for the variables \n Page 2 of 2", gridExtra::tableGrob(ds[,(secondhalf:fullcols)], theme=mytheme))
# correlations
nums <- unlist(lapply(d, is.numeric))
numcols <- d[ , nums]
# graphics::pairs(numcols, lower.panel = NULL, pch=16,cex=0.3)
#Visualize the correlations among numeric variables as numbers or pie charts
correlations = stats::cor(numcols)
# corrplot::corrplot(correlations, method="pie")
# corrplot::corrplot(correlations, method="number")
## Estimate the logistic regression model and show its summary
logit1 = stats::glm(myformula, data=d, family = "binomial")
# Display the results
# summary of results from the full model
rez1 = jtools::summ(logit1)
# rez1_details =t(data.frame(analysistype= "Logistic Regression",
# Dependent.Var=attributes(rez1)$dv,
# Observations=nrow(d),
# R_Squared=round(attributes(rez1)$rsq,2),
# Adj_R_Squared=round(attributes(rez1)$arsq,2),
# Missing_Values=attributes(rez1)$missing,
# F_Stat=round(attributes(rez1)$fstat,2),
# Model_P_Value = round(attributes(rez1)$modpval,2)))
#
#
#
# metrics = c("Analysis:", "Dependent Variable:", "Observations:","R-Squared:", "Adjusted R-Squared:","Missing Values:", "Model F-stat:", "Model P-Value:" )
# rez1_deets = data.frame(metrics, rez1_details)
# row.names(rez1_deets) = NULL
# names(rez1_deets) = c(" ", " ")
coeftable1 = round(rez1$coeftable,2)
attributes(coeftable1)$dimnames[[2]] = c("Estimate", "Std. error", "t-statistic", "p-value")
# # Comparative table of all models
# jtools::export_summs(m1, m2, m3, m4, m5, m6)
# ## If it is difficult to see the above table
# ## uncoment the next line of code and run it
# ## to create this table in the Plots area,
# ## and then click the window-arrow icon
# ## located on the right side of the broom
# ## in the the Plots tab
# # tab_model(m1, m2, m3, m4, m5, m6)
# Visual summary
suppressWarnings(jtools::plot_summs(logit1, scale = TRUE, plot.distributions = TRUE, inner_ci_level = .95))
# Find the best model using stepwise regression.
# What variables should remain and what variables should be deleted
# from the equation?\
# selected_model = stats::step(m6)
# utils::capture.output({
# selected_model = stats::step(logit1)
# })
selected_model = logit1
suppressWarnings(jtools::plot_summs(selected_model, scale = TRUE, plot.distributions = TRUE, inner_ci_level = .95))
## Predict the probability of visiting
if (typeof(prediction_data)!="double"){
df = prediction_data
clickprobability = stats::predict(logit1, df, type="response")
predictions = cbind(df,predicted_probability = round(clickprobability,2))
sortedpredictions = predictions[order(predictions$predicted_probability, decreasing = TRUE),]
suppressWarnings(res002 <- try(utils::write.csv(sortedpredictions, file ="! Results_Logistic_Predictions.csv", row.names = FALSE), silent = TRUE))
}
# ### titanic example
#
# # A different example: Survivors of the Titanic
# dd = read.csv(file="titanic.csv")
# head(dd)
# str(dd)
#
# # drop the names column
# dd = dd[ ,-3]
# head(dd)
#
# # Let us first do a model-free descriptive
# # analysis on some of the variables:
#
# ft = table(dd$Survived,dd$Sex)
# barplot(ft[2 ,], col="cyan", main = "Survival by Sex")
#
# ft = table(dd$Survived,dd$PassengerClass)
# barplot(ft[2 ,], col="maroon", main = "Survival by Passenger Class")
#
#
# # Estimate the logistic regression model and show its summary
#
# titanic_Model = glm(Survived ~ ., data = dd, family = "binomial")
# jtools::summ(titanic_Model)
#
# # What do you think about these results?
# # What other insights do these results provide?
################# save results in a pdf file ############
# Set up some parameters
# Decide what to call your file name; make sure to put .pdf at the end of the name
filename = "! Results_Logistic_Analysis.pdf"
# grDevices::pdf(filename, height=larger*8.5, width=larger*11)
suppressWarnings(res00 <- try(grDevices::pdf(filename, height=larger*8.5, width=larger*11), silent = TRUE))
# gridExtra::grid.arrange(top="\n\n\n\n\n\n View the first 10 rows of the data set \n a max. of 14 columns can be shown here.", gridExtra::tableGrob(dsample, theme=mytheme))
gridExtra::grid.arrange(top="\n\n\n\n\n\n Summary Statistics for the variables \n Page 1 of 2", gridExtra::tableGrob(ds[,1:firsthalf], theme=mytheme))
gridExtra::grid.arrange(top="\n\n\n\n\n\n Summary Statistics for the variables \n Page 2 of 2", gridExtra::tableGrob(ds[,(secondhalf:fullcols)], theme=mytheme))
# graphics::pairs(numcols, lower.panel = NULL, pch=16,cex=0.3)
correlations = stats::cor(numcols)
corrplot::corrplot(correlations, method="pie")
corrplot::corrplot(correlations, method="number")
gridExtra::grid.arrange(top="\n Logistic Regression Results: \n\n Coefficient Estiamtes", gridExtra::tableGrob(coeftable1, theme=mytheme))
# jtools::export_summs(m1, m2, m3, m4, m5, m6)
jtools::plot_summs(logit1, scale = TRUE, plot.distributions = TRUE, inner_ci_level = .95)
# # Display the best model
# jtools::summ(selected_model)
#
# # Display the best model visually
# jtools::plot_summs(selected_model, scale = TRUE, plot.distributions = TRUE, inner_ci_level = .95)
# Model Diagnostics for the Best model
# selected_model = stats::step(m6)
# utils::capture.output({
# selected_model = stats::step(m6)
# })
utils::capture.output({
grDevices::dev.off()
})
if (!is.null(res00)){
cat("\n ERROR:\n Logistic Rgression analysis was performed, but we were not able to\n save the results in \"! Results_Logistic_Analysis.pdf\"")
cat(" \n This is probably due to a PDF file with the same name being open.\n")
cat(" Make sure you close that file, and then run the previous line of code again.")
} else {
cat("\n Logistic Regression analysis has been performed on these data!\n")
cat("\n Results have been saved in a file named: \"! Results_Logistic_Analysis.pdf\"\n")
cat(" You can find this file in the same folder as your data files, which is here:\n ")
cat(as.character(getwd()))
cat(" \n\n ")
cat(" If you see any warnings below, simply disregard them.\n\n ")
}
if (typeof(prediction_data)!="double"){
if (!is.null(res002)){
cat("\n ERROR:\n Prediction for the new data set was also performed,\n but we were not able to save the results in:\n \"! Results_Logistic_Predictions.csv\"")
cat(" \n This is probably due to a CSV file with the same name being open.\n")
cat(" Make sure you close that file, and then run the previous line of code again.")
} else {
cat("\nPrediction for the new data set has been performed!\n")
cat("\n Results have been saved in a file named: \"! Results_Logistic_Predictions.csv\"\n")
cat(" You can find this file in the same folder as your data files, which is here:\n ")
cat(as.character(getwd()))
cat(" \n\n ")
cat(" If you see any warnings below, simply disregard them.\n\n ")
}
}
}
1moein/listentodata documentation built on Nov. 14, 2024, 5:35 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions Run_Logistic
Documented in Run_Logistic
#' Logistic Regression Analysis
#'
#' This function conducts a Logistic regression analysis and provides the results in a pdf file.
#' It can also perform predictions on a new csv data file and provide the results in a csv file.
#'
#' @param mydata csv data file
#' @param myformula formula for the glm() model
#' @param prediction_data csv data file with the same set of variable names as those in the mydata data set; intended for prediction. The da
#' The default value set at 0 tells the function that no prediction data set has been provided.
#' @param resizepaper How much larger should the pdf paper size be to fit everything?
#'
#' @export
#'
#' @examples
#' \dontrun{
#' # This is the sample code to be copied and used in a new R Script:
#' library(listentodata)
#' clear_console()
#' mydata = load_csv_data()
#' head(mydata)
#' str(mydata)
#' myformula = "visited ~ ."
#' resizepaper = 1
#' Run_Logistic(mydata, myformula, resizepaper)
#' # If you have data for prediction:
#' prediction_data = load_csv_data()
#' Run_Logistic(mydata, myformula, prediction_data, resizepaper)
#' }
Run_Logistic = function(mydata,myformula, prediction_data=0, resizepaper = 1) {
# Companies constantly send out promotional emails to their customers hoping
# to get them to click a link within these emails and visit their websites.
# Such visits can engage customers with the company's website and potentially
# lead to online purchases.
#
# In this example, we analyze some characteristics of promotional emails that
# have been sent to 1000 customers. We will determine what factors increase
# the probability that customers might click the link inside these promotional
# emails to visit the company's website.
#
# The outcome of interest in this example is binary and can be coded as either
# 0 or 1; there are two possibilities: the email recipients either visit the
# website or not.
# # For testing purposes
# mydata = read.csv("T:\\MarketingAnalytics\\marketing_analytics\\Data for regression\\emailclickdata.csv")
# myformula = "visited ~ ."
# prediction_data = read.csv("T:\\MarketingAnalytics\\marketing_analytics\\Data for regression\\newemails.csv")
# if (datafortraining<=0.9 & datafortraining>=0.2) {
# set.seed(1378)
# index <- sample(1:nrow(mydata),round(datafortraining*nrow(mydata)))
# d <- mydata[index,]
# df_test <- d[-index,]
# } else {
# stop("The value of datafortraining represents \n the percentage of the data you will use for\n training your model. It must be between 0.2 and 0.9.\n Analysis was aborted; Please chage that value and try again!\n We suggest using 0.75")
# }
d = mydata
ds = summary(d)
larger = resizepaper
# showable = dim(d)[2]
# if (showable>14){
# showable = 14
# }
# dsample = d[1:10,1:showable]
options(scipen=999) # to prevent the use of scientific notation in results
# # Create a theme for formatting our tables
# if (!require(gridExtra)) install.packages("gridExtra")
# library(gridExtra)
mytablecolors = c("#e5f5e0","#fff7dc")
mytheme = gridExtra::ttheme_minimal(
core=list(bg_params = list(fill = mytablecolors, col=NA),
fg_params=list(fontface=3)),
colhead=list(fg_params=list(col="navyblue", fontface=4L)),
rowhead=list(fg_params=list(col="black", fontface=3L)))
# (Optional) In the above theme, we can use fill = hsv(0.61,seq(0.1,1,length.out = 25), 0.99,0.6) to paint the table with shades of blue
# Show the segment sizes table with the new formatting
fullcols = dim(ds)[2]
firsthalf = ceiling(fullcols/2)
secondhalf = firsthalf + 1
# gridExtra::grid.arrange(top="\n\n\n\n\n\n View the first 10 rows of the data set \n a max. of 14 columns can be shown here.", gridExtra::tableGrob(dsample, theme=mytheme))
# gridExtra::grid.arrange(top="\n\n\n\n\n\n Summary Statistics for the variables \n Page 1 of 2", gridExtra::tableGrob(ds[,1:firsthalf], theme=mytheme))
# gridExtra::grid.arrange(top="\n\n\n\n\n\n Summary Statistics for the variables \n Page 2 of 2", gridExtra::tableGrob(ds[,(secondhalf:fullcols)], theme=mytheme))
# correlations
nums <- unlist(lapply(d, is.numeric))
numcols <- d[ , nums]
# graphics::pairs(numcols, lower.panel = NULL, pch=16,cex=0.3)
#Visualize the correlations among numeric variables as numbers or pie charts
correlations = stats::cor(numcols)
# corrplot::corrplot(correlations, method="pie")
# corrplot::corrplot(correlations, method="number")
## Estimate the logistic regression model and show its summary
logit1 = stats::glm(myformula, data=d, family = "binomial")
# Display the results
# summary of results from the full model
rez1 = jtools::summ(logit1)
# rez1_details =t(data.frame(analysistype= "Logistic Regression",
# Dependent.Var=attributes(rez1)$dv,
# Observations=nrow(d),
# R_Squared=round(attributes(rez1)$rsq,2),
# Adj_R_Squared=round(attributes(rez1)$arsq,2),
# Missing_Values=attributes(rez1)$missing,
# F_Stat=round(attributes(rez1)$fstat,2),
# Model_P_Value = round(attributes(rez1)$modpval,2)))
#
#
#
# metrics = c("Analysis:", "Dependent Variable:", "Observations:","R-Squared:", "Adjusted R-Squared:","Missing Values:", "Model F-stat:", "Model P-Value:" )
# rez1_deets = data.frame(metrics, rez1_details)
# row.names(rez1_deets) = NULL
# names(rez1_deets) = c(" ", " ")
coeftable1 = round(rez1$coeftable,2)
attributes(coeftable1)$dimnames[[2]] = c("Estimate", "Std. error", "t-statistic", "p-value")
# # Comparative table of all models
# jtools::export_summs(m1, m2, m3, m4, m5, m6)
# ## If it is difficult to see the above table
# ## uncoment the next line of code and run it
# ## to create this table in the Plots area,
# ## and then click the window-arrow icon
# ## located on the right side of the broom
# ## in the the Plots tab
# # tab_model(m1, m2, m3, m4, m5, m6)
# Visual summary
suppressWarnings(jtools::plot_summs(logit1, scale = TRUE, plot.distributions = TRUE, inner_ci_level = .95))
# Find the best model using stepwise regression.
# What variables should remain and what variables should be deleted
# from the equation?\
# selected_model = stats::step(m6)
# utils::capture.output({
# selected_model = stats::step(logit1)
# })
selected_model = logit1
suppressWarnings(jtools::plot_summs(selected_model, scale = TRUE, plot.distributions = TRUE, inner_ci_level = .95))
## Predict the probability of visiting
if (typeof(prediction_data)!="double"){
df = prediction_data
clickprobability = stats::predict(logit1, df, type="response")
predictions = cbind(df,predicted_probability = round(clickprobability,2))
sortedpredictions = predictions[order(predictions$predicted_probability, decreasing = TRUE),]
suppressWarnings(res002 <- try(utils::write.csv(sortedpredictions, file ="! Results_Logistic_Predictions.csv", row.names = FALSE), silent = TRUE))
}
# ### titanic example
#
# # A different example: Survivors of the Titanic
# dd = read.csv(file="titanic.csv")
# head(dd)
# str(dd)
#
# # drop the names column
# dd = dd[ ,-3]
# head(dd)
#
# # Let us first do a model-free descriptive
# # analysis on some of the variables:
#
# ft = table(dd$Survived,dd$Sex)
# barplot(ft[2 ,], col="cyan", main = "Survival by Sex")
#
# ft = table(dd$Survived,dd$PassengerClass)
# barplot(ft[2 ,], col="maroon", main = "Survival by Passenger Class")
#
#
# # Estimate the logistic regression model and show its summary
#
# titanic_Model = glm(Survived ~ ., data = dd, family = "binomial")
# jtools::summ(titanic_Model)
#
# # What do you think about these results?
# # What other insights do these results provide?
################# save results in a pdf file ############
# Set up some parameters
# Decide what to call your file name; make sure to put .pdf at the end of the name
filename = "! Results_Logistic_Analysis.pdf"
# grDevices::pdf(filename, height=larger*8.5, width=larger*11)
suppressWarnings(res00 <- try(grDevices::pdf(filename, height=larger*8.5, width=larger*11), silent = TRUE))
# gridExtra::grid.arrange(top="\n\n\n\n\n\n View the first 10 rows of the data set \n a max. of 14 columns can be shown here.", gridExtra::tableGrob(dsample, theme=mytheme))
gridExtra::grid.arrange(top="\n\n\n\n\n\n Summary Statistics for the variables \n Page 1 of 2", gridExtra::tableGrob(ds[,1:firsthalf], theme=mytheme))
gridExtra::grid.arrange(top="\n\n\n\n\n\n Summary Statistics for the variables \n Page 2 of 2", gridExtra::tableGrob(ds[,(secondhalf:fullcols)], theme=mytheme))
# graphics::pairs(numcols, lower.panel = NULL, pch=16,cex=0.3)
correlations = stats::cor(numcols)
corrplot::corrplot(correlations, method="pie")
corrplot::corrplot(correlations, method="number")
gridExtra::grid.arrange(top="\n Logistic Regression Results: \n\n Coefficient Estiamtes", gridExtra::tableGrob(coeftable1, theme=mytheme))
# jtools::export_summs(m1, m2, m3, m4, m5, m6)
jtools::plot_summs(logit1, scale = TRUE, plot.distributions = TRUE, inner_ci_level = .95)
# # Display the best model
# jtools::summ(selected_model)
#
# # Display the best model visually
# jtools::plot_summs(selected_model, scale = TRUE, plot.distributions = TRUE, inner_ci_level = .95)
# Model Diagnostics for the Best model
# selected_model = stats::step(m6)
# utils::capture.output({
# selected_model = stats::step(m6)
# })
utils::capture.output({
grDevices::dev.off()
})
if (!is.null(res00)){
cat("\n ERROR:\n Logistic Rgression analysis was performed, but we were not able to\n save the results in \"! Results_Logistic_Analysis.pdf\"")
cat(" \n This is probably due to a PDF file with the same name being open.\n")
cat(" Make sure you close that file, and then run the previous line of code again.")
} else {
cat("\n Logistic Regression analysis has been performed on these data!\n")
cat("\n Results have been saved in a file named: \"! Results_Logistic_Analysis.pdf\"\n")
cat(" You can find this file in the same folder as your data files, which is here:\n ")
cat(as.character(getwd()))
cat(" \n\n ")
cat(" If you see any warnings below, simply disregard them.\n\n ")
}
if (typeof(prediction_data)!="double"){
if (!is.null(res002)){
cat("\n ERROR:\n Prediction for the new data set was also performed,\n but we were not able to save the results in:\n \"! Results_Logistic_Predictions.csv\"")
cat(" \n This is probably due to a CSV file with the same name being open.\n")
cat(" Make sure you close that file, and then run the previous line of code again.")
} else {
cat("\nPrediction for the new data set has been performed!\n")
cat("\n Results have been saved in a file named: \"! Results_Logistic_Predictions.csv\"\n")
cat(" You can find this file in the same folder as your data files, which is here:\n ")
cat(as.character(getwd()))
cat(" \n\n ")
cat(" If you see any warnings below, simply disregard them.\n\n ")
}
}
}
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