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Copy pathfunctions.R
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499 lines (439 loc) · 17.1 KB
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require(car)
require(tidyverse)
require(pander)
require(lindia)
RegVals <- function(y_varcol, x_varcol, output_type ="non-list"){
y <- y_varcol
x <- x_varcol
mydata <- data.frame(y=y_varcol, x=x_varcol)
lm_obj <- lm(y ~ x, data=mydata)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
b <- coefficients(lm_obj)
reg_plot<- ggplot(mydata, aes(x=x, y=y))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) (b[1]) + (b[2])*x, color="#A58AFF")
#gg_boxcox(lm_obj)
#fit <- gg_resfitted(lm_obj)
#qq <- gg_qqplot(lm_obj)
#pred <- gg_resX(lm_obj)
if(output_type == "list"){
#plot_all(list(gg_resX(lm_obj),reg_plot, fit, qq))
output<-list(RegressionValues = df, summary = sumry)
return(output)
} else{
#plot = plot_all(list(reg_plot, fit, qq, pred))
pander(df)
sumry
#return(plot)
}
}
QQplots <- function(lm_obj){
par(mfrow=c(1,3))
plot(lm_obj,which=1:2)
plot(lm_obj$residuals)
}
#3.25x36:12.39982
# RegVals <- function(y_varcol, x_varcol){
# y <- y_varcol
# x <- x_varcol
# lm_obj <- lm(y ~ x)
# sumry <- summary(lm_obj)
# SSE <- sum((y_varcol - lm_obj$fit)^2)
# SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
# SSTO <- sum((y_varcol - mean(y_varcol))^2)
# R2 <- SSR/SSTO
# r <- sqrt(R2)
# Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
# Value <- c(SSE, SSR, SSTO, R2,r)
# df <- data.frame(Item, Value)
#
#
# data <- data.frame(y, x)
# b <- coefficients(lm_obj)
# reg_plot<- ggplot(data, aes(x=x, y=y))+
# geom_point(fill="skyblue", color="lightblue")+
# labs(title= "Reg Plot", x="x-var", y="y-var")+
# theme_bw()+
# stat_function(fun = function(x) (b[1]) + (b[2])*x, color="#A58AFF")
#
# bc <- gg_boxcox(lm_obj)
#
#
#
# fit <- gg_resfitted(lm_obj)
# qq <- gg_qqplot(lm_obj)
# pred <- gg_resX(lm_obj)
#
# plots <- list(reg_plot, fit, qq, pred, bc)
#
# output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
# return(output)
# }
#T_var defaults to Y
RegValsT <- function(y_varcol, x_varcol, tranfrm=1, T_var=y){
if (tranfrm == "no" | tranfrm == 1) {
if (T_var == "x"){
lm_obj <- lm(y_varcol ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) (b[1]) + (b[2])*x, aes(color="Y′=X"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
} else {
lm_obj <- lm(y_varcol ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) (b[1]) + (b[2])*x, aes(color="Y′=Y"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
}
} else if (tranfrm == -2 | tranfrm == "oneoversq") {
if (T_var == "x"){
lm_obj <- lm(y_varcol ~ x_varcol^-2)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) ((b[1]) + (b[2])*x)^(-1/2), aes(color="Y′=Y−2=1/X^2"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
} else {
lm_obj <- lm(y_varcol^-2 ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) ((b[1]) + (b[2])*x)^(-1/2), aes(color="Y′=Y−2=1/Y2"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
}
} else if (tranfrm == -1 | tranfrm == "oneover") {
if (T_var == "x"){
lm_obj <- lm(y_varcol^-1 ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) ((b[1]) + (b[2])*x)^-1, aes(color="Y′=Y−1=1/Y"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
} else {
lm_obj <- lm(y_varcol^-1 ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) ((b[1]) + (b[2])*x)^-1, aes(color="Y′=Y−1=1/Y"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
}
} else if (tranfrm == 0 | tranfrm == "log") {
if (T_var == "x"){
lm_obj <- lm(log(y_varcol) ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) exp((b[1]) + (b[2])*x), aes(color="Y′=log(Y)"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
} else {
lm_obj <- lm(log(y_varcol) ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) exp((b[1]) + (b[2])*x), aes(color="Y′=log(Y)"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
}
} else if (tranfrm == 0.25 | tranfrm == "sqrtsqrt") {
if (T_var == "x"){
lm_obj <- lm(sqrt(sqrt(y_varcol)) ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) ((b[1]) + (b[2])*x)^4, aes(color="Y′=(√(√Y))"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
} else {
lm_obj <- lm(sqrt(sqrt(y_varcol)) ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) ((b[1]) + (b[2])*x)^4, aes(color="Y′=(√(√Y))"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
}
} else if (tranfrm == 0.5 | tranfrm == "sqrt") {
if (T_var == "x"){
lm_obj <- lm(sqrt(y_varcol) ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) ((b[1]) + (b[2])*x)^2, aes(color="Y′=(√Y)"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
} else {
lm_obj <- lm(sqrt(y_varcol) ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=(y_varcol)^2)+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) ((b[1]) + (b[2])*x)^2, aes(color="Y′=(√Y)"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
}
} else if (tranfrm == 2 | tranfrm == "sq") {
if (T_var == "x"){
#x <- names(x_varcol)
lm_obj <- lm(y_varcol ~ x_varcol^2)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) sqrt((b[1]) + (b[2])*x), aes(color="Y′=X^2"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
} else {
#x <- names(x_varcol)
lm_obj <- lm(y_varcol^2 ~ x_varcol)
sumry <- summary(lm_obj)
SSE <- sum((y_varcol - lm_obj$fit)^2)
SSR <- sum((lm_obj$fit - mean(y_varcol))^2)
SSTO <- sum((y_varcol - mean(y_varcol))^2)
R2 <- SSR/SSTO
r <- sqrt(R2)
Item <- c("SSE", "SSR", "SSTO", "R^2", "r")
Value <- c(SSE, SSR, SSTO, R2,r)
df <- data.frame(Item, Value)
data <- data.frame(y_varcol, x_varcol)
b <- coefficients(lm_obj)
reg_plot<- ggplot(data, aes(x=x_varcol, y=y_varcol))+
geom_point(fill="skyblue", color="lightblue")+
labs(title= "Reg Plot", x="x-var", y="y-var")+
theme_bw()+
stat_function(fun = function(x) sqrt((b[1]) + (b[2])*x), aes(color="Y′=Y^2"))
fit <- gg_resfitted(lm_obj)
qq <- gg_qqplot(lm_obj)
pred <- gg_resX(lm_obj)
plots <- list(reg_plot, fit, qq, pred)
output<-list(plot = plot_all(plots), RegressionValues = df, summary = sumry)
return(output)
}
} else {
paste("Bruh. Invalid Transformation argument")
}
}