R/Linearsystems.R
In JobNmadu/Dyn4cast: Dynamic Modeling and Machine Learning Environment
Defines functions
Effects_wide
Effects_long
Predicted_wide
Predicted_long
fitted_wide
fitted_long
mmmd
Linearsystems
Documented in
Linearsystems
#' Linear Model and various Transformations for Efficiency
#'
#' @description
#' The linear model still remains a reference point towards advanced modeling of
#' some datasets as foundation for **Machine Learning**, **Data Science** and
#' **Artificial Intelligence** in spite of some of her weaknesses. The major
#' task in **modeling** is to compare various models before a selection is
#' made for one or for advanced modeling. Often, some trial and error methods
#' are used to decide which model to select. This is where this function is
#' unique. It helps to estimate 14 different linear models and provide
#' their coefficients in a formatted Table for quick comparison so that
#' time and energy are saved. The interesting thing about this function is
#' the simplicity, and it is a _one line_ code.
#'
#' @param y Vector of the dependent variable. This must be numeric.
#' @param x Data frame of the explanatory variables.
#' @param mod The group of linear models to be estimated. It takes value from 0
#' to 6. 0 = EDA (correlation, summary tables, Visuals means); 1 = Linear
#' systems, 2 = power models, 3 = polynomial models, 4 = root models,
#' 5 = inverse models, 6 = all the 14 models
#' @param limit Number of variables to be included in the coefficients plots
#' @param Test test data to be used to predict y. If not supplied, the fitted y
#' is used hence may be identical with the fitted value. It is important to be
#' cautious if the data is to be divided between train and test subsets in
#' order to train and test the model. If the sample size is not sufficient to
#' have enough data for the test, errors are thrown up.
#'
#' @return A list with the following components:
#' \item{\code{Visual means of the numeric variable}}{Plot of the means of the
#' _numeric_ variables.}
#' \item{\code{Correlation plot}}{Plot of the Correlation Matrix of the
#' _numeric_ variables. To recover the plot, please use this canonical form
#' _object$`Correlation plot`$plot()_.}
#' \item{\code{Linear}}{The full estimates of the Linear Model.}
#' \item{\code{Linear with interaction}}{The full estimates of the Linear Model
#' with full interaction among the _numeric_ variables.}
#' \item{\code{Semilog}}{The full estimates of the Semilog Model. Here the
#' independent variable(s) is/are log-transformed.}
#' \item{\code{Growth}}{The full estimates of the Growth Model. Here the
#' dependent variable is log-transformed.}
#' \item{\code{Double Log}}{The full estimates of the double-log Model. Here
#' the both the dependent and independent variables are log-transformed.}
#' \item{\code{Mixed-power model}}{The full estimates of the Mixed-power Model.
#' This is a combination of linear and double log models. It has significant
#' gains over the two models separately.}
#' \item{\code{Translog model}}{The full estimates of the double-log Model with
#' full interaction of the _numeric_ variables.}
#' \item{\code{Quadratic}}{The full estimates of the Quadratic Model.
#' Here the square of _numeric_ independent variable(s) is/are included as
#' independent variables.}
#' \item{\code{Cubic model}}{The full estimates of the Cubic Model. Here the
#' third-power (x^3) of _numeric_ independent variable(s) is/are included as
#' independent variables.}
#' \item{\code{Inverse y}}{The full estimates of the Inverse Model. Here the
#' dependent variable is inverse-transformed (1 / y).}
#' \item{\code{Inverse x}}{The full estimates of the Inverse Model. Here the
#' independent variable is inverse-transformed (1 / x).}
#' \item{\code{Inverse y & x}}{The full estimates of the Inverse Model. Here
#' the dependent and independent variables are inverse-transformed 1 / y & 1 /
#' x).}
#' \item{\code{Square root}}{The full estimates of the Square root Model. Here
#' the independent variable is square root-transformed (x^0.5).}
#' \item{\code{Cubic root}}{The full estimates of the cubic root Model. Here
#' the independent variable is cubic root-transformed (x^1 / 3).}
#' \item{\code{Significant plot of Linear}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Linear with interaction}}{Plots of order of
#' importance and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Semilog}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Growth}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Double Log}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Mixed-power model}}{Plots of order of
#' importance and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Translog model}}{Plots of order of
#' importance and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Quadratic}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Cubic model}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Inverse y}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Inverse x}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Inverse y & x}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Square root}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Cubic root}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Model Table}}{Formatted Tables of the coefficient estimates of
#' all the models}
#' \item{\code{Machine Learning Metrics}}{Metrics (47) for assessing model
#' performance and metrics for diagnostic analysis of the error in estimation.}
#' \item{\code{Table of Marginal effects}}{Tables of marginal effects of each
#' model. Because of computational limitations, if you choose to estimate all
#' the 14 models, the Tables are produced separately for the major
#' transformations. They can easily be compiled into one.}
#' \item{\code{Fitted plots long format}}{Plots of the fitted estimates from
#' each of the model.}
#' \item{\code{Fitted plots wide format}}{Plots of the fitted estimates from
#' each of the model.}
#' \item{\code{Prediction plots long format}}{Plots of the predicted estimates
#' from each of the model.}
#' \item{\code{Prediction plots wide format}}{Plots of the predicted estimates
#' from each of the model.}
#' \item{\code{Naive effects plots long format}}{Plots of the `lm` effects.
#' May be identical with plots of marginal effects if performed.}
#' \item{\code{Naive effects plots wide format}}{Plots of the `lm` effects.
#' May be identical with plots of marginal effects if performed.}
#' \item{\code{Summary of numeric variables}}{of the dataset.}
#' \item{\code{Summary of character variables}}{of the dataset.}
#'
#' @export Linearsystems
#'
#' @importFrom modelsummary modelsummary
#' @importFrom dplyr select_if
#' @importFrom ggplot2 geom_line
#' @importFrom ggplot2 scale_fill_hue
#' @importFrom ggplot2 scale_color_hue
#' @importFrom ggplot2 theme_minimal
#' @importFrom ggplot2 facet_wrap
#' @importFrom ggplot2 vars
#' @importFrom ggplot2 stat_summary
#' @importFrom ggplot2 geom_vline
#' @importFrom ggplot2 geom_jitter
#' @importFrom ggplot2 element_blank
#' @importFrom marginaleffects avg_slopes
#' @importFrom tibble rownames_to_column
#' @importFrom utils globalVariables
#'
#' @name Linearsystems
#'
#' @aliases linearsystems
#' @aliases sampling
#'
#' @examples
#' ## Without test data (not run)
#' # library(tidyverse)
#' # y <- linearsystems$MKTcost # to run all the exercises, uncomment.
#' # x <- select(linearsystems, -MKTcost)
#' # Linearsystems(y, x, 6, 15) # NaNs produced if run
#' ## Without test data (not run)
#' # x <- sampling[, -1]
#' # y <- sampling$qOutput
#' # limit <- 20
#' # mod <-3
#' # Test <- NA
#' # Linearsystems(y, x, 3, 15) # NaNs produced if run
#' # # with test data
#' # x <- sampling[, -1]
#' # y <- sampling$qOutput
#' # Data <- cbind(y, x)
#' # # 80% of data is sampled
#' # sampling <- sample(1 : nrow(Data), 0.8 * nrow(Data))
#' # # for training the model
#' # train <- Data[sampling, ]
#' # Test <- Data[-sampling, ]
#' # # 20% of data is reserved for testing (predicting) the model
#' # y <- train$y
#' # x <- train[, -1]
#' # mod <- 4
#' # Linearsystems(y, x, 4, 15, Test) # NaNs produced if run
#'
#' @usage Linearsystems(y, x, mod, limit, Test = NA)
utils::globalVariables(c("Variables", "Model", "values", "Observed"))
Linearsystems <- function(y, x, mod, limit, Test = NA) {
y1 <- y
Data <- cbind(y, x)
Names <- names(Data)
case <- if (ncol(Data) > 9) {
"complex"
} else {
"normal"
}
mod <- mod
xnum <- x %>%
dplyr::select_if(is.numeric)
xnum_n <- names(xnum)
xcha <- x %>%
dplyr::select_if(is.character)
xcha_n <- names(xcha)
y <- Data$y
Linear <- lm(y ~ ., data = Data)
Linears <- MLMetrics(Observed = Data, yvalue = y, modeli = Linear,
K = 2, Name = "Linear", Form = "LM", kutuf = 0,
TTy = "Number")
v_Linear <- estimate_plot(model25 = Linear, limit = limit)
if (case != "complex") {
e_Linear <- marginaleffects::avg_slopes(Linear, by = TRUE)
} else {
e_Linear <- NULL
}
KNN <- Data %>% dplyr::select_if(is.numeric)
KNN1 <- quicksummary(x = KNN, Type = 1)
KKC <- Data %>%
dplyr::select_if(is.character) %>%
summary()
if (length(KNN) < 10) {
YYY <- paste(c("y", paste(c(paste("x", 1 : (length(KNN)-1), sep = "")))))
} else if (length(KNN) == 10) {
YYY <- paste(c("y", paste(c(paste("x0", 1 : 9, sep = "")))))
} else{
YYY <- paste(c("y", paste(c(paste("x0", 1 : 9, sep = ""),
(paste("x", 10 : (length(KNN) - 1),
sep = ""))))))
}
colnames(KNN) <- YYY
e_corplot <- corplot(stats::cor(KNN))
Data1 <- tidyr::pivot_longer(KNN, tidyr::everything(), names_to = "Variables",
values_to = "values")
e_meanplot <- ggplot2::ggplot(Data1, ggplot2::aes(x = Variables,
y = values)) +
ggplot2::geom_jitter(alpha = 0.7,
shape = 16,
width = 0.2,
color = "cadetblue") +
ggplot2::geom_vline(
xintercept = seq(.5, length(unique(Data1$Variables)), by = 1),
color = "gray90",
linewidth = 1) +
ggplot2::stat_summary(fun = "mean",
geom = "point",
size = 5,
alpha = 0.6,
shape = 16,
color = "tomato") +
ggplot2::stat_summary(
ggplot2::aes(label = round(ggplot2::after_stat(y), 1), fontface = "bold"),
fun = mean,
geom = "label", #try text if you want
#binwidth = 4,
alpha = 0.6,
label.size = NA, # remove border around label
vjust = -0.5,
color = "tomato") +
ggplot2::labs(
title = "Visualised means of the numeric variables in the model",
subtitle = "",
x = "Variables",
y = "Values") +
ggplot2::theme_minimal() +
ggplot2::theme(panel.grid = ggplot2::element_blank(),
plot.subtitle = mmmd())
if (!mod %in% 0:6) {
message("Module out of range")
stop(message(paste("Choose between", 0,
"and", 6)))
}
if (mod == 5) {
Data <- cbind(y, xnum, xcha)
`reciprocal in Y` <- lm((1 / (y + 1)) ~ ., data = Data)
reciY <- MLMetrics(Observed = Data, yvalue = y, modeli = `reciprocal in Y`,
K = 2, Name = "Semilog in Y", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciY <- estimate_plot(model25 = `reciprocal in Y`, limit = limit)
if (case != "complex") {
e_reciY <- marginaleffects::avg_slopes(`reciprocal in Y`, by = TRUE)
} else {
e_reciY <- NULL
}
xnum8 <- 1 / (xnum + 1)
Data <- cbind(y = y, xnum8, xcha)
y <- Data$y
`reciprocal in X` <- lm(y ~ ., data = Data)
reciX <- MLMetrics(Observed = Data, yvalue = y, modeli = `reciprocal in X`,
K = 2, Name = "Inverse in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciX <- estimate_plot(model25 = `reciprocal in X`, limit = limit)
if (case != "complex") {
e_reciX <- marginaleffects::avg_slopes(`reciprocal in X`, by = TRUE)
} else {
e_reciX <- NULL
}
Data$y <- y <- 1 / (y + 1)
`double reciprocal` <- lm(y ~ ., data = Data)
reciD <- MLMetrics(Observed = Data, yvalue = y, modeli = `double reciprocal`,
K = 2, Name = "Inverse in Y & X", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciD <- estimate_plot(model25 = `double reciprocal`, limit = limit)
if (case != "complex") {
e_reciD <- marginaleffects::avg_slopes(`double reciprocal`, by = TRUE)
} else {
e_reciD <- NULL
}
} else if (mod == 4) {
xnum3 <- xnum^.5
names(xnum3) <- paste("I", names(xnum3), sep = "")
DD3 <- cbind(y, xnum, xcha, xnum3)
Data <- DD3
y <- Data$y
`square root` <- lm(y ~ ., data = Data)
squares <- MLMetrics(Observed = Data, yvalue = y, modeli = `square root`,
K = 2, Name = "Square root", Form = "LM", kutuf = 0,
TTy = "Number")
v_square <- estimate_plot(model25 = `square root`, limit = limit)
if (case != "complex") {
e_square <- marginaleffects::avg_slopes(`square root`, by = TRUE)
} else {
e_square <- NULL
}
xnum4 <- xnum^(1 / 3)
names(xnum4) <- paste("I", names(xnum4), sep = "")
DD4 <- cbind(y, xnum, xcha, xnum4)
Data <- DD4
y <- Data$y
`cubic root` <- lm(y ~ ., data = Data)
cubics <- MLMetrics(Observed = Data, yvalue = y, modeli = `cubic root`,
K = 2, Name = "Cubic root", Form = "LM", kutuf = 0,
TTy = "Number")
v_cubic <- estimate_plot(model25 = `cubic root`, limit = limit)
if (case != "complex") {
e_cubic <- marginaleffects::avg_slopes(`cubic root`, by = TRUE)
} else {
e_cubic <- NULL
}
} else if (mod == 3) {
xnum1 <- xnum^2
names(xnum1) <- paste("I", names(xnum1), sep = "")
DD2 <- cbind(y, xnum, xcha, xnum1)
Data <- DD2
y <- Data$y
quadratic <- lm(y ~ ., data = Data)
quadratics <- MLMetrics(Observed = Data, yvalue = y, modeli = quadratic,
K = 2, Name = "Quadratic", Form = "LM", kutuf = 0,
TTy = "Number")
v_quadratic <- estimate_plot(model25 = quadratic, limit = limit)
if (case != "complex") {
e_quadratic <- marginaleffects::avg_slopes(quadratic, by = TRUE)
} else {
e_quadratic <- NULL
}
xnum2 <- xnum^3
names(xnum2) <- paste("IC", names(xnum), sep = "")
Data <- cbind(DD2, xnum2)
y <- Data$y
cube <- lm(y ~ ., data = Data)
cubes <- MLMetrics(Observed = Data, yvalue = y, modeli = cube,
K = 2, Name = "Cube", Form = "LM", kutuf = 0,
TTy = "Number")
v_cube <- estimate_plot(model25 = cube, limit = limit)
if (case != "complex") {
e_cube <- marginaleffects::avg_slopes(cube, by = TRUE)
} else {
e_cube <- NULL
}
}else if (mod == 2) {
Data <- cbind(y = log(y + 1), xnum, xcha)
y <- Data$y
loglin <- lm(y ~ ., data = Data)
loglins <- MLMetrics(Observed = Data, yvalue = y, modeli = loglin,
K = 2, Name = "Semilog in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_loglin <- estimate_plot(model25 = loglin, limit = limit)
if (case != "complex") {
e_loglin <- marginaleffects::avg_slopes(loglin, by = TRUE)
} else {
e_loglin <- NULL
}
xnum5 <- log(xnum + 1)
DD5 <- cbind(y, xnum5, xcha)
Data <- DD5
y <- Data$y
linlog <- lm(y ~ ., data = Data)
linlogs <- MLMetrics(Observed = Data, yvalue = y, modeli = linlog,
K = 2, Name = "Semilog in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_linlog <- estimate_plot(model25 = linlog, limit = limit)
if (case != "complex") {
e_linlog <- marginaleffects::avg_slopes(linlog, by = TRUE)
} else {
e_linlog <- NULL
}
names(xnum5) <- paste("I", names(xnum5), sep = "")
DD6 <- cbind(y, xnum, xcha, xnum5)
Data <- DD6
y <- Data$y
perlog <- lm(y ~ ., data = Data)
perlogs <- MLMetrics(Observed = Data, yvalue = y, modeli = perlog,
K = 2, Name = "Mixed-power", Form = "LM", kutuf = 0,
TTy = "Number")
v_perlog <- estimate_plot(model25 = perlog, limit = limit)
if (case != "complex") {
e_perlog <- marginaleffects::avg_slopes(perlog, by = TRUE)
} else {
e_perlog <- NULL
}
DD7 <- cbind(y = y, xnum)
DD7 <- log(DD7 + 1)
Data <- cbind(DD7, xcha)
y = Data$y
loglog <- lm(y ~ ., data = Data)
loglogs <- MLMetrics(Observed = Data, yvalue = y, modeli = loglog,
K = 2, Name = "Cobb Douglas", Form = "LM", kutuf = 0,
TTy = "Number")
v_loglog <- estimate_plot(model25 = loglog, limit = limit)
if (case != "complex") {
e_loglog <- marginaleffects::avg_slopes(loglog, by = TRUE)
} else {
e_loglog <- NULL
}
xnum6 <- log(xnum + 1)
xnum6_n <- names(xnum6)
xnum7 <- xnum6^2
xnum7_n <- names(xnum7)
DD7 <- cbind(xnum6, xnum7, xcha)
if (length(xcha) == 0) {
names(DD7) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum6_n, "`",
sep = "",
collapse = "*"),
paste("`", "I", xnum7_n, "`",
sep = "",
collapse = "+")),
collapse = "+")),
collapse = " ~ "))
} else {
names(DD7) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""), paste(xcha_n,
sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum6_n, "`",
sep = "",
collapse = "*"),
paste("`", "I", xnum7_n, "`",
sep = "",
collapse = "+"),
paste("`", xcha_n, "`",
sep = "",
collapse = " + ")),
collapse = "+")),
collapse = " ~ "))
}
Data <- cbind(y = log(y + 1), DD7)
y <- Data$y
if (case != "complex") {
translog <- lm(MOD, data = Data)
translogs <- MLMetrics(Observed = Data, yvalue = y, modeli = translog,
K = 2, Name = "Translog", Form = "LM", kutuf = 0,
TTy = "Number")
v_translog <- estimate_plot(model25 = translog, limit = limit)
e_translog <- marginaleffects::avg_slopes(translog, by = TRUE)
} else {
translog <- NULL
translogs <- NULL
v_translog <- NULL
e_translog <- NULL
}
} else if (mod == 1) {
DD0 <- cbind(xnum, xcha)
DDn <- names(DD0)
if (length(xcha) == 0) {
names(DD0) <- paste(c(paste(xnum_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum_n, "`",
sep = "",
collapse = " * ")),
collapse = "+")),
collapse = " ~ "))
} else {
names(DD0) <- paste(c(paste(xnum_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum_n, "`",
sep = "",
collapse = " * "),
paste("`", xcha_n, "`",
sep = "",
collapse = " + ")),
collapse = "+")),
collapse = " ~ "))
}
Data <- cbind(y, DD0)
if (case != "complex") {
LinearI <- lm(MOD, data = Data)
LinearIs <- MLMetrics(Observed = Data, yvalue = y, modeli = LinearI,
K = 2, Name = "Linear", Form = "LM", kutuf = 0,
TTy = "Number")
v_LinearI <- estimate_plot(model25 = LinearI, limit = limit)
e_LinearI <- marginaleffects::avg_slopes(LinearI, by = TRUE)
} else {
LinearI <- NULL
LinearIs <- NULL
v_LinearI <- NULL
e_LinearI <- NULL
}
} else if (mod == 0) {
KNN1 <- KNN1
KKC <- KKC
e_corplot <- e_corplot
e_meanplot <- e_meanplot
} else {
DD0 <- cbind(xnum, xcha)
DDn <- names(DD0)
if (length(xcha) == 0) {
names(DD0) <- paste(c(paste(xnum_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum_n, "`",
sep = "",
collapse = " * ")),
collapse = "+")),
collapse = " ~ "))
} else {
names(DD0) <- paste(c(paste(xnum_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum_n, "`",
sep = "",
collapse = " * "),
paste("`", xcha_n, "`",
sep = "",
collapse = " + ")),
collapse = "+")),
collapse = " ~ "))
}
Data <- cbind(y, DD0)
if (case != "complex") {
LinearI <- lm(MOD, data = Data)
LinearIs <- MLMetrics(Observed = Data, yvalue = y, modeli = LinearI,
K = 2, Name = "Linear", Form = "LM", kutuf = 0,
TTy = "Number")
v_LinearI <- estimate_plot(model25 = LinearI, limit = limit)
e_LinearI <- marginaleffects::avg_slopes(LinearI, by = TRUE)
} else {
LinearI <- NULL
LinearIs <- NULL
v_LinearI <- NULL
e_LinearI <- NULL
}
Data <- cbind(y, xnum, xcha)
`reciprocal in Y` <- lm((1 / (y + 1)) ~ ., data = Data)
reciY <- MLMetrics(Observed = Data, yvalue = y, modeli = `reciprocal in Y`,
K = 2, Name = "Semilog in Y", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciY <- estimate_plot(model25 = `reciprocal in Y`, limit = limit)
if (case != "complex") {
e_reciY <- marginaleffects::avg_slopes(`reciprocal in Y`, by = TRUE)
} else {
e_reciY <- NULL
}
Data <- cbind(y = log(y + 1), xnum, xcha)
y <- Data$y
loglin <- lm(y ~ ., data = Data)
loglins <- MLMetrics(Observed = Data, yvalue = y, modeli = loglin,
K = 2, Name = "Semilog in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_loglin <- estimate_plot(model25 = loglin, limit = limit)
if (case != "complex") {
e_loglin <- marginaleffects::avg_slopes(loglin, by = TRUE)
} else {
e_loglin <- NULL
}
xnum1 <- xnum^2
names(xnum1) <- paste("I", names(xnum1), sep = "")
DD2 <- cbind(y, xnum, xcha, xnum1)
Data <- DD2
y <- Data$y
quadratic <- lm(y ~ ., data = Data)
quadratics <- MLMetrics(Observed = Data, yvalue = y, modeli = quadratic,
K = 2, Name = "Quadratic", Form = "LM", kutuf = 0,
TTy = "Number")
v_quadratic <- estimate_plot(model25 = quadratic, limit = limit)
if (case != "complex") {
e_quadratic <- marginaleffects::avg_slopes(quadratic, by = TRUE)
} else {
e_quadratic <- NULL
}
xnum2 <- xnum^3
names(xnum2) <- paste("IC", names(xnum), sep = "")
Data <- cbind(DD2, xnum2)
y <- Data$y
cube <- lm(y ~ ., data = Data)
cubes <- MLMetrics(Observed = Data, yvalue = y, modeli = cube,
K = 2, Name = "Cube", Form = "LM", kutuf = 0,
TTy = "Number")
v_cube <- estimate_plot(model25 = cube, limit = limit)
if (case != "complex") {
e_cube <- marginaleffects::avg_slopes(cube, by = TRUE)
} else {
e_cube <- NULL
}
xnum3 <- xnum^.5
names(xnum3) <- paste("I", names(xnum3), sep = "")
DD3 <- cbind(y, xnum, xcha, xnum3)
Data <- DD3
y <- Data$y
`square root` <- lm(y ~ ., data = Data)
squares <- MLMetrics(Observed = Data, yvalue = y, modeli = `square root`,
K = 2, Name = "Square root", Form = "LM", kutuf = 0,
TTy = "Number")
v_square <- estimate_plot(model25 = `square root`, limit = limit)
if (case != "complex") {
e_square <- marginaleffects::avg_slopes(`square root`, by = TRUE)
} else {
e_square <- NULL
}
xnum4 <- xnum^(1 / 3)
names(xnum4) <- paste("I", names(xnum4), sep = "")
DD4 <- cbind(y, xnum, xcha, xnum4)
Data <- DD4
y <- Data$y
`cubic root` <- lm(y ~ ., data = Data)
cubics <- MLMetrics(Observed = Data, yvalue = y, modeli = `cubic root`,
K = 2, Name = "Cubic root", Form = "LM", kutuf = 0,
TTy = "Number")
v_cubic <- estimate_plot(model25 = `cubic root`, limit = limit)
if (case != "complex") {
e_cubic <- marginaleffects::avg_slopes(`cubic root`, by = TRUE)
} else {
e_cubic <- NULL
}
xnum5 <- log(xnum + 1)
DD5 <- cbind(y, xnum5, xcha)
Data <- DD5
y <- Data$y
linlog <- lm(y ~ ., data = Data)
linlogs <- MLMetrics(Observed = Data, yvalue = y, modeli = linlog,
K = 2, Name = "Semilog in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_linlog <- estimate_plot(model25 = linlog, limit = limit)
if (case != "complex") {
e_linlog <- marginaleffects::avg_slopes(linlog, by = TRUE)
} else {
e_linlog <- NULL
}
names(xnum5) <- paste("I", names(xnum5), sep = "")
DD6 <- cbind(y, xnum, xcha, xnum5 )
Data <- DD6
y <- Data$y
perlog <- lm(y ~ ., data = Data)
perlogs <- MLMetrics(Observed = Data, yvalue = y, modeli = perlog,
K = 2, Name = "Mixed-power", Form = "LM", kutuf = 0,
TTy = "Number")
v_perlog <- estimate_plot(model25 = perlog, limit = limit)
if (case != "complex") {
e_perlog <- marginaleffects::avg_slopes(perlog, by = TRUE)
} else {
e_perlog <- NULL
}
DD7 <- cbind(y = y, xnum)
DD7 <- log(DD7 + 1)
Data <- cbind(DD7, xcha)
y <- Data$y
loglog <- lm(y ~ ., data = Data)
loglogs <- MLMetrics(Observed = Data, yvalue = y, modeli = loglog,
K = 2, Name = "Cobb Douglas", Form = "LM", kutuf = 0,
TTy = "Number")
v_loglog <- estimate_plot(model25 = loglog, limit = limit)
if (case != "complex") {
e_loglog <- marginaleffects::avg_slopes(loglog, by = TRUE)
} else {
e_loglog <- NULL
}
xnum6 <- log(xnum + 1)
xnum6_n <- names(xnum6)
xnum7 <- xnum6^2
xnum7_n <- names(xnum7)
DD7 <- cbind(xnum6, xnum7, xcha)
if (length(xcha) == 0) {
names(DD7) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum6_n, "`",
sep = "",
collapse = "*"),
paste("`", "I", xnum7_n, "`",
sep = "",
collapse = "+")),
collapse = "+")),
collapse = " ~ "))
} else {
names(DD7) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""), paste(xcha_n,
sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum6_n, "`",
sep = "",
collapse = "*"),
paste("`", "I", xnum7_n, "`",
sep = "",
collapse = "+"),
paste("`", xcha_n, "`",
sep = "",
collapse = " + ")),
collapse = "+")),
collapse = " ~ "))
}
Data <- cbind(y = log(y + 1), DD7)
y <- Data$y
if (case != "complex") {
translog <- lm(MOD, data = Data)
translogs <- MLMetrics(Observed = Data, yvalue = y, modeli = translog,
K = 2, Name = "Translog", Form = "LM", kutuf = 0,
TTy = "Number")
v_translog <- estimate_plot(model25 = translog, limit = limit)
e_translog <- marginaleffects::avg_slopes(translog, by = TRUE)
} else {
translog <- NULL
translogs <- NULL
v_translog <- NULL
e_translog <- NULL
}
xnum8 <- 1 / (xnum + 1)
Data <- cbind(y = y, xnum8, xcha)
y <- Data$y
`reciprocal in X` <- lm(y ~ ., data = Data)
reciX <- MLMetrics(Observed = Data, yvalue = y, modeli = `reciprocal in X`,
K = 2, Name = "Inverse in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciX <- estimate_plot(model25 = `reciprocal in X`, limit = limit)
if (case != "complex") {
e_reciX <- marginaleffects::avg_slopes(`reciprocal in X`, by = TRUE)
} else {
e_reciX <- NULL
}
Data$y <- y <- 1 / (y + 1)
`double reciprocal` <- lm(y ~ ., data = Data)
reciD <- MLMetrics(Observed = Data, yvalue = y, modeli = `double reciprocal`,
K = 2, Name = "Inverse in Y & X", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciD <- estimate_plot(model25 = `double reciprocal`, limit = limit)
if (case != "complex") {
e_reciD <- marginaleffects::avg_slopes(`double reciprocal`, by = TRUE)
} else {
e_reciD <- NULL
}
}
Test <- Test
AA <- dim(Test)
if (is.null(AA)) {
Test <- Test
} else {
Test <- Test
xt <- Test[, -1]
xnum <- xt %>%
dplyr::select_if(is.numeric)
xnum_n <- names(xnum)
xcha <- xt %>%
dplyr::select_if(is.character)
xcha_n <- names(xcha)
xnum1 <- xnum^2
names(xnum1) <- paste("I", names(xnum1), sep = "")
Test_q <- cbind(xnum, xcha, xnum1)
xnum2 <- xnum^3
names(xnum2) <- paste("IC", names(xnum), sep = "")
Test_c <- cbind(Test_q, xnum2)
xnum3 <- xnum^.5
names(xnum3) <- paste("I", names(xnum3), sep = "")
Test_s <- cbind(xnum, xcha, xnum3)
xnum4 <- xnum^(1 / 3)
names(xnum4) <- paste("I", names(xnum4), sep = "")
Test_cr <- cbind(xnum, xcha, xnum4)
xnum5 <- log(xnum + 1)
Test_l <- cbind(xnum5, xcha)
names(xnum5) <- paste("I", names(xnum5), sep = "")
Test_p <- cbind(xnum, xcha, xnum5)
DD7 <- log(xnum + 1)
Test_ll <- cbind(DD7, xcha)
xnum6 <- log(xnum + 1)
xnum6_n <- names(xnum6)
xnum7 <- xnum6^2
xnum7_n <- names(xnum7)
Test_t <- cbind(xnum6, xnum7, xcha)
names(Test_t) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""), paste(xcha_n,
sep = "")))
xnum8 <- 1 / (xnum + 1)
Test_X <- cbind(xnum8, xcha)
}
if (mod == 5) {
if (case != "complex") {
e_list <- list(Linear = e_Linear,
`Reciprocal in X` = e_reciX,
`Reciprocal in Y` = e_reciY,
`Double reciprocal` = e_reciD)
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_table <- NULL
}
m_list <- list(Linear = Linear,
`Reciprocal in X` = `reciprocal in X`,
`Reciprocal in Y` = `reciprocal in Y`,
`Double reciprocal` = `double reciprocal`)
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
Anova <- stats::anova(Linear, `reciprocal in X`, `reciprocal in X`,
`double reciprocal`)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Reciprocal in X` = fitted.values(`reciprocal in X`),
`Reciprocal in Y` =
1 / fitted.values(`reciprocal in Y`),
`Double reciprocal` =
1 / fitted.values(`double reciprocal`))
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Reciprocal in X` = predict(`reciprocal in X`),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`),
`Double reciprocal` =
1 / predict(`double reciprocal`))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Reciprocal in X` =
predict(`reciprocal in X`, Test_X),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`, Test),
`Double reciprocal` =
1 / predict(`double reciprocal`, Test_X))
}
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Reciprocal in X` =
`reciprocal in X`[["effects"]],
`Reciprocal in Y` =
1 / `reciprocal in Y`[["effects"]],
`Double reciprocal` =
1 / `double reciprocal`[["effects"]])
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- data.frame(cbind(Linear = Linears,
`Reciprocal in X` = reciX,
`Reciprocal in Y` = reciY,
`Double reciprocal` = reciD))
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
} else if (mod == 4) {
if (case != "complex") {
e_list <- list(Linear = e_Linear,
`Square root` = e_square,
`Cubic root` = e_cubic)
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_table <- NULL
}
m_list <- list(Linear = Linear,
`Square root` = `square root`,
`Cubic root` = `cubic root`)
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
Anova <- stats::anova(Linear, `square root`, `cubic root`)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Square root` = fitted.values(`square root`),
`Cubic root` = fitted.values(`cubic root`))
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Square root` = predict(`square root`),
`Cubic root` = predict(`cubic root`))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Square root` = predict(`square root`, Test_s),
`Cubic root` = predict(`cubic root`, Test_cr))
}
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Square root` = `square root`[["effects"]],
`Cubic root` = `cubic root`[["effects"]])
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- data.frame(cbind(Linear = Linears,
`Square root` = squares,
`Cubic root` = cubics))
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
} else if (mod == 3) {
if (case != "complex") {
e_list <- list(Linear = e_Linear,
Quadratic = e_quadratic,
Cubic = e_cube)
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_table <- NULL
}
m_list <- list(Linear = Linear,
Quadratic = quadratic,
Cubic = cube)
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
Anova <- stats::anova(Linear, quadratic, cube)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
Quadratic = fitted.values(quadratic),
Cubic = fitted.values(cube))
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
Quadratic = predict(quadratic),
Cubic = predict(cube))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Square root` = predict(quadratic, Test_s),
`Cubic root` = predict(cube, Test_c))
}
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
Quadratic = quadratic[["effects"]],
Cubic = cube[["effects"]])
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- data.frame(cbind(Linear = Linears,
Quadratic = quadratics,
Cubic = cubes))
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
}else if (mod == 2) {
if (case != "complex") {
e_list <- list(Linear = e_Linear,
`Cobb Douglas` = e_loglog,
Linlog = e_linlog,
Loglin = e_loglin,
`Mixed-power` = e_perlog,
Translog = e_translog)
m_list <- list(Linear = Linear,
`Cobb Douglas` = loglog,
Linlog = linlog,
Loglin = loglin,
`Mixed-power` = perlog,
Translog = translog)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Cobb Douglas` = exp(fitted.values(loglog)),
Translog = exp(fitted.values(translog)),
`Mixed-power` = exp(fitted.values(perlog)),
Linlog = fitted.values(linlog),
Loglin = exp(fitted.values(loglin)))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Cobb Douglas` = exp(predict(loglog)),
Translog = exp(predict(translog)),
`Mixed-power` = exp(predict(perlog)),
Linlog = predict(linlog),
Loglin = exp(predict(loglin)))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Cobb Douglas` = exp(predict(loglog, Test_ll)),
Translog = exp(predict(translog, Test_t)),
`Mixed-power` = exp(predict(perlog, Test_p)),
Linlog = predict(linlog, Test_l),
Loglin = exp(predict(loglin, Test)))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Cobb Douglas` = exp(loglog[["effects"]]),
Translog = exp(translog[["effects"]]),
`Mixed-power` = exp(perlog[["effects"]]),
Linlog = linlog[["effects"]],
Loglin = exp(loglin[["effects"]]))
evaluation <- data.frame(cbind(Linear = Linears,
`Cobb Douglas` = loglogs,
Linlog = linlogs,
Loglin = loglins,
`Mixed-power` = perlogs,
Translog = translogs))
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_list <- NULL
e_table <- NULL
m_list <- list(Linear = Linear,
`Cobb Douglas` = loglog,
Linlog = linlog,
Loglin = loglin,
`Mixed-power` = perlog)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Cobb Douglas` = exp(fitted.values(loglog)),
`Mixed-power` = exp(fitted.values(perlog)),
Linlog = fitted.values(linlog),
Loglin = exp(fitted.values(loglin)))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Cobb Douglas` = exp(predict(loglog)),
`Mixed-power` = exp(predict(perlog)),
Linlog = predict(linlog),
Loglin = exp(predict(loglin)))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Cobb Douglas` = exp(predict(loglog, Test_ll)),
`Mixed-power` = exp(predict(perlog, Test_p)),
Linlog = predict(linlog, Test_l),
Loglin = exp(predict(loglin, Test)))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Cobb Douglas` = exp(loglog[["effects"]]),
`Mixed-power` = exp(perlog[["effects"]]),
Linlog = linlog[["effects"]],
Loglin = exp(loglin[["effects"]]))
evaluation <- data.frame(cbind(Linear = Linears,
`Cobb Douglas` = loglogs,
Linlog = linlogs,
Loglin = loglins,
`Mixed-power` = perlogs))
}
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
Anova <- stats::anova(Linear, loglog, perlog, translog, linlog, loglin)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
} else if (mod == 1) {
if (case != "complex") {
if (is.null(e_LinearI)) {
e_list <- list(Linear = e_Linear)
m_list <- list(Linear = Linear)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears,
`Linear with interaction` = LinearIs))
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_list <- list(Linear = e_Linear,
`Linear with interaction` = e_LinearI)
m_list <- list(Linear = Linear,
`Linear with interaction` = LinearI)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Linear with interaction` =
fitted.values(LinearI))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Linear with interaction` = predict(LinearI))
} else {
Predicted <- data.frame(Observed = y1,
Linear =
predict(Linear, Test),
`Linear with interaction` =
predict(LinearI, Test))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Linear with interaction` =
LinearI[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears,
`Linear with interaction` = LinearIs))
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
}
} else {
e_list <- NULL
m_list <- list(Linear = Linear)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears))
e_table <- NULL
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
}
Anova <- stats::anova(Linear, LinearI)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
} else if (mod == 0) {
cat("EDA...", "\n")
} else {
if (case != "complex") {
m_list <- list(Linear = Linear,
`Cobb Douglas` = loglog,
Linlog = linlog,
Loglin = loglin,
`Reciprocal in X` = `reciprocal in X`,
`Reciprocal in Y` = `reciprocal in Y`,
`Double reciprocal` = `double reciprocal`,
Quadratic = quadratic,
`Square root` = `square root`,
`Cubic root` = `cubic root`,
Cubic = cube,
`Mixed-power` = perlog,
Translog = translog,
`Linear with interaction` = LinearI)
e_list <- list(Linear = e_Linear,
`Linear with interaction` = e_LinearI)
e_table1 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
e_list <- list(`Cobb Douglas` = e_loglog,
Linlog = e_linlog,
Loglin = e_loglin,
`Mixed-power` = e_perlog,
Translog = e_translog)
e_table2 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
e_list <- list(`Reciprocal in X` = e_reciX,
`Reciprocal in Y` = e_reciY,
`Double reciprocal` = e_reciD)
e_table3 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
e_list <- list(Quadratic = e_quadratic,
Cubic = e_cube)
e_table4 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
e_list <- list(`Square root` = e_square,
`Cubic root` = e_cubic)
e_table5 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Linear with interaction` = fitted.values(LinearI),
`Cobb Douglas` = exp(fitted.values(loglog)),
Translog = exp(fitted.values(translog)),
`Mixed-power` = exp(fitted.values(perlog)),
Linlog = fitted.values(linlog),
Loglin = exp(fitted.values(loglin)),
Quadratic = fitted.values(quadratic),
Cubic = fitted.values(cube),
`Square root` = fitted.values(`square root`),
`Cubic root` = fitted.values(`cubic root`),
`Reciprocal in X` =
fitted.values(`reciprocal in X`),
`Reciprocal in Y` =
1 / fitted.values(`reciprocal in Y`),
`Double reciprocal` =
1 / fitted.values(`double reciprocal`))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Linear with interaction` = predict(LinearI),
`Cobb Douglas` = exp(predict(loglog)),
Translog = exp(predict(translog)),
`Mixed-power` = exp(predict(perlog)),
Linlog = predict(linlog),
Loglin = exp(predict(loglin)),
Quadratic = predict(quadratic),
Cubic = predict(cube),
`Square root` = predict(`square root`),
`Cubic root` = predict(`cubic root`),
`Reciprocal in X`
= predict(`reciprocal in X`),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`),
`Double reciprocal` =
1 / predict(`double reciprocal`))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Linear with interaction`
= predict(LinearI, Test),
`Cobb Douglas` = exp(predict(loglog, Test_ll)),
Translog = exp(predict(translog, Test_t)),
`Mixed-power` = exp(predict(perlog, Test_p)),
Linlog = predict(linlog, Test_l),
Loglin = exp(predict(loglin, Test)),
Quadratic = predict(quadratic, Test_q),
Cubic = predict(cube, Test_c),
`Square root` = predict(`square root`, Test_s),
`Cubic root` = predict(`cubic root`, Test_cr),
`Reciprocal in X` =
predict(`reciprocal in X`, Test_X),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`, Test),
`Double reciprocal` =
1 / predict(`double reciprocal`, Test_X))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Linear with interaction` =
LinearI[["effects"]],
`Cobb Douglas` = exp(loglog[["effects"]]),
Translog = exp(translog[["effects"]]),
`Mixed-power` = exp(perlog[["effects"]]),
Linlog = linlog[["effects"]],
Loglin = exp(loglin[["effects"]]),
Quadratic = quadratic[["effects"]],
Cubic = cube[["effects"]],
`Square root` = `square root`[["effects"]],
`Cubic root` = `cubic root`[["effects"]],
`Reciprocal in X` =
`reciprocal in X`[["effects"]],
`Reciprocal in Y` =
1 / `reciprocal in Y`[["effects"]],
`Double reciprocal` =
1 / `double reciprocal`[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears,
`Cobb Douglas` = loglogs,
Linlog = linlogs,
Loglin = loglins,
`Reciprocal in X` = reciX,
`Reciprocal in Y` = reciY,
`Double reciprocal` = reciD,
Quadratic = quadratics,
`Square root` = squares,
`Cubic root` = cubics,
Cubic = cubes,
`Mixed-power` = perlogs,
Translog = translogs,
`Linear with interaction` = LinearIs))
e_table <- list(e_table1, e_table2, e_table3, e_table4, e_table5)
Anova <- stats::anova(Linear, LinearI, loglog, perlog, translog, linlog,
loglin, quadratic,
cube, `square root`, `cubic root`,
`reciprocal in X`,
`reciprocal in X`, `double reciprocal`)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
} else {
m_list <- list(Linear = Linear,
`Cobb Douglas` = loglog,
Linlog = linlog,
Loglin = loglin,
`Reciprocal in X` = `reciprocal in X`,
`Reciprocal in Y` = `reciprocal in Y`,
`Double reciprocal` = `double reciprocal`,
Quadratic = quadratic,
`Square root` = `square root`,
`Cubic root` = `cubic root`,
Cubic = cube,
`Mixed-power` = perlog)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Cobb Douglas` = exp(fitted.values(loglog)),
`Mixed-power` = exp(fitted.values(perlog)),
Linlog = fitted.values(linlog),
Loglin = exp(fitted.values(loglin)),
Quadratic = fitted.values(quadratic),
Cubic = fitted.values(cube),
`Square root` = fitted.values(`square root`),
`Cubic root` = fitted.values(`cubic root`),
`Reciprocal in X` =
fitted.values(`reciprocal in X`),
`Reciprocal in Y` =
1 / fitted.values(`reciprocal in Y`),
`Double reciprocal` =
1 / fitted.values(`double reciprocal`))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Cobb Douglas` = exp(predict(loglog)),
`Mixed-power` = exp(predict(perlog)),
Linlog = predict(linlog),
Loglin = exp(predict(loglin)),
Quadratic = predict(quadratic),
Cubic = predict(cube),
`Square root` = predict(`square root`),
`Cubic root` = predict(`cubic root`),
`Reciprocal in X` =
predict(`reciprocal in X`),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`),
`Double reciprocal` =
1 / predict(`double reciprocal`))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Cobb Douglas` = exp(predict(loglog, Test_ll)),
`Mixed-power` = exp(predict(perlog, Test_p)),
Linlog = predict(linlog, Test_l),
Loglin = exp(predict(loglin, Test)),
Quadratic = predict(quadratic, Test_q),
Cubic = predict(cube, Test_c),
`Square root` = predict(`square root`, Test_s),
`Cubic root` = predict(`cubic root`, Test_cr),
`Reciprocal in X` =
predict(`reciprocal in X`, Test_X),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`, Test),
`Double reciprocal` =
1 / predict(`double reciprocal`, Test_X))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Cobb Douglas` = exp(loglog[["effects"]]),
`Mixed-power` = exp(perlog[["effects"]]),
Linlog = linlog[["effects"]],
Loglin = exp(loglin[["effects"]]),
Quadratic = quadratic[["effects"]],
Cubic = cube[["effects"]],
`Square root` = `square root`[["effects"]],
`Cubic root` = `cubic root`[["effects"]],
`Reciprocal in X` =
`reciprocal in X`[["effects"]],
`Reciprocal in Y` =
1 / `reciprocal in Y`[["effects"]],
`Double reciprocal` =
1 / `double reciprocal`[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears,
`Cobb Douglas` = loglogs,
Linlog = linlogs,
Loglin = loglins,
`Reciprocal in X` = reciX,
`Reciprocal in Y` = reciY,
`Double reciprocal` = reciD,
Quadratic = quadratics,
`Square root` = squares,
`Cubic root` = cubics,
Cubic = cubes,
`Mixed-power` = perlogs))
Anova <- stats::anova(Linear, loglog, perlog, linlog, loglin,
quadratic, cube, `square root`, `cubic root`,
`reciprocal in X`, `reciprocal in X`,
`double reciprocal`)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
}
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
}
ezhe <- list("Visual means of the numeric variable" = e_meanplot,
"Correlation plot" = e_corplot,
"Summary of numeric variables" = KNN1,
"Summary of character variables" = KKC,
"Linear" = Linear,
"Significant plot of Linear" = v_Linear)
if (mod == 0) {
results <- list("Visual means of the numeric variable" = e_meanplot,
"Correlation plot" = e_corplot,
"Summary of numeric variables" = KNN1,
"Summary of character variables" = KKC)
} else if (mod == 1) {
result <- list("Linear with interaction" = LinearI,
"Significant plot of Linear with interaction" = v_LinearI)
} else if (mod == 2) {
result <- list("Semilog" = linlog,
"Significant plot of Semilog" = v_linlog,
"Growth" = loglin,
"Significant plot of Growth" = v_loglin,
"Double Log" = loglog,
"Significant plot of Double Log" = v_loglog,
"Mixed-power model" = perlog,
"Significant plot of Mixed-power model" = v_perlog,
"Translog model" = translog,
"Significant plot of Translog model" = v_translog)
} else if (mod == 3) {
result <- list("Quadratic" = quadratic,
"Significant plot of Quadratic" = v_quadratic,
"Cubic model" = cube,
"Significant plot of Cubic model" = v_cube)
} else if (mod == 4) {
result <- list("Square root" = `square root`,
"Significant plot of Square root" = v_square,
"Cubic root" = `cubic root`,
"Significant plot of Cubic root" = v_cubic)
} else if (mod == 5) {
result <- list("Inverse y" = `reciprocal in Y`,
"Significant plot of Inverse y" = v_reciY,
"Inverse x" = `reciprocal in X`,
"Significant plot of Inverse x" = v_reciX,
"Inverse y & x" = `double reciprocal`,
"Significant plot of Inverse y & x" = v_reciD)
} else {
result <- list("Linear with interaction" = LinearI,
"Significant plot of Linear with interaction" = v_LinearI,
"Semilog" = linlog,
"Significant plot of Semilog" = v_linlog,
"Growth" = loglin,
"Significant plot of Growth" = v_loglin,
"Double Log" = loglog,
"Significant plot of Double Log" = v_loglog,
"Mixed-power model" = perlog,
"Significant plot of Mixed-power model" = v_perlog,
"Translog model" = translog,
"Significant plot of Translog model" = v_translog,
"Quadratic" = quadratic,
"Significant plot of Quadratic" = v_quadratic,
"Cubic model" = cube,
"Significant plot of Cubic model" = v_cube,
"Square root" = `square root`,
"Significant plot of Square root" = v_square,
"Cubic root" = `cubic root`,
"Significant plot of Cubic root" = v_cubic,
"Inverse y" = `reciprocal in Y`,
"Significant plot of Inverse y" = v_reciY,
"Inverse x" = `reciprocal in X`,
"Significant plot of Inverse x" = v_reciX,
"Inverse y & x" = `double reciprocal`,
"Significant plot of Inverse y & x" = v_reciD)
}
if (mod != 0) {
fitted_long <- fitted_long(Fitted)
fitted_wide <- fitted_wide(Fitted)
Predicted_long <- Predicted_long(Predicted)
Predicted_wide <- Predicted_wide(Predicted)
Effects_long <- Effects_long(Effects)
Effects_wide <- Effects_wide(Effects)
if (case != "complex") {
rezult <- list("Model Table" = ModelTable,
"Machine Learning Metrics" = evaluation,
"Tables of marginal effects" = e_table,
"Fitted plots long format" = fitted_long,
"Fitted plots wide format" = fitted_wide,
"Prediction plots long format" = Predicted_long,
"Prediction plots wide format" = Predicted_wide,
"Naive effects plots long format" = Effects_long,
"Naive effects plots wide format" = Effects_wide)
} else {
rezult <- list("Model Table" = ModelTable,
"Machine Learning Metrics" = evaluation,
"Fitted plots long format" = fitted_long,
"Fitted plots wide format" = fitted_wide,
"Prediction plots long format" = Predicted_long,
"Prediction plots wide format" = Predicted_wide,
"Naive effects plots long format" = Effects_long,
"Naive effects plots wide format" = Effects_wide)
}
results <- c(ezhe, result, rezult)
}
return(results)
}
mmmd <- function(family = NULL, face = NULL, size = NULL, colour = NULL,
fill = NULL, box.colour = NULL, linetype = NULL,
linewidth = NULL,
hjust = NULL, vjust = NULL, halign = NULL, valign = NULL,
angle = NULL, lineheight = NULL, margin = NULL,
padding = NULL,
r = NULL, color = NULL, box.color = NULL, align_widths = NULL,
align_heights = NULL, rotate_margins = NULL, debug = FALSE,
inherit.blank = FALSE)
{
if (!is.null(color))
colour <- color
if (!is.null(box.color))
box.colour <- box.color
structure(list(family = family, face = face, size = size,
colour = colour, fill = fill, box.colour = box.colour,
linetype = linetype, linewidth = linewidth, hjust = hjust,
vjust = vjust, halign = halign, valign = valign, angle = angle,
lineheight = lineheight, margin = margin, padding = padding,
r = r, align_widths = align_widths,
align_heights = align_heights,
rotate_margins = rotate_margins, debug = debug,
inherit.blank = inherit.blank),
class = c("element_markdown", "element_text", "element"))
}
fitted_long <- function(Fitted) {
ggplot2::ggplot(Fitted) +
ggplot2::aes(x = Observed,
y = Fitted,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal()
}
fitted_wide <- function(Fitted) {
ggplot2::ggplot(Fitted) +
ggplot2::aes(
x = Observed,
y = Fitted,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal() +
ggplot2::facet_wrap(ggplot2::vars(Model))
}
Predicted_long <- function(Predicted) {
ggplot2::ggplot(Predicted) +
ggplot2::aes(x = Observed,
y = Predicted,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal()
}
Predicted_wide <- function(Predicted) {
ggplot2::ggplot(Predicted) +
ggplot2::aes(x = Observed,
y = Predicted,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal() +
ggplot2::facet_wrap(ggplot2::vars(Model))
}
Effects_long <- function(Effects) {
ggplot2::ggplot(Effects) +
ggplot2::aes(x = Observed,
y = Effects,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal()
}
Effects_wide <- function(Effects) {
ggplot2::ggplot(Effects) +
ggplot2::aes(x = Observed,
y = Effects,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal() +
ggplot2::facet_wrap(ggplot2::vars(Model))
}
JobNmadu/Dyn4cast documentation built on June 15, 2025, 9:28 a.m.
R Package Documentation
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Defines functions Effects_wide Effects_long Predicted_wide Predicted_long fitted_wide fitted_long mmmd Linearsystems
Documented in Linearsystems
#' Linear Model and various Transformations for Efficiency
#'
#' @description
#' The linear model still remains a reference point towards advanced modeling of
#' some datasets as foundation for **Machine Learning**, **Data Science** and
#' **Artificial Intelligence** in spite of some of her weaknesses. The major
#' task in **modeling** is to compare various models before a selection is
#' made for one or for advanced modeling. Often, some trial and error methods
#' are used to decide which model to select. This is where this function is
#' unique. It helps to estimate 14 different linear models and provide
#' their coefficients in a formatted Table for quick comparison so that
#' time and energy are saved. The interesting thing about this function is
#' the simplicity, and it is a _one line_ code.
#'
#' @param y Vector of the dependent variable. This must be numeric.
#' @param x Data frame of the explanatory variables.
#' @param mod The group of linear models to be estimated. It takes value from 0
#' to 6. 0 = EDA (correlation, summary tables, Visuals means); 1 = Linear
#' systems, 2 = power models, 3 = polynomial models, 4 = root models,
#' 5 = inverse models, 6 = all the 14 models
#' @param limit Number of variables to be included in the coefficients plots
#' @param Test test data to be used to predict y. If not supplied, the fitted y
#' is used hence may be identical with the fitted value. It is important to be
#' cautious if the data is to be divided between train and test subsets in
#' order to train and test the model. If the sample size is not sufficient to
#' have enough data for the test, errors are thrown up.
#'
#' @return A list with the following components:
#' \item{\code{Visual means of the numeric variable}}{Plot of the means of the
#' _numeric_ variables.}
#' \item{\code{Correlation plot}}{Plot of the Correlation Matrix of the
#' _numeric_ variables. To recover the plot, please use this canonical form
#' _object$`Correlation plot`$plot()_.}
#' \item{\code{Linear}}{The full estimates of the Linear Model.}
#' \item{\code{Linear with interaction}}{The full estimates of the Linear Model
#' with full interaction among the _numeric_ variables.}
#' \item{\code{Semilog}}{The full estimates of the Semilog Model. Here the
#' independent variable(s) is/are log-transformed.}
#' \item{\code{Growth}}{The full estimates of the Growth Model. Here the
#' dependent variable is log-transformed.}
#' \item{\code{Double Log}}{The full estimates of the double-log Model. Here
#' the both the dependent and independent variables are log-transformed.}
#' \item{\code{Mixed-power model}}{The full estimates of the Mixed-power Model.
#' This is a combination of linear and double log models. It has significant
#' gains over the two models separately.}
#' \item{\code{Translog model}}{The full estimates of the double-log Model with
#' full interaction of the _numeric_ variables.}
#' \item{\code{Quadratic}}{The full estimates of the Quadratic Model.
#' Here the square of _numeric_ independent variable(s) is/are included as
#' independent variables.}
#' \item{\code{Cubic model}}{The full estimates of the Cubic Model. Here the
#' third-power (x^3) of _numeric_ independent variable(s) is/are included as
#' independent variables.}
#' \item{\code{Inverse y}}{The full estimates of the Inverse Model. Here the
#' dependent variable is inverse-transformed (1 / y).}
#' \item{\code{Inverse x}}{The full estimates of the Inverse Model. Here the
#' independent variable is inverse-transformed (1 / x).}
#' \item{\code{Inverse y & x}}{The full estimates of the Inverse Model. Here
#' the dependent and independent variables are inverse-transformed 1 / y & 1 /
#' x).}
#' \item{\code{Square root}}{The full estimates of the Square root Model. Here
#' the independent variable is square root-transformed (x^0.5).}
#' \item{\code{Cubic root}}{The full estimates of the cubic root Model. Here
#' the independent variable is cubic root-transformed (x^1 / 3).}
#' \item{\code{Significant plot of Linear}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Linear with interaction}}{Plots of order of
#' importance and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Semilog}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Growth}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Double Log}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Mixed-power model}}{Plots of order of
#' importance and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Translog model}}{Plots of order of
#' importance and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Quadratic}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Cubic model}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Inverse y}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Inverse x}}{Plots of order of importance and
#' significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Inverse y & x}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Square root}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Significant plot of Cubic root}}{Plots of order of importance
#' and significance of estimates coefficients of the model.}
#' \item{\code{Model Table}}{Formatted Tables of the coefficient estimates of
#' all the models}
#' \item{\code{Machine Learning Metrics}}{Metrics (47) for assessing model
#' performance and metrics for diagnostic analysis of the error in estimation.}
#' \item{\code{Table of Marginal effects}}{Tables of marginal effects of each
#' model. Because of computational limitations, if you choose to estimate all
#' the 14 models, the Tables are produced separately for the major
#' transformations. They can easily be compiled into one.}
#' \item{\code{Fitted plots long format}}{Plots of the fitted estimates from
#' each of the model.}
#' \item{\code{Fitted plots wide format}}{Plots of the fitted estimates from
#' each of the model.}
#' \item{\code{Prediction plots long format}}{Plots of the predicted estimates
#' from each of the model.}
#' \item{\code{Prediction plots wide format}}{Plots of the predicted estimates
#' from each of the model.}
#' \item{\code{Naive effects plots long format}}{Plots of the `lm` effects.
#' May be identical with plots of marginal effects if performed.}
#' \item{\code{Naive effects plots wide format}}{Plots of the `lm` effects.
#' May be identical with plots of marginal effects if performed.}
#' \item{\code{Summary of numeric variables}}{of the dataset.}
#' \item{\code{Summary of character variables}}{of the dataset.}
#'
#' @export Linearsystems
#'
#' @importFrom modelsummary modelsummary
#' @importFrom dplyr select_if
#' @importFrom ggplot2 geom_line
#' @importFrom ggplot2 scale_fill_hue
#' @importFrom ggplot2 scale_color_hue
#' @importFrom ggplot2 theme_minimal
#' @importFrom ggplot2 facet_wrap
#' @importFrom ggplot2 vars
#' @importFrom ggplot2 stat_summary
#' @importFrom ggplot2 geom_vline
#' @importFrom ggplot2 geom_jitter
#' @importFrom ggplot2 element_blank
#' @importFrom marginaleffects avg_slopes
#' @importFrom tibble rownames_to_column
#' @importFrom utils globalVariables
#'
#' @name Linearsystems
#'
#' @aliases linearsystems
#' @aliases sampling
#'
#' @examples
#' ## Without test data (not run)
#' # library(tidyverse)
#' # y <- linearsystems$MKTcost # to run all the exercises, uncomment.
#' # x <- select(linearsystems, -MKTcost)
#' # Linearsystems(y, x, 6, 15) # NaNs produced if run
#' ## Without test data (not run)
#' # x <- sampling[, -1]
#' # y <- sampling$qOutput
#' # limit <- 20
#' # mod <-3
#' # Test <- NA
#' # Linearsystems(y, x, 3, 15) # NaNs produced if run
#' # # with test data
#' # x <- sampling[, -1]
#' # y <- sampling$qOutput
#' # Data <- cbind(y, x)
#' # # 80% of data is sampled
#' # sampling <- sample(1 : nrow(Data), 0.8 * nrow(Data))
#' # # for training the model
#' # train <- Data[sampling, ]
#' # Test <- Data[-sampling, ]
#' # # 20% of data is reserved for testing (predicting) the model
#' # y <- train$y
#' # x <- train[, -1]
#' # mod <- 4
#' # Linearsystems(y, x, 4, 15, Test) # NaNs produced if run
#'
#' @usage Linearsystems(y, x, mod, limit, Test = NA)
utils::globalVariables(c("Variables", "Model", "values", "Observed"))
Linearsystems <- function(y, x, mod, limit, Test = NA) {
y1 <- y
Data <- cbind(y, x)
Names <- names(Data)
case <- if (ncol(Data) > 9) {
"complex"
} else {
"normal"
}
mod <- mod
xnum <- x %>%
dplyr::select_if(is.numeric)
xnum_n <- names(xnum)
xcha <- x %>%
dplyr::select_if(is.character)
xcha_n <- names(xcha)
y <- Data$y
Linear <- lm(y ~ ., data = Data)
Linears <- MLMetrics(Observed = Data, yvalue = y, modeli = Linear,
K = 2, Name = "Linear", Form = "LM", kutuf = 0,
TTy = "Number")
v_Linear <- estimate_plot(model25 = Linear, limit = limit)
if (case != "complex") {
e_Linear <- marginaleffects::avg_slopes(Linear, by = TRUE)
} else {
e_Linear <- NULL
}
KNN <- Data %>% dplyr::select_if(is.numeric)
KNN1 <- quicksummary(x = KNN, Type = 1)
KKC <- Data %>%
dplyr::select_if(is.character) %>%
summary()
if (length(KNN) < 10) {
YYY <- paste(c("y", paste(c(paste("x", 1 : (length(KNN)-1), sep = "")))))
} else if (length(KNN) == 10) {
YYY <- paste(c("y", paste(c(paste("x0", 1 : 9, sep = "")))))
} else{
YYY <- paste(c("y", paste(c(paste("x0", 1 : 9, sep = ""),
(paste("x", 10 : (length(KNN) - 1),
sep = ""))))))
}
colnames(KNN) <- YYY
e_corplot <- corplot(stats::cor(KNN))
Data1 <- tidyr::pivot_longer(KNN, tidyr::everything(), names_to = "Variables",
values_to = "values")
e_meanplot <- ggplot2::ggplot(Data1, ggplot2::aes(x = Variables,
y = values)) +
ggplot2::geom_jitter(alpha = 0.7,
shape = 16,
width = 0.2,
color = "cadetblue") +
ggplot2::geom_vline(
xintercept = seq(.5, length(unique(Data1$Variables)), by = 1),
color = "gray90",
linewidth = 1) +
ggplot2::stat_summary(fun = "mean",
geom = "point",
size = 5,
alpha = 0.6,
shape = 16,
color = "tomato") +
ggplot2::stat_summary(
ggplot2::aes(label = round(ggplot2::after_stat(y), 1), fontface = "bold"),
fun = mean,
geom = "label", #try text if you want
#binwidth = 4,
alpha = 0.6,
label.size = NA, # remove border around label
vjust = -0.5,
color = "tomato") +
ggplot2::labs(
title = "Visualised means of the numeric variables in the model",
subtitle = "",
x = "Variables",
y = "Values") +
ggplot2::theme_minimal() +
ggplot2::theme(panel.grid = ggplot2::element_blank(),
plot.subtitle = mmmd())
if (!mod %in% 0:6) {
message("Module out of range")
stop(message(paste("Choose between", 0,
"and", 6)))
}
if (mod == 5) {
Data <- cbind(y, xnum, xcha)
`reciprocal in Y` <- lm((1 / (y + 1)) ~ ., data = Data)
reciY <- MLMetrics(Observed = Data, yvalue = y, modeli = `reciprocal in Y`,
K = 2, Name = "Semilog in Y", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciY <- estimate_plot(model25 = `reciprocal in Y`, limit = limit)
if (case != "complex") {
e_reciY <- marginaleffects::avg_slopes(`reciprocal in Y`, by = TRUE)
} else {
e_reciY <- NULL
}
xnum8 <- 1 / (xnum + 1)
Data <- cbind(y = y, xnum8, xcha)
y <- Data$y
`reciprocal in X` <- lm(y ~ ., data = Data)
reciX <- MLMetrics(Observed = Data, yvalue = y, modeli = `reciprocal in X`,
K = 2, Name = "Inverse in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciX <- estimate_plot(model25 = `reciprocal in X`, limit = limit)
if (case != "complex") {
e_reciX <- marginaleffects::avg_slopes(`reciprocal in X`, by = TRUE)
} else {
e_reciX <- NULL
}
Data$y <- y <- 1 / (y + 1)
`double reciprocal` <- lm(y ~ ., data = Data)
reciD <- MLMetrics(Observed = Data, yvalue = y, modeli = `double reciprocal`,
K = 2, Name = "Inverse in Y & X", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciD <- estimate_plot(model25 = `double reciprocal`, limit = limit)
if (case != "complex") {
e_reciD <- marginaleffects::avg_slopes(`double reciprocal`, by = TRUE)
} else {
e_reciD <- NULL
}
} else if (mod == 4) {
xnum3 <- xnum^.5
names(xnum3) <- paste("I", names(xnum3), sep = "")
DD3 <- cbind(y, xnum, xcha, xnum3)
Data <- DD3
y <- Data$y
`square root` <- lm(y ~ ., data = Data)
squares <- MLMetrics(Observed = Data, yvalue = y, modeli = `square root`,
K = 2, Name = "Square root", Form = "LM", kutuf = 0,
TTy = "Number")
v_square <- estimate_plot(model25 = `square root`, limit = limit)
if (case != "complex") {
e_square <- marginaleffects::avg_slopes(`square root`, by = TRUE)
} else {
e_square <- NULL
}
xnum4 <- xnum^(1 / 3)
names(xnum4) <- paste("I", names(xnum4), sep = "")
DD4 <- cbind(y, xnum, xcha, xnum4)
Data <- DD4
y <- Data$y
`cubic root` <- lm(y ~ ., data = Data)
cubics <- MLMetrics(Observed = Data, yvalue = y, modeli = `cubic root`,
K = 2, Name = "Cubic root", Form = "LM", kutuf = 0,
TTy = "Number")
v_cubic <- estimate_plot(model25 = `cubic root`, limit = limit)
if (case != "complex") {
e_cubic <- marginaleffects::avg_slopes(`cubic root`, by = TRUE)
} else {
e_cubic <- NULL
}
} else if (mod == 3) {
xnum1 <- xnum^2
names(xnum1) <- paste("I", names(xnum1), sep = "")
DD2 <- cbind(y, xnum, xcha, xnum1)
Data <- DD2
y <- Data$y
quadratic <- lm(y ~ ., data = Data)
quadratics <- MLMetrics(Observed = Data, yvalue = y, modeli = quadratic,
K = 2, Name = "Quadratic", Form = "LM", kutuf = 0,
TTy = "Number")
v_quadratic <- estimate_plot(model25 = quadratic, limit = limit)
if (case != "complex") {
e_quadratic <- marginaleffects::avg_slopes(quadratic, by = TRUE)
} else {
e_quadratic <- NULL
}
xnum2 <- xnum^3
names(xnum2) <- paste("IC", names(xnum), sep = "")
Data <- cbind(DD2, xnum2)
y <- Data$y
cube <- lm(y ~ ., data = Data)
cubes <- MLMetrics(Observed = Data, yvalue = y, modeli = cube,
K = 2, Name = "Cube", Form = "LM", kutuf = 0,
TTy = "Number")
v_cube <- estimate_plot(model25 = cube, limit = limit)
if (case != "complex") {
e_cube <- marginaleffects::avg_slopes(cube, by = TRUE)
} else {
e_cube <- NULL
}
}else if (mod == 2) {
Data <- cbind(y = log(y + 1), xnum, xcha)
y <- Data$y
loglin <- lm(y ~ ., data = Data)
loglins <- MLMetrics(Observed = Data, yvalue = y, modeli = loglin,
K = 2, Name = "Semilog in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_loglin <- estimate_plot(model25 = loglin, limit = limit)
if (case != "complex") {
e_loglin <- marginaleffects::avg_slopes(loglin, by = TRUE)
} else {
e_loglin <- NULL
}
xnum5 <- log(xnum + 1)
DD5 <- cbind(y, xnum5, xcha)
Data <- DD5
y <- Data$y
linlog <- lm(y ~ ., data = Data)
linlogs <- MLMetrics(Observed = Data, yvalue = y, modeli = linlog,
K = 2, Name = "Semilog in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_linlog <- estimate_plot(model25 = linlog, limit = limit)
if (case != "complex") {
e_linlog <- marginaleffects::avg_slopes(linlog, by = TRUE)
} else {
e_linlog <- NULL
}
names(xnum5) <- paste("I", names(xnum5), sep = "")
DD6 <- cbind(y, xnum, xcha, xnum5)
Data <- DD6
y <- Data$y
perlog <- lm(y ~ ., data = Data)
perlogs <- MLMetrics(Observed = Data, yvalue = y, modeli = perlog,
K = 2, Name = "Mixed-power", Form = "LM", kutuf = 0,
TTy = "Number")
v_perlog <- estimate_plot(model25 = perlog, limit = limit)
if (case != "complex") {
e_perlog <- marginaleffects::avg_slopes(perlog, by = TRUE)
} else {
e_perlog <- NULL
}
DD7 <- cbind(y = y, xnum)
DD7 <- log(DD7 + 1)
Data <- cbind(DD7, xcha)
y = Data$y
loglog <- lm(y ~ ., data = Data)
loglogs <- MLMetrics(Observed = Data, yvalue = y, modeli = loglog,
K = 2, Name = "Cobb Douglas", Form = "LM", kutuf = 0,
TTy = "Number")
v_loglog <- estimate_plot(model25 = loglog, limit = limit)
if (case != "complex") {
e_loglog <- marginaleffects::avg_slopes(loglog, by = TRUE)
} else {
e_loglog <- NULL
}
xnum6 <- log(xnum + 1)
xnum6_n <- names(xnum6)
xnum7 <- xnum6^2
xnum7_n <- names(xnum7)
DD7 <- cbind(xnum6, xnum7, xcha)
if (length(xcha) == 0) {
names(DD7) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum6_n, "`",
sep = "",
collapse = "*"),
paste("`", "I", xnum7_n, "`",
sep = "",
collapse = "+")),
collapse = "+")),
collapse = " ~ "))
} else {
names(DD7) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""), paste(xcha_n,
sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum6_n, "`",
sep = "",
collapse = "*"),
paste("`", "I", xnum7_n, "`",
sep = "",
collapse = "+"),
paste("`", xcha_n, "`",
sep = "",
collapse = " + ")),
collapse = "+")),
collapse = " ~ "))
}
Data <- cbind(y = log(y + 1), DD7)
y <- Data$y
if (case != "complex") {
translog <- lm(MOD, data = Data)
translogs <- MLMetrics(Observed = Data, yvalue = y, modeli = translog,
K = 2, Name = "Translog", Form = "LM", kutuf = 0,
TTy = "Number")
v_translog <- estimate_plot(model25 = translog, limit = limit)
e_translog <- marginaleffects::avg_slopes(translog, by = TRUE)
} else {
translog <- NULL
translogs <- NULL
v_translog <- NULL
e_translog <- NULL
}
} else if (mod == 1) {
DD0 <- cbind(xnum, xcha)
DDn <- names(DD0)
if (length(xcha) == 0) {
names(DD0) <- paste(c(paste(xnum_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum_n, "`",
sep = "",
collapse = " * ")),
collapse = "+")),
collapse = " ~ "))
} else {
names(DD0) <- paste(c(paste(xnum_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum_n, "`",
sep = "",
collapse = " * "),
paste("`", xcha_n, "`",
sep = "",
collapse = " + ")),
collapse = "+")),
collapse = " ~ "))
}
Data <- cbind(y, DD0)
if (case != "complex") {
LinearI <- lm(MOD, data = Data)
LinearIs <- MLMetrics(Observed = Data, yvalue = y, modeli = LinearI,
K = 2, Name = "Linear", Form = "LM", kutuf = 0,
TTy = "Number")
v_LinearI <- estimate_plot(model25 = LinearI, limit = limit)
e_LinearI <- marginaleffects::avg_slopes(LinearI, by = TRUE)
} else {
LinearI <- NULL
LinearIs <- NULL
v_LinearI <- NULL
e_LinearI <- NULL
}
} else if (mod == 0) {
KNN1 <- KNN1
KKC <- KKC
e_corplot <- e_corplot
e_meanplot <- e_meanplot
} else {
DD0 <- cbind(xnum, xcha)
DDn <- names(DD0)
if (length(xcha) == 0) {
names(DD0) <- paste(c(paste(xnum_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum_n, "`",
sep = "",
collapse = " * ")),
collapse = "+")),
collapse = " ~ "))
} else {
names(DD0) <- paste(c(paste(xnum_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum_n, "`",
sep = "",
collapse = " * "),
paste("`", xcha_n, "`",
sep = "",
collapse = " + ")),
collapse = "+")),
collapse = " ~ "))
}
Data <- cbind(y, DD0)
if (case != "complex") {
LinearI <- lm(MOD, data = Data)
LinearIs <- MLMetrics(Observed = Data, yvalue = y, modeli = LinearI,
K = 2, Name = "Linear", Form = "LM", kutuf = 0,
TTy = "Number")
v_LinearI <- estimate_plot(model25 = LinearI, limit = limit)
e_LinearI <- marginaleffects::avg_slopes(LinearI, by = TRUE)
} else {
LinearI <- NULL
LinearIs <- NULL
v_LinearI <- NULL
e_LinearI <- NULL
}
Data <- cbind(y, xnum, xcha)
`reciprocal in Y` <- lm((1 / (y + 1)) ~ ., data = Data)
reciY <- MLMetrics(Observed = Data, yvalue = y, modeli = `reciprocal in Y`,
K = 2, Name = "Semilog in Y", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciY <- estimate_plot(model25 = `reciprocal in Y`, limit = limit)
if (case != "complex") {
e_reciY <- marginaleffects::avg_slopes(`reciprocal in Y`, by = TRUE)
} else {
e_reciY <- NULL
}
Data <- cbind(y = log(y + 1), xnum, xcha)
y <- Data$y
loglin <- lm(y ~ ., data = Data)
loglins <- MLMetrics(Observed = Data, yvalue = y, modeli = loglin,
K = 2, Name = "Semilog in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_loglin <- estimate_plot(model25 = loglin, limit = limit)
if (case != "complex") {
e_loglin <- marginaleffects::avg_slopes(loglin, by = TRUE)
} else {
e_loglin <- NULL
}
xnum1 <- xnum^2
names(xnum1) <- paste("I", names(xnum1), sep = "")
DD2 <- cbind(y, xnum, xcha, xnum1)
Data <- DD2
y <- Data$y
quadratic <- lm(y ~ ., data = Data)
quadratics <- MLMetrics(Observed = Data, yvalue = y, modeli = quadratic,
K = 2, Name = "Quadratic", Form = "LM", kutuf = 0,
TTy = "Number")
v_quadratic <- estimate_plot(model25 = quadratic, limit = limit)
if (case != "complex") {
e_quadratic <- marginaleffects::avg_slopes(quadratic, by = TRUE)
} else {
e_quadratic <- NULL
}
xnum2 <- xnum^3
names(xnum2) <- paste("IC", names(xnum), sep = "")
Data <- cbind(DD2, xnum2)
y <- Data$y
cube <- lm(y ~ ., data = Data)
cubes <- MLMetrics(Observed = Data, yvalue = y, modeli = cube,
K = 2, Name = "Cube", Form = "LM", kutuf = 0,
TTy = "Number")
v_cube <- estimate_plot(model25 = cube, limit = limit)
if (case != "complex") {
e_cube <- marginaleffects::avg_slopes(cube, by = TRUE)
} else {
e_cube <- NULL
}
xnum3 <- xnum^.5
names(xnum3) <- paste("I", names(xnum3), sep = "")
DD3 <- cbind(y, xnum, xcha, xnum3)
Data <- DD3
y <- Data$y
`square root` <- lm(y ~ ., data = Data)
squares <- MLMetrics(Observed = Data, yvalue = y, modeli = `square root`,
K = 2, Name = "Square root", Form = "LM", kutuf = 0,
TTy = "Number")
v_square <- estimate_plot(model25 = `square root`, limit = limit)
if (case != "complex") {
e_square <- marginaleffects::avg_slopes(`square root`, by = TRUE)
} else {
e_square <- NULL
}
xnum4 <- xnum^(1 / 3)
names(xnum4) <- paste("I", names(xnum4), sep = "")
DD4 <- cbind(y, xnum, xcha, xnum4)
Data <- DD4
y <- Data$y
`cubic root` <- lm(y ~ ., data = Data)
cubics <- MLMetrics(Observed = Data, yvalue = y, modeli = `cubic root`,
K = 2, Name = "Cubic root", Form = "LM", kutuf = 0,
TTy = "Number")
v_cubic <- estimate_plot(model25 = `cubic root`, limit = limit)
if (case != "complex") {
e_cubic <- marginaleffects::avg_slopes(`cubic root`, by = TRUE)
} else {
e_cubic <- NULL
}
xnum5 <- log(xnum + 1)
DD5 <- cbind(y, xnum5, xcha)
Data <- DD5
y <- Data$y
linlog <- lm(y ~ ., data = Data)
linlogs <- MLMetrics(Observed = Data, yvalue = y, modeli = linlog,
K = 2, Name = "Semilog in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_linlog <- estimate_plot(model25 = linlog, limit = limit)
if (case != "complex") {
e_linlog <- marginaleffects::avg_slopes(linlog, by = TRUE)
} else {
e_linlog <- NULL
}
names(xnum5) <- paste("I", names(xnum5), sep = "")
DD6 <- cbind(y, xnum, xcha, xnum5 )
Data <- DD6
y <- Data$y
perlog <- lm(y ~ ., data = Data)
perlogs <- MLMetrics(Observed = Data, yvalue = y, modeli = perlog,
K = 2, Name = "Mixed-power", Form = "LM", kutuf = 0,
TTy = "Number")
v_perlog <- estimate_plot(model25 = perlog, limit = limit)
if (case != "complex") {
e_perlog <- marginaleffects::avg_slopes(perlog, by = TRUE)
} else {
e_perlog <- NULL
}
DD7 <- cbind(y = y, xnum)
DD7 <- log(DD7 + 1)
Data <- cbind(DD7, xcha)
y <- Data$y
loglog <- lm(y ~ ., data = Data)
loglogs <- MLMetrics(Observed = Data, yvalue = y, modeli = loglog,
K = 2, Name = "Cobb Douglas", Form = "LM", kutuf = 0,
TTy = "Number")
v_loglog <- estimate_plot(model25 = loglog, limit = limit)
if (case != "complex") {
e_loglog <- marginaleffects::avg_slopes(loglog, by = TRUE)
} else {
e_loglog <- NULL
}
xnum6 <- log(xnum + 1)
xnum6_n <- names(xnum6)
xnum7 <- xnum6^2
xnum7_n <- names(xnum7)
DD7 <- cbind(xnum6, xnum7, xcha)
if (length(xcha) == 0) {
names(DD7) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""),
paste(xcha_n, sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum6_n, "`",
sep = "",
collapse = "*"),
paste("`", "I", xnum7_n, "`",
sep = "",
collapse = "+")),
collapse = "+")),
collapse = " ~ "))
} else {
names(DD7) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""), paste(xcha_n,
sep = "")))
MOD <- stats::formula(paste(c("y", paste(c(paste("`", xnum6_n, "`",
sep = "",
collapse = "*"),
paste("`", "I", xnum7_n, "`",
sep = "",
collapse = "+"),
paste("`", xcha_n, "`",
sep = "",
collapse = " + ")),
collapse = "+")),
collapse = " ~ "))
}
Data <- cbind(y = log(y + 1), DD7)
y <- Data$y
if (case != "complex") {
translog <- lm(MOD, data = Data)
translogs <- MLMetrics(Observed = Data, yvalue = y, modeli = translog,
K = 2, Name = "Translog", Form = "LM", kutuf = 0,
TTy = "Number")
v_translog <- estimate_plot(model25 = translog, limit = limit)
e_translog <- marginaleffects::avg_slopes(translog, by = TRUE)
} else {
translog <- NULL
translogs <- NULL
v_translog <- NULL
e_translog <- NULL
}
xnum8 <- 1 / (xnum + 1)
Data <- cbind(y = y, xnum8, xcha)
y <- Data$y
`reciprocal in X` <- lm(y ~ ., data = Data)
reciX <- MLMetrics(Observed = Data, yvalue = y, modeli = `reciprocal in X`,
K = 2, Name = "Inverse in X", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciX <- estimate_plot(model25 = `reciprocal in X`, limit = limit)
if (case != "complex") {
e_reciX <- marginaleffects::avg_slopes(`reciprocal in X`, by = TRUE)
} else {
e_reciX <- NULL
}
Data$y <- y <- 1 / (y + 1)
`double reciprocal` <- lm(y ~ ., data = Data)
reciD <- MLMetrics(Observed = Data, yvalue = y, modeli = `double reciprocal`,
K = 2, Name = "Inverse in Y & X", Form = "LM", kutuf = 0,
TTy = "Number")
v_reciD <- estimate_plot(model25 = `double reciprocal`, limit = limit)
if (case != "complex") {
e_reciD <- marginaleffects::avg_slopes(`double reciprocal`, by = TRUE)
} else {
e_reciD <- NULL
}
}
Test <- Test
AA <- dim(Test)
if (is.null(AA)) {
Test <- Test
} else {
Test <- Test
xt <- Test[, -1]
xnum <- xt %>%
dplyr::select_if(is.numeric)
xnum_n <- names(xnum)
xcha <- xt %>%
dplyr::select_if(is.character)
xcha_n <- names(xcha)
xnum1 <- xnum^2
names(xnum1) <- paste("I", names(xnum1), sep = "")
Test_q <- cbind(xnum, xcha, xnum1)
xnum2 <- xnum^3
names(xnum2) <- paste("IC", names(xnum), sep = "")
Test_c <- cbind(Test_q, xnum2)
xnum3 <- xnum^.5
names(xnum3) <- paste("I", names(xnum3), sep = "")
Test_s <- cbind(xnum, xcha, xnum3)
xnum4 <- xnum^(1 / 3)
names(xnum4) <- paste("I", names(xnum4), sep = "")
Test_cr <- cbind(xnum, xcha, xnum4)
xnum5 <- log(xnum + 1)
Test_l <- cbind(xnum5, xcha)
names(xnum5) <- paste("I", names(xnum5), sep = "")
Test_p <- cbind(xnum, xcha, xnum5)
DD7 <- log(xnum + 1)
Test_ll <- cbind(DD7, xcha)
xnum6 <- log(xnum + 1)
xnum6_n <- names(xnum6)
xnum7 <- xnum6^2
xnum7_n <- names(xnum7)
Test_t <- cbind(xnum6, xnum7, xcha)
names(Test_t) <- paste(c(paste(xnum6_n, sep = ""),
paste("I", xnum7_n, sep = ""), paste(xcha_n,
sep = "")))
xnum8 <- 1 / (xnum + 1)
Test_X <- cbind(xnum8, xcha)
}
if (mod == 5) {
if (case != "complex") {
e_list <- list(Linear = e_Linear,
`Reciprocal in X` = e_reciX,
`Reciprocal in Y` = e_reciY,
`Double reciprocal` = e_reciD)
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_table <- NULL
}
m_list <- list(Linear = Linear,
`Reciprocal in X` = `reciprocal in X`,
`Reciprocal in Y` = `reciprocal in Y`,
`Double reciprocal` = `double reciprocal`)
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
Anova <- stats::anova(Linear, `reciprocal in X`, `reciprocal in X`,
`double reciprocal`)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Reciprocal in X` = fitted.values(`reciprocal in X`),
`Reciprocal in Y` =
1 / fitted.values(`reciprocal in Y`),
`Double reciprocal` =
1 / fitted.values(`double reciprocal`))
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Reciprocal in X` = predict(`reciprocal in X`),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`),
`Double reciprocal` =
1 / predict(`double reciprocal`))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Reciprocal in X` =
predict(`reciprocal in X`, Test_X),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`, Test),
`Double reciprocal` =
1 / predict(`double reciprocal`, Test_X))
}
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Reciprocal in X` =
`reciprocal in X`[["effects"]],
`Reciprocal in Y` =
1 / `reciprocal in Y`[["effects"]],
`Double reciprocal` =
1 / `double reciprocal`[["effects"]])
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- data.frame(cbind(Linear = Linears,
`Reciprocal in X` = reciX,
`Reciprocal in Y` = reciY,
`Double reciprocal` = reciD))
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
} else if (mod == 4) {
if (case != "complex") {
e_list <- list(Linear = e_Linear,
`Square root` = e_square,
`Cubic root` = e_cubic)
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_table <- NULL
}
m_list <- list(Linear = Linear,
`Square root` = `square root`,
`Cubic root` = `cubic root`)
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
Anova <- stats::anova(Linear, `square root`, `cubic root`)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Square root` = fitted.values(`square root`),
`Cubic root` = fitted.values(`cubic root`))
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Square root` = predict(`square root`),
`Cubic root` = predict(`cubic root`))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Square root` = predict(`square root`, Test_s),
`Cubic root` = predict(`cubic root`, Test_cr))
}
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Square root` = `square root`[["effects"]],
`Cubic root` = `cubic root`[["effects"]])
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- data.frame(cbind(Linear = Linears,
`Square root` = squares,
`Cubic root` = cubics))
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
} else if (mod == 3) {
if (case != "complex") {
e_list <- list(Linear = e_Linear,
Quadratic = e_quadratic,
Cubic = e_cube)
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_table <- NULL
}
m_list <- list(Linear = Linear,
Quadratic = quadratic,
Cubic = cube)
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
Anova <- stats::anova(Linear, quadratic, cube)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
Quadratic = fitted.values(quadratic),
Cubic = fitted.values(cube))
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
Quadratic = predict(quadratic),
Cubic = predict(cube))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Square root` = predict(quadratic, Test_s),
`Cubic root` = predict(cube, Test_c))
}
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
Quadratic = quadratic[["effects"]],
Cubic = cube[["effects"]])
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- data.frame(cbind(Linear = Linears,
Quadratic = quadratics,
Cubic = cubes))
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
}else if (mod == 2) {
if (case != "complex") {
e_list <- list(Linear = e_Linear,
`Cobb Douglas` = e_loglog,
Linlog = e_linlog,
Loglin = e_loglin,
`Mixed-power` = e_perlog,
Translog = e_translog)
m_list <- list(Linear = Linear,
`Cobb Douglas` = loglog,
Linlog = linlog,
Loglin = loglin,
`Mixed-power` = perlog,
Translog = translog)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Cobb Douglas` = exp(fitted.values(loglog)),
Translog = exp(fitted.values(translog)),
`Mixed-power` = exp(fitted.values(perlog)),
Linlog = fitted.values(linlog),
Loglin = exp(fitted.values(loglin)))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Cobb Douglas` = exp(predict(loglog)),
Translog = exp(predict(translog)),
`Mixed-power` = exp(predict(perlog)),
Linlog = predict(linlog),
Loglin = exp(predict(loglin)))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Cobb Douglas` = exp(predict(loglog, Test_ll)),
Translog = exp(predict(translog, Test_t)),
`Mixed-power` = exp(predict(perlog, Test_p)),
Linlog = predict(linlog, Test_l),
Loglin = exp(predict(loglin, Test)))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Cobb Douglas` = exp(loglog[["effects"]]),
Translog = exp(translog[["effects"]]),
`Mixed-power` = exp(perlog[["effects"]]),
Linlog = linlog[["effects"]],
Loglin = exp(loglin[["effects"]]))
evaluation <- data.frame(cbind(Linear = Linears,
`Cobb Douglas` = loglogs,
Linlog = linlogs,
Loglin = loglins,
`Mixed-power` = perlogs,
Translog = translogs))
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_list <- NULL
e_table <- NULL
m_list <- list(Linear = Linear,
`Cobb Douglas` = loglog,
Linlog = linlog,
Loglin = loglin,
`Mixed-power` = perlog)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Cobb Douglas` = exp(fitted.values(loglog)),
`Mixed-power` = exp(fitted.values(perlog)),
Linlog = fitted.values(linlog),
Loglin = exp(fitted.values(loglin)))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Cobb Douglas` = exp(predict(loglog)),
`Mixed-power` = exp(predict(perlog)),
Linlog = predict(linlog),
Loglin = exp(predict(loglin)))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Cobb Douglas` = exp(predict(loglog, Test_ll)),
`Mixed-power` = exp(predict(perlog, Test_p)),
Linlog = predict(linlog, Test_l),
Loglin = exp(predict(loglin, Test)))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Cobb Douglas` = exp(loglog[["effects"]]),
`Mixed-power` = exp(perlog[["effects"]]),
Linlog = linlog[["effects"]],
Loglin = exp(loglin[["effects"]]))
evaluation <- data.frame(cbind(Linear = Linears,
`Cobb Douglas` = loglogs,
Linlog = linlogs,
Loglin = loglins,
`Mixed-power` = perlogs))
}
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
Anova <- stats::anova(Linear, loglog, perlog, translog, linlog, loglin)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
} else if (mod == 1) {
if (case != "complex") {
if (is.null(e_LinearI)) {
e_list <- list(Linear = e_Linear)
m_list <- list(Linear = Linear)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears,
`Linear with interaction` = LinearIs))
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
} else {
e_list <- list(Linear = e_Linear,
`Linear with interaction` = e_LinearI)
m_list <- list(Linear = Linear,
`Linear with interaction` = LinearI)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Linear with interaction` =
fitted.values(LinearI))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Linear with interaction` = predict(LinearI))
} else {
Predicted <- data.frame(Observed = y1,
Linear =
predict(Linear, Test),
`Linear with interaction` =
predict(LinearI, Test))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Linear with interaction` =
LinearI[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears,
`Linear with interaction` = LinearIs))
e_table <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
}
} else {
e_list <- NULL
m_list <- list(Linear = Linear)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears))
e_table <- NULL
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
}
Anova <- stats::anova(Linear, LinearI)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
} else if (mod == 0) {
cat("EDA...", "\n")
} else {
if (case != "complex") {
m_list <- list(Linear = Linear,
`Cobb Douglas` = loglog,
Linlog = linlog,
Loglin = loglin,
`Reciprocal in X` = `reciprocal in X`,
`Reciprocal in Y` = `reciprocal in Y`,
`Double reciprocal` = `double reciprocal`,
Quadratic = quadratic,
`Square root` = `square root`,
`Cubic root` = `cubic root`,
Cubic = cube,
`Mixed-power` = perlog,
Translog = translog,
`Linear with interaction` = LinearI)
e_list <- list(Linear = e_Linear,
`Linear with interaction` = e_LinearI)
e_table1 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
e_list <- list(`Cobb Douglas` = e_loglog,
Linlog = e_linlog,
Loglin = e_loglin,
`Mixed-power` = e_perlog,
Translog = e_translog)
e_table2 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
e_list <- list(`Reciprocal in X` = e_reciX,
`Reciprocal in Y` = e_reciY,
`Double reciprocal` = e_reciD)
e_table3 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
e_list <- list(Quadratic = e_quadratic,
Cubic = e_cube)
e_table4 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
e_list <- list(`Square root` = e_square,
`Cubic root` = e_cubic)
e_table5 <- modelsummary::modelsummary(e_list,
shape = term : contrast ~ model,
stars = TRUE)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Linear with interaction` = fitted.values(LinearI),
`Cobb Douglas` = exp(fitted.values(loglog)),
Translog = exp(fitted.values(translog)),
`Mixed-power` = exp(fitted.values(perlog)),
Linlog = fitted.values(linlog),
Loglin = exp(fitted.values(loglin)),
Quadratic = fitted.values(quadratic),
Cubic = fitted.values(cube),
`Square root` = fitted.values(`square root`),
`Cubic root` = fitted.values(`cubic root`),
`Reciprocal in X` =
fitted.values(`reciprocal in X`),
`Reciprocal in Y` =
1 / fitted.values(`reciprocal in Y`),
`Double reciprocal` =
1 / fitted.values(`double reciprocal`))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Linear with interaction` = predict(LinearI),
`Cobb Douglas` = exp(predict(loglog)),
Translog = exp(predict(translog)),
`Mixed-power` = exp(predict(perlog)),
Linlog = predict(linlog),
Loglin = exp(predict(loglin)),
Quadratic = predict(quadratic),
Cubic = predict(cube),
`Square root` = predict(`square root`),
`Cubic root` = predict(`cubic root`),
`Reciprocal in X`
= predict(`reciprocal in X`),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`),
`Double reciprocal` =
1 / predict(`double reciprocal`))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Linear with interaction`
= predict(LinearI, Test),
`Cobb Douglas` = exp(predict(loglog, Test_ll)),
Translog = exp(predict(translog, Test_t)),
`Mixed-power` = exp(predict(perlog, Test_p)),
Linlog = predict(linlog, Test_l),
Loglin = exp(predict(loglin, Test)),
Quadratic = predict(quadratic, Test_q),
Cubic = predict(cube, Test_c),
`Square root` = predict(`square root`, Test_s),
`Cubic root` = predict(`cubic root`, Test_cr),
`Reciprocal in X` =
predict(`reciprocal in X`, Test_X),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`, Test),
`Double reciprocal` =
1 / predict(`double reciprocal`, Test_X))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Linear with interaction` =
LinearI[["effects"]],
`Cobb Douglas` = exp(loglog[["effects"]]),
Translog = exp(translog[["effects"]]),
`Mixed-power` = exp(perlog[["effects"]]),
Linlog = linlog[["effects"]],
Loglin = exp(loglin[["effects"]]),
Quadratic = quadratic[["effects"]],
Cubic = cube[["effects"]],
`Square root` = `square root`[["effects"]],
`Cubic root` = `cubic root`[["effects"]],
`Reciprocal in X` =
`reciprocal in X`[["effects"]],
`Reciprocal in Y` =
1 / `reciprocal in Y`[["effects"]],
`Double reciprocal` =
1 / `double reciprocal`[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears,
`Cobb Douglas` = loglogs,
Linlog = linlogs,
Loglin = loglins,
`Reciprocal in X` = reciX,
`Reciprocal in Y` = reciY,
`Double reciprocal` = reciD,
Quadratic = quadratics,
`Square root` = squares,
`Cubic root` = cubics,
Cubic = cubes,
`Mixed-power` = perlogs,
Translog = translogs,
`Linear with interaction` = LinearIs))
e_table <- list(e_table1, e_table2, e_table3, e_table4, e_table5)
Anova <- stats::anova(Linear, LinearI, loglog, perlog, translog, linlog,
loglin, quadratic,
cube, `square root`, `cubic root`,
`reciprocal in X`,
`reciprocal in X`, `double reciprocal`)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
} else {
m_list <- list(Linear = Linear,
`Cobb Douglas` = loglog,
Linlog = linlog,
Loglin = loglin,
`Reciprocal in X` = `reciprocal in X`,
`Reciprocal in Y` = `reciprocal in Y`,
`Double reciprocal` = `double reciprocal`,
Quadratic = quadratic,
`Square root` = `square root`,
`Cubic root` = `cubic root`,
Cubic = cube,
`Mixed-power` = perlog)
Fitted <- data.frame(Observed = y1,
Linear = fitted.values(Linear),
`Cobb Douglas` = exp(fitted.values(loglog)),
`Mixed-power` = exp(fitted.values(perlog)),
Linlog = fitted.values(linlog),
Loglin = exp(fitted.values(loglin)),
Quadratic = fitted.values(quadratic),
Cubic = fitted.values(cube),
`Square root` = fitted.values(`square root`),
`Cubic root` = fitted.values(`cubic root`),
`Reciprocal in X` =
fitted.values(`reciprocal in X`),
`Reciprocal in Y` =
1 / fitted.values(`reciprocal in Y`),
`Double reciprocal` =
1 / fitted.values(`double reciprocal`))
if (is.null(AA)) {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear),
`Cobb Douglas` = exp(predict(loglog)),
`Mixed-power` = exp(predict(perlog)),
Linlog = predict(linlog),
Loglin = exp(predict(loglin)),
Quadratic = predict(quadratic),
Cubic = predict(cube),
`Square root` = predict(`square root`),
`Cubic root` = predict(`cubic root`),
`Reciprocal in X` =
predict(`reciprocal in X`),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`),
`Double reciprocal` =
1 / predict(`double reciprocal`))
} else {
Predicted <- data.frame(Observed = y1,
Linear = predict(Linear, Test),
`Cobb Douglas` = exp(predict(loglog, Test_ll)),
`Mixed-power` = exp(predict(perlog, Test_p)),
Linlog = predict(linlog, Test_l),
Loglin = exp(predict(loglin, Test)),
Quadratic = predict(quadratic, Test_q),
Cubic = predict(cube, Test_c),
`Square root` = predict(`square root`, Test_s),
`Cubic root` = predict(`cubic root`, Test_cr),
`Reciprocal in X` =
predict(`reciprocal in X`, Test_X),
`Reciprocal in Y` =
1 / predict(`reciprocal in Y`, Test),
`Double reciprocal` =
1 / predict(`double reciprocal`, Test_X))
}
Effects <- data.frame(Observed = y1,
Linear = Linear[["effects"]],
`Cobb Douglas` = exp(loglog[["effects"]]),
`Mixed-power` = exp(perlog[["effects"]]),
Linlog = linlog[["effects"]],
Loglin = exp(loglin[["effects"]]),
Quadratic = quadratic[["effects"]],
Cubic = cube[["effects"]],
`Square root` = `square root`[["effects"]],
`Cubic root` = `cubic root`[["effects"]],
`Reciprocal in X` =
`reciprocal in X`[["effects"]],
`Reciprocal in Y` =
1 / `reciprocal in Y`[["effects"]],
`Double reciprocal` =
1 / `double reciprocal`[["effects"]])
evaluation <- data.frame(cbind(Linear = Linears,
`Cobb Douglas` = loglogs,
Linlog = linlogs,
Loglin = loglins,
`Reciprocal in X` = reciX,
`Reciprocal in Y` = reciY,
`Double reciprocal` = reciD,
Quadratic = quadratics,
`Square root` = squares,
`Cubic root` = cubics,
Cubic = cubes,
`Mixed-power` = perlogs))
Anova <- stats::anova(Linear, loglog, perlog, linlog, loglin,
quadratic, cube, `square root`, `cubic root`,
`reciprocal in X`, `reciprocal in X`,
`double reciprocal`)
Anova <- tibble::rownames_to_column(Anova, var = "Model")
Anova <- modelsummary::datasummary_df(Anova, stars = TRUE)
evaluation <- tibble::rownames_to_column(evaluation, var = "Name")
ModelTable <- modelsummary::modelsummary(m_list, stars = TRUE)
}
Fitted <- tidyr::pivot_longer(Fitted, -Observed, names_to = "Model",
values_to = "Fitted")
Predicted <- tidyr::pivot_longer(Predicted, -Observed, names_to = "Model",
values_to = "Predicted")
Effects <- tidyr::pivot_longer(Effects, -Observed, names_to = "Model",
values_to = "Effects")
}
ezhe <- list("Visual means of the numeric variable" = e_meanplot,
"Correlation plot" = e_corplot,
"Summary of numeric variables" = KNN1,
"Summary of character variables" = KKC,
"Linear" = Linear,
"Significant plot of Linear" = v_Linear)
if (mod == 0) {
results <- list("Visual means of the numeric variable" = e_meanplot,
"Correlation plot" = e_corplot,
"Summary of numeric variables" = KNN1,
"Summary of character variables" = KKC)
} else if (mod == 1) {
result <- list("Linear with interaction" = LinearI,
"Significant plot of Linear with interaction" = v_LinearI)
} else if (mod == 2) {
result <- list("Semilog" = linlog,
"Significant plot of Semilog" = v_linlog,
"Growth" = loglin,
"Significant plot of Growth" = v_loglin,
"Double Log" = loglog,
"Significant plot of Double Log" = v_loglog,
"Mixed-power model" = perlog,
"Significant plot of Mixed-power model" = v_perlog,
"Translog model" = translog,
"Significant plot of Translog model" = v_translog)
} else if (mod == 3) {
result <- list("Quadratic" = quadratic,
"Significant plot of Quadratic" = v_quadratic,
"Cubic model" = cube,
"Significant plot of Cubic model" = v_cube)
} else if (mod == 4) {
result <- list("Square root" = `square root`,
"Significant plot of Square root" = v_square,
"Cubic root" = `cubic root`,
"Significant plot of Cubic root" = v_cubic)
} else if (mod == 5) {
result <- list("Inverse y" = `reciprocal in Y`,
"Significant plot of Inverse y" = v_reciY,
"Inverse x" = `reciprocal in X`,
"Significant plot of Inverse x" = v_reciX,
"Inverse y & x" = `double reciprocal`,
"Significant plot of Inverse y & x" = v_reciD)
} else {
result <- list("Linear with interaction" = LinearI,
"Significant plot of Linear with interaction" = v_LinearI,
"Semilog" = linlog,
"Significant plot of Semilog" = v_linlog,
"Growth" = loglin,
"Significant plot of Growth" = v_loglin,
"Double Log" = loglog,
"Significant plot of Double Log" = v_loglog,
"Mixed-power model" = perlog,
"Significant plot of Mixed-power model" = v_perlog,
"Translog model" = translog,
"Significant plot of Translog model" = v_translog,
"Quadratic" = quadratic,
"Significant plot of Quadratic" = v_quadratic,
"Cubic model" = cube,
"Significant plot of Cubic model" = v_cube,
"Square root" = `square root`,
"Significant plot of Square root" = v_square,
"Cubic root" = `cubic root`,
"Significant plot of Cubic root" = v_cubic,
"Inverse y" = `reciprocal in Y`,
"Significant plot of Inverse y" = v_reciY,
"Inverse x" = `reciprocal in X`,
"Significant plot of Inverse x" = v_reciX,
"Inverse y & x" = `double reciprocal`,
"Significant plot of Inverse y & x" = v_reciD)
}
if (mod != 0) {
fitted_long <- fitted_long(Fitted)
fitted_wide <- fitted_wide(Fitted)
Predicted_long <- Predicted_long(Predicted)
Predicted_wide <- Predicted_wide(Predicted)
Effects_long <- Effects_long(Effects)
Effects_wide <- Effects_wide(Effects)
if (case != "complex") {
rezult <- list("Model Table" = ModelTable,
"Machine Learning Metrics" = evaluation,
"Tables of marginal effects" = e_table,
"Fitted plots long format" = fitted_long,
"Fitted plots wide format" = fitted_wide,
"Prediction plots long format" = Predicted_long,
"Prediction plots wide format" = Predicted_wide,
"Naive effects plots long format" = Effects_long,
"Naive effects plots wide format" = Effects_wide)
} else {
rezult <- list("Model Table" = ModelTable,
"Machine Learning Metrics" = evaluation,
"Fitted plots long format" = fitted_long,
"Fitted plots wide format" = fitted_wide,
"Prediction plots long format" = Predicted_long,
"Prediction plots wide format" = Predicted_wide,
"Naive effects plots long format" = Effects_long,
"Naive effects plots wide format" = Effects_wide)
}
results <- c(ezhe, result, rezult)
}
return(results)
}
mmmd <- function(family = NULL, face = NULL, size = NULL, colour = NULL,
fill = NULL, box.colour = NULL, linetype = NULL,
linewidth = NULL,
hjust = NULL, vjust = NULL, halign = NULL, valign = NULL,
angle = NULL, lineheight = NULL, margin = NULL,
padding = NULL,
r = NULL, color = NULL, box.color = NULL, align_widths = NULL,
align_heights = NULL, rotate_margins = NULL, debug = FALSE,
inherit.blank = FALSE)
{
if (!is.null(color))
colour <- color
if (!is.null(box.color))
box.colour <- box.color
structure(list(family = family, face = face, size = size,
colour = colour, fill = fill, box.colour = box.colour,
linetype = linetype, linewidth = linewidth, hjust = hjust,
vjust = vjust, halign = halign, valign = valign, angle = angle,
lineheight = lineheight, margin = margin, padding = padding,
r = r, align_widths = align_widths,
align_heights = align_heights,
rotate_margins = rotate_margins, debug = debug,
inherit.blank = inherit.blank),
class = c("element_markdown", "element_text", "element"))
}
fitted_long <- function(Fitted) {
ggplot2::ggplot(Fitted) +
ggplot2::aes(x = Observed,
y = Fitted,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal()
}
fitted_wide <- function(Fitted) {
ggplot2::ggplot(Fitted) +
ggplot2::aes(
x = Observed,
y = Fitted,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal() +
ggplot2::facet_wrap(ggplot2::vars(Model))
}
Predicted_long <- function(Predicted) {
ggplot2::ggplot(Predicted) +
ggplot2::aes(x = Observed,
y = Predicted,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal()
}
Predicted_wide <- function(Predicted) {
ggplot2::ggplot(Predicted) +
ggplot2::aes(x = Observed,
y = Predicted,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal() +
ggplot2::facet_wrap(ggplot2::vars(Model))
}
Effects_long <- function(Effects) {
ggplot2::ggplot(Effects) +
ggplot2::aes(x = Observed,
y = Effects,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal()
}
Effects_wide <- function(Effects) {
ggplot2::ggplot(Effects) +
ggplot2::aes(x = Observed,
y = Effects,
fill = Model,
colour = Model,
group = Model) +
ggplot2::geom_line(linewidth = 1.5) +
ggplot2::scale_fill_hue(direction = 1) +
ggplot2::scale_color_hue(direction = 1) +
ggplot2::theme_minimal() +
ggplot2::facet_wrap(ggplot2::vars(Model))
}
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