R/MLMetrics.R
In JobNmadu/Dyn4cast: Dynamic Modeling and Machine Learning Environment
Defines functions
MLMetrics
Documented in
MLMetrics
#' Collection of Machine Learning Model Metrics for Easy Reference
#'
#' @description
#' This function estimates over 40 Metrics for assessing the quality of Machine Learning Models. The purpose is to provide a wrapper which brings all the metrics on the table and makes it easier to use them to select a model.
#'
#' @param Observed The Observed data in a data frame format
#' @param yvalue The Response variable of the estimated Model
#' @param Model The Estimated Model (*Model* = a + bx)
#' @param K The number of variables in the estimated Model to consider
#' @param Name The Name of the Models that need to be specified. They are ARIMA, Values if the model computes the fitted value without estimation like Essembles, SMOOTH (smooth.spline), Logit, Ensembles based on weight - EssemWet, QUADRATIC polynomial, SPLINE polynomial.
#' @param Form Form of the Model Estimated (LM, ALM, GLM, N-LM, ARDL)
#' @param kutuf Cutoff for the Estimated values (defaults to 0.5 if not specified)
#' @param TTy Type of response variable (Numeric or Response - like *binary*)
#'
#' @export MLMetrics
#'
#' @import tidyverse
#' @importFrom broom augment
#' @importFrom stats AIC
#' @importFrom stats BIC
#' @importFrom stats na.omit
#' @importFrom Metrics accuracy
#' @importFrom Metrics ae
#' @importFrom Metrics ape
#' @importFrom Metrics apk
#' @importFrom Metrics bias
#' @importFrom Metrics f1
#' @importFrom Metrics ll
#' @importFrom Metrics mape
#' @importFrom Metrics mapk
#' @importFrom Metrics mase
#' @importFrom Metrics mdae
#' @importFrom Metrics msle
#' @importFrom Metrics percent_bias
#' @importFrom Metrics rae
#' @importFrom Metrics rmsle
#' @importFrom Metrics rrse
#' @importFrom Metrics rse
#' @importFrom Metrics se
#' @importFrom Metrics sle
#' @importFrom Metrics smape
#' @importFrom Metrics sse
#' @importFrom ModelMetrics auc
#' @importFrom ModelMetrics ce
#' @importFrom ModelMetrics f1Score
#' @importFrom ModelMetrics logLoss
#' @importFrom ModelMetrics mae
#' @importFrom ModelMetrics mse
#' @importFrom ModelMetrics precision
#' @importFrom ModelMetrics recall
#' @importFrom ModelMetrics rmse
#' @importFrom ModelMetrics brier
#' @importFrom ModelMetrics gini
#' @importFrom ModelMetrics kappa
#' @importFrom ModelMetrics sensitivity
#' @importFrom ModelMetrics specificity
#' @importFrom ModelMetrics fScore
#' @importFrom ModelMetrics mcc
#' @importFrom ModelMetrics tnr
#' @importFrom ModelMetrics tpr
#' @importFrom ModelMetrics ppv
#' @importFrom ModelMetrics npv
# #' @importFrom qpcR PRESS
#' @importFrom dplyr mutate
#' @importFrom stats predict
#'
#' @return A list with the following components:
#' \item{\code{Absolute Error}}{of the Model.}
#' \item{\code{Absolute Percent Error}}{of the Model.}
#' \item{\code{Accuracy}}{of the Model.}
#' \item{\code{Adjusted R Square}}{of the Model.}
#' \item{\code{`Akaike's` Information Criterion AIC}}{of the Model.}
# #' \item{\code{`Allen's` Prediction Sum-Of-Squares (PRESS, P-Square)}}{of the Model.}
#' \item{\code{Area under the ROC curve (AUC)}}{of the Model.}
#' \item{\code{Average Precision at k}}{of the Model.}
#' \item{\code{Bias}}{of the Model.}
#' \item{\code{Brier score}}{of the Model.}
#' \item{\code{Classification Error}}{of the Model.}
#' \item{\code{F1 Score}}{of the Model.}
#' \item{\code{fScore}}{of the Model.}
#' \item{\code{GINI Coefficient}}{of the Model.}
#' \item{\code{kappa statistic}}{of the Model.}
#' \item{\code{Log Loss}}{of the Model.}
#' \item{\code{`Mallow's` cp}}{of the Model.}
#' \item{\code{Matthews Correlation Coefficient}}{of the Model.}
#' \item{\code{Mean Log Loss}}{of the Model.}
#' \item{\code{Mean Absolute Error}}{of the Model.}
#' \item{\code{Mean Absolute Percent Error}}{of the Model.}
#' \item{\code{Mean Average Precision at k}}{of the Model.}
#' \item{\code{Mean Absolute Scaled Error}}{of the Model.}
#' \item{\code{Median Absolute Error}}{of the Model.}
#' \item{\code{Mean Squared Error}}{of the Model.}
#' \item{\code{Mean Squared Log Error}}{of the Model.}
#' \item{\code{Model turning point error}}{of the Model.}
#' \item{\code{Negative Predictive Value}}{of the Model.}
#' \item{\code{Percent Bias}}{of the Model.}
#' \item{\code{Positive Predictive Value}}{of the Model.}
#' \item{\code{Precision}}{of the Model.}
#' \item{\code{R Square}}{of the Model.}
#' \item{\code{Relative Absolute Error}}{of the Model.}
#' \item{\code{Recall}}{of the Model.}
#' \item{\code{Root Mean Squared Error}}{of the Model.}
#' \item{\code{Root Mean Squared Log Error}}{of the Model.}
#' \item{\code{Root Relative Squared Error}}{of the Model.}
#' \item{\code{Relative Squared Error}}{of the Model.}
#' \item{\code{`Schwarz's` Bayesian criterion BIC}}{of the Model.}
#' \item{\code{Sensitivity}}{of the Model.}
#' \item{\code{specificity}}{of the Model.}
#' \item{\code{Squared Error}}{of the Model.}
#' \item{\code{Squared Log Error}}{of the Model.}
#' \item{\code{Symmetric Mean Absolute Percentage Error}}{of the Model.}
#' \item{\code{Sum of Squared Errors}}{of the Model.}
#' \item{\code{True negative rate}}{of the Model.}
#' \item{\code{True positive rate}}{of the Model.}
#'
#' @name MLMetrics
#'
#' @examples
#' library(splines)
#' Model <- lm(states ~ bs(sequence, knots = c(30, 115)), data = Data)
#' MLMetrics(Observed = Data, yvalue = Data$states, Model = Model, K = 2,
#' Name = "Linear", Form = "LM", kutuf = 0, TTy = "Number")
MLMetrics <- function(Observed, yvalue, Model, K, Name, Form, kutuf, TTy){
Predy = 0
Preds = 0
Probable = 0
Predy <- if(Name == "ARIMA"){
Model[["fitted"]]
}else if (Form == "ALM"){
Model[["fitted"]]
}else if(Form == "ARDL"){
c(0, 0, 0, Model[["fitted.values"]])
}else if(Name == "Values"){
Model
}else{
fitted.values(Model)
}
if(Form == "LM"){
Preds = fitted.values(Model)
}else if(Form == "ALM"){
Preds = Model[["fitted"]]
}else if(Form == "N-LM"){
Preds = 0
}else if(Form == "GLM" & Name != "Log"){
KK <- broom::augment(Model, data = Observed)
KK$Probable = 1/(1 + exp(-KK$.fitted))
KK$Predicted = ifelse(Probable > 0.5, 1, 0)
Preds = KK$Predicted
}else{
Preds = stats::predict(Model, type = "response")
}
ppk <- if(sum(Preds) == 0) 1 else 2
kutuf <- if (kutuf != 0) kutuf else .5
RD01 <- signif(ifelse(Name == "ARIMA", Model$aic,
ifelse(Name == "SMOOTH"| Name == "Values", 0,
stats::AIC(Model))), 2)
RD02 <- signif(ifelse(Name == "ARIMA", Model$bic,
ifelse(Name == "SMOOTH"| Name == "Values", 0,
stats::BIC(Model))), 2)
RD03 <- if(Name == "ARIMA" | Name == "SMOOTH"| Form == "GLM"|
Name == "Values"| Name == "Logit"){
0
}else if(Name == "ALM"){
signif(summary(Model[["r.squared"]]), 2)
}else{
signif(summary(Model)$r.squared, 2)
}
RD04 <- if(Name == "ARIMA" | Name == "SMOOTH"| Form == "GLM"|
Name == "Values"| Name == "Logit"){
0
} else if (Name == "ALM"){
signif(summary(Model[["adj.r.squared"]]), 2)
} else {
signif(summary(Model)$adj.r.squared, 2)
}
RD05 = signif(Metrics::accuracy(yvalue, Preds), 2)
RD06 = signif(sum(Metrics::ae(yvalue, Preds)), 2)
RD07 = signif(sum(Metrics::ape(yvalue, Predy)), 2)
RD08 = signif(Metrics::apk(actual = yvalue, predicted = Preds, k = K), 2)
RD09 = signif(ifelse(Form == "LM"| Form == "ARDL" |
TTy == "Number" | Name == "nil" & ppk == 1, 0,
ModelMetrics::auc(yvalue, Preds)), 2)
RD10 = signif(Metrics::bias(yvalue, Preds), 2)
RD11 = signif(ifelse(Form == "LM" | TTy == "Number"| Form == "ALM",
ModelMetrics::ce(yvalue, Predy),
ModelMetrics::ce(Model)), 2)
RD12 = signif(ifelse(Form == "LM" | Form == "ALM",
Metrics::f1(yvalue, Predy),
ModelMetrics::f1Score(yvalue, Preds,
cutoff = kutuf)), 2)
RD13 = signif(sum(na.omit((Metrics::ll(yvalue, Preds))), 2))
RD14 = signif(ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::logLoss(yvalue, Predy),
ModelMetrics::logLoss(yvalue, Preds)), 2)
RD15 = if(Form == "GLM"){
signif(ModelMetrics::mae(actual = yvalue, predicted = Preds), 2)
}else if (Form == "LM"| TTy == "Number"| Form == "ALM"){
signif(ModelMetrics::mae(actual = yvalue, predicted = Preds), 2)
}else{
signif(ModelMetrics::mae(actual = yvalue, predicted = Predy), 2)
}
RD16 = signif(Metrics::mape(yvalue, Predy), 2)
RD17 = signif(ifelse(Name != "Values",
Metrics::mapk(actual = yvalue, predicted = Predy,
k = K), 0), 2)
RD18 = signif(Metrics::mase(yvalue, Preds), 2)
RD19 = signif(Metrics::mdae(yvalue, Predy), 2)
RD20 = signif(ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::mse(yvalue, Predy),
ModelMetrics::mse(yvalue, Preds)), 2)
RD21 = signif(Metrics::msle(yvalue, Preds), 2)
RD22 = signif(Metrics::percent_bias(yvalue, Predy), 2)
RD23 = signif(ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::precision(yvalue, Predy),
ModelMetrics::precision(yvalue, Preds,
cutoff = kutuf)), 2)
RD24 = signif(Metrics::rae(yvalue, Predy), 2)
RD25 = signif((ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::recall(yvalue, Predy),
ModelMetrics::recall(yvalue, Preds,
cutoff = kutuf))), 2)
RD26 = signif(ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::rmse(yvalue, Predy),
ModelMetrics::rmse(yvalue, Preds)), 2)
RD27 = signif(Metrics::rmsle(yvalue, Preds), 2)
RD28 = signif(Metrics::rrse(yvalue, Preds), 2)
RD29 = signif(Metrics::rse(yvalue, Preds), 2)
RD30 = signif(sum(Metrics::se(yvalue, Preds)), 2)
RD31 = signif(sum(Metrics::sle(yvalue, Preds)), 2)
RD32 = signif(Metrics::smape(yvalue, Predy), 2)
RD33 = signif(Metrics::sse(yvalue, Preds), 2)
ptp = diff(yvalue, lag = 1) / diff(Predy, lag = 1)
ptpe = ifelse(ptp > 0, 0, 1)
RD34 = sum(ptpe)
if(Name == "QUADRATIC"){
Nlevels = 1
}else if(Name == "SPLINE"){
Nlevels = Model[["rank"]] - 4
}else{
Nlevels = 0
}
if(Name != "SPLINE"){
Type = Form
}else{
Type = "SPLINE"
}
RD37 = MallowsCp(Model = Model, y = yvalue, x = Observed[, -1],
type = Type, Nlevels = Nlevels)
# RD38 <- ifelse(ppk == 1 & Name == "QUADRATIC",
# signif(qpcR::PRESS(Model, verbose = FALSE)$P.square, 2), 0)
RD39 = signif(ifelse(Form == "LM"| TTy == "Number" | Form == "ALM",
ModelMetrics::brier(yvalue, Preds),
ModelMetrics::brier(Model)), 0)
RD40 = signif(ifelse(Form == "LM"| TTy == "Number" | Form == "ALM",
ModelMetrics::gini(yvalue, Predy),
ModelMetrics::gini(Model)), 0)
RD41 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::kappa(yvalue, Preds,
cutoff = kutuf)), 0)
RD42 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::sensitivity(yvalue, Predy,
cutoff = kutuf)), 0)
RD43 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::specificity(yvalue, Preds,
cutoff = kutuf)), 0)
RD44 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::fScore(yvalue, Preds,
cutoff = kutuf, beta = 1)), 0)
RD45 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::mcc(yvalue, Preds, cutoff = kutuf)), 0)
RD46 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::tnr(yvalue, Preds, cutoff = kutuf)), 0)
RD47 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::tpr(yvalue, Preds, cutoff = kutuf)), 0)
RD48 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::ppv(yvalue, Preds, cutoff = kutuf)), 0)
RD49 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::npv(yvalue, Preds, cutoff = kutuf)), 0)
results <- list(
"Absolute Error" = RD06,
"Absolute Percent Error" = RD07,
"Accuracy" = RD05,
"Adjusted R Square" = RD04,
"Akaike's Information Criterion AIC" = RD01,
# "Allen's Prediction Sum-Of-Squares (PRESS, P-Square)" = RD38,
"Area under the ROC curve (AUC)" = RD09,
"Average Precision at k" = RD08,
"Bias" = RD10,
"Brier score" = RD39,
"Classification Error" = RD11,
"F1 Score" = RD12,
"fScore" = RD44,
"GINI Coefficient" = RD40,
"kappa statistic" = RD41,
"Log Loss" = RD13,
"Mallow's cp" = RD37,
"Matthews Correlation Coefficient" = RD45,
"Mean Log Loss" = RD14,
"Mean Absolute Error" = RD15,
"Mean Absolute Percent Error" = RD16,
"Mean Average Precision at k" = RD17,
"Mean Absolute Scaled Error" = RD18,
"Median Absolute Error" = RD19,
"Mean Squared Error" = RD20,
"Mean Squared Log Error" = RD21,
"Model turning point error" = RD34,
"Negative Predictive Value" = RD49,
# "Observed turning point error" = RD35$tp,
"Percent Bias" = RD22,
"Positive Predictive Value" = RD48,
"Precision" = RD23,
"R Square" = RD03,
"Relative Absolute Error" = RD24,
"Recall" = RD25,
"Root Mean Squared Error" = RD26,
"Root Mean Squared Log Error" = RD27,
"Root Relative Squared Error" = RD28,
"Relative Squared Error" = RD29,
"Schwarz's Bayesian criterion BIC" = RD02,
"Sensitivity" = RD42,
"specificity" = RD43,
"Squared Error" = RD30,
"Squared Log Error" = RD31,
"Symmetric Mean Absolute Percentage Error" = RD32,
"Sum of Squared Errors" = RD33,
"True negative rate" = RD46,
"True positive rate" = RD47
# "Turning point error using random tests" = RD36$tp
)
return(results)
}
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Defines functions MLMetrics
Documented in MLMetrics
#' Collection of Machine Learning Model Metrics for Easy Reference
#'
#' @description
#' This function estimates over 40 Metrics for assessing the quality of Machine Learning Models. The purpose is to provide a wrapper which brings all the metrics on the table and makes it easier to use them to select a model.
#'
#' @param Observed The Observed data in a data frame format
#' @param yvalue The Response variable of the estimated Model
#' @param Model The Estimated Model (*Model* = a + bx)
#' @param K The number of variables in the estimated Model to consider
#' @param Name The Name of the Models that need to be specified. They are ARIMA, Values if the model computes the fitted value without estimation like Essembles, SMOOTH (smooth.spline), Logit, Ensembles based on weight - EssemWet, QUADRATIC polynomial, SPLINE polynomial.
#' @param Form Form of the Model Estimated (LM, ALM, GLM, N-LM, ARDL)
#' @param kutuf Cutoff for the Estimated values (defaults to 0.5 if not specified)
#' @param TTy Type of response variable (Numeric or Response - like *binary*)
#'
#' @export MLMetrics
#'
#' @import tidyverse
#' @importFrom broom augment
#' @importFrom stats AIC
#' @importFrom stats BIC
#' @importFrom stats na.omit
#' @importFrom Metrics accuracy
#' @importFrom Metrics ae
#' @importFrom Metrics ape
#' @importFrom Metrics apk
#' @importFrom Metrics bias
#' @importFrom Metrics f1
#' @importFrom Metrics ll
#' @importFrom Metrics mape
#' @importFrom Metrics mapk
#' @importFrom Metrics mase
#' @importFrom Metrics mdae
#' @importFrom Metrics msle
#' @importFrom Metrics percent_bias
#' @importFrom Metrics rae
#' @importFrom Metrics rmsle
#' @importFrom Metrics rrse
#' @importFrom Metrics rse
#' @importFrom Metrics se
#' @importFrom Metrics sle
#' @importFrom Metrics smape
#' @importFrom Metrics sse
#' @importFrom ModelMetrics auc
#' @importFrom ModelMetrics ce
#' @importFrom ModelMetrics f1Score
#' @importFrom ModelMetrics logLoss
#' @importFrom ModelMetrics mae
#' @importFrom ModelMetrics mse
#' @importFrom ModelMetrics precision
#' @importFrom ModelMetrics recall
#' @importFrom ModelMetrics rmse
#' @importFrom ModelMetrics brier
#' @importFrom ModelMetrics gini
#' @importFrom ModelMetrics kappa
#' @importFrom ModelMetrics sensitivity
#' @importFrom ModelMetrics specificity
#' @importFrom ModelMetrics fScore
#' @importFrom ModelMetrics mcc
#' @importFrom ModelMetrics tnr
#' @importFrom ModelMetrics tpr
#' @importFrom ModelMetrics ppv
#' @importFrom ModelMetrics npv
# #' @importFrom qpcR PRESS
#' @importFrom dplyr mutate
#' @importFrom stats predict
#'
#' @return A list with the following components:
#' \item{\code{Absolute Error}}{of the Model.}
#' \item{\code{Absolute Percent Error}}{of the Model.}
#' \item{\code{Accuracy}}{of the Model.}
#' \item{\code{Adjusted R Square}}{of the Model.}
#' \item{\code{`Akaike's` Information Criterion AIC}}{of the Model.}
# #' \item{\code{`Allen's` Prediction Sum-Of-Squares (PRESS, P-Square)}}{of the Model.}
#' \item{\code{Area under the ROC curve (AUC)}}{of the Model.}
#' \item{\code{Average Precision at k}}{of the Model.}
#' \item{\code{Bias}}{of the Model.}
#' \item{\code{Brier score}}{of the Model.}
#' \item{\code{Classification Error}}{of the Model.}
#' \item{\code{F1 Score}}{of the Model.}
#' \item{\code{fScore}}{of the Model.}
#' \item{\code{GINI Coefficient}}{of the Model.}
#' \item{\code{kappa statistic}}{of the Model.}
#' \item{\code{Log Loss}}{of the Model.}
#' \item{\code{`Mallow's` cp}}{of the Model.}
#' \item{\code{Matthews Correlation Coefficient}}{of the Model.}
#' \item{\code{Mean Log Loss}}{of the Model.}
#' \item{\code{Mean Absolute Error}}{of the Model.}
#' \item{\code{Mean Absolute Percent Error}}{of the Model.}
#' \item{\code{Mean Average Precision at k}}{of the Model.}
#' \item{\code{Mean Absolute Scaled Error}}{of the Model.}
#' \item{\code{Median Absolute Error}}{of the Model.}
#' \item{\code{Mean Squared Error}}{of the Model.}
#' \item{\code{Mean Squared Log Error}}{of the Model.}
#' \item{\code{Model turning point error}}{of the Model.}
#' \item{\code{Negative Predictive Value}}{of the Model.}
#' \item{\code{Percent Bias}}{of the Model.}
#' \item{\code{Positive Predictive Value}}{of the Model.}
#' \item{\code{Precision}}{of the Model.}
#' \item{\code{R Square}}{of the Model.}
#' \item{\code{Relative Absolute Error}}{of the Model.}
#' \item{\code{Recall}}{of the Model.}
#' \item{\code{Root Mean Squared Error}}{of the Model.}
#' \item{\code{Root Mean Squared Log Error}}{of the Model.}
#' \item{\code{Root Relative Squared Error}}{of the Model.}
#' \item{\code{Relative Squared Error}}{of the Model.}
#' \item{\code{`Schwarz's` Bayesian criterion BIC}}{of the Model.}
#' \item{\code{Sensitivity}}{of the Model.}
#' \item{\code{specificity}}{of the Model.}
#' \item{\code{Squared Error}}{of the Model.}
#' \item{\code{Squared Log Error}}{of the Model.}
#' \item{\code{Symmetric Mean Absolute Percentage Error}}{of the Model.}
#' \item{\code{Sum of Squared Errors}}{of the Model.}
#' \item{\code{True negative rate}}{of the Model.}
#' \item{\code{True positive rate}}{of the Model.}
#'
#' @name MLMetrics
#'
#' @examples
#' library(splines)
#' Model <- lm(states ~ bs(sequence, knots = c(30, 115)), data = Data)
#' MLMetrics(Observed = Data, yvalue = Data$states, Model = Model, K = 2,
#' Name = "Linear", Form = "LM", kutuf = 0, TTy = "Number")
MLMetrics <- function(Observed, yvalue, Model, K, Name, Form, kutuf, TTy){
Predy = 0
Preds = 0
Probable = 0
Predy <- if(Name == "ARIMA"){
Model[["fitted"]]
}else if (Form == "ALM"){
Model[["fitted"]]
}else if(Form == "ARDL"){
c(0, 0, 0, Model[["fitted.values"]])
}else if(Name == "Values"){
Model
}else{
fitted.values(Model)
}
if(Form == "LM"){
Preds = fitted.values(Model)
}else if(Form == "ALM"){
Preds = Model[["fitted"]]
}else if(Form == "N-LM"){
Preds = 0
}else if(Form == "GLM" & Name != "Log"){
KK <- broom::augment(Model, data = Observed)
KK$Probable = 1/(1 + exp(-KK$.fitted))
KK$Predicted = ifelse(Probable > 0.5, 1, 0)
Preds = KK$Predicted
}else{
Preds = stats::predict(Model, type = "response")
}
ppk <- if(sum(Preds) == 0) 1 else 2
kutuf <- if (kutuf != 0) kutuf else .5
RD01 <- signif(ifelse(Name == "ARIMA", Model$aic,
ifelse(Name == "SMOOTH"| Name == "Values", 0,
stats::AIC(Model))), 2)
RD02 <- signif(ifelse(Name == "ARIMA", Model$bic,
ifelse(Name == "SMOOTH"| Name == "Values", 0,
stats::BIC(Model))), 2)
RD03 <- if(Name == "ARIMA" | Name == "SMOOTH"| Form == "GLM"|
Name == "Values"| Name == "Logit"){
0
}else if(Name == "ALM"){
signif(summary(Model[["r.squared"]]), 2)
}else{
signif(summary(Model)$r.squared, 2)
}
RD04 <- if(Name == "ARIMA" | Name == "SMOOTH"| Form == "GLM"|
Name == "Values"| Name == "Logit"){
0
} else if (Name == "ALM"){
signif(summary(Model[["adj.r.squared"]]), 2)
} else {
signif(summary(Model)$adj.r.squared, 2)
}
RD05 = signif(Metrics::accuracy(yvalue, Preds), 2)
RD06 = signif(sum(Metrics::ae(yvalue, Preds)), 2)
RD07 = signif(sum(Metrics::ape(yvalue, Predy)), 2)
RD08 = signif(Metrics::apk(actual = yvalue, predicted = Preds, k = K), 2)
RD09 = signif(ifelse(Form == "LM"| Form == "ARDL" |
TTy == "Number" | Name == "nil" & ppk == 1, 0,
ModelMetrics::auc(yvalue, Preds)), 2)
RD10 = signif(Metrics::bias(yvalue, Preds), 2)
RD11 = signif(ifelse(Form == "LM" | TTy == "Number"| Form == "ALM",
ModelMetrics::ce(yvalue, Predy),
ModelMetrics::ce(Model)), 2)
RD12 = signif(ifelse(Form == "LM" | Form == "ALM",
Metrics::f1(yvalue, Predy),
ModelMetrics::f1Score(yvalue, Preds,
cutoff = kutuf)), 2)
RD13 = signif(sum(na.omit((Metrics::ll(yvalue, Preds))), 2))
RD14 = signif(ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::logLoss(yvalue, Predy),
ModelMetrics::logLoss(yvalue, Preds)), 2)
RD15 = if(Form == "GLM"){
signif(ModelMetrics::mae(actual = yvalue, predicted = Preds), 2)
}else if (Form == "LM"| TTy == "Number"| Form == "ALM"){
signif(ModelMetrics::mae(actual = yvalue, predicted = Preds), 2)
}else{
signif(ModelMetrics::mae(actual = yvalue, predicted = Predy), 2)
}
RD16 = signif(Metrics::mape(yvalue, Predy), 2)
RD17 = signif(ifelse(Name != "Values",
Metrics::mapk(actual = yvalue, predicted = Predy,
k = K), 0), 2)
RD18 = signif(Metrics::mase(yvalue, Preds), 2)
RD19 = signif(Metrics::mdae(yvalue, Predy), 2)
RD20 = signif(ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::mse(yvalue, Predy),
ModelMetrics::mse(yvalue, Preds)), 2)
RD21 = signif(Metrics::msle(yvalue, Preds), 2)
RD22 = signif(Metrics::percent_bias(yvalue, Predy), 2)
RD23 = signif(ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::precision(yvalue, Predy),
ModelMetrics::precision(yvalue, Preds,
cutoff = kutuf)), 2)
RD24 = signif(Metrics::rae(yvalue, Predy), 2)
RD25 = signif((ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::recall(yvalue, Predy),
ModelMetrics::recall(yvalue, Preds,
cutoff = kutuf))), 2)
RD26 = signif(ifelse(Form == "LM" | Form == "ALM",
ModelMetrics::rmse(yvalue, Predy),
ModelMetrics::rmse(yvalue, Preds)), 2)
RD27 = signif(Metrics::rmsle(yvalue, Preds), 2)
RD28 = signif(Metrics::rrse(yvalue, Preds), 2)
RD29 = signif(Metrics::rse(yvalue, Preds), 2)
RD30 = signif(sum(Metrics::se(yvalue, Preds)), 2)
RD31 = signif(sum(Metrics::sle(yvalue, Preds)), 2)
RD32 = signif(Metrics::smape(yvalue, Predy), 2)
RD33 = signif(Metrics::sse(yvalue, Preds), 2)
ptp = diff(yvalue, lag = 1) / diff(Predy, lag = 1)
ptpe = ifelse(ptp > 0, 0, 1)
RD34 = sum(ptpe)
if(Name == "QUADRATIC"){
Nlevels = 1
}else if(Name == "SPLINE"){
Nlevels = Model[["rank"]] - 4
}else{
Nlevels = 0
}
if(Name != "SPLINE"){
Type = Form
}else{
Type = "SPLINE"
}
RD37 = MallowsCp(Model = Model, y = yvalue, x = Observed[, -1],
type = Type, Nlevels = Nlevels)
# RD38 <- ifelse(ppk == 1 & Name == "QUADRATIC",
# signif(qpcR::PRESS(Model, verbose = FALSE)$P.square, 2), 0)
RD39 = signif(ifelse(Form == "LM"| TTy == "Number" | Form == "ALM",
ModelMetrics::brier(yvalue, Preds),
ModelMetrics::brier(Model)), 0)
RD40 = signif(ifelse(Form == "LM"| TTy == "Number" | Form == "ALM",
ModelMetrics::gini(yvalue, Predy),
ModelMetrics::gini(Model)), 0)
RD41 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::kappa(yvalue, Preds,
cutoff = kutuf)), 0)
RD42 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::sensitivity(yvalue, Predy,
cutoff = kutuf)), 0)
RD43 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::specificity(yvalue, Preds,
cutoff = kutuf)), 0)
RD44 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::fScore(yvalue, Preds,
cutoff = kutuf, beta = 1)), 0)
RD45 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::mcc(yvalue, Preds, cutoff = kutuf)), 0)
RD46 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::tnr(yvalue, Preds, cutoff = kutuf)), 0)
RD47 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::tpr(yvalue, Preds, cutoff = kutuf)), 0)
RD48 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::ppv(yvalue, Preds, cutoff = kutuf)), 0)
RD49 = signif(ifelse(Form == "LM" | Form == "ALM", 0,
ModelMetrics::npv(yvalue, Preds, cutoff = kutuf)), 0)
results <- list(
"Absolute Error" = RD06,
"Absolute Percent Error" = RD07,
"Accuracy" = RD05,
"Adjusted R Square" = RD04,
"Akaike's Information Criterion AIC" = RD01,
# "Allen's Prediction Sum-Of-Squares (PRESS, P-Square)" = RD38,
"Area under the ROC curve (AUC)" = RD09,
"Average Precision at k" = RD08,
"Bias" = RD10,
"Brier score" = RD39,
"Classification Error" = RD11,
"F1 Score" = RD12,
"fScore" = RD44,
"GINI Coefficient" = RD40,
"kappa statistic" = RD41,
"Log Loss" = RD13,
"Mallow's cp" = RD37,
"Matthews Correlation Coefficient" = RD45,
"Mean Log Loss" = RD14,
"Mean Absolute Error" = RD15,
"Mean Absolute Percent Error" = RD16,
"Mean Average Precision at k" = RD17,
"Mean Absolute Scaled Error" = RD18,
"Median Absolute Error" = RD19,
"Mean Squared Error" = RD20,
"Mean Squared Log Error" = RD21,
"Model turning point error" = RD34,
"Negative Predictive Value" = RD49,
# "Observed turning point error" = RD35$tp,
"Percent Bias" = RD22,
"Positive Predictive Value" = RD48,
"Precision" = RD23,
"R Square" = RD03,
"Relative Absolute Error" = RD24,
"Recall" = RD25,
"Root Mean Squared Error" = RD26,
"Root Mean Squared Log Error" = RD27,
"Root Relative Squared Error" = RD28,
"Relative Squared Error" = RD29,
"Schwarz's Bayesian criterion BIC" = RD02,
"Sensitivity" = RD42,
"specificity" = RD43,
"Squared Error" = RD30,
"Squared Log Error" = RD31,
"Symmetric Mean Absolute Percentage Error" = RD32,
"Sum of Squared Errors" = RD33,
"True negative rate" = RD46,
"True positive rate" = RD47
# "Turning point error using random tests" = RD36$tp
)
return(results)
}
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