| Title: | Regression Data Analysis System |
|---|---|
| Description: | Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines. |
| Authors: | Oldemar Rodriguez [aut, cre], Andres Navarro D. [ctb, prg], Diego Jimenez A. [ctb, prg], Ariel Arroyo S. [ctb, prg], Joseline Quiros M. [ctb, prg] |
| Maintainer: | Oldemar Rodriguez <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 4.0.3 |
| Built: | 2024-12-25 07:12:53 UTC |
| Source: | CRAN |
creates a string representative of a vector
as_string_c(vect, quote = TRUE)as_string_c(vect, quote = TRUE)
vect |
a vector with values |
quote |
a logical value. If TRUE, the values on the vector will be surrounded by quotes. |
as_string_c(c("A", "B", "C")) as_string_c(c(5, 6, 7)) as_string_c(c(5, 6, 7), quote = FALSE) as_string_c(iris$Species)as_string_c(c("A", "B", "C")) as_string_c(c(5, 6, 7)) as_string_c(c(5, 6, 7), quote = FALSE) as_string_c(iris$Species)
Error Evolution
e_boost_evol_error(modelo, label = "Iterations")e_boost_evol_error(modelo, label = "Iterations")
modelo |
a adabag model. |
label |
a label plot. |
echarts4r plot
Joseline Quiros <[email protected]>
model <- traineR::train.gbm(Sepal.Length~., data = iris, distribution = "gaussian", n.trees = 5, shrinkage = 0.01) e_boost_evol_error(model, iris)model <- traineR::train.gbm(Sepal.Length~., data = iris, distribution = "gaussian", n.trees = 5, shrinkage = 0.01) e_boost_evol_error(model, iris)
Var importance Adabag
e_boost_importance(modelo)e_boost_importance(modelo)
modelo |
a adabag model. |
echarts4r plot
Joseline Quiros <[email protected]>
model <- traineR::train.gbm(Sepal.Length~., data = iris, distribution = "gaussian", n.trees = 5, shrinkage = 0.01) e_boost_importance(model)model <- traineR::train.gbm(Sepal.Length~., data = iris, distribution = "gaussian", n.trees = 5, shrinkage = 0.01) e_boost_importance(model)
Plot the coefficients and selected lambda of a glmnet model.
e_coeff_lambda(model, sel.lambda = NULL, label = "Log Lambda")e_coeff_lambda(model, sel.lambda = NULL, label = "Log Lambda")
model |
a glmnet model. |
sel.lambda |
the selected lambda. |
label |
a character specifying the title to use on selected lambda tooltip. |
echarts4r plot
Joseline Quiros <[email protected]>
x <- model.matrix(Sepal.Length ~ ., iris)[, -1] y <- iris$Sepal.Length modelo <- glmnet::cv.glmnet(x, y, standardize = TRUE, alpha = 1, family = "gaussian") e_coeff_lambda(modelo, log(modelo$lambda[1]))x <- model.matrix(Sepal.Length ~ ., iris)[, -1] y <- iris$Sepal.Length modelo <- glmnet::cv.glmnet(x, y, standardize = TRUE, alpha = 1, family = "gaussian") e_coeff_lambda(modelo, log(modelo$lambda[1]))
Eval character vectors to JS code
e_JS(...)e_JS(...)
... |
character vectors to evaluate |
Joseline Quiros <[email protected]>
e_JS('5 * 3')e_JS('5 * 3')
Possible lambda
e_posib_lambda( cv.glm, labels = c("Valor Superior", "Valor Inferior", "lambda") )e_posib_lambda( cv.glm, labels = c("Valor Superior", "Valor Inferior", "lambda") )
cv.glm |
a cv.glmnet model. |
labels |
a character vector of length 3 specifying the titles to use on legend. |
echarts4r plot
Joseline Quiros <[email protected]>
x <- model.matrix(Species~., iris)[, -1] y <- iris[,'Species'] cv.glm <- glmnet::cv.glmnet(x, y, standardize = TRUE, alpha = 1, family = 'multinomial') e_posib_lambda(cv.glm)x <- model.matrix(Species~., iris)[, -1] y <- iris[,'Species'] cv.glm <- glmnet::cv.glmnet(x, y, standardize = TRUE, alpha = 1, family = 'multinomial') e_posib_lambda(cv.glm)
Error Evolution
e_rf_error(modelo, label = "Trees")e_rf_error(modelo, label = "Trees")
modelo |
a random forest model. |
label |
a label plot. |
echarts4r plot
Joseline Quiros <[email protected]>
model <- traineR::train.randomForest(Sepal.Length~., iris, mtry = 2, ntree = 20) e_rf_error(model, "Trees")model <- traineR::train.randomForest(Sepal.Length~., iris, mtry = 2, ntree = 20) e_rf_error(model, "Trees")
Var importance Random Forest
e_rndf_importance(modelo, error = "X.IncMSE")e_rndf_importance(modelo, error = "X.IncMSE")
modelo |
a random forest model. |
error |
a character specifying the type of importance. |
echarts4r plot
Joseline Quiros <[email protected]>
model <- traineR::train.randomForest(Species~., iris, mtry = 2, ntree = 20) e_rndf_importance(model)model <- traineR::train.randomForest(Species~., iris, mtry = 2, ntree = 20) e_rndf_importance(model)
graph the root mean square error of cross validation according to components used.
e.rdim.rmse(modelo, ncomp, titles = c("RMSE", "Componente"))e.rdim.rmse(modelo, ncomp, titles = c("RMSE", "Componente"))
modelo |
a dimension reduction model. |
ncomp |
the optimum number of components. |
titles |
labels on the chart |
echarts4r plot
Diego Jimenez <[email protected]>
graph of variance explained in the predictors according to components used.
e.rdim.vare(modelo, ncomp, titles = c("Varianza Explicada", "Componente"))e.rdim.vare(modelo, ncomp, titles = c("Varianza Explicada", "Componente"))
modelo |
a dimension reduction model. |
ncomp |
the optimum number of components. |
titles |
labels on the chart |
echarts4r plot
Ariel Arroyo <[email protected]>
concat and execute a text in R.
exe(..., envir = parent.frame())exe(..., envir = parent.frame())
... |
one or more texts to be concatenated and executed. |
envir |
the environment in which expr is to be evaluated. |
the result of the execute.
exe("5+5") exe("5","+","5") exe("plot(iris$Species)")exe("5+5") exe("5","+","5") exe("plot(iris$Species)")
gets the code of a function in text form.
extract_code(funcion, envir = parent.frame())extract_code(funcion, envir = parent.frame())
funcion |
the name of the function to be extracted. |
envir |
the environment in which expr is to be evaluated. |
extract_code("cat") extract_code("plot") parse(text = extract_code("plot"))extract_code("cat") extract_code("plot") parse(text = extract_code("plot"))
calculates indices to measure accuracy of a model.
calculates indices to measure accuracy of a model.
general.indices(real, prediccion) general.indices(real, prediccion)general.indices(real, prediccion) general.indices(real, prediccion)
real |
the real values in traning-testing. |
prediccion |
the prediction values in traning-testing. |
a list with the Correlation, Relative Error, Mean Absolute Error and Root Mean Square Error.
a list with the Correlation, Relative Error, Mean Absolute Error and Root Mean Square Error.
real <- rnorm(45) prediction <- rnorm(45) model <- "KNN" general.indices(real, prediction) real <- rnorm(45) prediction <- rnorm(45) model <- "KNN" general.indices(real, prediction)real <- rnorm(45) prediction <- rnorm(45) model <- "KNN" general.indices(real, prediction) real <- rnorm(45) prediction <- rnorm(45) model <- "KNN" general.indices(real, prediction)
scatter plot between the actual value of the variable to be predicted and the prediction of the model.
plot_real_prediction(real, pred, titles = c("Real", "Prediccion"))plot_real_prediction(real, pred, titles = c("Real", "Prediccion"))
real |
the real values in traning-testing. |
pred |
the prediction values in traning-testing. |
titles |
Labels on the chart |
echarts4r plot
Ariel Arroyo <[email protected]>
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.
| Package: | regressoR |
| Type: | Package |
| Version: | 4.0.2 |
| Date: | 2024-11-15 |
| License: | GPL (>=2) |
Oldemar Rodriguez Rojas
Maintainer: Oldemar Rodriguez Rojas <[email protected]>
Useful links:
Run the Shiny Application
run_app(...)run_app(...)
... |
A series of options to be used inside the app. |
summarizes a variable by returning the minimum, first quartile, third quartile and maximum value.
summary_indices(data)summary_indices(data)
data |
a numeric vector. |
summary_indices(iris$Sepal.Length)summary_indices(iris$Sepal.Length)