| Title: | Regression Data Analysis System |
| Type: | Package |
| Version: | 4.0.4 |
| Depends: | R (≥ 4.1) |
| Imports: | DT (≥ 0.27), gbm (≥ 2.2.2), pls (≥ 2.8-1), dplyr (≥ 1.1.0), psych (≥ 2.4.6), shiny (≥ 1.7.4), golem (≥ 0.3.5), rlang (≥ 1.0.6), glmnet (≥ 4.1-6), loadeR (≥ 1.1.3), shinyjs (≥ 2.1.0), traineR (≥ 2.2.0), shinyAce (≥ 0.4.2), echarts4r (≥ 0.4.4), htmltools (≥ 0.5.4), rpart.plot (≥ 3.1.1), shinydashboard (≥ 0.7.2), shinycustomloader (≥ 0.9.0), shinydashboardPlus (≥ 2.0.3) |
| 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. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| URL: | https://promidat.website/ |
| BugReports: | https://github.com/PROMiDAT/predictoR/issues |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-05-28 14:39:56 UTC; r583594 |
| Author: | 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 <oldemar.rodriguez@ucr.ac.cr> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-28 20:30:02 UTC |
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.
Details
| Package: | regressoR |
| Type: | Package |
| Version: | 4.0.2 |
| Date: | 2024-11-15 |
| License: | GPL (>=2) |
Author(s)
Oldemar Rodriguez Rojas
Maintainer: Oldemar Rodriguez Rojas <oldemar.rodriguez@ucr.ac.cr>
See Also
Useful links:
as_string_c
Description
creates a string representative of a vector
Usage
as_string_c(vect, quote = TRUE)
Arguments
vect |
a vector with values |
quote |
a logical value. If TRUE, the values on the vector will be surrounded by quotes. |
Examples
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)
e.rdim.rmse
Description
graph the root mean square error of cross validation according to components used.
Usage
e.rdim.rmse(modelo, ncomp, titles = c("RMSE", "Componente"))
Arguments
modelo |
a dimension reduction model. |
ncomp |
the optimum number of components. |
titles |
labels on the chart |
Value
echarts4r plot
Author(s)
Diego Jimenez <diego.jimenez@promidat.com>
plot_pred_rd
Description
graph of variance explained in the predictors according to components used.
Usage
e.rdim.vare(modelo, ncomp, titles = c("Varianza Explicada", "Componente"))
Arguments
modelo |
a dimension reduction model. |
ncomp |
the optimum number of components. |
titles |
labels on the chart |
Value
echarts4r plot
Author(s)
Ariel Arroyo <luis.ariel.arroyo@promidat.com>
Eval character vectors to JS code
Description
Eval character vectors to JS code
Usage
e_JS(...)
Arguments
... |
character vectors to evaluate |
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
e_JS('5 * 3')
Error Evolution
Description
Error Evolution
Usage
e_boost_evol_error(modelo, label = "Iterations")
Arguments
modelo |
a adabag model. |
label |
a label plot. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
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
Description
Var importance Adabag
Usage
e_boost_importance(modelo)
Arguments
modelo |
a adabag model. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
model <- traineR::train.gbm(Sepal.Length~., data = iris,
distribution = "gaussian", n.trees = 5, shrinkage = 0.01)
e_boost_importance(model)
Coefficients and lambda
Description
Plot the coefficients and selected lambda of a glmnet model.
Usage
e_coeff_lambda(model, sel.lambda = NULL, label = "Log Lambda")
Arguments
model |
a glmnet model. |
sel.lambda |
the selected lambda. |
label |
a character specifying the title to use on selected lambda tooltip. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
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$lambda[1]))
Possible lambda
Description
Possible lambda
Usage
e_posib_lambda(
cv.glm,
labels = c("Valor Superior", "Valor Inferior", "lambda")
)
Arguments
cv.glm |
a cv.glmnet model. |
labels |
a character vector of length 3 specifying the titles to use on legend. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
x <- model.matrix(Species~., iris)[, -1]
y <- iris[,'Species']
cv.glm <- glmnet::cv.glmnet(x, y, standardize = TRUE, alpha = 1, family = 'multinomial')
e_posib_lambda(cv.glm)
Error Evolution
Description
Error Evolution
Usage
e_rf_error(modelo, label = "Trees")
Arguments
modelo |
a random forest model. |
label |
a label plot. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
model <- traineR::train.randomForest(Sepal.Length~., iris, mtry = 2, ntree = 20)
e_rf_error(model, "Trees")
Var importance Random Forest
Description
Var importance Random Forest
Usage
e_rndf_importance(modelo, error = "X.IncMSE")
Arguments
modelo |
a random forest model. |
error |
a character specifying the type of importance. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
model <- traineR::train.randomForest(Species~., iris, mtry = 2, ntree = 20)
e_rndf_importance(model)
exe
Description
concat and execute a text in R.
Usage
exe(..., envir = parent.frame())
Arguments
... |
one or more texts to be concatenated and executed. |
envir |
the environment in which expr is to be evaluated. |
Value
the result of the execute.
Examples
exe("5+5")
exe("5","+","5")
exe("plot(iris$Species)")
extract_code
Description
gets the code of a function in text form.
Usage
extract_code(funcion, envir = parent.frame())
Arguments
funcion |
the name of the function to be extracted. |
envir |
the environment in which expr is to be evaluated. |
Examples
extract_code("cat")
extract_code("plot")
parse(text = extract_code("plot"))
general.indices
Description
calculates indices to measure accuracy of a model.
calculates indices to measure accuracy of a model.
Usage
general.indices(real, prediccion)
general.indices(real, prediccion)
Arguments
real |
the real values in traning-testing. |
prediccion |
the prediction values in traning-testing. |
Value
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.
Examples
real <- rnorm(45)
prediction <- rnorm(45)
model <- "KNN"
general.indices(real, prediction)
real <- rnorm(45)
prediction <- rnorm(45)
model <- "KNN"
general.indices(real, prediction)
ind_nuevos Server Function
Description
ind_nuevos Server Function
Usage
mod_ind_nuevos_server(input, output, session, newCases, updateData2, codedioma)
plot_real_prediction
Description
scatter plot between the actual value of the variable to be predicted and the prediction of the model.
Usage
plot_real_prediction(real, pred, titles = c("Real", "Prediccion"))
Arguments
real |
the real values in traning-testing. |
pred |
the prediction values in traning-testing. |
titles |
Labels on the chart |
Value
echarts4r plot
Author(s)
Ariel Arroyo <luis.ariel.arroyo@promidat.com>
Run the Shiny Application
Description
Run the Shiny Application
Usage
run_app(...)
Arguments
... |
A series of options to be used inside the app. |
summary_indices
Description
summarizes a variable by returning the minimum, first quartile, third quartile and maximum value.
Usage
summary_indices(data)
Arguments
data |
a numeric vector. |
Examples
summary_indices(iris$Sepal.Length)