| Type: | Package |
| Title: | Predictive Data Analysis System |
| Version: | 4.1.5 |
| Description: | Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Imports: | DT (≥ 0.27), dplyr (≥ 1.1.0), shiny (≥ 1.7.4), golem (≥ 0.3.5), rlang (≥ 1.0.6), loadeR (≥ 1.0.1), config (≥ 0.3.1), glmnet (≥ 4.1-6), traineR (≥ 2.2.0), shinyjs (≥ 2.1.0), xgboost (≥ 1.7.3.1), shinyAce (≥ 0.4.2), echarts4r (≥ 0.4.4), htmltools (≥ 0.5.4), rpart.plot (≥ 3.1.1), colourpicker (≥ 1.1.1), shinydashboard (≥ 0.7.2), shinycustomloader (≥ 0.9.0), shinydashboardPlus (≥ 2.0.3) |
| Depends: | R (≥ 4.1) |
| Encoding: | UTF-8 |
| URL: | https://promidat.website/ |
| BugReports: | https://github.com/PROMiDAT/predictoR/issues |
| RoxygenNote: | 7.3.2 |
| Language: | en-US |
| NeedsCompilation: | no |
| Packaged: | 2025-05-27 22:50:00 UTC; r583594 |
| Author: | Oldemar Rodriguez [aut, cre], Diego Jiménez [ctb, prg], Andrés Navarro [ctb, prg] |
| Maintainer: | Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-28 20:30:05 UTC |
Predictive Data Analysis System
Description
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
Details
| Package: | predictoR |
| Type: | Package |
| Version: | 4.1.2 |
| Date: | 2024-11-01 |
| License: | GPL (>=2) |
Author(s)
Oldemar Rodriguez Rojas
Maintainer: Oldemar Rodriguez Rojas <oldemar.rodriguez@ucr.ac.cr>
See Also
Useful links:
Returns a matrix of contrasts for the train.kknn.
Description
Returns a matrix of contrasts for the train.kknn.
Usage
contr.dummy(n, contrasts = TRUE)
Arguments
n |
A vector containing levels of a factor, or the number of levels. |
contrasts |
A logical value indicating whether contrasts should be computed. |
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
contr.dummy(5)
Returns a matrix of contrasts for the train.kknn.
Description
Returns a matrix of contrasts for the train.kknn.
Usage
contr.metric(n, contrasts = TRUE)
Arguments
n |
A vector containing levels of a factor, or the number of levels. |
contrasts |
A logical value indicating whether contrasts should be computed. |
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
contr.metric(5)
Returns a matrix of contrasts for the train.kknn.
Description
Returns a matrix of contrasts for the train.kknn.
Usage
contr.ordinal(n, contrasts = TRUE)
Arguments
n |
A vector containing levels of a factor, or the number of levels. |
contrasts |
A logical value indicating whether contrasts should be computed. |
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
contr.ordinal(5)
Convierte toda la tabla a código dummy.
Description
Convierte toda la tabla a código dummy.
Usage
data.frame.dummy(DF, exclude = NULL)
Arguments
DF |
a data.frame. |
exclude |
variables of data.frame exclude of conversion. |
Author(s)
Diego Jimenez <diego.jimenezs@promidat.com>
Examples
data.frame.dummy(iris)
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_ada_evol_error(modelo, datos, label = "Iterations")
Arguments
modelo |
a adabag model. |
datos |
a data.frame object. |
label |
a label plot. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
model <- traineR::train.adabag(Species~., iris, mfinal = 20, coeflearn = 'Freund')
e_ada_evol_error(model, iris)
Var importance Random Forest
Description
Var importance Random Forest
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.adabag(Species~., iris, mfinal = 20, coeflearn = 'Freund')
e_boost_importance(model)
Coefficients and lambda
Description
Plot the coefficients and selected lambda of a glmnet model.
Usage
e_coeff_lambda(model, cat, sel.lambda = NULL, label = "Log Lambda")
Arguments
model |
a glmnet model. |
cat |
a category of the variable to be predicted. |
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(Species ~ ., iris)[, -1]
y <- iris$Species
modelo <- glmnet::cv.glmnet(x, y, standardize = TRUE, alpha = 1, family = "multinomial")
e_coeff_lambda(modelo, 'setosa', log(modelo$lambda[1]))
Gauge Plot
Description
Gauge Plot
Usage
e_global_gauge(
value = 100,
label = "Label",
color1 = "#B5E391",
color2 = "#90C468"
)
Arguments
value |
a number specifying the value of the graph. |
label |
a character specifying the title to use on legend. |
color1 |
a color for the gauge. |
color2 |
a shadowColor for the gauge. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
e_global_gauge(87, "Global Precision")
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(model, label = "Trees")
Arguments
model |
a random forest model. |
label |
a label plot. |
Value
echarts4r plot
Author(s)
Joseline Quiros <joseline.quiros@promidat.com>
Examples
model <- traineR::train.randomForest(Species~., iris, mtry = 2, ntree = 20)
label <- "Trees"
e_rf_error(model, label)
Var importance Random Forest
Description
Var importance Random Forest
Usage
e_rndf_importance(modelo, error = "MeanDecreaseAccuracy")
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)
Var importance XGBoosting
Description
Var importance XGBoosting
Usage
e_xgb_importance(modelo, error = "Gain")
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.xgboost(Species ~ ., data = iris, nrounds = 20)
e_xgb_importance(model)
Run the Shiny Application
Description
Run the Shiny Application
Usage
run_app(...)
Arguments
... |
A series of options to be used inside the app. |
Voronoi Plot SVM
Description
Voronoi Plot SVM
Usage
voronoi_svm_plot(datos, varpred, vars, kernel = "linear")
Arguments
datos |
a data.frame object. |
varpred |
variable to predict. |
vars |
predictor variables. |
kernel |
the kernel used in training and predicting. |
Value
plot
Author(s)
Diego Jimenez <diego.jimenez@promidat.com>
Examples
voronoi_svm_plot(iris, "Species", c("Sepal.Length", "Sepal.Width"), "linear")