| Title: | 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. |
| Authors: | Oldemar Rodriguez [aut, cre], Diego Jiménez [ctb, prg] |
| Maintainer: | Oldemar Rodriguez <[email protected]> |
| License: | GPL (>=2) |
| Version: | 3.0.5 |
| Built: | 2024-10-30 03:46:14 UTC |
| Source: | https://github.com/PROMiDAT/predictoR |
Plot the coefficients and selected lambda of a glmnet model.
e_coeff_landa(model, category, sel.lambda = NULL, label = "Log Lambda")e_coeff_landa(model, category, sel.lambda = NULL, label = "Log Lambda")
model |
a glmnet model. |
category |
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. |
echarts4r plot
Joseline Quiros <[email protected]>
modelo <- traineR::train.glmnet(Species~., iris) e_coeff_landa(modelo, 'setosa', log(modelo$lambda[1]))modelo <- traineR::train.glmnet(Species~., iris) e_coeff_landa(modelo, 'setosa', log(modelo$lambda[1]))
Gauge Plot
e_global_gauge( value = 100, label = "Label", color1 = "#B5E391", color2 = "#90C468" )e_global_gauge( value = 100, label = "Label", color1 = "#B5E391", color2 = "#90C468" )
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. |
echarts4r plot
Joseline Quiros <[email protected]>
e_global_gauge(87, "Global Precision")e_global_gauge(87, "Global Precision")
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(model, label = "Trees")e_rf_error(model, label = "Trees")
model |
a random forest model. |
label |
a label plot. |
echarts4r plot
Joseline Quiros <[email protected]>
model <- traineR::train.randomForest(Species~., iris, mtry = 2, ntree = 20) label <- "Trees" e_rf_error(model, label)model <- traineR::train.randomForest(Species~., iris, mtry = 2, ntree = 20) label <- "Trees" e_rf_error(model, label)
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.
| Package: | predictoR |
| Type: | Package |
| Version: | 3.0.1 |
| Date: | 2022-02-09 |
| License: | GPL (>=2) |
Oldemar Rodriguez Rojas
Maintainer: Oldemar Rodriguez Rojas <[email protected]>
Run the Shiny Application
run_app(...)run_app(...)
... |
A series of options to be used inside the app. |