| Title: | Build, Predict and Analyse Artificial Intelligence Models |
| Version: | 1.0.4.44 |
| Description: | An interface for data processing, building models, predicting values and analysing outcomes. Fitting Linear Models, Robust Fitting of Linear Models, k-Nearest Neighbor Classification, 1-Nearest Neighbor Classification, and Conditional Inference Trees are available. |
| Depends: | R (≥ 4.4.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| URL: | https://github.com/urniaz/ai |
| BugReports: | https://github.com/urniaz/ai/issues |
| biocViews: | Software |
| Imports: | base, class, stats, caTools, MASS, party, Metrics |
| Suggests: | testthat (≥ 3.0.0) |
| RoxygenNote: | 7.3.2 |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2024-09-30 22:26:49 UTC; urniaz |
| Author: | Rafal Urniaz 
[aut, cre] |
| Maintainer: | Rafal Urniaz <rafal.urniaz@cantab.net> |
| Repository: | CRAN |
| Date/Publication: | 2024-10-02 13:30:06 UTC |
Models parameters
Description
The config function sets additional models parameters
Usage
config(formula = NULL, k = NULL)
Arguments
formula |
formula parameter for eg. linear models including lm, rlm, read more: lm |
k |
number of neighbors considered from knn models, read more: knn |
Value
configuration list contains models parameters different than defaults
Examples
config(formula = "Status ~ Value")
AI/ML models
Description
The model function generates AI/ML models
Usage
model(data, type = "lm", config = NULL, verbose = FALSE)
Arguments
data |
data object with data to be modeled, read more prodata |
type |
model type, lm (Fitting Linear Models) by default; available are lm, rlm, ctree, knn, knn1 |
config |
additional parameters for model, read more config |
verbose |
if true the messages are displayed in console, false by default |
Value
model list contains model, predicted, and expected values for all generated models
Examples
model_data <- data.frame(a = c(1,2,3,4,5,6),
b = c(1,2,3,4,5,6),
s = c(1,2,3,4,5,6))
config <- config(formula = "a ~ b + s")
model_data <- prodata(model_data, status_colname = "s")
model(model_data, config)
Data processing
Description
The prodata function generates an data list for models. It additionally splits data for training and testing set by split ratio.
Usage
prodata(data, status_colname, SplitRatio = 0.75)
Arguments
data |
data.frame with data to be modeled |
status_colname |
name of the column in data where the true results (true positive, expected) values are listed |
SplitRatio |
Splitting ratio; 0.75 means 75% data for training and 25% for testing, more: sample.split |
Value
data list
Examples
model_data <- data.frame(a = c(1,2,3,4,5,6),
b = c(1,2,3,4,5,6),
s = c(1,2,3,4,5,6))
prodata(data = model_data, status_colname = "s")
Models statistics
Description
The stats function calculates models statistics. Read more auc
Usage
stats(modelA, modelB = NULL)
Arguments
modelA |
Model generated by model function |
modelB |
Model generated by model function |
Value
modified model list contains additional statistics
Examples
model_data <- data.frame(a = c(1,2,3,4,5,6),
b = c(1,2,3,4,5,6),
s = c(1,2,3,4,5,6))
model_data <- prodata(model_data, status_colname = "s")
config <- config(formula = "a ~ b + s")
model <- model(model_data, config)
stats(model)
stats_compare_models()
Description
stats_compare_models()
Usage
stats_compare_models(modelA, modelB)
Arguments
modelA |
modelA |
modelB |
modelB |
Value
data.frame contains comparison of both models statistics
stats_model()
Description
stats_model()
Usage
stats_model(model)
Arguments
model |
model |
Value
list contains model statistics