| Type: | Package |
| Title: | Decision Forest |
| Version: | 0.4.2 |
| Date: | 2017-11-28 |
| Author: | Leihong Wu <leihong.wu@fda.hhs.gov>, Weida Tong (Weida.tong@fda.hhs.gov) |
| Maintainer: | Leihong Wu <leihong.wu@fda.hhs.gov> |
| Depends: | R (≥ 3.0) |
| Imports: | rpart, ggplot2, methods, stats |
| Description: | Provides R-implementation of Decision forest algorithm, which combines the predictions of multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel pattern-recognition method which can be used to analyze: (1) DNA microarray data; (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) data; and (3) Structure-Activity Relation (SAR) data. In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV. run Dforest() to see more instructions. Weida Tong (2003) <doi:10.1021/ci020058s>. |
| License: | GPL-2 |
| LazyLoad: | yes |
| LazyData: | yes |
| Encoding: | UTF-8 |
| NeedsCompilation: | no |
| RoxygenNote: | 6.0.1 |
| Packaged: | 2017-11-28 19:17:32 UTC; Leihong.Wu |
| Repository: | CRAN |
| Date/Publication: | 2017-11-28 22:03:57 UTC |
Construct Decision Tree model with pruning
Description
Construct Decision Tree model with pruning
Usage
Con_DT(X, Y, min_split = 10, cp = 0.01)
Arguments
X |
dataset |
Y |
data_Labels |
min_split |
minimum number of node in each leaf |
cp |
pre-defined Complexity Parameter (CP) rpart program |
Value
Decision Tree Model with pruning Implemented by rpart
See Also
rpart
Decision Forest algorithm: Model training with Cross-validation
Description
Decision Forest algorithm: Model training with Cross-validation Default is 5-fold cross-validation
Usage
DF_CV(X, Y, stop_step = 10, CV_fold = 5, Max_tree = 20, min_split = 10,
cp = 0.1, Filter = F, p_val = 0.05, Method = "bACC", Quiet = T,
Grace_val = 0.05, imp_accu_val = 0.01, imp_accu_criteria = F)
Arguments
X |
Training Dataset |
Y |
Training data endpoint |
stop_step |
How many extra step would be processed when performance not improved, 1 means one extra step |
CV_fold |
Fold of cross-validation (Default = 5) |
Max_tree |
Maximum tree number in Forest |
min_split |
minimum leaves in tree nodes |
cp |
parameters to pruning decision tree, default is 0.1 |
Filter |
doing feature selection before training |
p_val |
P-value threshold measured by t-test used in feature selection, default is 0.05 |
Method |
Which is used for evaluating training process. MIS: Misclassification rate; ACC: accuracy |
Quiet |
if TRUE (default), don't show any message during the process |
Grace_val |
Grace Value in evaluation: the next model should have a performance (Accuracy, bACC, MCC) not bad than previous model with threshold |
imp_accu_val |
improvement in evaluation: adding new tree should improve the overall model performance (Accuracy, bACC, MCC) by threshold |
imp_accu_criteria |
if TRUE, model must have improvement in accumulated accuracy |
Value
.$performance: Overall training accuracy (Cross-validation)
.$pred: Detailed training prediction (Cross-validation)
.$detail: Detailed usage of Decision tree Features/Models and their performances in all CVs
.$Method: pass evaluating Methods used in training
.$cp: pass cp value used in training decision trees
Examples
##data(iris)
X = iris[,1:4]
Y = iris[,5]
names(Y)=rownames(X)
random_seq=sample(nrow(X))
split_rate=3
split_sample = suppressWarnings(split(random_seq,1:split_rate))
Train_X = X[-random_seq[split_sample[[1]]],]
Train_Y = Y[-random_seq[split_sample[[1]]]]
CV_result = DF_CV(Train_X, Train_Y)
output summary for Dforest Cross-validation results
Description
Draw plot for Dforest Cross-validation results
Usage
DF_CVsummary(CV_result, plot = T)
Arguments
CV_result |
Training Dataset |
plot |
if TRUE (default), draw plot |
Decision Forest algorithm: confidence level accumulated plot
Description
Draw accuracy curve according to the confidence level of predictions
Usage
DF_ConfPlot(Pred_result, Label, bin = 20, plot = T, smooth = F)
Arguments
Pred_result |
Predictions |
Label |
known label for Test Dataset |
bin |
How many bins occurred in Conf Plot (Default is 20) |
plot |
Draw Plot if True, otherwise output the datamatrix |
smooth |
if TRUE, Fit the performance curve with smooth function (by ggplot2) |
Value
ACC_Conf: return data Matrix ("ConfidenceLevel", "Accuracy", "Matched Samples") for confidence plot (no plot)
ConfPlot: Draw Confidence Plot if True, need install ggplot2
Decision Forest algorithm: confidence level accumulated plot (accumulated version)
Description
Draw accuracy curve according to the confidence level of predictions
Usage
DF_ConfPlot_accu(Pred_result, Label, bin = 20, plot = T, smooth = F)
Arguments
Pred_result |
Predictions |
Label |
known label for Test Dataset |
bin |
How many bins occurred in Conf Plot (Default is 20) |
plot |
Draw Plot if True, otherwise output the datamatrix |
smooth |
if TRUE, Fit the performance curve with smooth function (by ggplot2) |
Value
ACC_Conf: return data Matrix ("ConfidenceLevel", "Accuracy", "Matched Samples") for confidence plot (no plot)
ConfPlot: Draw Confidence Plot if True, need install ggplot2
output summary for Dforest test results
Description
Draw plot for Dforest test results
Usage
DF_Trainsummary(used_model, plot = T)
Arguments
used_model |
Training result |
plot |
if TRUE (default), draw plot |
Performance evaluation from Decision Tree Predictions
Description
Performance evaluation from Decision Tree Predictions
Usage
DF_acc(pred, label)
Arguments
pred |
Predictions |
label |
Known-endpoint |
Value
result$ACC: Predicting Accuracy
result$MIS: MisClassfication Counts
result$MCC: Matthew's Correlation Coefficients
result$bACC: balanced Accuracy
T-test for feature selection
Description
T-test for feature selection
Usage
DF_calp(X, Y)
Arguments
X |
X variable matrix |
Y |
Y label |
Decision Forest algorithm: Feature Selection in pre-processing
Description
Decision Forest algorithm: feature selection for two-class predictions, kept statistical significant features pass the t-test
Usage
DF_dataFs(X, Y, p_val = 0.05)
Arguments
X |
Training Dataset |
Y |
Training Labels |
p_val |
Correlation Coefficient threshold to filter out high correlated features; default is 0.95 |
Value
Keep_feat: qualified features in data matrix after filtering
Examples
##data(iris)
X = iris[iris[,5]!="setosa",1:4]
Y = iris[iris[,5]!="setosa",5]
used_feat = DF_dataFs(X, Y)
Decision Forest algorithm: Data pre-processing
Description
Decision Forest algorithm: Data pre-processing, remove All-Zero columns/features and high correlated features
Usage
DF_dataPre(X, thres = 0.95)
Arguments
X |
Training Dataset |
thres |
Correlation Coefficient threshold to filter out high correlated features; default is 0.95 |
Value
Keep_feat: qualified features in data matrix after filtering
Examples
##data(iris)
X = iris[,1:4]
Keep_feat = DF_dataPre(X)
Simple pre-defined pipeline for Decision forest
Description
This is a script of decision forest for easy use t
Usage
DF_easy(Train_X, Train_Y, Test_X, Test_Y, mode = "default")
Arguments
Train_X |
Training Dataset |
Train_Y |
Training data endpoint |
Test_X |
Testing Dataset |
Test_Y |
Testing data endpoint |
mode |
pre-defined modeling |
Value
data_matrix training and testing result
Examples
# data(demo_simple)
X = iris[,1:4]
Y = iris[,5]
names(Y)=rownames(X)
random_seq=sample(nrow(X))
split_rate=3
split_sample = suppressWarnings(split(random_seq,1:split_rate))
Train_X = X[-random_seq[split_sample[[1]]],]
Train_Y = Y[-random_seq[split_sample[[1]]]]
Test_X = X[random_seq[split_sample[[1]]],]
Test_Y = Y[random_seq[split_sample[[1]]]]
Result = DF_easy(Train_X, Train_Y, Test_X, Test_Y)
performance evaluation between two factors
Description
performance evaluation between two factors
Usage
DF_perf(pred, label)
Arguments
pred |
Predictions |
label |
Known-endpoint |
Value
result$ACC: Predicting Accuracy
result$MIS: MisClassfication Counts
result$MCC: Matthew's Correlation Coefficients
result$bACC: balanced Accuracy
Decision Forest algorithm: Model prediction
Description
Decision Forest algorithm: Model prediction with constructed DF models. DT_models is a list of Decision Tree models (rpart.objects) generated by DF_train() DT_train_CV() is only designed for Cross-validation and won't generate models
Usage
DF_pred(DT_models, X, Y = NULL)
Arguments
DT_models |
Constructed DF models |
X |
Test Dataset |
Y |
Test data endpoint |
Value
.$accuracy: Overall test accuracy
.$predictions: Detailed test prediction
Examples
# data(demo_simple)
X = data_dili$X
Y = data_dili$Y
names(Y)=rownames(X)
random_seq=sample(nrow(X))
split_rate=3
split_sample = suppressWarnings(split(random_seq,1:split_rate))
Train_X = X[-random_seq[split_sample[[1]]],]
Train_Y = Y[-random_seq[split_sample[[1]]]]
Test_X = X[random_seq[split_sample[[1]]],]
Test_Y = Y[random_seq[split_sample[[1]]]]
used_model = DF_train(Train_X, Train_Y)
Pred_result = DF_pred(used_model,Test_X,Test_Y)
Decision Forest algorithm: Model training
Description
Decision Forest algorithm: Model training
Usage
DF_train(X, Y, stop_step = 5, Max_tree = 20, min_split = 10, cp = 0.1,
Filter = F, p_val = 0.05, Method = "bACC", Quiet = T,
Grace_val = 0.05, imp_accu_val = 0.01, imp_accu_criteria = F)
Arguments
X |
Training Dataset |
Y |
Training data endpoint |
stop_step |
How many extra step would be processed when performance not improved, 1 means one extra step |
Max_tree |
Maximum tree number in Forest |
min_split |
minimum leaves in tree nodes |
cp |
parameters to pruning decision tree, default is 0.1 |
Filter |
doing feature selection before training |
p_val |
P-value threshold measured by t-test used in feature selection, default is 0.05 |
Method |
Which is used for evaluating training process. MIS: Misclassification rate; ACC: accuracy |
Quiet |
if TRUE (default), don't show any message during the process |
Grace_val |
Grace Value in evaluation: the next model should have a performance (Accuracy, bACC, MCC) not bad than previous model with threshold |
imp_accu_val |
improvement in evaluation: adding new tree should improve the overall model performance (Accuracy, bACC, MCC) by threshold |
imp_accu_criteria |
if TRUE, model must have improvement in accumulated accuracy |
Value
.$accuracy: Overall training accuracy
.$pred: Detailed training prediction (fitting)
.$detail: Detailed usage of Decision tree Features/Models and their performances
.$models: Constructed (list of) Decision tree models
.$Method: pass evaluating Methods used in training
.$cp: pass cp value used in training decision trees
Examples
##data(iris)
X = iris[,1:4]
Y = iris[,5]
names(Y)=rownames(X)
used_model = DF_train(X,factor(Y))
Demo script to lean Decision Forest package Demo data are located in data/ folder
Description
Demo script to lean Decision Forest package Demo data are located in data/ folder
Usage
Dforest()
Author(s)
Leihong.Wu
Examples
Dforest()
Doing Prediction with Decision Tree model
Description
Doing Prediction with Decision Tree model
Usage
Pred_DT(model, X)
Arguments
model |
Decision Tree Model |
X |
dataset |
Value
Decision Tree Predictions Different endpoints presented in multiple columns
Source
rpart
See Also
rpart
Performance evaluation from other modeling algorithm Result
Description
Performance evaluation from other modeling algorithm Result
Usage
cal_MCC(pred, label)
Arguments
pred |
Predictions |
label |
Known-endpoint |
Value
result$ACC: Predicting Accuracy
result$MIS: MisClassfication Counts
result$MCC: Matthew's Correlation Coefficients
result$bACC: balanced Accuracy
QSAR dataset with DILI endpoint for demo
Description
This data set gives the DILI endpoint of various compounds (Most or No DILI-concern) with QSAR descriptors generated by MOLD2
Usage
riversFormat
A List containing two vectors: X contains 958 observations and 777 variables. Y contains DILI endpoints of 958 observations
Source
In-house data
References
Minjun Chen (2011) FDA-approved drug labeling for the study of drug-induced liver injury. Drug discovery today
multiplot
Description
Multiple plot function
If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE), then plot 1 will go in the upper left, 2 will go in the upper right, and 3 will go all the way across the bottom.
Usage
multiplot(..., plotlist = NULL, cols = 1, layout = NULL)
Arguments
... |
ggplot objects |
plotlist |
a list of ggplot objects |
cols |
Number of columns in layout |
layout |
A matrix specifying the layout. If present, 'cols' is ignored. |