Start to apply logging data and parameter matrix in R

Today, I started to apply logging data and parameter matrix in R.

Summary:

I try to select 6 most possible models to predict:

ANN, Boosted tree, Cubist, Random forest, SVM and Elastic Net.

As what I read last semester, these six are the best ones used to predict other kinds of data.

At this evening, I code for ANN model. Now the laptop is calculating and it has not finished yet. So I do not know if it is good or not for now. I will leave it calculating by itself. Tomorrow, I may get some results.

Codes:

## T2 Prediction

head(betapredict)

head(logmatch)

betaorigin=betapredict

logorigin=logmatch

logchange1=logorigin

betachange1=betaorigin

library(caret)

set.seed(1)

trainingrows=createDataPartition((1:3055), p=0.8, list=FALSE)

head(trainingrows)

trainlog=logchange1[trainingrows, ]

trainbeta=betachange1[trainingrows,]

testlog=logchange1[-trainingrows, ]

testbeta=betachange1[-trainingrows,]

library(earth)

library(kernlab)

library(nnet)

library(MASS)

parameter1=trainbeta$P1

parameter2=trainbeta$P2

parameter3=trainbeta$P3

parameter4=trainbeta$P4

parameter5=trainbeta$P5

parameter6=trainbeta$P6

ctr1=trainControl(method = "cv", number = 10)

nnetgrid=expand.grid(.decay=c(0, 0.01, 0.1), .size=c(1:10), .bag=FALSE)

set.seed(100)

nnettune=train(trainlog, parameter1, method="avNNet", tuneGrid = nnetgrid, trControl = ctr1,

               preProcess = c("center", "scale"), linout = TRUE, trace = FALSE, 

               MaxNWts = 10*(ncol(trainlog)+1)+10+1, maxit=200)

nnettune

Tomorrow, I will continue to build models and compare them.

Results：

Model Averaged Neural Network 

2447 samples
  16 predictor

Pre-processing: centered (16), scaled (16) 
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 2202, 2202, 2202, 2201, 2202, 2203, ... 
Resampling results across tuning parameters:

  decay  size  RMSE         Rsquared 
  0.00    1    0.001182810  0.2659956
  0.00    2    0.001178855  0.2652559
  0.00    3    0.001176240  0.2700417
  0.00    4    0.001171193  0.2758881
  0.00    5    0.001171282  0.2757166
  0.00    6    0.001175032  0.2719288
  0.00    7    0.001169831  0.2771893
  0.00    8    0.001170633  0.2768607
  0.00    9    0.001173294  0.2730265
  0.00   10    0.001174102  0.2723126
  0.01    1    0.001202662  0.2380567
  0.01    2    0.001202690  0.2380329
  0.01    3    0.001202191  0.2388848
  0.01    4    0.001201014  0.2403814
  0.01    5    0.001208686  0.2342891
  0.01    6    0.001192577  0.2520885
  0.01    7    0.001187736  0.2564749
  0.01    8    0.001188651  0.2550046
  0.01    9    0.001189708  0.2548316
  0.01   10    0.001190979  0.2544052
  0.10    1    0.001212622  0.2285682
  0.10    2    0.001211344  0.2293782
  0.10    3    0.001209365  0.2291870
  0.10    4    0.001211336  0.2291943
  0.10    5    0.001211175  0.2295812
  0.10    6    0.001204673  0.2362646
  0.10    7    0.001203994  0.2369177
  0.10    8    0.001202633  0.2369342
  0.10    9    0.001203141  0.2373625
  0.10   10    0.001204911  0.2364219

Tuning parameter 'bag' was held constant at a value of FALSE
RMSE was used to select the optimal model using  the smallest value.


The final values used for the model were size = 7, decay = 0 and bag = FALSE. 

The results come out now but R2 of training model is not very good. The largest is smaller than 0.3. It should be at least 0.7. I will try to figure it out today.