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Weight Lifting Wearables

Raymond C
19/03/2015

###Preliminaries Load the necessary libraries and set the random seed.

library(caret)
library(dplyr)
library(reshape2)
library(gridExtra)
set.seed(1935)

Load the training and validation data. Data from http://groupware.les.inf.puc-rio.br/har

train_dat <- read.csv("pml-training.csv")
test_dat <- read.csv("pml-testing.csv")

###Data Cleaning Remove features that are not used in the validation dataset, i.e. the feature have NA for all values.

excl_list <- character(0)
for (n in names(test_dat)){
  if (all(is.na(test_dat[n]))){
    excl_list <- c(excl_list, n)
  }
}
train_dat <- select(train_dat, -one_of(excl_list))

Remove features that are not relevant from the dataset. Feature X is the record sequence and should be removed. The problem is categorical hence temporal features are not revelant (Actually, the raw temporal data has been preprocessed already).

train_dat <- select(train_dat, -X, -(raw_timestamp_part_1:num_window))

Convert all movement features to the numeric type.

dyn_names <- names(train_dat)[2:(ncol(train_dat)-1)]
train_dat <- mutate_each_(train_dat, funs(as.numeric), 
                          dyn_names)

###Parition Dataset Partition dataset into training and testing sets. Test set will be used to compute out of sample error.

inTrain <- createDataPartition(train_dat$classe, p = 0.75, 
                               list = FALSE)
train <- train_dat[inTrain,]
test <- train_dat[-inTrain,]

###Train the Model Train the model using the random tree algorithm. Note that random tree does not require much preprocessing unlike regression. Cross validation is built into the model training.

fitControl <- trainControl(method="cv", number=10)
modFit <- train(classe ~ ., method="rf", data=train, 
                 trControl = fitControl, prox=TRUE)
## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.

###Model Diagnostics Plot K fold cross validation vs. accuracy and kappa with in the training dataset.

p1 <- qplot(Resample, Accuracy, data=modFit$resample)
p2 <- qplot(Resample, Kappa, data=modFit$resample)
grid.arrange(p1, p2)

Plot of the confusion matrix. Note that the test set is used as a reference for computing out of sample error.

pred <- predict(modFit, test)
truth <- test$classe

cm <- confusionMatrix(pred, truth)
cm_tab <- melt(cm$table)
p <- qplot(Reference, Prediction, 
           color=value, size=value, data=cm_tab) +
  scale_size_area(max_size=25)
plot(p)

Plot of various metrics by prediction class.

byClass <- mutate(melt(cm$byClass), Class = substr(Var1, 7, 8))
p <- ggplot(byClass, aes(x=Class, y=value, group=Var2)) + 
  geom_point() + facet_wrap(~ Var2)
plot(p)

Overall performance including the final out of sample error.

print(cm$overall)
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.9942904      0.9927770      0.9917585      0.9962027      0.2844617 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN