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<h2>Practical Machine Learning Project Write up</h2>
<p>We first consider the dataset and see what are the variables which are being used in the model. We note that many of the elements being used are actually descriptive statistics of the observations done for every window of the data. Also these descriptive statistics ( like average, max, min, amplitude, skewness, kurtosis, standard deviation, variance) are present for only a few of the rows - i.e. rows which are having new_window=yes. We omit all those columns which are these statistical observations for our predictions. </p>
<pre><code class="r"># set up environment
options(warn=-1)
library(lattice)
library(caret)
library(rattle)
library(randomForest)
library(cvTools)
library(png)
library(grid)
library(rpart.plot)
library(RColorBrewer)
library(rpart)
# remove statistics columns
pml.training <- read.csv("C:/SujoyRc/Temp/Coursera/pml-training.csv")
pml.testing <- read.csv("C:/SujoyRc/Temp/Coursera/pml-testing.csv")
</code></pre>
<p>For simplicity of coding we create a new dataset removing these columns.We can also confirm that none of the other columns will have any NAs (and thus no rows will be excluded from our analysis)</p>
<pre><code class="r">cols_exclude_pattern<-c("max","min","skewness","kurtosis","avg","var","stddev","amplitude")
pml_use<-pml.training[ , -grep(paste(cols_exclude_pattern,collapse="|"),names(pml.training))]
apply(pml_use, 2, function(x) length(which(is.na(x))))
</code></pre>
<pre><code>## user_name raw_timestamp_part_1 raw_timestamp_part_2
## 0 0 0
## cvtd_timestamp new_window num_window
## 0 0 0
## roll_belt pitch_belt yaw_belt
## 0 0 0
## total_accel_belt gyros_belt_x gyros_belt_y
## 0 0 0
## gyros_belt_z accel_belt_x accel_belt_y
## 0 0 0
## accel_belt_z magnet_belt_x magnet_belt_y
## 0 0 0
## magnet_belt_z roll_arm pitch_arm
## 0 0 0
## yaw_arm total_accel_arm gyros_arm_x
## 0 0 0
## gyros_arm_y gyros_arm_z accel_arm_x
## 0 0 0
## accel_arm_y accel_arm_z magnet_arm_x
## 0 0 0
## magnet_arm_y magnet_arm_z roll_dumbbell
## 0 0 0
## pitch_dumbbell yaw_dumbbell total_accel_dumbbell
## 0 0 0
## gyros_dumbbell_x gyros_dumbbell_y gyros_dumbbell_z
## 0 0 0
## accel_dumbbell_x accel_dumbbell_y accel_dumbbell_z
## 0 0 0
## magnet_dumbbell_x magnet_dumbbell_y magnet_dumbbell_z
## 0 0 0
## roll_forearm pitch_forearm yaw_forearm
## 0 0 0
## total_accel_forearm gyros_forearm_x gyros_forearm_y
## 0 0 0
## gyros_forearm_z accel_forearm_x accel_forearm_y
## 0 0 0
## accel_forearm_z magnet_forearm_x magnet_forearm_y
## 0 0 0
## magnet_forearm_z classe
## 0 0
</code></pre>
<pre><code class="r">summary(pml_use$new_window)
</code></pre>
<pre><code>## no yes
## 19216 406
</code></pre>
<p>One question is how distribution of the columns differ for new_window=yes as against new_window=no values - this is to be confident that there is nothing specific about these rows in that they will need to be excluded from the analysis or some special treatment is required. We plot a few of these and see that there is not much difference between them visually. </p>
<pre><code class="r">transparentTheme (trans = .9)
featurePlot(x = pml_use[, 8:59],
y = pml_use$new_window,
plot = "density",
## Pass in options to xyplot() to
## make it prettier
scales = list(x = list(relation="free"),
y = list(relation="free")),
adjust = 1.5,
pch = "|",
layout = c(9, 6),
auto.key = list(columns = 52))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>To start our modelling, we must split the data into testing and training sets - strictly speaking the testing dataset is an out-of-sample set as it does not have the response variable in it.</p>
<pre><code class="r">set.seed(12345)
inTrain<-createDataPartition(y=pml_use$classe,p=0.7,list=FALSE)
training<-pml_use[inTrain,]
testing<-pml_use[-inTrain,]
### List all columns into one single variable
fmla<-as.formula(paste("classe ~",paste(names(pml_use[8:59]),collapse=" + ")))
</code></pre>
<p>We start with a RPART model and see the performance. </p>
<pre><code class="r">modFit<-train(fmla, method="rpart",data=training)
</code></pre>
<pre><code class="r">fancyRpartPlot(modFit$finalModel)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-8"/> </p>
<p>And see the model fit statistics both for in-sample and out of sample errors.</p>
<p><strong>IN-SAMPLE ERRORS</strong></p>
<pre><code class="r">confusionMatrix(training$classe,predict(modFit))
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 3564 51 280 0 11
## B 1090 918 650 0 0
## C 1115 70 1211 0 0
## D 1004 402 846 0 0
## E 357 325 665 0 1178
##
## Overall Statistics
##
## Accuracy : 0.5
## 95% CI : (0.492, 0.509)
## No Information Rate : 0.519
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.347
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.500 0.5198 0.3316 NA 0.9907
## Specificity 0.948 0.8546 0.8825 0.836 0.8927
## Pos Pred Value 0.912 0.3454 0.5054 NA 0.4665
## Neg Pred Value 0.637 0.9235 0.7848 NA 0.9990
## Prevalence 0.519 0.1286 0.2659 0.000 0.0866
## Detection Rate 0.259 0.0668 0.0882 0.000 0.0858
## Detection Prevalence 0.284 0.1935 0.1744 0.164 0.1838
## Balanced Accuracy 0.724 0.6872 0.6070 NA 0.9417
</code></pre>
<p><strong>OUT OF SAMPLE ERRORS</strong></p>
<pre><code class="r">confusionMatrix(testing$classe,predict(modFit,newdata=testing))
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1528 21 123 0 2
## B 473 389 277 0 0
## C 480 31 515 0 0
## D 415 151 398 0 0
## E 148 161 292 0 481
##
## Overall Statistics
##
## Accuracy : 0.495
## 95% CI : (0.482, 0.508)
## No Information Rate : 0.517
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.34
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.502 0.5166 0.3209 NA 0.9959
## Specificity 0.949 0.8539 0.8806 0.836 0.8887
## Pos Pred Value 0.913 0.3415 0.5019 NA 0.4445
## Neg Pred Value 0.640 0.9233 0.7757 NA 0.9996
## Prevalence 0.517 0.1280 0.2727 0.000 0.0821
## Detection Rate 0.260 0.0661 0.0875 0.000 0.0817
## Detection Prevalence 0.284 0.1935 0.1743 0.164 0.1839
## Balanced Accuracy 0.725 0.6852 0.6007 NA 0.9423
</code></pre>
<p>These two results show a match of only 60% approximately - clearly it is not good enough. We attempt a Random Forest result for the same and note the variable importance in a plot.</p>
<p>For simplicity we assign the randomForest object in the train model into a separate model. This will be useful as we will be using randomForest package functions for variable importance plots and cross-validation.</p>
<pre><code class="r">modFit_rf<-train(fmla, method="rf",data=training)
rf<-modFit_rf$finalModel
</code></pre>
<p>And visualize the node importance in the following plot</p>
<pre><code class="r">varImpPlot(rf,type=2,main="mean decrease in node impurity")
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAGwCAMAAABy/YdMAAAA2FBMVEX9/v0AAAAAABcAADkAAGUAOWUAOY8AZo8AZrUNAAAXAAAgAAAoAAA5AAA5ADk5AGU5OWU5OY85Zo85ZrU5j485j9plAABlADllAGVlOQBlOTllOY9lZgBlZjllZmVlZrVlj49ltbVltf1m2/2PMQCPOQCPOTmPOWWPZgCPZo+PjzmPj2WPtY+PvI+P27WP29qP2/21WAC1ZgC1Zjm1tWW124+1/rW1/tq1/v2+vr7aeyDajznaj2Xa24/a/rXa/tra/v39tWX9vFH923v924/9/rX9/tr9/v1lU8kfAAAASHRSTlP//////////////////////////////////////////////////////////////////////////////////////////////wCc7PJgAAAACXBIWXMAAAsSAAALEgHS3X78AAAZcklEQVR4nO2dj3/jNJrGN7QMOfbK0B0yFLiZZcO2sDscm+3QoVyOpblL4///P1rrly07tiT7lSzZer4fMm1jKxZ+Kkt6+urVHwqQJX+IXQEQBwifKRA+UyB8pkD4TIHwmQLhMwXCZwqEzxQInykQPlMgfKZA+EyB8JkC4TMFwmcKhM8UCJ8pED5TIHymQPhMgfCZAuEzBcJnCoTPlDyFf3794on4AZePoy7kXpCdd78dUTc3IHzCF7pfQXgLh9Unt6vLx/2K36ryC/96KL9e7IrT7eXPq5Vqaafb1eovXA91GnunPHq6vXjDTm+VLo5XsrA6UIgG2fhY7Qd22qbzQrKgXtmqXPnNI/89EdX46fXlL2X58szy7QP/PL8sRnhFeZ/u+Teb8jayr+xW8jfW/Ez5Q6mHOk28w4Tk77dLq6/V+Qwh/PnHrgt52rrjQqqgXtnqLE149vO/pPDrffnbcc9+/zyzGOEvH8uWuX1+fbE7XpU3m9/HolJoU54hHrrHq/LAvvyhOo2/Uz5Vy9PW7HirNH+j6DjQ+Njqh+NVqZKoRvtCqqBeWV6u/N3a6sKvZR/PHvXHq7Uq65XFCL+WYlzsZINirYS1VvFm1dsexG198VSdph6k/LTirDRv8fx5Wx0oqmtVH1v9wD+/FHx7fiFVUK+sEHW/2uiP+p0m/Om2bP1r/3dsUcKLu1bdaNY5C+HlPVVntoVXT+uG8Ko07+NXjd+nqo9/1ISXPzgLLyprFb7YX/x45b+LX6Lw6tnMbid/sjcUaj6BtXc24o6fleY/lBJUBxj9wvc86vWCTeGrR32p8n51Lnz5exegi1+i8HIwpZramULNMZca+8lHd3FWumr56gD7lH7hewd3dcGW8GfV0YQXnxNi7rlE4fk9ZDer/LrZa+Mmfiq71V+8lofFPb2Xsz7Rslql+aO++pWQH2IQvjGda12o81H/szjIxhJ/aT7qyyvz34gAXfxShJ8tDiP2fRAbB8LHxS58kMkchI+NVdb9KoxvC+EzBcJnCoTPFAifKRA+UyB8pkD4TIHwmQLhMwXCZwqEzxQInykQPlMgfKZA+EyB8JkC4TMFwmcKhM8UCJ8piQq/AmTMdzhV4WNXYP5A+EyB8JkC4TMFwmeKN+GPLx/L/9RPjURczUM+gPBkIHw+NOZwNOGPn3/Fs8CwxdpN4d/yxXzq0IfXK69rOiH8CFaFfuOIwl+V6t5visOLp5bw/A12aL8O0eJ/LYpf8Rr2WsmX+JkofKnn8035+nJ3/qi/35Zvs8N41CeB1xb/kj/ji9NdS/j3Oy48SwV2sYPwaeCzjze0+NPtlh0qMLhLErrw3X0879k7D/kAwpPxIHzPqF4mfyy/lO97ztQC4cnAucsUv8KLbH8y1WJAEwfCkwnY4iF8AKwRFM4fZD5sEb7U9nj9xNr5thzEF8fPdmwwpw6Gc++yFb45F6d+kgGL8Idy1L5hNk3ZmvcbnmRxX6VSDujeZevcNd03yosm/PM3T+9Fuu2bx7Lpv3+3Pt1VqZQDundo8V4+yYBF+NPdT9+ILLyXj883H24eXn64mcK9y1b4VPr4Yv+WpVPfCrfm21enu3d1KuWA7l2+wnuDKDwbznHDng3rVptGKuWA7h2EJ0MU/vQ93zlj9fFXckyvDdwDuncQngy1xb/yVpMhQHgyNOH3HduhNNw7+VbjIc9m/OdIu8fN9YHwZCbx6iH8KLyN4Ds/3HzYOp17t1ptDnyTFe7f8S794693x+sfatvuWfPs2OGvtjKAQy8ufb6W3acJz3ZuUVOGLIT3N2fv+3QDNuFv12yvQ6aQ9O/4IJ5trsXH8rVtp0qww6tK+Lp45fMZ7L7aHMrCufPn0vl37pgU7MVUL7h/x6btIhBL/TYI204W4Idv6xZfFxc+n9Huq91gtHgvn25giPDcvzteP+nCV7adLCBCdrqEFz6fye5jH+1W64WQdB9fKSf9u1aLr2w7WcDY4tmor9/u0/4KkInwQfEmvPLvZB8vw28r206VEH08U3p/2RRedvW9dt9e20EVwpPx96iX/l35lP6kavHSttM8u9MtP6s8+YubpvDC52vbfZXwYp/2jVOtgR0v83hdoNaALEwYDoQn48vAUYKe/to07aTw0s776Lvhwnc4gcsUPuhY7vxq5sO2QIybv4tH8gdp1LDe/b/0zQ/DxF8tUfiws7fOyxmwCc82vr+U8202KD+8+E1aN5Iw8VdLNHBWv076/0UVfluvmxOaqmGdEj5I/BVavJfLGbAJz42brRSeGywt4cPEXy1R+Hn18V/u7C0+QPzVIoWfFuqjfl0bLTyy/uVDS/gg8VcQngy1xb/R4qrkqL4pfJD4KwhPxkMfHwEITyaQ8E3XBRE46YHQq/hMO5xXFzUfTin0alv/RX5Rwk88gdeuaiCh0Cu+QFPVOgGnzdsrbIhVGOduytCraoFmgRbv6aoGEgq9kgs0XWo9M2bZx08XesUXaLrVGtiZT+gV/3S3WgM7swm9kgs0nWoN7ISYx/vx488/tl6gCeHJ+I65a4VelQ2cd8xd8VOOqKJvLhBe7RHfMXet5h6o8S9G+CgDenFl82FizB1/R7pw9Z4G0uRj7/yieXf+aj0b4kzhq0sboMbcaSN0uafBfl1Ik4+9o3t3Q2od223z9Jo4zs6fc2eNuZNzeTndk4YcLyn8vJb15yz8kJMTZr4t3hZzJ38fpKUjDTll8kH4+fbxtpi7VouXrV2YfBA+JoFj7lruXf3t8bMdhI9J4Jg7+YC/fKz2NGBPemHyQfiYTBJzhwic9EDMXSzijevE5c2HUwq94h6AU63nQMSZXHV9AwmFXrHs2Or7BRg4K/mKdn2a8FNmvdqvlhSIMfMWP2nWq2UFYsy8j58w9Or+bZULfwnCR2Y+oVfH69/vFhllG4fZhF6Jv/0tcUFFHGYUeqUB4cl4E1791eWWjcpUxFV9lB56tfisV9PiW/jqh/FVcmCuwkceyevQhNfiqXThQ0VcudY6VWLP3XWIwst4qrMReJiIq7rW8Z23Obp1/pw7FV0hpnPBI65q4UeVis6CWryctVVROPX7ISKuXGudLMvp491avK+IK9daAzt04S19vM+IK9daAzsehO/423mgiCvXWgM7oZIfeQ+6aQDhyfgVvnbZIHyThIZ1AqLwdbxVKXb176UeaOsp0dWQWqdHShM5AT3Y8qhWzf0u/n4WKNFVs9bxDZBhrziZrcIZOFz8G20NTVH4jLbSE101hbdXKy2W1uK1eCu1IWCoRFdDap0gC+vjNU+mavGBEl0NqTWw48Gy/WwnVs39wv99DJToakitgR3io76Ot9JH9c3pnJ9EV0NqDexMkr16NFqiqwYQnkw44bWQKf257+LWdCW6agDhyUydr97PlqOzET65wXzF1Ism+3ee7LXphtc6GdKbvldMvmhyuE3XVev4zpfTK2JWq7DO3eBFk2Nsui7hHc+LzXJb/NBFk2NsuuG1Tofl9vEDF02OsemG1xrYmXzR5HCbbnitgZ2pF00adp7ss+mG1xrYSWjRZJ9N1wGEJxNm0SR7oxFuVdl45+FWVptueK2BnTCLJhm+4+x05iF8ukP6ItCiSUqcnY9ap0HCk/gi1KJJQpydW63jO1/2V3pxdv6cO7VAqr2EipLVzEn4ISfHYtEtvn/R5OisZh5qnQhL7uPHtHhLnJ2HWgM7YRZNUuLsPNQa2AmzaJISZ+eh1sDOBIsm/QTdNIDwZCZYNJmt8Ase3DktmjREW5W3Zt330YRap8GSp3NuiyYNZs3pblRmBBg4kQ0cLr5l0aTRrNmPS3SXbkPSWHKLd1k0aUxB7xpWO6zWibDgPt5l0aTBrBn5oJ+L8EkzxaLJXrNm5IMewntggkWTvdFW/Ok/Kp0thCeT9qLJPiA8mSiLJtWh/1782rl0waLJICQ9oOcktGhygI03h7uaeB3TWTQ5ZHaXunOX8GJJP86dz0WTQ2Z3ibemDFq8x0WTQ2y8xG9qDn28v0WTQ2y81O/qDEhn0eQQGw/Ck0lm0eQgGw/Ck0lo0eQAIDwZ38KrRZOtdXR//bRjp0lh5hz/Uz/k5ulAeDITBFsWuWxUkP5QXmOCmDvCRgV9Zl6SN3gGk3eNKWLuCAsoe+Z4SbpiacfY+XXuuPjhNironeMl2bByavGBNyroNfPSvL8Z9fGBNyroNfNmdINTZYqYu9ELKHvNPAhPZoKYu9ELKPvNPAhPJlzyo5BAeDLhhNdi7oZiLZqg8LMa2RXBsl4JY4cd/kVzbLyR3j2e11yuCJX1Sho77LDu2HgjPQNnJV+x6zGRgXPsyYHDkC4OdcfwTtJrW7m1+J6sV8rYyUf43Pr4vqxXtbGTi/BzI0zWK2nsQPh0CZT1Shg7ED5dsGgyU8JnvTrdep3CcyA8mfChVyGM3LSEn9uAnhM89Iq/Kd+rfT6jtSdicQi1npbZTeE54UOvtD+5Sp+vPGy09p5vPtyYnxJJOXfzCrny49wJmcyhV3IllVxsIw9rBTuG/XvbqCCpBpZji3cMvarNPXXYbO1Zd6BL6z5n2Me7hF61Wrxs7UZr7/6tJUPCDG90aoQPvWqtnay/7bX2jte/W1bOQngy4UOv5ANejfTFYZO1J4aKxrXyEJ4MnLtMmSLdmeviOPdgLQhPZooW73+ngnSEn+WInkEUXiQ3K5U9Xv+m+XKHVZ3gLMROBcnc7HnO4RlE4UXik0P570aLv2NzNs109b9TQTLO3TxdO7pzJ1MdPX/z9L6apbNESF+eJUDwu1NBMo0s1xYvk5ud7n765kmLv2PDNG2I5n+ngnTudaZ9vGzxxf7tRo+/Y4e0mbj/nQpmerNTwksfz811zZdjouvCe9+pAMKToY/qP2HO2/e1L8dG7PftUb3nnQogPBn6PJ737a+63q3JI/nRrKBmr+ZG2/7cbONRFpUR1xa+4dHNVvjZDuwYaWe96ovBSuKGz3cqx0g661VvDFYSBs58zRu6gSPUCZf1qi8GK4mGlnOLD571qicGK437nXEfHzjrVW8M1oxveCoknfWqNwYLwpNJOetVfwwWhCeD0KtMmSL0qo3VsZtj1qu5EbbF+7ZqFUkIP+tBPVF4HnP1xO07PsArZ191pzzesaPWehLmPY0nCs9jrqR9t9+U47et9pdWSp56W63jO19z2E0ynHPHY674NzfMxXn/bl2HVpDy1NuEdz0xIFm3eB5zJe07Zqw/vKzNdYpjR6z1NOTcx/OYK2nfFfffvip78spqozh2xFoDO9Q/y8qYKz6sW4leXjHesaPWGtghCs9irpR9x8XXIq5GO3bkWgM71BZ/FnM1CRCeDE34//noO/ld3Vz/7z/sSx+pEXkQnoyHzJa937mUY8xS+HmP6YnCc9Ot/kemMNPiKEJF5MW/5zOfxfto8dWoXKUwq0MwgkXkxXfMZrcxgV/n7thIbFQUzWi7xhteI/LitzW0+GOdyqxOYaYIFZGXwC3Puo9vtvhGAB4jXETevO95Enjs4491CrP6aJiIPAhPhrqESmU/Yd/VKcwkwSLyIDyZSWLuBpixbkB4MiGEPwuZ6xCesA9lEV/4uY/sCvrf42vDpXZrPv56d7z+ofZdnus0V+Y89VvX1CiRb/vs53IFvY+vDBfp1sgcGccrPhgr2n6NMU89D+Ryq3Vk80O+Ypsw8QycVh7actIls+LI6dmZXyPPkpP/VvLiKpDLLrzTWcFAi2+IV7k1mvBtv8aYp14GctFrHRz08bV40q1ptfgzv8aYp54FcvmoNbDjTXgVgCX7eCF80ZW8vn8LSuvOFI61Bnb8Peprt+aTqsVL36VKc2XZgpIHcnmoNbATZB4/1q9xDuSC8GR8Cy/zYDU5vnww+TXKzHnjbOdAeDKwbEddH6N6r86ddYdJx1qHBvN4v86dfYfJqtZw7pbk3Nl3mKyEdzstFGjxfp27+czj0cf7de6sO0w61hrYScm5s+8w6VhrYCch585hh0nHWgM7STl3zkB4MpM4d0Xx/3/scu7kurh/fqodPLqsl4bwZCZKcOg571lc4RcwpicL77TTJGGngp4ovKg3fgmzeLLwbjtNjs571heFF9W5m/cGBX6cO6edJgl5z/qi8NDiyRCXULnsNDluXSSnLwoPfTwZLy3evNMkJe9ZTxTeAm58bLz08badJsfnPetx7yE8Gfqo3r7TJCHvWU8UHoQnQ5/H80e4ZafJ0fRE4UF4MtRADLedJodii8LD4I4MNcGhQ14rP9FWQ2odFkzn3PJaeYq2atYaBk5cA0doZ85rpZ1DibZqCj+ijDfQ4hn2vFaeoq2G1Dow6OPd8lr5ibYaUmtgh571ypbXylO01ZBaAzvER71LXis/0VZDag3sTBSI4RkITyac8Jp/ozV/GW0jD3703TiHD8KTmXrRJGF7SY2Ywi9iTD/9oslWmNWH+tAQEy/irV/GLH7yRZOGMKshJl5E524JCybpzt3wRZOG7SUHmHho8WSmXjRp2F5ygImHPp7MxIsmTWFWA0y8Rdz6uEy8aNIQZjXExIPwZKZeNNkOs6oODTLxIDwZLJrMlInTnf3j9mzoPiZCC8KTmdi58/QwiDmdW8agfmLnjnft0rXT1lgao/LKO3022o9375cyjZ/audNG9HKNZXnYGJUnRpDtWkdzvGa/w2Qk5+7Y2JpSHpZFe2J0iv25oYcWT2Zi5+7Y2JpSHrZE5XUskEcfT2Zi567V4mVrN0XldTzoF9Lm4hIh3Zn2Xv1tb1Rex4MewntgYudOvndZr7FkT3pDVB7vDc7m/hCeDJy7TJko3dlwe85YAoM7MjThRVYq7r/w2Pqyj6//0GKyZYhgOkeGJjzPSiX9l3IUti+FrgZjFluGBAycyAZOlZWK+S/XT+/frZuzL4MtQwItngx1VM+zUnH/hQVLPrzUAyZNtgwN9PFkqEuo3m6U/1Lcf/vqdPeu+oOKebEkjWXc+6hQM2LIdFd8WLcSvbw6ZLJliEB4MkTheVYqad1w8bVkV8ZNJWlAeDLUFu+6N6RfIDwZmvAd6a66nJd6Gnc6D70aA4Qng9CrYRdeyJh+DqFXXWspY939xcziZxB61bmWMpZzt/o1cqq1VJy7KUKvutZSosWTmUHoVcdaSvTxZJIPvepcS7mUux+R5EOvOtdSQngyqYdeda+lhPBkEHqVKfEXTfaQaujVUoBzN+SyixnTz8G569psMs79X9AsfgbOXedmk3Hcs8XE283CuevcbBItnkzyzl3nZpPo48mk79x1bTa5nPsfjeSdu7S8+uWQunPXvdkkhCdD35rk/LsH62Rdm85b85h3hfVBeDK+hXdbNOkgvHGzSQzuyJCEr+0Z/o/0YWpRXfah7E9gP77WgcB0rqIxVlM+TB015bAPpSGBvanWMHDiGjjN2VlR9O4x2b8PpSGBvUF4+ykBQIuvaPoxtQ+jcNiH0pDAfnStQ4E+XtFo8Y1tJxku+1CaEtiPrjWw47GP13yY+qhtH0pDAvvxtQZ2qH+dU39aZd/VPozEZR/K3gT2hFoDO5MEYngHwpMJEnPnkOJqTJb6GghPBi0+UyB8psxUeEDGfIcTFV5nfOtHyX4gfKYlIXymJSF8piUhfKYlIXymJSF8piVnIDwIAYTPFAifKRA+UyB8pkD4TIHwmQLhMwXCZwqEz5TUhX9+vTpLe2lFLc1nJYd8QL0GdGjJw4ovBR1RUuT2HFNyv+IXHXXN5IVnN+V8GwMLB7EOgJcc8gF8DahYHzKwJPtNq4sMrDTb2GtMSbH+fNw1kxeerbwamjPx/uJHmX5HLgBz/YADu3H32xElGXWRYSWPn7MlKcNLyi0/R9Y2deHZSszhe5rxpV2i5NAPqIsMLckWgY8oebr7x+12TEm+DHVUSU7iwrMciuOElyUHfsDpdjOu5PHqYjeq5H6j8ncPLMlyRpWtftz/Z/LCT9zin19vimmfFeW5p3EtnnO/XWiLH9PHy9RbI/o+noV1dK85anSw5yHwm0mvyUlcePboHTyq53dAlhzyAUL3MSXlg3ZEyUKMy0de8/T947hrJi/8pPN40fq2I+fUci34pPP40ddMX3gQCAifKRA+UyB8pkD4TIHwmQLhMwXCZwqEzxQInykQPlMgfKZA+EyB8JkC4TMFwmcKhM8UCJ8pED5TIHymQPjieMWXTvXslCN22th2HjNxKEutuzfnTQIIXxw/+9NT8fx1jy4yYneo8odLEeGdLBC+lPbNrji+rfY/l6ulj9c/1Dvp8oUqr9VS6Ivd8fOv1PZcanF1vVJartXlpdh+a/oHJQOELxX5eVv874/V/rdqC90rbQc99t79Rq1HPrz4ja29aJzOlrKwFQ2tvXXbH5QMEL7U5uHV6fsHsWmy3BFTbo9b7Y94uts1ds3li7Qap8uFa+qs6yf14c0PSgYIzzbJ/Nu//sy3UF2JxSnVFrqVXqWeandcsZGu6Bi009kTX+y3qPbW5W9A+HQpFXn/biOf1kVjx9xKr/JR3dg1Vy3LrE8vGmeJPl5syQvhE6VU5CDaaL0vrtxCV+nFR/XiqNhI94Hvra2fztYwspd4sziwR8EBLT5lhCjy4c30qrfQ5UMzNY+XR8Wo/uVj0TxdPvDVm3ysf/mIPh4kBoTPFAifKRA+UyB8pkD4TIHwmQLhMwXCZwqEzxQInykQPlMgfKZA+EyB8JkC4TMFwmcKhM+UfwNHbjm4MMhXQAAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-13"/> </p>
<p>And the confusion matrices are analyzed</p>
<p><strong>IN-SAMPLE ERRORS</strong></p>
<pre><code class="r">confusionMatrix(training$classe,predict(modFit_rf))
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 3906 0 0 0 0
## B 0 2658 0 0 0
## C 0 0 2396 0 0
## D 0 0 0 2252 0
## E 0 0 0 0 2525
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (1, 1)
## No Information Rate : 0.284
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 1
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.000 1.000 1.000 1.000 1.000
## Specificity 1.000 1.000 1.000 1.000 1.000
## Pos Pred Value 1.000 1.000 1.000 1.000 1.000
## Neg Pred Value 1.000 1.000 1.000 1.000 1.000
## Prevalence 0.284 0.193 0.174 0.164 0.184
## Detection Rate 0.284 0.193 0.174 0.164 0.184
## Detection Prevalence 0.284 0.193 0.174 0.164 0.184
## Balanced Accuracy 1.000 1.000 1.000 1.000 1.000
</code></pre>
<p>The confusion matrix for the training dataset gives a result of 100% which creates some concerns if this data is overfitted.</p>
<p><strong>OUT OF SAMPLE ERRORS</strong></p>
<pre><code class="r">confusionMatrix(testing$classe,predict(modFit_rf,newdata=testing))
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1674 0 0 0 0
## B 2 1137 0 0 0
## C 0 5 1021 0 0
## D 0 0 10 954 0
## E 0 0 0 0 1082
##
## Overall Statistics
##
## Accuracy : 0.997
## 95% CI : (0.995, 0.998)
## No Information Rate : 0.285
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.996
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.999 0.996 0.990 1.000 1.000
## Specificity 1.000 1.000 0.999 0.998 1.000
## Pos Pred Value 1.000 0.998 0.995 0.990 1.000
## Neg Pred Value 1.000 0.999 0.998 1.000 1.000
## Prevalence 0.285 0.194 0.175 0.162 0.184
## Detection Rate 0.284 0.193 0.173 0.162 0.184
## Detection Prevalence 0.284 0.194 0.174 0.164 0.184
## Balanced Accuracy 0.999 0.998 0.995 0.999 1.000
</code></pre>
<p>However the confusion matrix for the testing dataset belies those fears and it is sufficient.</p>
<p>We then run a cross-validation and observer the out-of-sample errors for trees involving 1,3,6,13,26,52 variables.</p>
<pre><code class="r">cv_results<-rfcv(pml_use[,8:59],pml_use[,60])
cv_results$error.cv
</code></pre>
<pre><code>## 52 26 13 6 3 1
## 0.003618 0.004994 0.007135 0.038732 0.105035 0.592906
</code></pre>
<pre><code class="r">with(cv_results, plot(n.var, error.cv, log="x", type="o", lwd=2))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-18"/> </p>
<p>The errrors are very small and provide a good estimae of out-of-sample errors.</p>
<p>These results, all encouraging, allow us to choose the random forest as a model of choice for this problem.</p>
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