Monroe Week 9 Homework, R

Monroe Week 9 Homework

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### Step 1: Load the data 
#Load your libraries
library("kernlab")
library("ggplot2")
library("e1071")
library("gridExtra")

# Let go back and analyze the air quality dataset (if you remember, we used that previously, in the visualization lab). Remember to think about how to deal with the NAs in the data. 
myairquality <- airquality
myairquality$Ozone[is.na(myairquality$Ozone)] <- round(mean(myairquality$Ozone, na.rm = TRUE))
myairquality$Solar.R[is.na(myairquality$Solar.R)] <- round(mean(myairquality$Solar.R, na.rm = TRUE))
myairquality

### Step 2: Create train and test data sets one for training and one for testing.
## 75% of the sample size
smp_size <- floor(0.75 * nrow(myairquality))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(myairquality)), size = smp_size)

train <- myairquality[train_ind, ]
test <- myairquality[-train_ind, ]
test

### Step 3: Build a Model using KSVM & visualize the results 
# Build a model (using the ksvm function, trying to predict onzone). You can use all the possible attributes, or select the attributes that you think would be the most helpful.
modelKSVM <- ksvm(Ozone ~ ., data = myairquality)
modelKSVM

predictOzone <- function(a, myairquality){
  predictedOzone <- predict(a, myairquality)
  results1 <- table(predictedOzone, myairquality$Ozone)
  print(results1)
  percentCorrect1 <- (results1[1,1]+results1[2,2])/(results1[1,1]+results1[1,2]+results1[2,1]+results1[2,2])*100
  round(percentCorrect1)  
  return(percentCorrect1)
}
predictOzone(modelKSVM, myairquality)

# 1) Test the model on the testing dataset, 2) compute the Root Mean Squared Error 
#3) Plot the results. Use a scatter plot. Have the x-axis represent temperature, the y-axis represent wind, the point size and color represent the error, as defined by the actual ozone level minus the predicted ozone level). 
root_square <- function(error)
{
  sqrt(mean(error^2))
}

modelKSVM.first <- predict(modelKSVM, myairquality)
modelKSVM.error <- (myairquality$Ozone - modelKSVM.first)
root_square(modelKSVM.error)

# Compute models and plot the results for svm (in the e1071 package) and lm. Generate similar charts for each model

# Show all three results (charts) in one window, using the grid.arrange function

### Step 4: Create a goodOzone variable 
# This variable should be either 0 or 1. It should be 0 if the ozone is below the average for all the data observations, and 1 if it is equal to or above the average ozone observed. 
goodOzone <-c()

for (i in 1:nrow(myairquality)) {
  if (myairquality$Ozone[i] < mean(myairquality$Ozone)){
      #cat(i, "goodozone", "\n")
      myairquality$goodOzone[i] <- 0
  }
  else {
      myairquality$goodOzone[i] <- 1
      #cat(i, "badozone", "\n")
   }
}

predictGoodozone <- function(m, myairquality){
  predictedGoodozone <- predict(m, myairquality)
  predictedGoodozone
  myairquality$Ozone
  results1 <- table(predictedGoodozone, myairquality$goodOzone)
  print(results1)
  percentCorrect1 <- (results1[1,1]+results1[2,2])/(results1[1,1]+results1[1,2]+results1[2,1]+results1[2,2])*100
  round(percentCorrect1)  
  return(percentCorrect1)
}

modelKSVM1 <- ksvm(goodOzone ~ ., data = myairquality)
modelKSVM1

predictGoodozone(modelKSVM1, myairquality)

### Step 5: See if we can do a better job predicting good and bad days 
# Build a model (using the ksvm function, trying to predict goodOzone). You can use all the possible attributes, or select the attributes that you think would be the most helpful.

modelKSVM <- ksvm(Ozone ~ ., data = myairquality)
predictOzone(modelKSVM, myairquality)
modelKSVM.first <- predict(modelKSVM, myairquality)
modelKSVM.error <- (myairquality$Ozone - modelKSVM.first)
root_square(modelKSVM.error)

modelSVM <- svm(Ozone ~ ., data = myairquality)
predictOzone(modelSVM, myairquality)
modelSVM.first <- predict(modelSVM, myairquality)
modelSVM.error <- (myairquality$Ozone - modelSVM.first)
root_square(modelSVM.error)

dfnew <- data.frame(myairquality$Wind,myairquality$Temp,modelKSVM.error)
colnames(dfnew) <- c("Wind","Temp","Error")
plotdf <- ggplot(data = dfnew,aes(x=myairquality$Temp,y=myairquality$Wind)) + geom_point(aes(size=modelKSVM.error), color = "red") + ggtitle("KSVM Model")
plotdf

dfnew1 <- data.frame(myairquality$Wind,myairquality$Temp,modelKSVM.error)
colnames(dfnew1) <- c("Wind","Temp","Error")
plotdf1 <- ggplot(data = dfnew1,aes(x=myairquality$Temp,y=myairquality$Wind)) + geom_point(aes(size=modelKSVM.error), color = "red") + ggtitle("SVM Model")
plotdf1

modelLM <- lm(Ozone ~., data=myairquality)
modelLM
modelLM.first <- predict(modelLM, myairquality)
modelLM.error <- (myairquality$Ozone - modelLM.first)
root_square(modelLM.error)

dfnew2 <- data.frame(myairquality$Wind,airquality$Temp,modelLM.error)
colnames(dfnew2) <- c("Wind","Temp","Error")
plotdf2 <- ggplot(data = dfnew2,aes(x=myairquality$Temp,y=airquality$Wind)) + geom_point(aes(size=modelLM.error), color = "red") + ggtitle("LM Model")
plotdf2

grid.arrange(plotdf,plotdf1, plotdf2, ncol = 2)

# Test the model on the testing dataset, and compute the percent of goodOzone that was correctly predicted.

# Plot the results. Use a scatter plot. Have the x-axis represent temperature, the y-axis represent wind, the shape representing what was predicted (good or bad day), the color representing the actual value of goodOzone (i.e. if the actual ozone level was good) and the size represent if the prediction was correct (larger symbols should be the observations the model got wrong).

# Compute models and plot the results for svm (in the e1071 package) and nb (Naive Bayes, also in the e1071 package).

# Show all three results (charts) in one window, using the grid.arrange function (have two charts in one row).

[ + ] Show input
Absolute running time: 4.38 sec, cpu time: 5.34 sec, memory peak: 73 Mb, absolute service time: 4,44 sec

Error(s), warning(s):

Attaching package: ‘ggplot2’

The following object is masked from ‘package:kernlab’:

    alpha

Ozone Solar.R Wind Temp Month Day 1 41 190 7.4 67 5 1 2 36 118 8.0 72 5 2 3 12 149 12.6 74 5 3 4 18 313 11.5 62 5 4 5 42 186 14.3 56 5 5 6 28 186 14.9 66 5 6 7 23 299 8.6 65 5 7 8 19 99 13.8 59 5 8 9 8 19 20.1 61 5 9 10 42 194 8.6 69 5 10 11 7 186 6.9 74 5 11 12 16 256 9.7 69 5 12 13 11 290 9.2 66 5 13 14 14 274 10.9 68 5 14 15 18 65 13.2 58 5 15 16 14 334 11.5 64 5 16 17 34 307 12.0 66 5 17 18 6 78 18.4 57 5 18 19 30 322 11.5 68 5 19 20 11 44 9.7 62 5 20 21 1 8 9.7 59 5 21 22 11 320 16.6 73 5 22 23 4 25 9.7 61 5 23 24 32 92 12.0 61 5 24 25 42 66 16.6 57 5 25 26 42 266 14.9 58 5 26 27 42 186 8.0 57 5 27 28 23 13 12.0 67 5 28 29 45 252 14.9 81 5 29 30 115 223 5.7 79 5 30 31 37 279 7.4 76 5 31 32 42 286 8.6 78 6 1 33 42 287 9.7 74 6 2 34 42 242 16.1 67 6 3 35 42 186 9.2 84 6 4 36 42 220 8.6 85 6 5 37 42 264 14.3 79 6 6 38 29 127 9.7 82 6 7 39 42 273 6.9 87 6 8 40 71 291 13.8 90 6 9 41 39 323 11.5 87 6 10 42 42 259 10.9 93 6 11 43 42 250 9.2 92 6 12 44 23 148 8.0 82 6 13 45 42 332 13.8 80 6 14 46 42 322 11.5 79 6 15 47 21 191 14.9 77 6 16 48 37 284 20.7 72 6 17 49 20 37 9.2 65 6 18 50 12 120 11.5 73 6 19 51 13 137 10.3 76 6 20 52 42 150 6.3 77 6 21 53 42 59 1.7 76 6 22 54 42 91 4.6 76 6 23 55 42 250 6.3 76 6 24 56 42 135 8.0 75 6 25 57 42 127 8.0 78 6 26 58 42 47 10.3 73 6 27 59 42 98 11.5 80 6 28 60 42 31 14.9 77 6 29 61 42 138 8.0 83 6 30 62 135 269 4.1 84 7 1 63 49 248 9.2 85 7 2 64 32 236 9.2 81 7 3 65 42 101 10.9 84 7 4 66 64 175 4.6 83 7 5 67 40 314 10.9 83 7 6 68 77 276 5.1 88 7 7 69 97 267 6.3 92 7 8 70 97 272 5.7 92 7 9 71 85 175 7.4 89 7 10 72 42 139 8.6 82 7 11 73 10 264 14.3 73 7 12 74 27 175 14.9 81 7 13 75 42 291 14.9 91 7 14 76 7 48 14.3 80 7 15 77 48 260 6.9 81 7 16 78 35 274 10.3 82 7 17 79 61 285 6.3 84 7 18 80 79 187 5.1 87 7 19 81 63 220 11.5 85 7 20 82 16 7 6.9 74 7 21 83 42 258 9.7 81 7 22 84 42 295 11.5 82 7 23 85 80 294 8.6 86 7 24 86 108 223 8.0 85 7 25 87 20 81 8.6 82 7 26 88 52 82 12.0 86 7 27 89 82 213 7.4 88 7 28 90 50 275 7.4 86 7 29 91 64 253 7.4 83 7 30 92 59 254 9.2 81 7 31 93 39 83 6.9 81 8 1 94 9 24 13.8 81 8 2 95 16 77 7.4 82 8 3 96 78 186 6.9 86 8 4 97 35 186 7.4 85 8 5 98 66 186 4.6 87 8 6 99 122 255 4.0 89 8 7 100 89 229 10.3 90 8 8 101 110 207 8.0 90 8 9 102 42 222 8.6 92 8 10 103 42 137 11.5 86 8 11 104 44 192 11.5 86 8 12 105 28 273 11.5 82 8 13 106 65 157 9.7 80 8 14 107 42 64 11.5 79 8 15 108 22 71 10.3 77 8 16 109 59 51 6.3 79 8 17 110 23 115 7.4 76 8 18 111 31 244 10.9 78 8 19 112 44 190 10.3 78 8 20 113 21 259 15.5 77 8 21 114 9 36 14.3 72 8 22 115 42 255 12.6 75 8 23 116 45 212 9.7 79 8 24 117 168 238 3.4 81 8 25 118 73 215 8.0 86 8 26 119 42 153 5.7 88 8 27 120 76 203 9.7 97 8 28 121 118 225 2.3 94 8 29 122 84 237 6.3 96 8 30 123 85 188 6.3 94 8 31 124 96 167 6.9 91 9 1 125 78 197 5.1 92 9 2 126 73 183 2.8 93 9 3 127 91 189 4.6 93 9 4 128 47 95 7.4 87 9 5 129 32 92 15.5 84 9 6 130 20 252 10.9 80 9 7 131 23 220 10.3 78 9 8 132 21 230 10.9 75 9 9 133 24 259 9.7 73 9 10 134 44 236 14.9 81 9 11 135 21 259 15.5 76 9 12 136 28 238 6.3 77 9 13 137 9 24 10.9 71 9 14 138 13 112 11.5 71 9 15 139 46 237 6.9 78 9 16 140 18 224 13.8 67 9 17 141 13 27 10.3 76 9 18 142 24 238 10.3 68 9 19 143 16 201 8.0 82 9 20 144 13 238 12.6 64 9 21 145 23 14 9.2 71 9 22 146 36 139 10.3 81 9 23 147 7 49 10.3 69 9 24 148 14 20 16.6 63 9 25 149 30 193 6.9 70 9 26 150 42 145 13.2 77 9 27 151 14 191 14.3 75 9 28 152 18 131 8.0 76 9 29 153 20 223 11.5 68 9 30 Ozone Solar.R Wind Temp Month Day 2 36 118 8.0 72 5 2 4 18 313 11.5 62 5 4 10 42 194 8.6 69 5 10 14 14 274 10.9 68 5 14 16 14 334 11.5 64 5 16 20 11 44 9.7 62 5 20 22 11 320 16.6 73 5 22 23 4 25 9.7 61 5 23 31 37 279 7.4 76 5 31 33 42 287 9.7 74 6 2 39 42 273 6.9 87 6 8 42 42 259 10.9 93 6 11 51 13 137 10.3 76 6 20 53 42 59 1.7 76 6 22 54 42 91 4.6 76 6 23 55 42 250 6.3 76 6 24 60 42 31 14.9 77 6 29 61 42 138 8.0 83 6 30 64 32 236 9.2 81 7 3 69 97 267 6.3 92 7 8 72 42 139 8.6 82 7 11 76 7 48 14.3 80 7 15 82 16 7 6.9 74 7 21 83 42 258 9.7 81 7 22 94 9 24 13.8 81 8 2 95 16 77 7.4 82 8 3 102 42 222 8.6 92 8 10 103 42 137 11.5 86 8 11 108 22 71 10.3 77 8 16 109 59 51 6.3 79 8 17 118 73 215 8.0 86 8 26 121 118 225 2.3 94 8 29 126 73 183 2.8 93 9 3 129 32 92 15.5 84 9 6 134 44 236 14.9 81 9 11 142 24 238 10.3 68 9 19 143 16 201 8.0 82 9 20 145 23 14 9.2 71 9 22 153 20 223 11.5 68 9 30 Support Vector Machine object of class "ksvm" SV type: eps-svr (regression) parameter : epsilon = 0.1 cost C = 1 Gaussian Radial Basis kernel function. Hyperparameter : sigma = 0.178614552914356 Number of Support Vectors : 121 Objective Function Value : -47.2592 Training error : 0.314828 predictedOzone 1 4 6 7 8 9 10 11 12 13 14 16 18 19 20 21 22 23 24 27 28 29 9.93449789749265 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10.2833178197347 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 10.7621815937222 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10.8591006573457 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11.4965743110482 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 11.8801366683124 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12.229163423381 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12.5970698518691 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 13.8612659234197 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15.0740770478819 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15.127548401671 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 15.8655670534654 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 15.9243697601157 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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Hyperparameter : sigma = 0.135202738492947 Number of Support Vectors : 93 Objective Function Value : -35.9403 Training error : 0.228976 predictedGoodozone 0 1 -0.0820062622902299 1 0 -0.0799958784447251 1 0 -0.0607873416441906 1 0 -0.0584779416604097 1 0 -0.0456582788973636 1 0 -0.0455846944735364 1 0 -0.045573463659 1 0 -0.0455528773499818 1 0 -0.0454584792819768 1 0 -0.0454385893465047 1 0 -0.0453424057552383 1 0 -0.0453423063076724 1 0 -0.0453311163528926 1 0 -0.0437598831317214 1 0 -0.0424976774972079 1 0 -0.0407509301923614 1 0 -0.0393521323260755 1 0 -0.0372935122774068 1 0 -0.0368853045933202 1 0 -0.0350704364673404 1 0 -0.0337233941376635 1 0 -0.0328541824630442 1 0 -0.0301302596127875 1 0 -0.0299993181273832 1 0 -0.0288394946159778 1 0 -0.0265911402611791 1 0 -0.0249355560526409 1 0 -0.0240183551579639 1 0 -0.021109928678258 1 0 -0.0203473052631194 1 0 -0.0187395598035096 1 0 -0.018011705547435 1 0 -0.0163081440540763 1 0 -0.013045300004409 1 0 -0.010809806133788 1 0 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21.3211285691257 0 0 0 0 0 0 21.3861611577124 0 0 0 0 0 0 21.4236495672894 0 0 0 0 0 0 21.641195867335 0 0 0 0 0 0 21.8386755495311 0 0 0 0 0 0 22.0330812383989 0 0 0 0 0 0 22.1324028062579 0 0 0 0 0 0 22.2204033114294 0 0 0 0 0 0 22.3119110429736 0 0 0 0 0 0 22.8761544873682 0 0 0 0 0 0 22.9971282362152 0 0 0 0 0 0 23.7484791005699 0 0 0 0 0 0 23.8737508354255 0 0 0 0 0 0 23.8781544276688 0 0 0 0 0 0 24.5771533639091 0 0 0 0 0 0 24.7592192464251 0 0 0 0 0 0 25.7774860992545 0 0 0 0 0 0 25.866445326291 0 0 0 0 0 0 25.8736242183361 0 0 0 0 0 0 26.0375266527 0 0 0 0 0 0 26.3768534394722 0 0 0 0 0 0 26.538093004797 0 0 0 0 0 0 26.605113808543 0 0 0 0 0 0 26.768754047691 0 0 0 0 0 0 26.8382193409421 0 0 0 0 0 0 26.8685952691813 0 0 0 0 0 0 26.9408781822484 0 0 0 0 0 0 27.1304103620878 0 0 0 0 0 0 27.6056725658302 0 0 0 0 0 0 27.6074499230351 0 0 0 0 0 0 28.1282031263293 0 0 0 0 0 0 28.8385765722076 0 0 0 0 0 0 29.1259746440799 0 0 0 0 0 0 30.2444452099151 0 0 0 0 0 0 30.3824124092141 0 0 0 0 0 0 30.8781123060451 0 0 0 0 0 0 31.8659441792213 0 0 0 0 0 0 32.490188999695 0 0 0 0 0 0 33.2043696488721 0 0 0 0 0 0 33.4131028320745 0 0 0 0 0 0 33.6255541054142 0 0 0 0 0 0 34.1328574843199 0 0 0 0 0 0 34.346947837186 0 0 0 0 0 0 34.7090848544591 0 0 0 0 0 0 34.9324586402226 0 0 0 0 0 0 35.6757230888073 0 0 0 0 0 0 35.731085016072 0 0 0 0 0 0 35.9257643971534 0 0 0 0 0 0 36.1115388495523 0 0 0 0 0 0 36.2758164837505 0 0 0 0 0 0 36.6065916592165 0 0 0 0 0 0 36.8425327227427 0 0 0 0 0 0 37.1123785361761 0 0 0 0 0 0 37.1848156102798 0 0 0 0 0 0 37.4465896212449 0 0 0 0 0 0 37.4804810551868 0 0 0 0 0 0 37.8701279739399 0 0 0 0 0 0 38.1842331075033 0 0 0 0 0 0 38.3493911180696 0 0 0 0 0 0 38.3676611790202 0 0 0 0 0 0 38.4553171937753 0 0 0 0 0 0 39.1170207087965 0 0 0 0 0 0 39.1286605673356 0 0 0 0 0 0 39.1298655678766 0 0 0 0 0 0 39.1305407896757 0 0 0 0 0 0 39.1305415772776 0 0 0 0 0 0 39.135812533112 0 0 0 0 0 0 39.1387355780491 0 0 0 0 0 0 39.139811715878 0 0 0 0 0 0 39.2480799822333 0 0 0 0 0 0 39.4522518289522 0 0 0 0 0 0 39.869654890421 0 0 0 0 0 0 39.9791114618309 0 0 0 0 0 0 41.0380633005601 0 0 0 0 0 0 41.2610383936273 0 0 0 0 0 0 42.0713503215405 0 0 0 0 0 0 42.4360741908155 0 0 0 0 0 0 43.2751129519553 0 0 0 0 0 0 43.7658526121848 0 0 0 0 0 0 44.1395598990936 0 0 0 0 0 0 44.8692862701749 0 0 0 0 0 0 44.8788420076596 0 0 0 0 0 0 45.3639567729511 0 0 0 0 0 0 45.8856807505616 0 0 0 0 0 0 47.8636398830171 0 0 0 0 0 0 48.8675612783223 0 0 0 0 0 0 48.9429300966055 0 0 0 0 0 0 53.7262557414284 0 0 0 0 0 0 54.8754539001243 0 0 0 0 0 0 55.9426825189403 0 0 0 0 0 0 57.1914064468975 0 0 0 0 0 0 61.7839001612161 0 0 0 0 0 0 62.3839561895705 0 0 0 0 0 0 62.7843107509281 0 0 0 0 0 0 65.074029305579 0 0 0 0 0 0 66.0614861649791 0 1 0 0 0 0 66.8531357948137 0 0 0 0 0 0 66.8780034770578 0 0 0 0 0 0 68.0769844023109 0 0 0 0 0 0 68.123718120462 0 0 0 0 0 0 70.5856195598491 0 0 0 0 0 0 70.593286311525 0 0 0 0 0 0 70.7041053606457 0 0 0 0 0 0 71.1278737077131 0 0 0 0 0 0 71.2899234089064 0 0 0 0 0 1 71.6140886326 0 0 0 0 1 0 72.6040125023594 0 0 0 0 0 0 75.1387700472196 0 0 0 0 0 0 75.8786830737229 0 0 0 0 0 0 76.0230674437088 0 0 0 0 0 0 76.083883450713 0 0 0 0 0 0 77.9085544253774 0 0 0 0 0 0 78.8348561729486 0 0 0 0 0 0 80.8741251618977 0 0 0 0 0 0 81.0232818072858 0 0 0 0 0 0 82.0405211277509 0 0 0 0 0 0 82.1189372311201 0 0 0 0 0 0 82.1192762193328 0 0 0 0 0 0 82.8318108246526 0 0 0 0 0 0 83.1641387319494 0 0 0 0 0 0 84.2514860998349 0 0 0 0 0 0 85.7999843640364 0 0 0 1 0 0 86.7384695466205 1 0 0 0 0 0 88.2188790412166 0 0 1 0 0 0 91.7802094439678 0 0 0 0 0 0 91.8747000373767 0 0 0 0 0 0 [1] 0 [1] 13.92238 Call: lm(formula = Ozone ~ ., data = myairquality) Coefficients: (Intercept) Solar.R Wind Temp Month Day 6.31081 0.03611 -1.64458 0.64321 -2.31866 0.07863 goodOzone 35.91191 [1] 15.88528