Code for Lecture 5

Author: LProcopi15

Lecture5.R

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+fire <- read.csv("C:/Users/Student/Documents/forestfires.csv")
+View(fire)
+
+#####################
+#
+# Explore Data
+#
+####################
+
+# Get summary information
+summary(fire)
+# No NA's, all numeric data is normalized
+
+# Check class of each attribute
+for (i in 1:ncol(fire)){
+  print(paste(colnames(fire[i]), ": ", class(fire[,i]),sep = ""))
+}
+ 
+# Getting a subset of the data
+summary(fire$area)
+areabox <- boxplot(fire$area)
+upperwhisk <- areabox$stats[5,]
+
+xtm_fire <- subset(fire, area >= upperwhisk, select = c('FFMC', 'DMC', 'DC', 'ISI', 'temp', 'RH', 'wind', 'rain', 'area'))
+
+####################
+#
+# Basic modeling - Linear Regression
+#
+####################
+
+# Plot two variables
+plot(xtm_fire$temp, xtm_fire$area) 
+# Create linear regression model between the two
+lm1 <- lm(area~temp, data = xtm_fire)
+# Add regression line to plot
+abline(lm1, col = "orange")
+# Statistical information about this lm
+summary(lm1)
+
+# Can add many more factors to this lm
+lm2 <- lm(area~temp+FFMC+wind, data = xtm_fire)
+summary(lm2)
+
+# Could interactions between variables help us?
+pairs(xtm_fire[c('FFMC', 'DMC', 'DC', 'ISI', 'temp', 'RH', 'wind', 'area')])
+symnum(cor(xtm_fire[c('FFMC', 'DMC', 'DC', 'ISI', 'temp', 'RH', 'wind', 'area')]))
+# Highly correlated: DC and DMC, ISI and FFMC, temp and FFMC
+# Add these correlated attributes to a model
+lm3 <- lm(area~temp+FFMC+wind+(DC+DMC)^2+(ISI+FFMC)^2+(temp+FFMC)^2, data = xtm_fire)
+summary(lm3)
+
+# compare models
+anova(lm1, lm2)
+anova(lm1, lm3)
+anova(lm2, lm3)
+
+#####################
+#
+# Practice Problems
+#
+####################
+
+# 1. Create a new subset that includes only with an ISI (inital spread index) greater than the median
+
+# 2. Create a linear model with area and wind as your predictors, and area as your response
+# Call this model lm1_ISI
+
+# 3. Determine if there are any correlated attributes
+
+# 4. If there are any correlated attributes add the interaction between them to a new model 
+# Call this model lm2_ISI
+
+# Compare these two models and determine which model is better at predicting the size of the area burned