.Rhistory

training <- read.csv("train-with-numbers.csv")
setwd("~/GitHub/TanzaniaWater")
training <- read.csv("train-with-numbers.csv")
View(training)
fit <- lm(numStatus ~ amount_tsh + construction_year + longitude, data=training)
summary(fit) # show results
fit <- lm(numStatus ~ amount_tsh + construction_year + longitude + latitude + numSource + numQuantity, data=training)
summary(fit) # show results
library(leaps)
regsubsets.out <-
regsubsets(numStatus ~ amount_tsh + construction_year + longitude + latitude + numSource + numQuantity, data = training)
regsubsets.out
summary.out <- summary(regsubsets.out)
as.data.frame(summary.out$outmat)
plot(regsubsets.out, scale = "adjr2", main = "Adjusted R^2")
regsubsets.out <-
regsubsets(numStatus ~ amount_tsh + gps_height + population + construction_year + longitude + latitude + numSource +
numBasin + numQuantity + numLga + numExtraction_type + numPayment_type + numWater_quality + numQuantity + numWaterpoint_type, data = training)
regsubsets.out
summary.out <- summary(regsubsets.out)
as.data.frame(summary.out$outmat)
plot(regsubsets.out, scale = "adjr2", main = "Adjusted R^2")
training[amount_tsh == 0 & construction_year == 0,]
View(training)
training[training$amount_tsh == 0 & training$construction_year == 0,]
length(training[training$amount_tsh == 0 & training$construction_year == 0,training$id])
length(training[training$amount_tsh == 0 & training$construction_year == 0,id])
length(training[training$amount_tsh == 0 & training$construction_year == 0,1])
length(training[training$amount_tsh == 0,1])
length(training[training$construction_year == 0,1])