96 lines
3.2 KiB
R
96 lines
3.2 KiB
R
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# ZAD1
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auto <- read.csv("http://ls.home.amu.edu.pl/data_sets/Automobile.csv",
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sep = ",", header = TRUE, na.strings = "?")
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head(auto)
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auto$num.of.doors <- ifelse(auto$num.of.doors == "four", 4, 2)
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auto_wna <- na.omit(auto)
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cat("wymiar nowych danych")
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dim(auto_wna)
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auto_wna_sel <- subset(auto_wna, select = c(horsepower, city.mpg, peak.rpm,
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curb.weight, num.of.doors, price))
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pairs(auto_wna_sel)
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model_1 <- lm(price ~ horsepower + city.mpg + peak.rpm + curb.weight +
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num.of.doors, data = auto_wna)
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model_1
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coef(model_1)
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confint(model_1)
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summary(model_1)
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fitted(model_1)
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residuals(model_1)
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step(model_1)
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step(model_1, k = log(nrow(auto_wna)))
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model_0 <- lm(price ~ 1, data = auto_wna)
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step(model_0, direction = "forward", scope = formula(model_1))
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step(model_0, direction = "forward", scope = formula(model_1), k = log(nrow(auto_wna)))
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model_1_1 <- lm(price ~ horsepower + city.mpg + curb.weight + num.of.doors, data = auto_wna)
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summary(model_1_1)$coefficients
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summary(model_1_1)$adj.r.squared
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model_1_2 <- lm(price ~ horsepower + curb.weight + num.of.doors, data = auto_wna)
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summary(model_1_2)$coefficients
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summary(model_1_2)$adj.r.squared
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model_1_3 <- lm(price ~ horsepower + curb.weight, data = auto_wna)
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summary(model_1_3)$coefficients
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summary(model_1_3)$adj.r.squared
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auto_sel <- subset(auto, select = c(horsepower, city.mpg, peak.rpm,
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curb.weight, num.of.doors, price))
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summary(auto_sel)
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install.packages("Hmisc")
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library(Hmisc)
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auto_sel$price <- as.numeric(impute(auto_sel$price, mean))
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auto_sel$horsepower <- as.numeric(impute(auto_sel$horsepower, mean))
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auto_sel$peak.rpm <- as.numeric(impute(auto_sel$peak.rpm, mean))
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auto_sel$num.of.doors <- as.numeric(impute(auto_sel$num.of.doors, median))
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summary(auto_sel)
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cat("2.", "\n")
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pairs(auto_sel)
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model_1_i <- lm(price ~ horsepower + city.mpg + peak.rpm + curb.weight +
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num.of.doors, data = auto_sel)
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model_1_i
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coef(model_1_i)
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confint(model_1_i)
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summary(model_1_i)
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fitted(model_1_i)
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residuals(model_1_i)
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cat("3.", "\n")
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step(model_1_i)
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step(model_1_i, k = log(nrow(auto_sel)))
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model_0_i <- lm(price ~ 1, data = auto_sel)
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step(model_0_i, direction = "forward", scope = formula(model_1_i))
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step(model_0_i, direction = "forward", scope = formula(model_1_i), k = log(nrow(auto_sel)))
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cat("4.", "\n")
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model_1_i_1 <- lm(price ~ horsepower + city.mpg + curb.weight + peak.rpm, data = auto_sel)
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summary(model_1_i_1)$coefficients
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summary(model_1_i_1)$adj.r.squared
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model_1_i_2 <- lm(price ~ horsepower + city.mpg + curb.weight, data = auto_sel)
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summary(model_1_i_2)$coefficients
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summary(model_1_i_2)$adj.r.squared
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model_1_i_3 <- lm(price ~ horsepower + curb.weight, data = auto_sel)
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summary(model_1_i_3)$coefficients
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summary(model_1_i_3)$adj.r.squared
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new_data <- data.frame(curb.weight = 2823, horsepower = 154)
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model_2 <- lm(price ~ curb.weight + horsepower, data = auto_wna)
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model_2_i <- lm(price ~ curb.weight + horsepower, data = auto_sel)
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stats::predict(model_2, new_data, interval = "prediction")
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stats::predict(model_2_i, new_data, interval = "prediction")
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summary(model_2)$adj.r.squared
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summary(model_2_i)$adj.r.squared
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