ds: r6

ds/25-1/r/6.R

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+setwd('/home/sek1ro/git/public/lab/ds/25-1/r')
+df = read.csv('zeta.csv')
+head(df)
+df = df[df$sex == 'F', -which(names(df) %in% c("X", "zcta", "sex"))]
+df = subset(df, 8 < meaneducation & meaneducation < 18 &
+              10000 < meanhouseholdincome & meanhouseholdincome < 200000 &
+              0 < meanemployment & meanemployment < 3 &
+              20 < meanage & meanage < 60)
+
+log_income = log10(df$meanhouseholdincome)
+colnames(df) = c("age", "education", "employment", "householdincome")
+
+library(ggplot2)
+ggplot(df, aes(x = age, y = log_income)) +
+  geom_point() +
+  geom_smooth(method = "lm")
+  theme_minimal()
+  
+lmmod = lm(log_income ~ age, data = df)
+summary(lmmod)
+
+# xm = sum(xi) / n
+# sd = sqrt(sum(xi - xm) / (n - 1))
+# mr = sd / sqrt(n): вариация
+# t = (xm1 - xm2) / sqrt(mr1 ^ 2 + mr2 ^ 2)
+# f = (n1 + n2) - 2: степень свободы
+# p: по таблице: Отличаются с вероятностью ошибки ...
+
+ggplot(df, aes(x = education, y = log_income)) +
+  geom_point() +
+  geom_smooth(method = "lm")
+  theme_minimal()
+
+lmmod = lm(log_income ~ education, data = df)
+summary(lmmod)
+
+ggplot(df, aes(x = employment, y = log_income)) +
+  geom_point() +
+  geom_smooth(method = "lm")
+theme_minimal()
+
+lmmod = lm(log_income ~ employment, data = df)
+summary(lmmod)
+
+lmmod = lm(householdincome ~ age + education + employment, data = df)
+summary(lmmod)
+
+set.seed(Sys.Date())
+test_idx = sample(1:nrow(df), 5000, replace = FALSE)
+test_df = df[test_idx, ]
+train_df = df[-test_idx, ]
+
+lmmod = lm(householdincome ~ age + education + employment, data = train_df)
+test_df$p_income = predict(lmmod, newdata = test_df)
+
+ggplot(test_df, aes(x = p_income, y = householdincome)) +
+  geom_abline(intercept = 0, slope = 1) +
+  geom_point()+
+  theme_minimal()
+
+test_df = test_df[order(test_df$p_income), ]
+slice_n = 10
+slice_size = floor(nrow(test_df) / slice_n)
+for (i in 0 : (slice_n - 1)) {
+  start_idx = slice_size * i + 1
+  end_idx = ifelse(i == slice_n - 1, nrow(test_df), slice_size * (i + 1))
+  slice = test_df[start_idx : end_idx,]
+  
+  error = slice$p_income - slice$householdincome
+  rmse = sqrt(mean(error ^ 2))
+  mae = mean(abs(error))
+  bias = mean(error)
+  cat(sprintf("range: %.2f-%.2f, RMSE: %.2f, MAE: %.2f, Bias: %.2f\n",
+              test_df[start_idx, ]$p_income,
+              test_df[end_idx, ]$p_income,
+              rmse, mae, bias)
+      )
+}
+