R'da Hiyerarşik ve Karma Etkili Modeller
Richard Erickson
Data Scientist
$y = \beta + \epsilon$
$y = \beta_0 + \beta_2 x_2+ \beta_3 x_3+ \epsilon$
$y = \beta_1 x_1 + \beta_2 x_2+ \beta_3 x_3+ \epsilon$
lm(formula, data)
lm(y ~ x, data = myData)
anova(lm(y ~ x, data = myData))
$y \sim \beta_0 + \beta_1 x + \epsilon$
\(y \sim \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \epsilon\)
lm(y ~ x - 1)
lm(y ~ x1 + x2 + x1:x2)
lm(y ~ x1 * x2)
reg_model <- lm(response ~ predictor, data = reg_demo) summary(reg_model) reg_model reg_coef_plot <- tidy(reg_model) ggplot(reg_model, aes(x = predictor, y = response)) + geom_point() + theme_minimal() + geom_abline(intercept = reg_model$estimate[1], slope = reg_model$estimate[2])
reg_model <- lm(response ~ predictor, data = reg_demo)
summary(reg_model) reg_model reg_coef_plot <- tidy(reg_model)
ggplot(reg_model, aes(x = predictor, y = response)) + geom_point() + theme_minimal() + geom_abline(intercept = reg_model$estimate[1], slope = reg_model$estimate[2])
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