library(ggplot2)
library(patchwork)
set.seed(31987)
n <- 100
g <- sample(c("a", "b"), n, replace = TRUE)
x <- rexp(n, 1 / 5)
b <- c(5, 10)
y <- b[1] * log(x) + b[2] * (g == "a") + rnorm(n)
geom_lm <- function(...) {
geom_smooth(method = "lm", se = FALSE, ...)
}p <- qplot(x, y, colour = g) + geom_lm()
p + p + aes(x = log(x)) + plot_layout(guides = "collect")
summary(lm(y ~ x * g))
#>
#> Call:
#> lm(formula = y ~ x * g)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -12.9211 -1.1970 0.9806 2.4664 5.1550
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 9.5530 0.7450 12.823 <2e-16 ***
#> x 1.0804 0.1014 10.653 <2e-16 ***
#> gb -11.6882 1.1656 -10.028 <2e-16 ***
#> x:gb 0.4419 0.1825 2.421 0.0174 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.738 on 96 degrees of freedom
#> Multiple R-squared: 0.8099, Adjusted R-squared: 0.804
#> F-statistic: 136.3 on 3 and 96 DF, p-value: < 2.2e-16
summary(lm(y ~ log(x) * g))
#>
#> Call:
#> lm(formula = y ~ log(x) * g)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.38847 -0.66242 0.02218 0.58159 2.54086
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 9.87491 0.17507 56.406 <2e-16 ***
#> log(x) 4.98848 0.10375 48.082 <2e-16 ***
#> gb -9.87115 0.25183 -39.197 <2e-16 ***
#> log(x):gb 0.04631 0.15103 0.307 0.76
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.9799 on 96 degrees of freedom
#> Multiple R-squared: 0.9869, Adjusted R-squared: 0.9865
#> F-statistic: 2417 on 3 and 96 DF, p-value: < 2.2e-16
