CODEWITHSUNDEEP
rstatistics
Mary
Mary

Reputation: 115

How to convert log function in RStudio?

fit1 = lm(price ~ . , data = car)

fit2 = lm(log(price) ~ . , data = car)

I'm not sure how to convert log(price) to price in fit2 Won't it just become the same thing as fit1 if I do convert it? Please help.

Upvotes: 0

Views: 520

Answers (1)

Allan Cameron
Allan Cameron

Reputation: 174546

Let's take a very simple example. Suppose I have some data points like this:

library(ggplot2)

df <- data.frame(x = 1:10, y = (1:10)^2)
(p <- ggplot(df, aes(x, y)) + geom_point())

I want to try to fit a model to them, but don't know what form this should take. I try a linear regression first and plot the resultant prediction:


mod1 <- lm(y ~ x, data = df)
(p <- p + geom_line(aes(y = predict(mod1)), color = "blue"))

Next I try a linear regression on log(y). Whatever results I get from predictions from this model will be predicted values of log(y). But I don't want log(y) predictions, I want y predictions, so I need to take the 'anti-log' of the prediction. We do this in R by doing exp:

mod2 <- lm(log(y) ~ x, data = df)
(p <- p + geom_line(aes(y = exp(predict(mod2))), color = "red"))

But we can see that we have different regression lines. That's because when we took the log of y, we were effectively fitting a straight line on the plot of log(y) against x. When we transform the axis back to a non-log axis, our straight line becomes an exponential curve. We can see this more clearly by drawing our plot again with a log-transformed y axis:

p + scale_y_log10(limits = c(1, 500))

enter image description here

Created on 2020-08-04 by the reprex package (v0.3.0)

Upvotes: 1

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