R linear model function lm looks like have a bug.
with no intercept term.
I want to know this is really a bug or my mistake on something...
library(dplyr)
rm(list=ls())
m2 <-
lm(data=iris, Sepal.Length ~ Sepal.Width + Species - 1)
res2 <-
iris %>%
mutate(real = Sepal.Length) %>%
mutate(pred = predict(m2, ., type='response')) %>%
mutate(error = real - pred) %>%
mutate(error.sq = error^2) %>%
mutate(sse = sum(error.sq)) %>%
mutate(sst = sum((real - mean(real))^2)) %>%
mutate(r.sq = 1-sse/sst) %>%
mutate(idx = 1:n())
m2 %>% .$coef
Sepal.Width Speciessetosa Speciesversicolor Speciesvirginica
0.8035609 2.2513932 3.7101363 4.1982099
m2 %>% summary %>% .$r.square
[1] 0.9946393
res2$r.sq %>% unique
[1] 0.7259066
without intercept linear model result shows different r-square value with manual calculation.
m1 <-
lm(data=iris, Sepal.Length ~ Sepal.Width + Species)
res1 <-
iris %>%
mutate(real = Sepal.Length) %>%
mutate(pred = predict(m1, ., type='response')) %>%
mutate(error = real - pred) %>%
mutate(error.sq = error^2) %>%
mutate(sse = sum(error.sq)) %>%
mutate(sst = sum((real - mean(real))^2)) %>%
mutate(r.sq = 1-sse/sst) %>%
mutate(idx = 1:n())
m1 %>% .$coef
(Intercept) Sepal.Width Speciesversicolor Speciesvirginica
2.2513932 0.8035609 1.4587431 1.9468166
m1 %>% summary %>% .$r.square
[1] 0.7259066
res1$r.sq %>% unique
[1] 0.7259066
in contrast, with intercept linear model result looks ok
