问题
I would like to create a function which can run a regression model (e.g. using lm) over different variables in a given dataset. In this function, I would specify as arguments the dataset I'm using, the dependent variable y and the independent variable x. I want this to be a function and not a loop as I would like to call the code in various places of my script. My naive function would look something like this:
lmfun <- function(data, y, x) {
lm(y ~ x, data = data)
}
This function obviously does not work because the lm function does not recognize y and x as variables of the dataset.
I have done some research and stumbled upon the following helpful vignette: programming with dplyr. The vignette gives the following solution to a similar problem as the one I am facing:
df <- tibble(
g1 = c(1, 1, 2, 2, 2),
g2 = c(1, 2, 1, 2, 1),
a = sample(5),
b = sample(5)
)
my_sum <- function(df, group_var) {
group_var <- enquo(group_var)
df %>%
group_by(!! group_var) %>%
summarise(a = mean(a))
}
I am aware that lm is not a function that is part of the dplyr package but would like to come up with a solution similar as this. I've tried the following:
lmfun <- function(data, y, x) {
y <- enquo(y)
x <- enquo(x)
lm(!! y ~ !! x, data = data)
}
lmfun(mtcars, mpg, disp)
Running this code gives the following error message:
Error in is_quosure(e2) : argument "e2" is missing, with no default
Anyone has an idea on how to amend the code to make this work?
Thanks,
Joost.
回答1:
You can fix this problem by using the quo_name
's and formula
:
lmfun <- function(data, y, x) {
y <- enquo(y)
x <- enquo(x)
model_formula <- formula(paste0(quo_name(y), "~", quo_name(x)))
lm(model_formula, data = data)
}
lmfun(mtcars, mpg, disp)
# Call:
# lm(formula = model_formula, data = data)
#
# Coefficients:
# (Intercept) disp
# 29.59985 -0.04122
回答2:
Another solution:
lmf2 <- function(data,y,x){
fml <- substitute(y~x, list(y=substitute(y), x=substitute(x)))
lm(eval(fml), data)
}
lmf2(mtcars, mpg, disp)
# Call:
# lm(formula = eval(fml), data = data)
#
# Coefficients:
# (Intercept) disp
# 29.59985 -0.04122
Or, equivalently:
lmf3 <- function(data,y,x){
lm(eval(call("~", substitute(y), substitute(x))), data)
}
回答3:
If the arguments are unquoted, then convert to symbol (sym
) after changing the quosure to string (quo_name
) and evaluate the expression in lm
(similar to the OP's syntax of lm
)
library(rlang)
lmfun <- function(data, y, x) {
y <- sym(quo_name(enquo(y)))
x <- sym(quo_name(enquo(x)))
expr1 <- expr(!! y ~ !! x)
model <- lm(expr1, data = data)
model$call$formula <- expr1 # change the call formula
model
}
lmfun(mtcars, mpg, disp)
#Call:
#lm(formula = mpg ~ disp, data = data)
#Coefficients:
#(Intercept) disp
# 29.59985 -0.04122
An option if we are passing strings would be convert to symbols with ensym
and then quote
it in lm
lmfun <- function(data, y, x) {
y <- ensym(y)
x <- ensym(x)
expr1 <- expr(!! y ~ !! x)
model <- lm(expr1, data = data)
model$call$formula <- expr1 # change the call formula
model
}
lmfun(mtcars, 'mpg', 'disp')
#Call:
#lm(formula = mpg ~ disp, data = data)
#Coefficients:
#(Intercept) disp
# 29.59985 -0.04122
NOTE: Both the options are from tidyverse
回答4:
Here is another option: EDIT: Here is a refactored answer
lmfun<-function(data,yname,xname){
formula1<-as.formula(paste(yname,"~",xname))
lm.fit<-do.call("lm",list(data=quote(data),formula1))
lm.fit
}
lmfun(mtcars,"mpg","disp")
And the Original Answer:
lmfun<-function(data,y,x){
formula1<-as.formula(y~x)
lm.fit<-do.call("lm",list(data=quote(data),formula1))
lm.fit
}
lmfun(mtcars,mtcars$mpg,mtcars$disp)
Yields:
Call:
lm(formula = y ~ x, data = data)
Coefficients:
(Intercept) x
29.59985 -0.04122
来源:https://stackoverflow.com/questions/54060985/function-which-runs-lm-over-different-variables