I have a dataframe:
df = data.frame(x1 = rnorm(50), x2 = rnorm(50), x3 = rnorm(50), x4 = rnorm(50)) I would like to regress each variable versus all the other variables, for instance:
fit1 <- lm(x1 ~ ., data = df) fit2 <- lm(x2 ~ ., data = df) etc. (Of course, the real dataframe has a lot more variables).
I tried putting them in a loop, but it didn't work. I also tried using lapply but couldn't produce the desired result either. Does anyone know the trick?
You can use reformulate to dynamically build formuals
df = data.frame(x1 = rnorm(50), x2 = rnorm(50), x3 = rnorm(50), x4 = rnorm(50)) vars <- names(df) result <- lapply(vars, function(resp) { lm(reformulate(".",resp), data=df) }) alternatively you could use do.call to get "prettier" formauls in each of the models
vars <- names(df) result <- lapply(vars, function(resp) { do.call("lm", list(reformulate(".",resp), data=quote(df))) }) each of these methods returns a list. You can extract individual models with result[[1]], result[[2]], etc
Or you can try this...
df = data.frame(x1 = rnorm(50), x2 = rnorm(50), x3 = rnorm(50), x4 = rnorm(50)) models = list() for (i in (1: ncol(df))){ formula = paste(colnames(df)[i], "~ .", sep="") models[[i]] = lm(formula, data = df) } This will save all models as a list
To retrieve stored models:
eg : model regressed on x4
#retrieve model - replace modelName with the name of the required column modelName = "x4" out = models[[which( colnames(df)== modelName )]] Output :
Call: lm(formula = formula, data = df) Coefficients: (Intercept) x1 x2 x3 -0.17383 0.07602 -0.09759 -0.23920