lm公式的可变长度不同

Question

I would like to calculate an r square coefficient for each ggplot in facetwrap. I thought of a solution using a for loop and save the coeficients in a list and than assign the items of the list to each facet. However, I cannot calculate the coefficients in the for loop. I get an error, that says:

Error in model.frame.default(formula = H60percent ~ choice, data = DATA50, : variable lengths differ (found for 'choice')

Here is my code:

xvalue <- c("Jnr3250","Jnr6450","Jnr12850","Jnr25650")
Rcoef_list <- list()
for (i in 1:length(xvalue)) {
  #print i as a number
  print(i)
  #choose elemnt from from xvalue according to the i (number)
  choice <-  noquote(xvalue[i], right = FALSE)
  print(choice)
  # counts R2
  LM1 =  lm(formula = H60percent ~ choice, data = DATA50)
  Rvalue <-round(as.numeric(summary(LM1)$r.squared[1]),2) 
  R2 <- paste("r^2 == ", Rvalue)
  print(R2)
  #put each R2 in a list
  Rcoef_list[[i]] <- R2
}

if I write the actual name of a column (for example Jnr3250) instead of choice in lm function it works (but only for one value obviously). I tried paste0(choice) and the error is the same. I will be glad for any tips or if someone could just point me in the right direction.

Answer 1

Are your variables separate predictor values, or values of a grouping variable? The answer below assumes the former; if it's the latter, use lme4::lmList() or nlme::lmList() ...

This is pretty close to a duplicate ... I haven't dissected your example to see exactly where you got in trouble, but it's usually best to do these kinds of problems with reformulate() . Stripped down, your list would look like this:

xvalue <- c("Jnr3250","Jnr6450","Jnr12850","Jnr25650")
Rcoef_list <- list()
for (x in xvalue) {
  form <- reformulate(x, response="H60percent")
  LM1 <- lm(form, data=DATA50)
  Rcoef_list[[x]] <- summary(LM1)$r.squared[1]
}

If you want the r^2 == stuff, you can use sprintf("r^2 == %1.1f", unlist(Rcoef_list)) after running the loop.

Answer 2

Your question isn't reproducible, but I think you'll have better luck creating a formula() object to pass into lm() . Here's an example with mtcars data, regressing mpg against three of the other variables in the dataset:

xcols <- c("disp", "hp", "qsec")
Rcoef_list <- list()

for (i in 1:length(xcols)){
  f <- as.formula(paste0("mpg ~", xcols[i]))
  LM1 <- lm(formula = f, data = mtcars)
  Rvalue <-round(as.numeric(summary(LM1)$r.squared[1]),2) 
  R2 <- paste("r^2 == ", Rvalue) 
  Rcoef_list[[i]] <- R2  
}
Rcoef_list
#> [[1]]
#> [1] "r^2 ==  0.72"
#> 
#> [[2]]
#> [1] "r^2 ==  0.6"
#> 
#> [[3]]
#> [1] "r^2 ==  0.18"

^{Created on 2019-01-27 by the reprex package (v0.2.1)}