How to return predicted values, residuals, R square from lm()?

Question

this piece of code will return coefficients :intercept , slop1 , slop2

set.seed(1)
n=10

y=rnorm(n)
x1=rnorm(n)
x2=rnorm(n)

lm.ft=function(y,x1,x2)
  return         


        

Answer 1

In your function, you return just the coefficients. Try returning the whole model:

lm.ft=function(y,x1,x2) lm(y~x1+x2) # You don't need the return statement.

Now try your code, and then run:

summary(res[[1]])

# Call:
#   lm(formula = y ~ x1 + x2)
# 
# Residuals:
#   Min       1Q   Median       3Q      Max 
# -0.88518 -0.25311  0.03868  0.43110  0.61753 
# 
# Coefficients:
#   Estimate Std. Error t value Pr(>|t|)  
# (Intercept) -0.44804    0.32615  -1.374   0.2119  
# x1           0.06398    0.24048   0.266   0.7979  
# x2          -0.62799    0.26915  -2.333   0.0524 .
# ---
#   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 0.6149 on 7 degrees of freedom
# Multiple R-squared:  0.5173,  Adjusted R-squared:  0.3794 
# F-statistic: 3.751 on 2 and 7 DF,  p-value: 0.07814