lm() is a linear model function, such like linear regression analysis.

lm(formula, data, subset, weights, ...)
formula: model description, such as x ~ y
data: optional, variables in the model
subset: optional, a subset vector of observations to be used in the fitting process
weights: optional, a vector of weights to be used in the fitting process

Let's create two vectors, and then fit a linear model:

>x <- c(rep(1:20))
>y <- x * 2
>f <- lm(x ~ y)
>f

Call:
lm(formula = x ~ y)
Coefficients:
(Intercept) y
-4.766e-15 5.000e-01

We can use summary() to see the details:

>summary(f)

Call:
lm(formula = x ~ y)
Residuals:
Min 1Q Median 3Q Max
-6.208e-15 8.400e-18 3.526e-16 6.074e-16 2.038e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.766e-15 7.696e-16 -6.193e+00 7.6e-06 ***
y 5.000e-01 3.212e-17 1.557e+16 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.657e-15 on 18 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 2.423e+32 on 1 and 18 DF, p-value: < 2.2e-16

Let's plot the results:

>plot(f)