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R Linear Model Function


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)