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I can't quite figure out how to do the following in one line:
data(attenu)
x_temp = attenu$accel^(1/4)
y_temp = log(attenu$dist)
best_line = lm(y_temp ~ x_temp)
Since the above works, I thought I could do the following:
data(attenu)
best_line = lm( log(attenu$dist) ~ (attenu$accel^(1/4)) )
But this gives the error:
Error in terms.formula(formula, data = data) : invalid power in formula
There's obviously something I'm missing when using transformed variables in R's formula format. Why doesn't this work?
343
The lm() function Note that the formula argument follows a specific format. For simple linear regression, this is “YVAR ~ XVAR” where YVAR is the dependent, or predicted, variable and XVAR is the independent, or predictor, variable.
To fit a linear model in the R Language by using the lm() function, We first use data. frame() function to create a sample data frame that contains values that have to be fitted on a linear model using regression function. Then we use the lm() function to fit a certain function to a given data frame.
Summary: R linear regression uses the lm() function to create a regression model given some formula, in the form of Y~X+X2. To look at the model, you use the summary() function. To analyze the residuals, you pull out the $resid variable from your new model.
You're looking for the function I so that the ^ operator is treated as arithmetic in the formula, ie.
x <- runif(1:100)
y <- x + rnorm(100,0, 3)
lm(log(y) ~ I(x^(1/4))
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