multiple linear regression: error in user defined function

Question

I have written my function for MLR. However, there seems to an issue with output (see examples in the end).

But when I run the code, line by line, the output is correct.

mlr <- function(dependentvar, dataset) {

x <- model.matrix(dependentvar ~., dataset) # Design Matrix for x

y <- dependentvar # dependent variable

betas <- solve(crossprod(x))%*%crossprod(x,y) # beta values

SST <- t(y)%*%y - (sum(y)^2/dim(dataset)[1]) # total sum of squares

SSres <- t(y)%*%y -(t(betas)%*%crossprod(x,y))  # sum of squares of residuals

SSreg <- SST - SSres  # regression sum of squares

sigmasqr <- SSres/(length(y) - dim(dataset)[2])  # variance or (MSE)

varofbeta <- sigmasqr[1]*solve( crossprod(x)) # variance of beta

cat("SST:", SST,"SSresiduals:", SSres,"SSregression:", SSreg, sep = "\n", append = FALSE)

return(betas)

}

To see the problem, try

mlr(trees$Height, trees)

I get the same problem even if I get rid of $

Height <- trees$Height
mlr(Height, trees)

Zheyuan Li · Accepted Answer · 2017-06-05 11:58:55Z

1

Use the following:

x <- model.matrix(reformulate(".", dependentvar), dataset)
y <- dataset[[dependentvar]]

and pass in dependentvar as a string.

Example:

mlr("Height", trees)

answered Jun 5, 2017 at 11:58

Zheyuan Li

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