Archive for June 13th, 2011

June 13, 2011

Calculate OLS regression manually using matrix algebra in R

The following code will attempt to replicate the results of the lm() function in R. For this exercise, we will be using a cross sectional data set provided by R called “women”, that has height and weight data for 15 individuals.

The OLS regression equation:

Y = X\beta + \varepsilon

where \varepsilon = a white noise error term. For this example Y = weight, and X = height. \beta = the marginal impact a one unit change in height has on weight.

## This is the OLS regression we will manually calculate:
reg = lm(weight ~ height, data=women)
summary(reg)

Recall that the following matrix equation is used to calculate the vector of estimated coefficients \hat{\beta} of an OLS regression:

\hat{\beta} = (X'X)^{-1}X'Y

where X = the matrix of regressor data (the first column is all 1’s for the intercept), and Y = the vector of the dependent variable data.

Matrix operators in R

  • as.matrix() coerces an object into the matrix class.
  • t() transposes a matrix.
  • %*% is the operator for matrix multiplication.
  • solve() takes the inverse of a matrix. Note, the matrix must be invertible.

For a more complete introduction to doing matrix operations in R, check out this page.

Back to OLS

The following code calculates the 2 x 1 matrix of coefficients, \hat{\beta}:

## Create X and Y matrices for this specific regression
X = as.matrix(cbind(1,women$height))
Y = as.matrix(women$weight)

## Choose beta-hat to minimize the sum of squared residuals
## resulting in matrix of estimated coefficients:
bh = round(solve(t(X)%*%X)%*%t(X)%*%Y, digits=2)

## Label and organize results into a data frame
beta.hat = as.data.frame(cbind(c("Intercept","Height"),bh))
names(beta.hat) = c("Coeff.","Est")
beta.hat

Calculating Standard Errors

To calculate the standard errors, you must first calculate the variance-covariance (VCV) matrix, as follows:

Var(\hat{\beta}|X) = \frac{1}{n-k}\hat{\varepsilon}'\hat{\varepsilon}(X'X)^{-1}

The VCV matrix will be a square k x k matrix. Standard errors for the estimated coefficients \hat{\beta} are found by taking the square root of the diagonal elements of the VCV matrix.

## Calculate vector of residuals
res = as.matrix(women$weight-bh[1]-bh[2]*women$height)

## Define n and k parameters
n = nrow(women)
k = ncol(X)

## Calculate Variance-Covariance Matrix
VCV = 1/(n-k) * as.numeric(t(res)%*%res) * solve(t(X)%*%X)

## Standard errors of the estimated coefficients
StdErr = sqrt(diag(VCV))

## Calculate p-value for a t-test of coefficient significance
P.Value = rbind(2*pt(abs(bh[1]/StdErr[1]), df=n-k,lower.tail= FALSE),
2*pt(abs(bh[2]/StdErr[2]), df=n-k,lower.tail= FALSE))

## concatenate into a single data.frame
beta.hat = cbind(beta.hat,StdErr,P.Value)
beta.hat

A Scatterplot with OLS line

Women's height vs. weight using plot() and abline() functions in R.

## Plot results
plot(women$height,women$weight, xlab = "Height", ylab = "Weight",
				main = "OLS: Height and Weight")
abline(a = bh[1], b = bh[2], col = 'red', lwd = 2, lty="dashed")

Now you can check the results above using the canned lm() function:

summary(lm(weight ~ height, data = women))
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