lestat (version 1.9)

leastsquares: Find the Least Squares Solution in a Linear Model

Description

Given a vector of data and a design matrix, the least squares estimates for a linear model is computed.

Usage

leastsquares(data, design)

Arguments

data

A data vector.

design

A design matrix. The number of rows must be equal to the length of the data vector.

Value

A vector of values of length equal to the number of columns in the design matrix.

Details

The fitted values represent the expected values all but the last variables in the posterior for the linear model.

See Also

linearmodel, fittedvalues, linearpredict

Examples

# NOT RUN {
xdata <- simulate(uniformdistribution(), 14)
ydata <- xdata + 4 + simulate(normal(), 14)*0.1
plot(xdata,ydata)
design <- cbind(1, xdata)
leastsquares(ydata, design)
# }