# Proj

0th

Percentile

##### Projection of Vector y on columns of X

Fitting a linear model, lm(y ~ X), by least squares can be thought of geometrically as the orthogonal projection of y on the column space of X. This function is designed to allow exploration of projections and orthogonality.

##### Usage
Proj(y, X, list = FALSE)
##### Arguments
y

a vector, treated as a one-column matrix

X

a vector or matrix. Number of rows of y and X must match

list

logical; if FALSE, return just the projected vector; otherwise returns a list

##### Details

The projection is defined as $P y$ where $P = X (X'X)^- X'$ and $X^-$ is a generalized inverse.

##### Value

the projection of y on X (if list=FALSE) or a list with elements y and P

Other vector diagrams: arc, arrows3d, circle3d, corner, plot.regvec3d, pointOnLine, regvec3d, vectors3d, vectors

• Proj
##### Examples
# NOT RUN {
X <- matrix( c(1, 1, 1, 1, 1, -1, 1, -1), 4,2, byrow=TRUE)
y <- 1:4
Proj(y, X[,1])  # project y on unit vector
Proj(y, X[,2])
Proj(y, X)

# orthogonal complements
yp <-Proj(y, X, list=TRUE)
yp$y P <- yp$P
IP <- diag(4) - P
yc <- c(IP %*% y)
crossprod(yp\$y, yc)

# P is idempotent:  P P = P
P %*% P
all.equal(P, P %*% P)
# }

Documentation reproduced from package matlib, version 0.9.2, License: GPL (>= 2)

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