mu_alt = mu/sqrt(n)

dd <- expand.grid(p=seq(.0001,.1,.0001),mu_alt = seq(.1,2,.1)) 
dim(dd)

## [1] 20000     2

dd$power05 <- with(dd, pnorm(qnorm(.05, lower.tail = FALSE), mean = mu_alt, lower.tail = FALSE))

z statistic has a n(0,1) distribution under H0 and a n(0,mu_alt) under HA

post prob with even prior odds is

post <- function(p,mu_alt) {
  crit <- qnorm(p,lower.tail = FALSE)
  dnorm(crit)/(dnorm(crit) + dnorm(crit, mu_alt))
}

post(.005,2)

## [1] 0.04102597

dd$post <- mapply(post, dd$p, dd$mu_alt)
head(dd)

##       p mu_alt    power05      post
## 1 1e-04    0.1 0.06119084 0.4092899
## 2 2e-04    0.1 0.06119084 0.4136229
## 3 3e-04    0.1 0.06119084 0.4162562
## 4 4e-04    0.1 0.06119084 0.4181726
## 5 5e-04    0.1 0.06119084 0.4196884
## 6 6e-04    0.1 0.06119084 0.4209468

library(latticeExtra)

## Loading required package: lattice

xyplot(post ~ p, dd, groups = mu_alt, type = 'l', auto.key=T, ylim = c(0,.5),
       scales = list(x = list(log=T)))+
  layer(panel.abline(h = .05))

xyplot(post ~ p, dd, groups = 100*round(power05,3), type = 'l', 
       auto.key=list(reverse.rows=F), 
       ylim = c(0,.5),
       scales = list(x = list(log=T, equispaced.log = F)))+
  layer(panel.abline(h = .05))

Figure 0.1: Posterior Probability

1 Relationship between Leverage and Mahalanobis Distance in Predictor Space

Given a data matrix \(X\), the ‘full’ \(n \times p\) data matrix including an intercept has the form \([1 X]\) where \(1\) is a \(n \times 1\) column of \(1\)s.

The vector of leverages in a regression in which the predictor variables are given by \(X\) plus an intercept term is the diagonal of the projection matrix (often known as the ‘hat matrix’) onto the linear space spanned by the unit vector and the columns of \(X\), \({\cal L}(1,X)\).

Under the assumption that \([1 X]\) is of full column rank: \[ \begin{aligned} H & = [1 X]\left([1 X]'[1 X]\right)^{-1}[1 X]' \\ & = [1 X] \begin{pmatrix} n & 1'X \\ X'1 & X'X \end{pmatrix} ^{-1}[1 X]' \\ \end{aligned} \] As a projection matrix, \(H\) is invariant under location scale transformations of the data matrix \(X\). Replacing \(X\) with \(X_c\) in which each column is centered so that \(X'_c 1 = 0\) \[ \begin{aligned} H & = [1 X_c]\left([1 X_c]'[1 X_c]\right)^{-1}[1 X_c]' \\ & = [1 X_c] \begin{pmatrix} n & 0 \\ 0 & X'_cX_c \end{pmatrix} ^{-1}[1 X_c]' \\ & = \frac{1}{n} 1 1' + X_c(X'_cX_c)^{-1}X'_c \\ & = \frac{1}{n} 1 1' + \frac{1}{n} X_c\Sigma_X^{-1}X'_c \\ \end{aligned} \] The diagonal elements of \(X_c\Sigma_X^{-1}X'_c\) are the squares of Mahalanobis distances of individual observations standardized by the maximum likelihood estimate of variance using division by \(n\).

Thus, using the diagonal elements:

\[ h_{ii} = \frac{1}{n} (1 + Z_i^2) \]

This is a result that is valid for linear multiple regression for any number of predictor variables with an intercept term. In simple regression, \(p = 1\) and \(Z_i\), with an appropriate sign, is simply the the ‘Z-score’ for \(i\)th predictor observation.

2 Some consequences

It is easily shown that \(\sum_i h_{ii} = 1 + p\): \[\begin{aligned} \sum_i h_{ii} & = \operatorname{trace}(H) \\ & = \operatorname{trace}\left( [1 X]\left([1 X]'[1 X]\right)^{-1}[1 X]'\right) \\ & = \operatorname{trace}\left( \left([1 X]'[1 X]\right)^{-1}[1 X]' [1 X]\right)\quad \textrm{since} \operatorname{trace}(AB) = \operatorname{trace}(BA)\\ & = \operatorname{trace}I_{(p+1) \times (p+1)} \\ & = p+1 \end{aligned}\] so that: \[\sum_{i=1}^n Z_i^2 = np\] and \[\overline{Z_i^2} = p\] which concords with expectation since \(Z_i^2\) has a distribution that is approximately \(\chi^2_p\).
We also know that \(\frac{1}{n} \le h_{ii} \le 1\) so that \[0 \le Z_i^2 \le n-1\]
If \(X\) is a \(n \times p\) matrix of predictor variables in a least-squares regression, then \(Z_i\) is the Mahalanobis distance of the \(i\)th case in predictor space.
The ‘residual-leverage’ plot, which is the fourth plot produced by the ‘plot’ command in R applied to ‘lm’ objects, plots \(h_{ii} = \frac{1 + Z_i^2}{n}\) on the horizontal axis.
Mahalanobis distance is the generalization of the univariate Z-score to all dimensions. Strictly speaking, in one dimension, Mahalanobis distance is the absolute value of the Z-score.
The ellipse in predictor space: \[{\cal E}_r = \left\{ x \in \mathbb{R}^p : (x -\bar{x})'\hat{\Sigma}_X^{-1} (x -\bar{x}) = r^2 \right\}\] contains the points (if any) with leverage equal to \(\frac{1}{n}(1 + r^2)\)
The concept works for linear regression in \(\beta\)s even if not linear in the predictors.

To compute Mahalanobis distance, you can write your own function, or you can consider the ‘mahalanobis’ function in base R.

For illustration, the following plot shows the predictor data ellipses of radii 1, 2 and 3, corresponding to Mahalanobis distances of 1, 2, and 3, and leverages of 0.1, 0.25, and 0.5 since \(n=20\).

The next plot also shows the predictor data ellipses of radii 1, 2 and 3, corresponding to Mahalanobis distances of 1, 2, and 3, and leverages of 0.1, 0.25, and 0.5 since \(n=20\) but in a quadratic regression. Note that although the predictors are not normally distributed, the ellipse captures the first and second moments of the predictors, and the least-squares linear regression only depends on the first and second moments.

P-values and posterior probabilities

1 Relationship between Leverage and Mahalanobis Distance in Predictor Space

2 Some consequences