ordFacReg {OrdFacReg}

Compute least squares or logistic regression for ordered predictors

Description

This function computes estimates in least squares or logistic regression where coefficients corresponding to dummy variables of ordered factors are estimated to be in non-decreasing order and at least 0. An active set algorithm as described in Duembgen et al. (2007) is used.

Usage

ordFacReg(D, Z, fact, ordfact, ordering = NA, type = c("LS", "logreg"), 
    intercept = TRUE, display = 0, eps = 0)

Arguments

D

Response vector, either in R^n (least squares) or in \{0, 1\}^n (logistic).

Z

Matrix of predictors. Factors are coded with levels from 1 to j.

fact

Specify columns in Z that correspond to unordered factors.

ordfact

Specify columns in Z that correspond to ordered factors.

ordering

Vector of the same length as ordfact. Specifies ordering of ordered factors: "i" means that the coefficients of the corresponding ordered factor are estimated in non-decreasing order and "d" means non-increasing order. See the examples below for details.

type

Specify type of response variable.

intercept

If TRUE, an intercept (= column of all 1's) is added to the design matrix.

display

If display == 1 progress of the algorithm is output.

eps

Quantity to which the criterion in the Basic Procedure 2 in Duembgen et al. (2007) is compared.

Details

For a detailed description of the problem and the algorithm we refer to Rufibach (2010).

Value

L

Value of the criterion function at the maximum.

beta

Computed regression coefficients.

A

Set A of active constraints.

design.matrix

Design matrix that was generated.

Author(s)

Kaspar Rufibach (maintainer)
[e-mail]
http://www.kasparrufibach.ch

References

Duembgen, L., Huesler, A. and Rufibach, K. (2010). Active set and EM algorithms for log-concave densities based on complete and censored data. Technical report 61, IMSV, Univ. of Bern, available at http://arxiv.org/abs/0707.4643.

Rufibach, K. (2010). An Active Set Algorithm to Estimate Parameters in Generalized Linear Models with Ordered Predictors. Comput. Statist. Data Anal., 54, 1442-1456.

See Also

ordFacRegCox computes estimates for Cox-regression.

Examples

## ========================================================
## To illustrate least squares estimation, we generate the same data
## that was used in Rufibach (2010), Table 1.
## ========================================================

## --------------------------------------------------------
## initialization
## --------------------------------------------------------
n <- 200
Z <- NULL
intercept <- FALSE

## --------------------------------------------------------
## quantitative variables
## --------------------------------------------------------
n.q <- 3
set.seed(14012009)
if (n.q > 0){for (i in 1:n.q){Z <- cbind(Z, rnorm(n, mean = 1, sd = 2))}}

## --------------------------------------------------------
## unordered factors
## --------------------------------------------------------
un.levels <- 3
for (i in 1:length(un.levels)){Z <- cbind(Z, sample(rep(1:un.levels[i], 
    each = ceiling(n / un.levels)))[1:n])}
fact <- n.q + 1:length(un.levels)

## --------------------------------------------------------
## ordered factors
## --------------------------------------------------------
levels <- 8
for (i in 1:length(un.levels)){Z <- cbind(Z, sample(rep(1:levels[i], 
    each = ceiling(n / levels)))[1:n])}
ordfact <- n.q + length(un.levels) + 1:length(levels)

## --------------------------------------------------------
## generate data matrices
## --------------------------------------------------------
Y <- prepareData(Z, fact, ordfact, ordering = NA, intercept)$Y

## --------------------------------------------------------
## generate response
## --------------------------------------------------------
D <- apply(Y * matrix(c(rep(c(2, -3, 0), each = n), rep(c(1, 1), each = n), 
    rep(c(0, 2, 2, 2, 2, 5, 5), each = n)), ncol = ncol(Y)), 1, sum) + 
    rnorm(n, mean = 0, sd = 4)

## --------------------------------------------------------
## compute estimates
## --------------------------------------------------------
res1 <- lmLSE(D, Y)
res2 <- ordFacReg(D, Z, fact, ordfact, ordering = "i", type = "LS", intercept, 
    display = 1, eps = 0)
b1 <- res1$beta
g1 <- lmSS(b1, D, Y)$dL
b2 <- res2$beta
g2 <- lmSS(b2, D, Y)$dL
Ls <- c(lmSS(b1, D, Y)$L, lmSS(b2, D, Y)$L)
names(Ls) <- c("LSE", "ordFact") 
disp <- cbind(1:length(b1), round(cbind(b1, g1, cumsum(g1)), 4), 
    round(cbind(b2, g2, cumsum(g2)), 4))

## --------------------------------------------------------
## display results
## --------------------------------------------------------
disp
Ls

## ========================================================
## Artificial data is used to illustrate logistic regression.
## ========================================================

## --------------------------------------------------------
## initialization
## --------------------------------------------------------
set.seed(1977)
n <- 500
Z <- NULL
intercept <- FALSE

## --------------------------------------------------------
## quantitative variables
## --------------------------------------------------------
n.q <- 2
if (n.q > 0){for (i in 1:n.q){Z <- cbind(Z, rnorm(n, rgamma(2, 2, 1)))}}

## --------------------------------------------------------
## unordered factors
## --------------------------------------------------------
un.levels <- c(8, 2)
for (i in 1:length(un.levels)){Z <- cbind(Z, sample(round(runif(n, 0, 
    un.levels[i] - 1)) + 1))}
fact <- n.q + 1:length(un.levels)

## --------------------------------------------------------
## ordered factors
## --------------------------------------------------------
levels <- c(2, 4, 10)
for (i in 1:length(levels)){Z <- cbind(Z, sample(round(runif(n, 0, 
    levels[i] - 1)) + 1))}
ordfact <- n.q + length(un.levels) + 1:length(levels)

## --------------------------------------------------------
## generate response
## --------------------------------------------------------
D <- sample(c(rep(0, n / 2), rep(1, n/2)))

## --------------------------------------------------------
## generate design matrix
## --------------------------------------------------------
Y <- prepareData(Z, fact, ordfact, ordering = NA, intercept)$Y

## --------------------------------------------------------
## compute estimates
## --------------------------------------------------------
res1 <- matrix(glm.fit(Y, D, family = binomial(link = logit))$coefficients, ncol = 1)
res2 <- ordFacReg(D, Z, fact, ordfact, ordering = NA, type = "logreg", 
    intercept = intercept, display = 1, eps = 0)
b1 <- res1
g1 <- logRegDeriv(b1, D, Y)$dL
b2 <- res2$beta
g2 <- logRegDeriv(b2, D, Y)$dL
Ls <- unlist(c(logRegLoglik(res1, D, Y), res2$L))
names(Ls) <- c("MLE", "ordFact") 
disp <- cbind(1:length(b1), round(cbind(b1, g1, cumsum(g1)), 4), 
    round(cbind(b2, g2, cumsum(g2)), 4))

## --------------------------------------------------------
## display results
## --------------------------------------------------------
disp
Ls

## --------------------------------------------------------
## compute estimates when the third ordered factor should
## have *decreasing* estimated coefficients
## --------------------------------------------------------
res3 <- ordFacReg(D, Z, fact, ordfact, ordering = c("i", "i", "d"), 
    type = "logreg", intercept = intercept, display = 1, eps = 0)
b3 <- res3$beta
g3 <- logRegDeriv(b3, D, Y)$dL
Ls <- unlist(c(logRegLoglik(res1, D, Y), res2$L, res3$L))
names(Ls) <- c("MLE", "ordFact ddd", "ordFact iid") 
disp <- cbind(1:length(b1), round(cbind(b1, b2, b3), 4))

## --------------------------------------------------------
## display results
## --------------------------------------------------------
disp
Ls