ordFacRegCox {OrdFacReg} | R Documentation |
This function computes estimates in Cox-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.
ordFacRegCox(ttf, tf, Z, fact, ordfact, intercept = TRUE, display = 0, eps = 0)
ttf |
Survival times. |
tf |
Censoring indicator (1 = event, 0 = censored). |
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. |
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. |
For a detailed description of the problem and the algorithm we refer to Rufibach (2009).
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. |
Kaspar Rufibach (maintainer)
kaspar.rufibach@ifspm.uzh.ch
http://www.biostat.uzh.ch/aboutus/people/rufibach.html
Duembgen, L., Huesler, A. and Rufibach, K. (2007) 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. (2009) An Active Set Algorithm to Estimate Parameters in Generalized Linear Models with Ordered Predictors. Preprint, available at http://arxiv.org/abs/0902.0240.
ordFacReg
computes estimates for least squares and logistic regression.
## ======================================================== ## Artificial data is used to illustrate Cox-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)[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(4, 5, 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 ## -------------------------------------------------------- ttf <- rexp(n) tf <- round(runif(n)) ## -------------------------------------------------------- ## generate design matrix ## -------------------------------------------------------- Y <- prepareData(Z, fact, ordfact, intercept)$Y ## -------------------------------------------------------- ## compute estimates ## -------------------------------------------------------- res1 <- coxreg.fit(Y, Surv(ttf, tf), max.survs = length(tf), strats = rep(1, length(tf)))$coefficients res2 <- ordFacRegCox(ttf, tf, Z, fact, ordfact, intercept = intercept, display = 1, eps = 0) b1 <- matrix(res1, ncol = 1) g1 <- coxDeriv(b1, ttf, tf, Y)$dL b2 <- res2$beta g2 <- coxDeriv(b2, ttf, tf, Y)$dL Ls <- c(coxLoglik(b1, ttf, tf, Y)$L, 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