--- tests/table.txt 	1970-01-01 01:00:00.000000000 +0100
+++ tests/table.txt	2008-01-17 15:04:57.706382509 +0000
@@ -0,0 +1,4 @@
+A B C D
+X1 4 5 6
+X2 7 8 9
+X3 6.0 2 Foo
--- tests/logit.r 	1970-01-01 01:00:00.000000000 +0100
+++ tests/logit.r	2008-01-17 15:04:57.778386612 +0000
@@ -0,0 +1,78 @@
+glm.od <-
+function(object, maxit = 30, verbose = TRUE)
+{
+  if (class(object)[1] != "glm")
+     stop("first argument must be a fitted model of class \"glm\" !")
+  class <- class(object)
+  if (!(family(object)$family == "binomial" & family(object)$link == "logit"))
+     stop("overdispersed model fitting available only for \nbinomial regression models with logit link function!")
+
+  pearson.X2 <- function(x) sum(residuals(x, "pearson")^2)
+
+  y <- object$model[,1]       # observed proportion of success & failures
+  trials <- apply(y, 1, sum)  # = object$prior.weights
+  X <- model.matrix(object)
+  p <- length(object$coefficients)
+  n <- dim(X)[[1]]
+  h <- lm.influence(object)$hat
+  X2 <- pearson.X2(object)
+  # initial estimate of dispersion parameter
+  phi <- (X2 - (n-p)) / sum((trials-1)*(1-h))
+  if(phi <0){phi <- 0;}
+  w <- 1/(1+phi*(trials-1))
+  if (verbose)
+      cat("\nBinomial overdispersed logit model fitting...\n")
+  # loop until Pearson X2 approx equal to 1
+  i <- 0
+
+  while( (X2/(n-p)-1) > object$control$epsilon )
+  {
+    i <- i + 1
+    if (i > maxit)
+    { warning("algoritm not converged after ", i, " iterations!")
+         break }
+    else
+       if (verbose) cat("Iter. ", i, " phi:", format(phi), "\n")
+    # computes weights
+    w <- 1/(1+phi*(trials-1))
+    # re-fit the model using update() evaluated in original model
+    # environment, usually R_GlobalEnv
+    disp.weights <<- w; object <<- object
+    object <- eval(expression(update(object, weights=disp.weights)),
+                   envir = object$data)
+
+    h <- lm.influence(object)$hat
+    X2 <- pearson.X2(object)
+    # current estimate of dispersion parameter
+    phi <- (X2 - sum(w*(1-h))) / sum(w*(trials-1)*(1-h))
+  }
+
+  if (verbose)
+     { cat("Converged after", i, "iterations. \n")
+       cat("Estimated dispersion parameter:", format(phi), "\n")
+           print(summary(object)) }
+  ##### Jun Lu
+  if (X2 < (n-p)){
+	  phi <- 0;
+	  w <- 1;
+  }
+  #cat(c("w is ",w,"\n"));
+  #if(phi <0){phi <- 0;}
+  #w[w>1] <- 1;
+  #cat(c("w is ",w,"\n"));
+  #####
+  object <- c(object, list(dispersion=phi, disp.weights=w))
+  class(object) <- class
+  invisible(object)
+}
+
+
+logit.1fact <- function(lib.size, counts, gp.des){
+   tmp <- counts
+   #if (sum(tmp)<5) next ;
+   #if(sum(tmp>0) <3) {tmp <- tmp+1;}
+   mod <- glm(cbind(tmp, lib.size-tmp) ~ gp.des, family=binomial(logit));
+   mod.disp <- glm.od(mod,verbose=FALSE);
+   test <- summary(mod.disp)$coefficients[2,];
+   return (list(zp = test))
+}