Dear list, I have a problem on calculating the standard error of Goodman-Kurskal's gamma using delta method. I exactly follow the method and forumla described in Problem 3.27 of Alan Agresti's Categorical Data Analysis (2nd edition). The data I used is also from the job satisfaction vs. income example from that book.
job <- matrix(c(1, 3, 10, 6, 2, 3, 10, 7, 1, 6, 14, 12, 0, 1, 9, 11), nrow = 4, ncol = 4, byrow = TRUE, dimnames = list(c("< 15,000", "15,000 - 25,000", "25,000 - 40,000", "> 40,000"), c("VD", "LD", "MS", "VS"))) The following code is for calculating gamma value, which is consistent with the result presented in section 2.4.5 of that book. C <- 0 D <- 0 for (i in 1:nrow(job)){ for (j in 1:ncol(job)){ pi_c <- 0 pi_d <- 0 for (h in 1:nrow(job)){ for (k in 1:ncol(job)){ if ((h > i & k > j) | (h < i & k < j)){ pi_c <- pi_c + job[h, k]/sum(job) } if ((h > i & k < j) | (h < i & k > j)){ pi_d <- pi_d + job[h, k]/sum(job) } } } C <- C + job[i, j] * pi_c D <- D + job[i, j] * pi_d } } gamma <- (C - D) / (C + D) # gamma = 0.221 for this example. The following code is for calculating stardard error of gamma. sigma.squared <- 0 for (i in 1:nrow(job)){ for (j in 1:ncol(job)){ pi_c <- 0 pi_d <- 0 for (h in 1:nrow(job)){ for (k in 1:ncol(job)){ if ((h > i & k > j) | (h < i & k < j)){ pi_c <- pi_c + job[h, k]/sum(job) } if ((h > i & k < j) | (h < i & k > j)){ pi_d <- pi_d + job[h, k]/sum(job) } } } phi <- 4 * (pi_c * D - pi_d * C) / (C + D)^2 sigma.squared <- sigma.squared + phi^2 } } se <- (sigma.squared/sum(job))^.5 # 0.00748, which is different from the SE 0.117 given in section 3.4.3 of that book. I am not able to figure out what is the problem with my code... Could anyone point out what the problem is? Thanks. Wuming ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html