Binghnetty Logistic Regression 6 (males and females)

R Software Module: rwasp_logisticregression.wasp (opens new window with default values)

Title produced by software: Bias-Reduced Logistic Regression

Date of computation: Wed, 30 Jan 2008 05:13:01 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Jan/30/t1201695322glqrbeoba4s4vw4.htm/, Retrieved Wed, 30 Jan 2008 13:15:22 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1 70.6 1 7.8 1 1 71.3 1 6.0 1 1 67.5 1 6.0 1 1 73.2 1 7.1 1 1 81.2 1 5.9 1 1 75.5 1 3.6 1 1 66.7 1 6.9 1 1 68.7 1 8.0 1 1 73.0 1 5.6 1 1 78.4 1 6.1 1 1 76.1 1 5.1 1 1 65.7 1 5.0 1 1 66.7 1 5.2 1 1 72.3 1 2.9 1 1 66.2 1 4.9 1 1 67.7 1 4.7 1 1 64.9 1 6.5 1 1 72.2 1 7.5 1 1 69.4 1 3.9 1 1 72.8 1 3.2 1 1 75.4 1 4.5 1 1 74.7 1 6.5 1 1 77.4 1 7.4 1 1 68.8 1 3.3 1 1 75.3 1 5.9 1 1 72.4 1 4.8 1 1 72.7 1 4.4 1 1 65.3 1 5.4 1 1 67.0 1 5.9 1 1 66.7 1 6.1 1 1 67.0 1 5.4 1 1 67.5 1 6.0 1 1 69.9 1 4.9 1 1 72.5 1 5.3 1 1 73.8 1 6.0 1 1 71.9 1 3.2 1 1 69.9 1 6.5 1 1 71.4 1 3.6 1 1 64.9 1 5.9 1 1 70.2 1 5.2 1 1 65.3 1 7.3 1 1 67.5 1 8.9 1 1 68.1 1 1.8 1 1 82.9 1 6.5 1 1 66.2 1 3.0 1 1 74.0 1 7.0 1 1 69.8 1 7.0 1 1 81.3 1 5.9 1 1 76.9 1 6.3 1 1 76.6 1 3.8 1 1 72.7 1 7.6 1 1 78.6 1 6.3 1 1 70.2 1 5.6 1 1 69.5 1 6.4 1 1 73.8 1 7.5 1 1 66.1 1 7.0 1 1 77.8 1 1.1 1 1 73.2 1 6.0 1 1 72.8 1 6.5 1 1 74.8 1 5.7 1 1 67.2 1 5.5 1 1 80.7 1 6.4 1 1 67.2 1 6.5 1 1 75.4 1 1.9 1 1 75.3 1 3.4 1 1 73.2 1 6.4 1 1 65.3 1 1.6 1 1 71.8 1 6.8 1 1 67.3 1 6.1 1 1 73.7 1 6.3 1 1 80.6 0 5.1 1 1 75.7 0 4.7 1 1 73.0 0 4.1 1 1 71.3 0 3.0 1 1 70.8 0 6.5 1 1 69.6 0 5.3 1 1 66.1 0 6.4 1 1 65.6 0 6.8 1 0 65.0 1 12.0 1 0 65.0 1 7.0 1 0 65.0 0 6.0 1 0 65.0 1 5.0 1 0 65.0 1 7.0 1 0 65.0 1 8.0 1 0 65.0 0 5.0 1 0 65.0 1 8.0 1 0 65.0 1 5.0 1 0 65.0 1 6.0 1 0 65.0 1 9.0 1 0 65.0 0 7.0 1 0 65.0 1 6.0 1 0 65.0 0 6.0 1 0 65.0 0 7.0 1 0 65.0 0 6.0 1 0 65.0 0 6.0 1 0 65.0 0 8.0 1 0 65.0 0 7.0 1 0 65.0 0 5.0 1 0 65.0 0 7.0 1 0 65.0 0 7.0 1 0 65.0 1 6.0 1 0 65.0 1 5.0 1 0 65.0 1 7.0 1 0 65.0 0 8.0 1 0 65.0 1 8.0 1 0 65.0 0 9.0 1 0 65.0 0 99.0 1 0 65.0 0 8.0 1 0 65.0 0 6.0 1 0 65.0 1 5.0 1 0 65.0 1 9.0 1 0 65.0 1 7.0 1 0 65.0 1 6.0 1 0 65.0 1 7.0 1 0 65.0 0 6.0 1 0 65.0 0 6.0 1 0 65.0 1 7.0 1 0 65.0 1 7.0 1 0 65.0 0 6.0 1 0 65.0 1 6.0 1 0 65.0 0 6.0 1 0 65.0 0 8.0 1 0 65.0 1 6.0 1 0 65.0 0 7.0 1 0 65.0 1 7.0 1 0 65.0 1 5.0 1 0 65.0 0 8.0 1 0 65.0 1 4.0 1 0 65.0 1 7.0 1 0 65.0 1 8.0 1 0 65.0 1 10.0 1 0 65.0 1 6.0 1 0 65.0 0 7.0 1 0 65.0 1 5.0 1 0 65.0 0 6.0 1 0 65.0 0 6.0 1 0 65.0 0 10.0 1 0 65.0 0 7.0 1 0 65.0 0 6.0 1 1 68.7 1 5.29 2 1 67.5 1 4.6 2 1 68.9 1 5.86 2 1 70.5 1 6.4 2 1 73.5 1 4.48 2 1 68.1 1 3.1 2 1 66.7 1 3.79 2 1 69.3 1 5.73 2 1 67.5 1 3.57 2 1 67.8 1 3.63 2 1 69.4 1 6.07 2 1 68.8 1 5.33 2 1 71.7 1 4.28 2 1 67 1 6.45 2 1 70.5 1 6.23 2 1 76.5 1 6.9 2 1 73.8 1 7.21 2 1 71.2 1 6.55 2 1 82.3 1 5.73 2 1 72.5 1 6.81 2 1 67.3 1 2.18 2 1 72.7 1 2.17 2 1 69.6 1 4.05 2 1 74.8 1 5.23 2 1 71.2 1 5.76 2 1 72.4 1 5.17 2 1 83.2 1 5.58 2 1 65.6 1 5.7 2 1 79.6 1 4.35 2 1 66.2 1 5.44 2 1 68.6 1 6.08 2 1 65.8 1 6.48 2 1 82.9 1 4.65 2 1 88 1 6.75 2 1 76.6 1 6.28 2 1 70.7 1 3.76 2 1 77.3 1 3.06 2 1 65.5 1 6.49 2 1 68.5 1 7.1 2 1 77.1 1 1.01 2 1 82.3 0 4.92 2 1 80.7 0 5.75 2 1 74.4 0 5.55 2 1 71.2 0 4.86 2 1 71.1 0 5.24 2 1 69.5 0 6.99 2 0 65 1 5 2 0 65 1 8 2 0 65 0 6 2 0 65 1 5 2 0 65 1 8 2 0 65 0 10 2 0 65 1 1 2 0 65 1 7 2 0 65 1 6 2 0 65 1 4 2 0 65 0 6 2 0 65 0 6 2 0 65 1 8 2 0 65 0 7 2 0 65 0 6 2 0 65 1 7 2 0 65 1 6 2 0 65 1 6 2 0 65 0 6 2 0 65 1 5 2 0 65 1 6 2 0 65 1 7 2 0 65 1 6 2 0 65 1 3 2 0 65 1 6 2 0 65 1 3 2 0 65 1 7 2 0 65 1 5 2 0 65 1 7 2 0 65 1 5 2 0 65 1 7 2 0 65 1 7 2 0 65 1 6 2 0 65 1 7 2 0 65 1 5 2 0 65 1 6 2 0 65 1 6 2 0 65 1 6 2 0 65 1 5 2 0 65 1 8 2 0 65 0 3 2 0 65 0 5 2 0 65 0 6 2 0 65 0 6 2 0 65 1 6 2 0 65 0 9 2 0 65 1 8 2 0 65 1 8 2 0 65 0 4 2 0 65 1 9 2 0 65 1 5 2 0 65 0 6 2 0 65 1 9 2 0 65 1 6 2 0 65 1 6 2 0 65 1 7 2 0 65 1 5 2 0 65 0 9 2 0 65 1 6 2 0 65 1 6 2 0 65 0 6 2 0 65 1 7 2 0 65 0 8 2 0 65 0 7 2 0 65 1 6 2 0 65 2 6 2 0 65 1 5 2 0 65 1 7 2 0 65 1 6 2 0 65 0 7 2 0 65 1 9 2 0 65 1 6 2 0 65 1 7 2 0 65 1 6 2 0 65 1 7 2 0 65 1 7 2 0 65 0 8 2 0 65 1 4 2 0 65 0 5 2 0 65 1 6 2 0 65 1 6 2 0 65 1 6 2 0 65 1 7 2

Text written by user:

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Coefficients of Bias-Reduced Logistic Regression Variable Parameter S.E. t-stat 2-sided p-value (Intercept) -287.117945384729 72.3549629452117 -3.96818592253497 9.34708308724108e-05 age 4.4130780166533 1.10922131620504 3.97853697200094 8.9715915304156e-05 snoring 1.42610726978632 0.88010062825373 1.62039115074370 0.106346625158878 sleep_time -0.226701378969611 0.222295695639293 -1.01981902221565 0.308751485869799 sex -1.54766196790315 0.725978073170543 -2.13183018206609 0.0339477442071952 Summary of Bias-Reduced Logistic Regression Deviance 39.8686178577758 Penalized deviance 33.8476247843873 Residual Degrees of Freedom 263 ROC Area 0.996975806451613 Fit of Logistic Regression Index Actual Fitted Error 1 1 0.99999999983995 1.6004964020766e-10 2 1 0.999999999995153 4.84656759169866e-12 3 1 0.999907022288002 9.29777119978992e-05 4 1 0.999999999999999 1.44328993201270e-15 5 1 1 0 6 1 1 0 7 1 0.996122053809969 0.00387794619003112 8 1 0.999999266434806 7.33565193700159e-07 9 1 0.999999999999998 2.44249065417534e-15 10 1 1 0 11 1 1 0 12 1 0.827243888088406 0.172756111911594 13 1 0.997359008706001 0.00264099129399864 14 1 0.99999999999997 2.90878432451791e-14 15 1 0.978020659770487 0.0219793402295128 16 1 0.999971351624335 2.86483756652967e-05 17 1 0.09076781193293 0.90923218806707 18 1 0.999999999999872 1.28341781646668e-13 19 1 0.999999986812269 1.31877310183270e-08 20 1 0.999999999999997 3.44169137633799e-15 21 1 1 0 22 1 1 0 23 1 1 0 24 1 0.999999837429336 1.62570663930772e-07 25 1 1 0 26 1 0.999999999999971 2.87547763377916e-14 27 1 0.999999999999993 6.99440505513849e-15 28 1 0.428079017173242 0.571920982826758 29 1 0.999174906900965 0.000825093099035334 30 1 0.996763190883795 0.00323680911620527 31 1 0.999263260801315 0.000736739198684777 32 1 0.999907022288002 9.29777119978992e-05 33 1 0.999999998178884 1.82111570268262e-09 34 1 0.99999999999998 2.07611705604904e-14 35 1 1 1.11022302462516e-16 36 1 0.999999999999818 1.81854531433601e-13 37 1 0.999999997382626 2.61737398421502e-09 38 1 0.99999999999819 1.80921944092916e-12 39 1 0.102635805710701 0.897364194289299 40 1 0.999999999481318 5.18682319317065e-10 41 1 0.327299256175724 0.672700743824276 42 1 0.999820583959527 0.000179416040472669 43 1 0.99999745925423 2.54074576988561e-06 44 1 1 0 45 1 0.98560195982244 0.0143980401775602 46 1 1 0 47 1 0.999999995442246 4.55775417407267e-09 48 1 1 0 49 1 1 0 50 1 1 0 51 1 0.999999999999986 1.44328993201270e-14 52 1 1 0 53 1 0.999999999432085 5.6791527036637e-10 54 1 0.999999985049682 1.49503179835975e-08 55 1 1 1.11022302462516e-16 56 1 0.946750499249857 0.0532495007501431 57 1 1 0 58 1 0.999999999999999 1.11022302462516e-15 59 1 0.999999999999993 7.21644966006352e-15 60 1 1 0 61 1 0.999688090354615 0.000311909645384523 62 1 1 0 63 1 0.99960875437377 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0.0183897050929530 -0.0183897050929530 257 0 0.0356639574520229 -0.0356639574520229 258 0 0.0286370008555614 -0.0286370008555614 259 0 0.0356639574520229 -0.0356639574520229 260 0 0.0286370008555614 -0.0286370008555614 261 0 0.0286370008555614 -0.0286370008555614 262 0 0.00561429427982578 -0.00561429427982578 263 0 0.0549976745859896 -0.0549976745859896 264 0 0.0110228031604347 -0.0110228031604347 265 0 0.0356639574520229 -0.0356639574520229 266 0 0.0356639574520229 -0.0356639574520229 267 0 0.0356639574520229 -0.0356639574520229 268 0 0.0286370008555614 -0.0286370008555614 Type I & II errors for various threshold values Threshold Type I Type II 0.01 0 0.875 0.02 0 0.819444444444444 0.03 0 0.631944444444444 0.04 0 0.423611111111111 0.05 0 0.25 0.06 0 0.236111111111111 0.07 0 0.215277777777778 0.08 0 0.215277777777778 0.09 0 0.201388888888889 0.1 0.00806451612903226 0.173611111111111 0.11 0.0161290322580645 0.166666666666667 0.12 0.0161290322580645 0.166666666666667 0.13 0.0161290322580645 0.104166666666667 0.14 0.0161290322580645 0.0972222222222222 0.15 0.0161290322580645 0.0486111111111111 0.16 0.0161290322580645 0.0486111111111111 0.17 0.0161290322580645 0.0486111111111111 0.18 0.0161290322580645 0.00694444444444444 0.19 0.0161290322580645 0.00694444444444444 0.2 0.0161290322580645 0.00694444444444444 0.21 0.0161290322580645 0.00694444444444444 0.22 0.0161290322580645 0 0.23 0.0161290322580645 0 0.24 0.0241935483870968 0 0.25 0.0241935483870968 0 0.26 0.0241935483870968 0 0.27 0.0241935483870968 0 0.28 0.0241935483870968 0 0.29 0.0241935483870968 0 0.3 0.0241935483870968 0 0.31 0.0241935483870968 0 0.32 0.0241935483870968 0 0.33 0.0403225806451613 0 0.34 0.0403225806451613 0 0.35 0.0403225806451613 0 0.36 0.0483870967741935 0 0.37 0.0483870967741935 0 0.38 0.0483870967741935 0 0.39 0.0483870967741935 0 0.4 0.0483870967741935 0 0.41 0.0483870967741935 0 0.42 0.0483870967741935 0 0.43 0.0564516129032258 0 0.44 0.0564516129032258 0 0.45 0.0564516129032258 0 0.46 0.0564516129032258 0 0.47 0.0564516129032258 0 0.48 0.0564516129032258 0 0.49 0.0564516129032258 0 0.5 0.0564516129032258 0 0.51 0.0564516129032258 0 0.52 0.0564516129032258 0 0.53 0.0564516129032258 0 0.54 0.064516129032258 0 0.55 0.064516129032258 0 0.56 0.064516129032258 0 0.57 0.064516129032258 0 0.58 0.064516129032258 0 0.59 0.064516129032258 0 0.6 0.064516129032258 0 0.61 0.064516129032258 0 0.62 0.064516129032258 0 0.63 0.064516129032258 0 0.64 0.0725806451612903 0 0.65 0.0725806451612903 0 0.66 0.0725806451612903 0 0.67 0.0725806451612903 0 0.68 0.0725806451612903 0 0.69 0.0725806451612903 0 0.7 0.0725806451612903 0 0.71 0.0725806451612903 0 0.72 0.0725806451612903 0 0.73 0.0725806451612903 0 0.74 0.0725806451612903 0 0.75 0.0725806451612903 0 0.76 0.0725806451612903 0 0.77 0.0725806451612903 0 0.78 0.0725806451612903 0 0.79 0.0725806451612903 0 0.8 0.0725806451612903 0 0.81 0.0725806451612903 0 0.82 0.0725806451612903 0 0.83 0.0806451612903226 0 0.84 0.0887096774193548 0 0.85 0.0887096774193548 0 0.86 0.0887096774193548 0 0.87 0.0887096774193548 0 0.88 0.0887096774193548 0 0.89 0.0887096774193548 0 0.9 0.096774193548387 0 0.91 0.096774193548387 0 0.92 0.096774193548387 0 0.93 0.096774193548387 0 0.94 0.096774193548387 0 0.95 0.104838709677419 0 0.96 0.104838709677419 0 0.97 0.104838709677419 0 0.98 0.112903225806452 0 0.99 0.120967741935484 0

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/30/t1201695322glqrbeoba4s4vw4/1m8431201695172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/30/t1201695322glqrbeoba4s4vw4/1m8431201695172.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/30/t1201695322glqrbeoba4s4vw4/244ze1201695172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/30/t1201695322glqrbeoba4s4vw4/244ze1201695172.ps (open in new window)

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

library(brlr) roc.plot <- function (sd, sdc, newplot = TRUE, ...) { sall <- sort(c(sd, sdc)) sens <- 0 specc <- 0 for (i in length(sall):1) { sens <- c(sens, mean(sd >= sall[i], na.rm = T)) specc <- c(specc, mean(sdc >= sall[i], na.rm = T)) } if (newplot) { plot(specc, sens, xlim = c(0, 1), ylim = c(0, 1), type = 'l', xlab = '1-specificity', ylab = 'sensitivity', main = 'ROC plot', ...) abline(0, 1) } else lines(specc, sens, ...) npoints <- length(sens) area <- sum(0.5 * (sens[-1] + sens[-npoints]) * (specc[-1] - specc[-npoints])) lift <- (sens - specc)[-1] cutoff <- sall[lift == max(lift)][1] sensopt <- sens[-1][lift == max(lift)][1] specopt <- 1 - specc[-1][lift == max(lift)][1] list(area = area, cutoff = cutoff, sensopt = sensopt, specopt = specopt) } roc.analysis <- function (object, newdata = NULL, newplot = TRUE, ...) { if (is.null(newdata)) { sd <- object$fitted[object$y == 1] sdc <- object$fitted[object$y == 0] } else { sd <- predict(object, newdata, type = 'response')[newdata$y == 1] sdc <- predict(object, newdata, type = 'response')[newdata$y == 0] } roc.plot(sd, sdc, newplot, ...) } hosmerlem <- function (y, yhat, g = 10) { cutyhat <- cut(yhat, breaks = quantile(yhat, probs = seq(0, 1, 1/g)), include.lowest = T) obs <- xtabs(cbind(1 - y, y) ~ cutyhat) expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat) chisq <- sum((obs - expect)^2/expect) P <- 1 - pchisq(chisq, g - 2) c('X^2' = chisq, Df = g - 2, 'P(>Chi)' = P) } x <- as.data.frame(t(y)) r <- brlr(x) summary(r) rc <- summary(r)$coeff bitmap(file='test0.png') ra <- roc.analysis(r) dev.off() te <- array(0,dim=c(2,99)) for (i in 1:99) { threshold <- i / 100 numcorr1 <- 0 numfaul1 <- 0 numcorr0 <- 0 numfaul0 <- 0 for (j in 1:length(r$fitted.values)) { if (y[1,j] > 0.99) { if (r$fitted.values[j] >= threshold) numcorr1 = numcorr1 + 1 else numfaul1 = numfaul1 + 1 } else { if (r$fitted.values[j] < threshold) numcorr0 = numcorr0 + 1 else numfaul0 = numfaul0 + 1 } } te[1,i] <- numfaul1 / (numfaul1 + numcorr1) te[2,i] <- numfaul0 / (numfaul0 + numcorr0) } bitmap(file='test1.png') op <- par(mfrow=c(2,2)) plot((1:99)/100,te[1,],xlab='Threshold',ylab='Type I error', main='1 - Specificity') plot((1:99)/100,te[2,],xlab='Threshold',ylab='Type II error', main='1 - Sensitivity') plot(te[1,],te[2,],xlab='Type I error',ylab='Type II error', main='(1-Sens.) vs (1-Spec.)') plot((1:99)/100,te[1,]+te[2,],xlab='Threshold',ylab='Sum of Type I & II error', main='(1-Sens.) + (1-Spec.)') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Coefficients of Bias-Reduced Logistic Regression',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'t-stat',header=TRUE) a<-table.element(a,'2-sided p-value',header=TRUE) a<-table.row.end(a) for (i in 1:length(rc[,1])) { a<-table.row.start(a) a<-table.element(a,labels(rc)[[1]][i],header=TRUE) a<-table.element(a,rc[i,1]) a<-table.element(a,rc[i,2]) a<-table.element(a,rc[i,3]) a<-table.element(a,2*(1-pt(abs(rc[i,3]),r$df.residual))) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Summary of Bias-Reduced Logistic Regression',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Deviance',1,TRUE) a<-table.element(a,r$deviance) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Penalized deviance',1,TRUE) a<-table.element(a,r$penalized.deviance) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual Degrees of Freedom',1,TRUE) a<-table.element(a,r$df.residual) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'ROC Area',1,TRUE) a<-table.element(a,ra$area) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Fit of Logistic Regression',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Index',1,TRUE) a<-table.element(a,'Actual',1,TRUE) a<-table.element(a,'Fitted',1,TRUE) a<-table.element(a,'Error',1,TRUE) a<-table.row.end(a) for (i in 1:length(r$fitted.values)) { a<-table.row.start(a) a<-table.element(a,i,1,TRUE) a<-table.element(a,y[1,i]) a<-table.element(a,r$fitted.values[i]) a<-table.element(a,y[1,i]-r$fitted.values[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Type I & II errors for various threshold values',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Threshold',1,TRUE) a<-table.element(a,'Type I',1,TRUE) a<-table.element(a,'Type II',1,TRUE) a<-table.row.end(a) for (i in 1:99) { a<-table.row.start(a) a<-table.element(a,i/100,1,TRUE) a<-table.element(a,te[1,i]) a<-table.element(a,te[2,i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable3.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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