R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> y <- c(13.5,16.2,17.6,15.8,17.6,15.2,15.9,12.0,13.3,14.8,16.1,16.9,17.6,13.9,10.0,7.6,7.1,8.1,8.1,7.7,4.0,1.4,0.3,-1.0,-1.9,-1.5,-0.2,3.4,3.0,4.1,3.4,3.2,6.1,5.8,6.2,5.8,5.9,6.7,5.9,3.8,1.7,1.4,1.8,3.0,3.6,4.8,4.3,4.2,2.9,4.9,7.2,8.7,9.1,8.9,9.0,11.6,9.6,9.1,9.2,10.8,11.0,8.5,6.5,7.2,7.8,8.7,7.8,7.5,7.7,7.5,8.3,7.9,10.4,11.5,14.0,11.9,11.9,10.3,11.3,9.9,8.9,9.2,8.8,6.7,7.1,6.6,7.2,5.0,5.3,6.3)
> x <- c(7.3,7.2,7.1,6.9,6.8,6.7,6.8,6.8,6.7,6.8,6.8,6.7,6.3,6.2,6.2,6.5,6.5,6.4,6.2,6.2,6.3,7.5,7.4,7.4,7.4,7.4,7.4,7.2,7.2,7.2,7.5,7.4,7.5,8.0,8.0,8.0,8.1,8.1,8.1,7.9,7.9,8.0,8.2,8.1,8.2,8.5,8.5,8.6,8.4,8.4,8.4,7.7,7.8,7.9,8.8,8.8,8.9,8.5,8.5,8.5,8.4,8.5,8.4,8.3,8.4,8.4,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.3,8.3,8.3,8.2,8.1,8.1,8.2,8.0,7.9,7.9,7.8,7.7,7.9,7.7,7.6)
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: Wessa P., (2007), Box-Cox Linearity Plot (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_boxcoxlin.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description
> n <- length(x)
> c <- array(NA,dim=c(401))
> l <- array(NA,dim=c(401))
> mx <- 0
> mxli <- -999
> for (i in 1:401)
+ {
+ l[i] <- (i-201)/100
+ if (l[i] != 0)
+ {
+ x1 <- (x^l[i] - 1) / l[i]
+ } else {
+ x1 <- log(x)
+ }
+ c[i] <- cor(x1,y)
+ if (mx < abs(c[i]))
+ {
+ mx <- abs(c[i])
+ mxli <- l[i]
+ }
+ }
> c
  [1] -0.2594200 -0.2593197 -0.2592191 -0.2591184 -0.2590175 -0.2589165
  [7] -0.2588153 -0.2587139 -0.2586123 -0.2585106 -0.2584087 -0.2583067
 [13] -0.2582045 -0.2581021 -0.2579995 -0.2578968 -0.2577939 -0.2576909
 [19] -0.2575877 -0.2574843 -0.2573808 -0.2572771 -0.2571732 -0.2570692
 [25] -0.2569650 -0.2568606 -0.2567561 -0.2566514 -0.2565465 -0.2564415
 [31] -0.2563363 -0.2562310 -0.2561255 -0.2560198 -0.2559140 -0.2558080
 [37] -0.2557018 -0.2555955 -0.2554890 -0.2553824 -0.2552756 -0.2551686
 [43] -0.2550615 -0.2549542 -0.2548467 -0.2547391 -0.2546314 -0.2545234
 [49] -0.2544153 -0.2543071 -0.2541987 -0.2540901 -0.2539814 -0.2538725
 [55] -0.2537634 -0.2536542 -0.2535448 -0.2534353 -0.2533256 -0.2532158
 [61] -0.2531058 -0.2529956 -0.2528853 -0.2527748 -0.2526642 -0.2525534
 [67] -0.2524424 -0.2523313 -0.2522200 -0.2521086 -0.2519970 -0.2518853
 [73] -0.2517734 -0.2516614 -0.2515492 -0.2514368 -0.2513243 -0.2512116
 [79] -0.2510988 -0.2509858 -0.2508727 -0.2507594 -0.2506459 -0.2505323
 [85] -0.2504186 -0.2503047 -0.2501906 -0.2500764 -0.2499620 -0.2498475
 [91] -0.2497328 -0.2496180 -0.2495030 -0.2493879 -0.2492726 -0.2491572
 [97] -0.2490416 -0.2489259 -0.2488100 -0.2486939 -0.2485777 -0.2484614
[103] -0.2483449 -0.2482283 -0.2481115 -0.2479945 -0.2478774 -0.2477602
[109] -0.2476428 -0.2475253 -0.2474076 -0.2472897 -0.2471717 -0.2470536
[115] -0.2469353 -0.2468169 -0.2466983 -0.2465796 -0.2464607 -0.2463417
[121] -0.2462225 -0.2461032 -0.2459837 -0.2458641 -0.2457444 -0.2456245
[127] -0.2455044 -0.2453842 -0.2452639 -0.2451434 -0.2450228 -0.2449020
[133] -0.2447811 -0.2446600 -0.2445388 -0.2444175 -0.2442960 -0.2441743
[139] -0.2440526 -0.2439306 -0.2438086 -0.2436864 -0.2435640 -0.2434415
[145] -0.2433189 -0.2431961 -0.2430732 -0.2429501 -0.2428269 -0.2427036
[151] -0.2425801 -0.2424565 -0.2423327 -0.2422088 -0.2420848 -0.2419606
[157] -0.2418363 -0.2417118 -0.2415872 -0.2414625 -0.2413376 -0.2412126
[163] -0.2410874 -0.2409621 -0.2408367 -0.2407111 -0.2405854 -0.2404596
[169] -0.2403336 -0.2402075 -0.2400812 -0.2399549 -0.2398283 -0.2397017
[175] -0.2395749 -0.2394479 -0.2393209 -0.2391937 -0.2390664 -0.2389389
[181] -0.2388113 -0.2386836 -0.2385557 -0.2384277 -0.2382995 -0.2381713
[187] -0.2380429 -0.2379143 -0.2377857 -0.2376569 -0.2375280 -0.2373989
[193] -0.2372697 -0.2371404 -0.2370109 -0.2368813 -0.2367516 -0.2366218
[199] -0.2364918 -0.2363617 -0.2362315 -0.2361011 -0.2359706 -0.2358400
[205] -0.2357092 -0.2355784 -0.2354474 -0.2353162 -0.2351850 -0.2350536
[211] -0.2349221 -0.2347904 -0.2346587 -0.2345268 -0.2343947 -0.2342626
[217] -0.2341303 -0.2339979 -0.2338654 -0.2337328 -0.2336000 -0.2334671
[223] -0.2333341 -0.2332009 -0.2330677 -0.2329343 -0.2328008 -0.2326671
[229] -0.2325334 -0.2323995 -0.2322655 -0.2321313 -0.2319971 -0.2318627
[235] -0.2317282 -0.2315936 -0.2314589 -0.2313240 -0.2311891 -0.2310540
[241] -0.2309188 -0.2307834 -0.2306480 -0.2305124 -0.2303767 -0.2302409
[247] -0.2301050 -0.2299689 -0.2298328 -0.2296965 -0.2295601 -0.2294236
[253] -0.2292869 -0.2291502 -0.2290133 -0.2288764 -0.2287393 -0.2286021
[259] -0.2284647 -0.2283273 -0.2281897 -0.2280521 -0.2279143 -0.2277764
[265] -0.2276384 -0.2275002 -0.2273620 -0.2272236 -0.2270852 -0.2269466
[271] -0.2268079 -0.2266691 -0.2265302 -0.2263912 -0.2262520 -0.2261128
[277] -0.2259734 -0.2258340 -0.2256944 -0.2255547 -0.2254149 -0.2252750
[283] -0.2251350 -0.2249949 -0.2248546 -0.2247143 -0.2245738 -0.2244333
[289] -0.2242926 -0.2241518 -0.2240110 -0.2238700 -0.2237289 -0.2235877
[295] -0.2234464 -0.2233050 -0.2231635 -0.2230218 -0.2228801 -0.2227383
[301] -0.2225963 -0.2224543 -0.2223122 -0.2221699 -0.2220276 -0.2218851
[307] -0.2217425 -0.2215999 -0.2214571 -0.2213143 -0.2211713 -0.2210282
[313] -0.2208851 -0.2207418 -0.2205984 -0.2204549 -0.2203114 -0.2201677
[319] -0.2200239 -0.2198801 -0.2197361 -0.2195920 -0.2194479 -0.2193036
[325] -0.2191592 -0.2190148 -0.2188702 -0.2187255 -0.2185808 -0.2184359
[331] -0.2182910 -0.2181460 -0.2180008 -0.2178556 -0.2177102 -0.2175648
[337] -0.2174193 -0.2172737 -0.2171280 -0.2169822 -0.2168363 -0.2166903
[343] -0.2165442 -0.2163980 -0.2162517 -0.2161054 -0.2159589 -0.2158124
[349] -0.2156657 -0.2155190 -0.2153722 -0.2152253 -0.2150783 -0.2149312
[355] -0.2147840 -0.2146367 -0.2144893 -0.2143419 -0.2141943 -0.2140467
[361] -0.2138990 -0.2137512 -0.2136033 -0.2134553 -0.2133072 -0.2131591
[367] -0.2130108 -0.2128625 -0.2127141 -0.2125656 -0.2124170 -0.2122683
[373] -0.2121196 -0.2119707 -0.2118218 -0.2116728 -0.2115237 -0.2113745
[379] -0.2112252 -0.2110759 -0.2109264 -0.2107769 -0.2106273 -0.2104776
[385] -0.2103279 -0.2101780 -0.2100281 -0.2098781 -0.2097280 -0.2095778
[391] -0.2094276 -0.2092772 -0.2091268 -0.2089763 -0.2088258 -0.2086751
[397] -0.2085244 -0.2083736 -0.2082227 -0.2080717 -0.2079207
> mx
[1] 0.2594200
> mxli
[1] -2
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> r<-lm(y~x)
> se <- sqrt(var(r$residuals))
> r1 <- lm(y~x1)
> se1 <- sqrt(var(r1$residuals))
> postscript(file="/var/www/html/rcomp/tmp/1zuu41199649132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/24vdv1199649132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y')
> abline(r)
> grid()
> mtext(paste('Residual Standard Deviation = ',se))
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/3m2gq1199649132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y')
> abline(r1)
> grid()
> mtext(paste('Residual Standard Deviation = ',se1))
> dev.off()
null device 
          1 
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Box-Cox Linearity Plot',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'# observations x',header=TRUE)
> a<-table.element(a,n)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'maximum correlation',header=TRUE)
> a<-table.element(a,mx)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'optimal lambda(x)',header=TRUE)
> a<-table.element(a,mxli)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Residual SD (orginial)',header=TRUE)
> a<-table.element(a,se)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Residual SD (transformed)',header=TRUE)
> a<-table.element(a,se1)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4iapw1199649132.tab") 
> 
> system("convert tmp/1zuu41199649132.ps tmp/1zuu41199649132.png")
> system("convert tmp/24vdv1199649132.ps tmp/24vdv1199649132.png")
> system("convert tmp/3m2gq1199649132.ps tmp/3m2gq1199649132.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.011   0.496   1.144