R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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. 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,1.07 + ,1.071 + ,1.068 + ,1.064 + ,1.067) > par1 = '12' > par1 <- '12' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P. (2012), Standard Deviation-Mean Plot (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_smp.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > par1 <- as.numeric(par1) > (n <- length(x)) [1] 1461 > (np <- floor(n / par1)) [1] 121 > arr <- array(NA,dim=c(par1,np)) > j <- 0 > k <- 1 > for (i in 1:(np*par1)) + { + j = j + 1 + arr[j,k] <- x[i] + if (j == par1) { + j = 0 + k=k+1 + } + } > arr [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 1.386 1.440 1.422 1.415 1.442 1.446 1.454 1.463 1.458 1.456 1.444 1.425 [2,] 1.388 1.437 1.417 1.420 1.442 1.447 1.455 1.464 1.456 1.451 1.446 1.425 [3,] 1.391 1.437 1.416 1.425 1.444 1.446 1.459 1.464 1.456 1.449 1.446 1.426 [4,] 1.394 1.439 1.414 1.425 1.444 1.445 1.465 1.464 1.456 1.445 1.445 1.426 [5,] 1.402 1.439 1.414 1.425 1.445 1.447 1.463 1.461 1.457 1.445 1.446 1.426 [6,] 1.414 1.438 1.415 1.424 1.448 1.447 1.463 1.462 1.457 1.447 1.442 1.424 [7,] 1.419 1.437 1.414 1.426 1.447 1.447 1.465 1.458 1.455 1.446 1.442 1.425 [8,] 1.419 1.437 1.414 1.428 1.445 1.445 1.467 1.458 1.456 1.446 1.443 1.423 [9,] 1.420 1.430 1.415 1.429 1.445 1.448 1.466 1.459 1.455 1.444 1.439 1.423 [10,] 1.424 1.430 1.415 1.433 1.447 1.450 1.466 1.459 1.455 1.446 1.434 1.425 [11,] 1.431 1.432 1.414 1.435 1.447 1.449 1.466 1.458 1.457 1.445 1.429 1.423 [12,] 1.438 1.427 1.414 1.440 1.447 1.452 1.462 1.457 1.457 1.445 1.427 1.422 [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [1,] 1.418 1.403 1.375 1.367 1.394 1.437 1.435 1.459 1.476 1.478 1.472 1.486 [2,] 1.417 1.400 1.377 1.365 1.396 1.437 1.435 1.466 1.476 1.479 1.471 1.484 [3,] 1.415 1.397 1.376 1.366 1.397 1.437 1.437 1.469 1.476 1.477 1.471 1.485 [4,] 1.415 1.397 1.374 1.367 1.402 1.436 1.437 1.469 1.476 1.476 1.472 1.484 [5,] 1.415 1.391 1.371 1.367 1.402 1.438 1.440 1.470 1.475 1.475 1.472 1.484 [6,] 1.417 1.391 1.369 1.371 1.404 1.436 1.445 1.474 1.474 1.476 1.473 1.486 [7,] 1.416 1.392 1.366 1.375 1.408 1.436 1.451 1.475 1.476 1.473 1.469 1.486 [8,] 1.416 1.388 1.367 1.380 1.415 1.437 1.450 1.475 1.476 1.473 1.468 1.486 [9,] 1.416 1.386 1.367 1.385 1.419 1.437 1.450 1.475 1.477 1.475 1.467 1.493 [10,] 1.407 1.384 1.367 1.385 1.429 1.437 1.452 1.473 1.476 1.474 1.467 1.496 [11,] 1.408 1.381 1.367 1.387 1.434 1.436 1.454 1.473 1.478 1.474 1.483 1.498 [12,] 1.409 1.376 1.367 1.391 1.435 1.437 1.457 1.475 1.478 1.472 1.487 1.499 [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [1,] 1.500 1.484 1.480 1.475 1.468 1.447 1.434 1.444 1.451 1.457 1.469 1.480 [2,] 1.501 1.485 1.479 1.474 1.470 1.443 1.433 1.445 1.452 1.461 1.474 1.482 [3,] 1.502 1.486 1.475 1.472 1.468 1.443 1.434 1.449 1.455 1.460 1.473 1.481 [4,] 1.500 1.486 1.475 1.474 1.466 1.445 1.433 1.449 1.454 1.460 1.478 1.479 [5,] 1.497 1.485 1.477 1.472 1.463 1.445 1.433 1.453 1.455 1.462 1.477 1.474 [6,] 1.494 1.483 1.475 1.472 1.460 1.442 1.435 1.452 1.453 1.463 1.478 1.475 [7,] 1.494 1.485 1.474 1.474 1.452 1.437 1.441 1.452 1.457 1.462 1.480 1.468 [8,] 1.496 1.481 1.472 1.474 1.452 1.437 1.443 1.455 1.456 1.462 1.482 1.467 [9,] 1.493 1.482 1.473 1.473 1.454 1.434 1.442 1.454 1.456 1.464 1.482 1.468 [10,] 1.492 1.483 1.471 1.471 1.449 1.434 1.444 1.454 1.458 1.464 1.480 1.466 [11,] 1.489 1.483 1.471 1.472 1.446 1.436 1.443 1.453 1.459 1.464 1.482 1.464 [12,] 1.489 1.483 1.473 1.468 1.445 1.434 1.443 1.453 1.458 1.466 1.480 1.462 [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [1,] 1.464 1.443 1.436 1.422 1.389 1.384 1.397 1.401 1.392 1.396 1.407 1.420 [2,] 1.460 1.437 1.438 1.422 1.391 1.383 1.396 1.398 1.390 1.397 1.408 1.427 [3,] 1.460 1.435 1.442 1.424 1.390 1.381 1.398 1.399 1.391 1.396 1.411 1.426 [4,] 1.461 1.432 1.440 1.418 1.391 1.384 1.399 1.401 1.390 1.395 1.411 1.429 [5,] 1.458 1.430 1.442 1.409 1.393 1.382 1.398 1.399 1.391 1.396 1.412 1.429 [6,] 1.455 1.434 1.438 1.402 1.391 1.382 1.400 1.395 1.393 1.395 1.416 1.429 [7,] 1.453 1.434 1.437 1.395 1.392 1.384 1.396 1.392 1.394 1.396 1.419 1.429 [8,] 1.450 1.433 1.440 1.391 1.387 1.384 1.397 1.393 1.393 1.397 1.418 1.427 [9,] 1.444 1.432 1.439 1.391 1.384 1.384 1.399 1.390 1.392 1.399 1.419 1.429 [10,] 1.444 1.434 1.439 1.392 1.381 1.382 1.398 1.390 1.396 1.396 1.421 1.426 [11,] 1.446 1.435 1.437 1.392 1.382 1.389 1.399 1.391 1.394 1.398 1.421 1.428 [12,] 1.444 1.436 1.433 1.391 1.383 1.394 1.399 1.392 1.394 1.404 1.421 1.428 [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [1,] 1.429 1.418 1.430 1.444 1.444 1.412 1.411 1.407 1.388 1.381 1.402 1.408 [2,] 1.428 1.419 1.427 1.442 1.443 1.413 1.413 1.407 1.390 1.379 1.403 1.408 [3,] 1.427 1.419 1.428 1.444 1.442 1.414 1.411 1.404 1.389 1.380 1.408 1.407 [4,] 1.429 1.419 1.430 1.443 1.439 1.413 1.413 1.401 1.386 1.382 1.405 1.405 [5,] 1.427 1.417 1.429 1.444 1.441 1.412 1.414 1.392 1.382 1.384 1.406 1.407 [6,] 1.428 1.418 1.433 1.446 1.435 1.411 1.410 1.393 1.380 1.384 1.408 1.403 [7,] 1.430 1.417 1.437 1.445 1.436 1.414 1.409 1.394 1.376 1.383 1.408 1.404 [8,] 1.430 1.419 1.442 1.444 1.438 1.410 1.407 1.392 1.378 1.391 1.407 1.405 [9,] 1.429 1.421 1.441 1.442 1.434 1.411 1.408 1.391 1.379 1.392 1.405 1.404 [10,] 1.426 1.424 1.443 1.444 1.426 1.413 1.405 1.388 1.379 1.394 1.409 1.399 [11,] 1.421 1.429 1.444 1.442 1.421 1.413 1.405 1.390 1.378 1.396 1.406 1.396 [12,] 1.418 1.427 1.445 1.442 1.418 1.412 1.407 1.386 1.378 1.398 1.407 1.398 [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [1,] 1.399 1.438 1.434 1.426 1.428 1.439 1.437 1.409 1.407 1.419 1.428 1.428 [2,] 1.400 1.441 1.431 1.427 1.426 1.438 1.438 1.409 1.410 1.419 1.429 1.424 [3,] 1.401 1.438 1.426 1.429 1.428 1.442 1.434 1.411 1.410 1.417 1.430 1.424 [4,] 1.402 1.439 1.426 1.427 1.429 1.441 1.429 1.410 1.411 1.419 1.429 1.425 [5,] 1.402 1.440 1.423 1.425 1.434 1.441 1.430 1.406 1.412 1.417 1.428 1.422 [6,] 1.400 1.440 1.425 1.427 1.434 1.442 1.424 1.400 1.418 1.418 1.431 1.420 [7,] 1.404 1.437 1.426 1.425 1.435 1.442 1.424 1.400 1.419 1.420 1.429 1.418 [8,] 1.403 1.434 1.427 1.426 1.439 1.440 1.425 1.400 1.416 1.421 1.430 1.420 [9,] 1.404 1.437 1.424 1.428 1.437 1.438 1.423 1.401 1.419 1.421 1.431 1.418 [10,] 1.404 1.435 1.422 1.427 1.438 1.439 1.420 1.402 1.417 1.425 1.431 1.419 [11,] 1.404 1.435 1.427 1.427 1.439 1.435 1.415 1.407 1.418 1.429 1.429 1.421 [12,] 1.440 1.437 1.424 1.425 1.439 1.435 1.413 1.408 1.420 1.428 1.428 1.421 [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [1,] 1.421 1.430 1.419 1.434 1.449 1.421 1.419 1.421 1.419 1.421 1.422 1.409 [2,] 1.423 1.430 1.420 1.442 1.444 1.422 1.417 1.418 1.415 1.421 1.419 1.407 [3,] 1.428 1.429 1.421 1.445 1.439 1.420 1.418 1.420 1.412 1.424 1.415 1.404 [4,] 1.427 1.427 1.421 1.447 1.439 1.420 1.420 1.419 1.408 1.425 1.415 1.405 [5,] 1.428 1.430 1.421 1.448 1.432 1.419 1.419 1.421 1.410 1.426 1.416 1.406 [6,] 1.430 1.428 1.420 1.448 1.433 1.420 1.422 1.421 1.409 1.422 1.413 1.406 [7,] 1.431 1.429 1.423 1.448 1.435 1.414 1.425 1.421 1.409 1.424 1.409 1.408 [8,] 1.429 1.430 1.427 1.447 1.433 1.415 1.427 1.419 1.411 1.427 1.408 1.408 [9,] 1.428 1.431 1.428 1.449 1.427 1.416 1.425 1.417 1.411 1.426 1.409 1.412 [10,] 1.430 1.427 1.430 1.447 1.424 1.416 1.426 1.419 1.414 1.428 1.408 1.414 [11,] 1.428 1.424 1.430 1.448 1.424 1.418 1.427 1.418 1.413 1.427 1.407 1.411 [12,] 1.429 1.422 1.429 1.449 1.420 1.418 1.425 1.418 1.421 1.427 1.410 1.406 [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [1,] 1.406 1.355 1.357 1.367 1.352 1.313 1.261 1.222 1.205 1.186 1.194 1.267 [2,] 1.409 1.351 1.355 1.372 1.352 1.313 1.252 1.223 1.198 1.185 1.201 1.268 [3,] 1.405 1.350 1.358 1.373 1.354 1.304 1.240 1.217 1.200 1.186 1.210 1.271 [4,] 1.400 1.352 1.357 1.371 1.347 1.304 1.236 1.212 1.201 1.184 1.219 1.276 [5,] 1.393 1.350 1.358 1.374 1.344 1.305 1.228 1.207 1.197 1.184 1.222 1.279 [6,] 1.387 1.349 1.361 1.365 1.342 1.299 1.228 1.208 1.191 1.187 1.225 1.280 [7,] 1.378 1.351 1.365 1.365 1.342 1.291 1.229 1.206 1.186 1.188 1.225 1.282 [8,] 1.378 1.356 1.366 1.368 1.330 1.283 1.227 1.206 1.187 1.189 1.229 1.280 [9,] 1.378 1.355 1.364 1.362 1.330 1.280 1.225 1.208 1.184 1.188 1.237 1.280 [10,] 1.374 1.355 1.366 1.360 1.332 1.270 1.221 1.207 1.185 1.190 1.249 1.282 [11,] 1.370 1.357 1.364 1.356 1.323 1.270 1.223 1.206 1.187 1.190 1.255 1.283 [12,] 1.361 1.357 1.364 1.357 1.319 1.271 1.222 1.203 1.187 1.192 1.260 1.283 [,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [1,] 1.282 1.294 1.278 1.270 1.269 1.309 1.312 1.308 1.309 1.302 1.295 [2,] 1.289 1.294 1.280 1.268 1.272 1.312 1.312 1.309 1.310 1.304 1.293 [3,] 1.291 1.292 1.279 1.268 1.282 1.312 1.313 1.308 1.310 1.305 1.293 [4,] 1.291 1.293 1.280 1.269 1.288 1.312 1.310 1.309 1.309 1.305 1.295 [5,] 1.293 1.292 1.277 1.270 1.289 1.310 1.309 1.309 1.303 1.304 1.295 [6,] 1.293 1.291 1.278 1.269 1.291 1.306 1.309 1.310 1.302 1.305 1.295 [7,] 1.293 1.293 1.276 1.268 1.294 1.306 1.311 1.313 1.304 1.299 1.293 [8,] 1.291 1.292 1.277 1.268 1.299 1.307 1.310 1.312 1.302 1.299 1.294 [9,] 1.293 1.287 1.278 1.261 1.300 1.306 1.310 1.310 1.301 1.301 1.294 [10,] 1.292 1.283 1.277 1.262 1.304 1.307 1.312 1.306 1.302 1.297 1.293 [11,] 1.292 1.284 1.275 1.263 1.310 1.312 1.313 1.306 1.302 1.296 1.295 [12,] 1.294 1.278 1.273 1.264 1.310 1.313 1.313 1.304 1.301 1.295 1.294 [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [1,] 1.289 1.255 1.218 1.194 1.172 1.188 1.199 1.182 1.200 1.170 [2,] 1.285 1.251 1.219 1.194 1.163 1.190 1.193 1.181 1.201 1.170 [3,] 1.284 1.245 1.220 1.193 1.157 1.196 1.194 1.184 1.199 1.172 [4,] 1.280 1.242 1.218 1.193 1.156 1.197 1.196 1.181 1.195 1.172 [5,] 1.280 1.242 1.215 1.194 1.155 1.199 1.194 1.181 1.193 1.170 [6,] 1.281 1.239 1.210 1.194 1.155 1.200 1.193 1.185 1.187 1.169 [7,] 1.280 1.240 1.209 1.194 1.157 1.199 1.193 1.184 1.188 1.175 [8,] 1.275 1.239 1.203 1.192 1.165 1.199 1.189 1.187 1.190 1.173 [9,] 1.267 1.233 1.203 1.190 1.174 1.201 1.185 1.190 1.186 1.173 [10,] 1.262 1.225 1.204 1.181 1.175 1.200 1.184 1.193 1.179 1.175 [11,] 1.254 1.220 1.201 1.181 1.181 1.198 1.186 1.196 1.172 1.180 [12,] 1.254 1.220 1.197 1.181 1.186 1.198 1.185 1.197 1.172 1.187 [,118] [,119] [,120] [,121] [1,] 1.196 1.199 1.180 1.139 [2,] 1.199 1.195 1.182 1.129 [3,] 1.198 1.191 1.177 1.129 [4,] 1.198 1.191 1.172 1.131 [5,] 1.202 1.186 1.168 1.122 [6,] 1.206 1.186 1.162 1.113 [7,] 1.207 1.188 1.157 1.107 [8,] 1.208 1.184 1.157 1.101 [9,] 1.208 1.181 1.159 1.094 [10,] 1.203 1.181 1.158 1.094 [11,] 1.204 1.182 1.151 1.096 [12,] 1.206 1.180 1.143 1.087 > arr.mean <- array(NA,dim=np) > arr.sd <- array(NA,dim=np) > arr.range <- array(NA,dim=np) > for (j in 1:np) + { + arr.mean[j] <- mean(arr[,j],na.rm=TRUE) + arr.sd[j] <- sd(arr[,j],na.rm=TRUE) + arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE) + } > arr.mean [1] 1.410500 1.435250 1.415333 1.427083 1.445250 1.447417 1.462583 1.460583 [9] 1.456250 1.447083 1.440250 1.424417 1.414083 1.390500 1.370250 1.375500 [17] 1.411250 1.436750 1.445250 1.471083 1.476167 1.475167 1.472667 1.488917 [25] 1.495583 1.483833 1.474583 1.472583 1.457750 1.439750 1.438167 1.451083 [33] 1.455333 1.462083 1.477917 1.472167 1.453250 1.434583 1.438417 1.404083 [41] 1.387833 1.384417 1.398000 1.395083 1.392500 1.397083 1.415333 1.427250 [49] 1.426833 1.420583 1.435750 1.443500 1.434750 1.412333 1.409417 1.395417 [57] 1.381917 1.387000 1.406167 1.403667 1.405250 1.437583 1.426250 1.426583 [65] 1.433833 1.439333 1.426000 1.405250 1.414750 1.421083 1.429417 1.421667 [73] 1.427667 1.428083 1.424083 1.446000 1.433250 1.418250 1.422500 1.419333 [81] 1.412667 1.424833 1.412583 1.408000 1.386583 1.353167 1.361250 1.365833 [89] 1.338917 1.291917 1.232667 1.210417 1.192333 1.187417 1.227167 1.277583 [97] 1.291167 1.289417 1.277333 1.266667 1.292333 1.309333 1.311167 1.308667 [105] 1.304583 1.301000 1.294083 1.274250 1.237583 1.209750 1.190083 1.166333 [113] 1.197083 1.190917 1.186750 1.188500 1.173833 1.202917 1.187000 1.163833 [121] 1.111833 > arr.sd [1] 0.0176763838 0.0043090813 0.0023094011 0.0066668561 0.0020056738 [6] 0.0020652243 0.0043788403 0.0027122059 0.0009653073 0.0034234043 [11] 0.0066895441 0.0013789544 0.0038009170 0.0079943162 0.0041368631 [16] 0.0095869229 0.0148882199 0.0006215816 0.0080467385 0.0048515852 [21] 0.0011146409 0.0021248886 0.0061693278 0.0058225008 0.0045016832 [26] 0.0015859229 0.0029063671 0.0018809250 0.0091365898 0.0048827153 [31] 0.0047831776 0.0035791907 0.0024984844 0.0023915888 0.0040778411 [36] 0.0071583814 0.0074605021 0.0032601822 0.0025390884 0.0139769832 [41] 0.0042604595 0.0036296339 0.0012792043 0.0042737749 0.0018340219 [46] 0.0024664414 0.0052454887 0.0025628464 0.0036886394 0.0039648073 [51] 0.0069429493 0.0013142575 0.0086458082 0.0012309149 0.0030883456 [56] 0.0073664884 0.0049627400 0.0067419986 0.0021248886 0.0040075686 [61] 0.0110874622 0.0021933094 0.0033608711 0.0012401124 0.0048772819 [66] 0.0024984844 0.0079658361 0.0043301270 0.0044543135 0.0040778411 [71] 0.0011645002 0.0030550505 0.0029024546 0.0027122059 0.0043371196 [76] 0.0042426407 0.0086352870 0.0025271256 0.0037294894 0.0014354811 [81] 0.0040301891 0.0024432963 0.0048515852 0.0030151134 0.0158082736 [86] 0.0029490625 0.0040254870 0.0061323781 0.0118203087 0.0165774125 [91] 0.0125505040 0.0066532061 0.0073772788 0.0025390884 0.0204754102 [96] 0.0057754706 0.0031861442 0.0051954234 0.0020150946 0.0032286595 [101] 0.0133575538 0.0028069179 0.0015275252 0.0025346089 0.0037284736 [106] 0.0037172815 0.0009003366 0.0120463121 0.0112205844 0.0080805603 [111] 0.0055996483 0.0109489172 0.0040330078 0.0049443877 0.0058794712 [116] 0.0100317677 0.0051316014 0.0042737749 0.0060151324 0.0120667336 [121] 0.0176935188 > arr.range [1] 0.052 0.013 0.008 0.025 0.006 0.007 0.013 0.007 0.003 0.012 0.019 0.004 [13] 0.011 0.027 0.011 0.026 0.041 0.002 0.022 0.016 0.004 0.007 0.020 0.015 [25] 0.013 0.005 0.009 0.007 0.025 0.013 0.011 0.011 0.008 0.009 0.013 0.020 [37] 0.020 0.013 0.009 0.033 0.012 0.013 0.004 0.011 0.006 0.009 0.014 0.009 [49] 0.012 0.012 0.018 0.004 0.026 0.004 0.009 0.021 0.014 0.019 0.007 0.012 [61] 0.041 0.007 0.012 0.004 0.013 0.007 0.025 0.011 0.013 0.012 0.003 0.010 [73] 0.010 0.009 0.011 0.015 0.029 0.008 0.010 0.004 0.013 0.007 0.015 0.010 [85] 0.048 0.008 0.011 0.018 0.035 0.043 0.040 0.020 0.021 0.008 0.066 0.016 [97] 0.012 0.016 0.007 0.009 0.041 0.007 0.004 0.009 0.009 0.010 0.002 0.035 [109] 0.035 0.023 0.013 0.031 0.013 0.015 0.016 0.029 0.018 0.012 0.019 0.039 [121] 0.052 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean 0.02773 -0.01626 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -4.317 -3.662 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean 0.08205 -0.04801 > postscript(file="/var/wessaorg/rcomp/tmp/1doue1483867926.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2rzn51483867926.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range') > dev.off() null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Section',header=TRUE) > a<-table.element(a,'Mean',header=TRUE) > a<-table.element(a,'Standard Deviation',header=TRUE) > a<-table.element(a,'Range',header=TRUE) > a<-table.row.end(a) > for (j in 1:np) { + a<-table.row.start(a) + a<-table.element(a,j,header=TRUE) + a<-table.element(a,arr.mean[j]) + a<-table.element(a,arr.sd[j] ) + a<-table.element(a,arr.range[j] ) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/3nec71483867926.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'alpha',header=TRUE) > a<-table.element(a,lm1$coefficients[[1]]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'beta',header=TRUE) > a<-table.element(a,lm1$coefficients[[2]]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,summary(lm1)$coefficients[2,2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'T-STAT',header=TRUE) > a<-table.element(a,summary(lm1)$coefficients[2,3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'p-value',header=TRUE) > a<-table.element(a,summary(lm1)$coefficients[2,4]) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/4ssad1483867926.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'alpha',header=TRUE) > a<-table.element(a,lnlm1$coefficients[[1]]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'beta',header=TRUE) > a<-table.element(a,lnlm1$coefficients[[2]]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,summary(lnlm1)$coefficients[2,2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'T-STAT',header=TRUE) > a<-table.element(a,summary(lnlm1)$coefficients[2,3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'p-value',header=TRUE) > a<-table.element(a,summary(lnlm1)$coefficients[2,4]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Lambda',header=TRUE) > a<-table.element(a,1-lnlm1$coefficients[[2]]) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/5hn6m1483867926.tab") > > try(system("convert tmp/1doue1483867926.ps tmp/1doue1483867926.png",intern=TRUE)) character(0) > try(system("convert tmp/2rzn51483867926.ps tmp/2rzn51483867926.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.549 0.099 1.659