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,14.47 + ,1 + ,14.45 + ,1 + ,14.46 + ,3 + ,14.45 + ,5 + ,14.45 + ,5 + ,14.43 + ,2 + ,14.68 + ,0 + ,14.47 + ,0 + ,14.74 + ,0 + ,14.75 + ,0 + ,14.81 + ,0 + ,14.54 + ,0 + ,14.51 + ,0 + ,14.43 + ,1 + ,14.53 + ,3 + ,14.43 + ,8 + ,14.46 + ,0 + ,14.48 + ,0 + ,14.51 + ,0 + ,14.33) + ,dim=c(2 + ,660) + ,dimnames=list(c('Orkanen' + ,'Temperatuur') + ,1:660)) > y <- array(NA,dim=c(2,660),dimnames=list(c('Orkanen','Temperatuur'),1:660)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par5 = '10' > par4 = '10' > par3 = 'Seasonal Differences (s=12)' > par2 = 'Include Monthly Dummies' > par1 = '1' > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > mywarning <- '' > par1 <- as.numeric(par1) > if(is.na(par1)) { + par1 <- 1 + mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.' + } > if (par4=='') par4 <- 0 > par4 <- as.numeric(par4) > if (par5=='') par5 <- 0 > par5 <- as.numeric(par5) > x <- na.omit(t(y)) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + (n <- n -1) + x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par3 == 'Seasonal Differences (s=12)'){ + (n <- n - 12) + x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) + for (i in 1:n) { + for (j in 1:k) { + x2[i,j] <- x[i+12,j] - x[i,j] + } + } + x <- x2 + } > if (par3 == 'First and Seasonal Differences (s=12)'){ + (n <- n -1) + x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + (n <- n - 12) + x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) + for (i in 1:n) { + for (j in 1:k) { + x2[i,j] <- x[i+12,j] - x[i,j] + } + } + x <- x2 + } > if(par4 > 0) { + x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep=''))) + for (i in 1:(n-par4)) { + for (j in 1:par4) { + x2[i,j] <- x[i+par4-j,par1] + } + } + x <- cbind(x[(par4+1):n,], x2) + n <- n - par4 + } > if(par5 > 0) { + x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) + for (i in 1:(n-par5*12)) { + for (j in 1:par5) { + x2[i,j] <- x[i+par5*12-j*12,par1] + } + } + x <- cbind(x[(par5*12+1):n,], x2) + n <- n - par5*12 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > (k <- length(x[n,])) [1] 33 > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x (1-B12)Orkanen (1-B12)Temperatuur (1-B12)Orkanen(t-1) (1-B12)Orkanen(t-2) 131 0 -0.14 1 -1 132 0 -0.07 0 1 133 0 -0.10 0 0 134 0 0.07 0 0 135 0 -0.51 0 0 136 0 -0.25 0 0 137 -1 -0.14 0 0 138 -1 -0.04 -1 0 139 1 -0.05 -1 -1 140 1 0.01 1 -1 141 -1 0.06 1 1 142 -2 0.06 -1 1 143 0 0.01 -2 -1 144 0 0.14 0 -2 145 0 0.05 0 0 146 0 0.01 0 0 147 0 0.43 0 0 148 0 0.19 0 0 149 0 0.22 0 0 150 -1 0.10 0 0 151 -1 -0.03 -1 0 152 -2 0.02 -1 -1 153 4 -0.03 -2 -1 154 2 0.06 4 -2 155 3 0.12 2 4 156 1 -0.15 3 2 157 0 0.05 1 3 158 0 0.01 0 1 159 0 0.22 0 0 160 0 0.12 0 0 161 0 -0.06 0 0 162 0 -0.09 0 0 163 -1 0.11 0 0 164 1 -0.05 -1 0 165 -6 0.07 1 -1 166 2 -0.07 -6 1 167 1 -0.02 2 -6 168 0 0.02 1 2 169 0 -0.05 0 1 170 0 -0.11 0 0 171 0 -0.27 0 0 172 0 -0.11 0 0 173 0 0.14 0 0 174 0 0.13 0 0 175 1 -0.04 0 0 176 1 0.07 1 0 177 1 -0.02 1 1 178 -3 -0.01 1 1 179 -1 -0.02 -3 1 180 1 -0.06 -1 -3 181 0 -0.14 1 -1 182 0 -0.07 0 1 183 0 -0.10 0 0 184 0 0.07 0 0 185 0 -0.51 0 0 186 0 -0.25 0 0 187 -1 -0.14 0 0 188 -1 -0.04 -1 0 189 1 -0.05 -1 -1 190 1 0.01 1 -1 191 -1 0.06 1 1 192 -2 0.06 -1 1 193 0 0.01 -2 -1 194 0 0.14 0 -2 195 0 0.05 0 0 196 0 0.01 0 0 197 0 0.43 0 0 198 0 0.19 0 0 199 0 0.22 0 0 200 -1 0.10 0 0 201 -1 -0.03 -1 0 202 -2 0.02 -1 -1 203 4 -0.03 -2 -1 204 2 -0.01 4 -2 205 2 0.11 2 4 206 0 -0.27 2 2 207 0 0.03 0 2 208 0 -0.04 0 0 209 0 -0.07 0 0 210 0 -0.03 0 0 211 0 -0.21 0 0 212 0 -0.12 0 0 213 -1 -0.01 0 0 214 2 -0.06 -1 0 215 -4 -0.04 2 -1 216 -1 -0.01 -4 2 217 -2 0.02 -1 -4 218 0 0.12 -2 -1 219 0 -0.12 0 -2 220 0 0.03 0 0 221 0 -0.12 0 0 222 0 -0.10 0 0 223 0 0.08 0 0 224 0 0.03 0 0 225 1 0.13 0 0 226 -1 0.23 1 0 227 3 0.21 -1 1 228 1 0.13 3 -1 229 0 0.11 1 3 230 0 0.01 0 1 231 0 0.05 0 0 232 0 -0.28 0 0 233 0 -0.16 0 0 234 0 -0.20 0 0 235 0 -0.20 0 0 236 1 -0.08 0 0 237 0 -0.23 1 0 238 0 -0.12 0 1 239 -5 -0.22 0 0 240 -2 -0.25 -5 0 241 0 -0.29 -2 -5 242 0 -0.14 0 -2 243 0 0.01 0 0 244 0 0.28 0 0 245 0 0.35 0 0 246 0 0.32 0 0 247 1 0.20 0 0 248 4 0.10 1 0 249 5 0.17 4 1 250 4 0.01 5 4 251 1 0.18 4 5 252 0 0.33 1 4 253 0 0.23 0 1 254 0 0.36 0 0 255 0 0.00 0 0 256 0 -0.12 0 0 257 1 -0.17 0 0 258 0 -0.09 1 0 259 1 -0.05 0 1 260 -3 -0.13 1 0 261 -3 -0.01 -3 1 262 -3 -0.13 -3 -3 263 -1 -0.17 -3 -3 264 0 -0.37 -1 -3 265 0 -0.13 0 -1 266 0 -0.45 0 0 267 0 -0.23 0 0 268 0 -0.18 0 0 269 -1 -0.11 0 0 270 0 -0.23 -1 0 271 -1 -0.09 0 -1 272 2 0.00 -1 0 273 3 -0.11 2 -1 274 -1 -0.06 3 2 275 1 -0.04 -1 3 276 0 0.05 1 -1 277 0 -0.22 0 1 278 0 -0.01 0 0 279 0 0.19 0 0 280 0 0.10 0 0 281 1 0.11 0 0 282 1 0.26 1 0 283 -1 0.12 1 1 284 -2 0.20 -1 1 285 -4 0.06 -2 -1 286 -1 0.15 -4 -2 287 0 0.08 -1 -4 288 0 0.27 0 -1 289 0 0.50 0 0 290 0 0.52 0 0 291 0 0.27 0 0 292 0 0.21 0 0 293 -1 0.19 0 0 294 -1 0.10 -1 0 295 2 0.07 -1 -1 296 0 -0.13 2 -1 297 0 0.03 0 2 298 2 0.01 0 0 299 -1 -0.04 2 0 300 0 -0.24 -1 2 301 0 -0.43 0 -1 302 0 -0.56 0 0 303 0 -0.34 0 0 304 0 -0.29 0 0 305 0 -0.27 0 0 306 1 -0.24 0 0 307 -1 -0.13 1 0 308 2 0.02 -1 1 309 2 -0.15 2 -1 310 -1 -0.19 2 2 311 0 -0.11 -1 2 312 0 -0.07 0 -1 313 0 0.20 0 0 314 0 0.21 0 0 315 0 0.14 0 0 316 0 0.07 0 0 317 0 0.17 0 0 318 0 0.05 0 0 319 0 0.00 0 0 320 -2 -0.18 0 0 321 -1 0.05 -2 0 322 0 -0.01 -1 -2 323 0 -0.06 0 -1 324 1 -0.07 0 0 325 0 -0.11 1 0 326 0 -0.06 0 1 327 0 -0.33 0 0 328 0 -0.11 0 0 329 1 -0.32 0 0 330 -1 -0.09 1 0 331 0 -0.05 -1 1 332 4 0.01 0 -1 333 -1 -0.04 4 0 334 0 -0.13 -1 4 335 0 0.04 0 -1 336 -1 0.17 0 0 337 0 0.05 -1 0 338 0 0.24 0 -1 339 0 0.45 0 0 340 0 0.30 0 0 341 -1 0.44 0 0 342 0 0.31 -1 0 343 -1 0.25 0 -1 344 -5 0.25 -1 0 345 1 0.14 -5 -1 346 1 0.26 1 -5 347 0 0.28 1 1 348 0 0.06 0 1 349 1 0.07 0 0 350 0 -0.09 1 0 351 0 -0.06 0 1 352 0 -0.08 0 0 353 0 -0.22 0 0 354 0 -0.23 0 0 355 1 -0.11 0 0 356 3 -0.24 1 0 357 0 -0.01 3 1 358 1 -0.02 0 3 359 0 -0.07 1 0 360 0 -0.02 0 1 361 -1 0.00 0 0 362 0 -0.15 -1 0 363 0 -0.03 0 -1 364 0 -0.05 0 0 365 0 -0.01 0 0 366 1 0.14 0 0 367 1 -0.05 1 0 368 -1 0.25 1 1 369 -1 0.13 -1 1 370 -2 0.19 -1 -1 371 0 0.10 -2 -1 372 0 0.37 0 -2 373 0 0.14 0 0 374 0 0.32 0 0 375 0 0.04 0 0 376 0 0.16 0 0 377 0 0.28 0 0 378 -1 0.06 0 0 379 -1 0.16 -1 0 380 -1 0.02 -1 -1 381 3 -0.06 -1 -1 382 0 -0.09 3 -1 383 2 0.05 0 3 384 0 -0.27 2 0 385 0 0.22 0 2 386 0 0.08 0 0 387 0 0.24 0 0 388 0 0.04 0 0 389 1 -0.11 0 0 390 1 0.03 1 0 391 -1 0.04 1 1 392 1 0.11 -1 1 393 -1 0.01 1 -1 394 0 -0.02 -1 1 395 0 -0.08 0 -1 396 0 0.17 0 0 397 0 -0.37 0 0 398 0 -0.24 0 0 399 0 -0.43 0 0 400 0 -0.19 0 0 401 -1 -0.02 0 0 402 1 -0.17 -1 0 403 0 -0.07 1 -1 404 -2 -0.25 0 1 405 -1 -0.07 -2 0 406 -1 -0.02 -1 -2 407 -2 -0.09 -1 -1 408 0 0.05 -2 -1 409 0 0.42 0 -2 410 0 0.33 0 0 411 0 0.45 0 0 412 0 0.18 0 0 413 0 0.13 0 0 414 -2 0.15 0 0 415 0 0.00 -2 0 416 1 0.27 0 -2 417 -1 0.24 1 0 418 0 0.07 -1 1 419 0 0.23 0 -1 420 0 -0.21 0 0 421 0 -0.23 0 0 422 0 -0.30 0 0 423 0 -0.22 0 0 424 0 -0.22 0 0 425 0 -0.03 0 0 426 0 -0.17 0 0 427 0 0.00 0 0 428 2 -0.17 0 0 429 4 -0.17 2 0 430 1 -0.07 4 2 431 1 -0.33 1 4 432 1 -0.31 1 1 433 0 -0.13 1 1 434 0 -0.22 0 1 435 0 -0.07 0 0 436 0 0.07 0 0 437 0 -0.13 0 0 438 0 0.10 0 0 439 2 -0.17 0 0 440 -1 -0.01 2 0 441 -3 -0.11 -1 2 442 1 0.00 -3 -1 443 0 0.02 1 -3 444 -1 0.23 0 1 445 0 0.15 -1 0 446 0 0.41 0 -1 447 0 0.13 0 0 448 0 0.08 0 0 449 0 0.02 0 0 450 2 -0.01 0 0 451 -2 0.12 2 0 452 -2 0.00 -2 2 453 -1 0.02 -2 -2 454 -2 0.03 -1 -2 455 0 0.03 -2 -1 456 0 0.04 0 -2 457 0 -0.01 0 0 458 0 0.11 0 0 459 0 -0.10 0 0 460 0 0.05 0 0 461 0 0.10 0 0 462 -2 0.19 0 0 463 0 0.29 -2 0 464 2 0.10 0 -2 465 1 0.32 2 0 466 1 0.18 1 2 467 -1 0.21 1 1 468 0 0.38 -1 1 469 0 0.28 0 -1 470 0 -0.07 0 0 471 0 0.31 0 0 472 0 0.14 0 0 473 0 0.09 0 0 474 0 0.06 0 0 475 0 -0.12 0 0 476 1 0.09 0 0 477 3 -0.08 1 0 478 0 0.00 3 1 479 1 -0.17 0 3 480 0 -0.24 1 0 481 0 -0.40 0 1 482 0 -0.07 0 0 483 0 -0.18 0 0 484 0 -0.19 0 0 485 0 -0.23 0 0 486 1 -0.26 0 0 487 3 -0.02 1 0 488 -1 -0.03 3 1 489 -4 -0.01 -1 3 490 0 -0.03 -4 -1 491 0 0.09 0 -4 492 0 0.07 0 0 493 0 0.22 0 0 494 0 0.09 0 0 495 0 0.39 0 0 496 0 0.27 0 0 497 0 0.25 0 0 498 -1 0.25 0 0 499 0 0.12 -1 0 500 2 0.04 0 -1 501 0 -0.02 2 0 502 3 0.17 0 2 503 -1 0.31 3 0 504 0 0.10 -1 3 505 0 0.02 0 -1 506 0 0.09 0 0 507 0 -0.36 0 0 508 0 -0.02 0 0 509 0 -0.01 0 0 510 1 0.11 0 0 511 -3 0.09 1 0 512 -4 0.02 -3 1 513 1 0.13 -4 -3 514 -1 -0.18 1 -4 515 0 -0.29 -1 1 516 0 -0.19 0 -1 517 0 0.03 0 0 518 0 -0.06 0 0 519 0 0.05 0 0 520 1 -0.22 0 0 521 0 -0.09 1 0 522 -1 -0.27 0 1 523 0 -0.43 -1 0 524 0 -0.29 0 -1 525 1 -0.42 0 0 526 -2 -0.22 1 0 527 0 -0.23 -2 1 528 0 -0.07 0 -2 529 0 -0.10 0 0 530 0 -0.11 0 0 531 0 -0.04 0 0 532 -1 -0.01 0 0 533 0 -0.04 -1 0 534 1 -0.05 0 -1 535 0 0.09 1 0 536 3 0.02 0 1 537 -1 0.08 3 0 538 -1 0.16 -1 3 539 0 0.07 -1 -1 540 0 0.01 0 -1 541 0 0.00 0 0 542 0 -0.32 0 0 543 0 -0.05 0 0 544 0 0.08 0 0 545 0 0.04 0 0 546 0 0.19 0 0 547 0 0.11 0 0 548 -2 0.16 0 0 549 -1 0.23 -2 0 550 0 0.22 -1 -2 551 2 0.37 0 -1 552 0 0.15 2 0 553 0 0.16 0 2 554 0 0.76 0 0 555 0 0.19 0 0 556 0 0.08 0 0 557 0 -0.11 0 0 558 0 0.04 0 0 559 5 0.26 0 0 560 5 0.18 5 0 561 1 -0.01 5 5 562 4 0.04 1 5 563 -2 0.00 4 1 564 0 -0.03 -2 4 565 0 -0.21 0 -2 566 0 -0.27 0 0 567 0 -0.19 0 0 568 0 -0.14 0 0 569 0 0.10 0 0 570 0 -0.15 0 0 571 -3 -0.16 0 0 572 -3 -0.05 -3 0 573 -1 -0.08 -3 -3 574 -1 -0.27 -1 -3 575 1 -0.09 -1 -1 576 0 0.05 1 -1 577 0 0.04 0 1 578 0 -0.12 0 0 579 0 0.21 0 0 580 0 0.13 0 0 581 0 0.10 0 0 582 0 0.25 0 0 583 1 -0.02 0 0 584 -4 0.04 1 0 585 -1 0.29 -4 1 586 -1 0.35 -1 -4 587 -1 0.27 -1 -1 588 0 0.22 -1 -1 589 0 0.28 0 -1 590 0 0.52 0 0 591 0 0.10 0 0 592 0 0.25 0 0 593 0 0.28 0 0 594 -1 0.16 0 0 595 -2 0.39 -1 0 596 4 0.24 -2 -1 597 5 -0.02 4 -2 598 -1 -0.08 5 4 599 1 -0.15 -1 5 600 0 -0.02 1 -1 601 0 -0.10 0 1 602 0 -0.19 0 0 603 0 -0.29 0 0 604 0 -0.30 0 0 605 0 -0.36 0 0 606 1 -0.31 0 0 607 -1 -0.41 1 0 608 0 -0.33 -1 1 609 -3 -0.18 0 -1 610 2 -0.12 -3 0 611 0 -0.08 2 -3 612 0 -0.14 0 2 613 0 -0.24 0 0 614 0 -0.15 0 0 615 0 0.19 0 0 616 0 0.22 0 0 617 0 0.07 0 0 618 -1 0.03 0 0 619 0 0.02 -1 0 620 0 0.10 0 -1 621 4 0.04 0 0 622 1 -0.09 4 0 623 -1 -0.11 1 4 624 0 -0.18 -1 1 625 0 0.17 0 -1 626 0 -0.09 0 0 627 0 0.10 0 0 628 0 -0.08 0 0 629 0 0.17 0 0 630 1 0.14 0 0 631 1 0.18 1 0 632 -1 0.08 1 1 633 -2 0.16 -1 1 634 1 0.22 -2 -1 635 2 0.47 1 -2 636 0 0.28 2 1 637 0 0.36 0 2 638 0 0.35 0 0 639 0 0.25 0 0 640 0 0.12 0 0 641 0 0.04 0 0 642 -1 -0.02 0 0 643 0 0.07 -1 0 644 0 -0.02 0 -1 645 3 0.01 0 0 646 -5 0.05 3 0 647 -2 -0.17 -5 3 648 0 -0.14 -2 -5 (1-B12)Orkanen(t-3) (1-B12)Orkanen(t-4) (1-B12)Orkanen(t-5) 131 -3 1 1 132 -1 -3 1 133 1 -1 -3 134 0 1 -1 135 0 0 1 136 0 0 0 137 0 0 0 138 0 0 0 139 0 0 0 140 -1 0 0 141 -1 -1 0 142 1 -1 -1 143 1 1 -1 144 -1 1 1 145 -2 -1 1 146 0 -2 -1 147 0 0 -2 148 0 0 0 149 0 0 0 150 0 0 0 151 0 0 0 152 0 0 0 153 -1 0 0 154 -1 -1 0 155 -2 -1 -1 156 4 -2 -1 157 2 4 -2 158 3 2 4 159 1 3 2 160 0 1 3 161 0 0 1 162 0 0 0 163 0 0 0 164 0 0 0 165 0 0 0 166 -1 0 0 167 1 -1 0 168 -6 1 -1 169 2 -6 1 170 1 2 -6 171 0 1 2 172 0 0 1 173 0 0 0 174 0 0 0 175 0 0 0 176 0 0 0 177 0 0 0 178 1 0 0 179 1 1 0 180 1 1 1 181 -3 1 1 182 -1 -3 1 183 1 -1 -3 184 0 1 -1 185 0 0 1 186 0 0 0 187 0 0 0 188 0 0 0 189 0 0 0 190 -1 0 0 191 -1 -1 0 192 1 -1 -1 193 1 1 -1 194 -1 1 1 195 -2 -1 1 196 0 -2 -1 197 0 0 -2 198 0 0 0 199 0 0 0 200 0 0 0 201 0 0 0 202 0 0 0 203 -1 0 0 204 -1 -1 0 205 -2 -1 -1 206 4 -2 -1 207 2 4 -2 208 2 2 4 209 0 2 2 210 0 0 2 211 0 0 0 212 0 0 0 213 0 0 0 214 0 0 0 215 0 0 0 216 -1 0 0 217 2 -1 0 218 -4 2 -1 219 -1 -4 2 220 -2 -1 -4 221 0 -2 -1 222 0 0 -2 223 0 0 0 224 0 0 0 225 0 0 0 226 0 0 0 227 0 0 0 228 1 0 0 229 -1 1 0 230 3 -1 1 231 1 3 -1 232 0 1 3 233 0 0 1 234 0 0 0 235 0 0 0 236 0 0 0 237 0 0 0 238 0 0 0 239 1 0 0 240 0 1 0 241 0 0 1 242 -5 0 0 243 -2 -5 0 244 0 -2 -5 245 0 0 -2 246 0 0 0 247 0 0 0 248 0 0 0 249 0 0 0 250 1 0 0 251 4 1 0 252 5 4 1 253 4 5 4 254 1 4 5 255 0 1 4 256 0 0 1 257 0 0 0 258 0 0 0 259 0 0 0 260 1 0 0 261 0 1 0 262 1 0 1 263 -3 1 0 264 -3 -3 1 265 -3 -3 -3 266 -1 -3 -3 267 0 -1 -3 268 0 0 -1 269 0 0 0 270 0 0 0 271 0 0 0 272 -1 0 0 273 0 -1 0 274 -1 0 -1 275 2 -1 0 276 3 2 -1 277 -1 3 2 278 1 -1 3 279 0 1 -1 280 0 0 1 281 0 0 0 282 0 0 0 283 0 0 0 284 1 0 0 285 1 1 0 286 -1 1 1 287 -2 -1 1 288 -4 -2 -1 289 -1 -4 -2 290 0 -1 -4 291 0 0 -1 292 0 0 0 293 0 0 0 294 0 0 0 295 0 0 0 296 -1 0 0 297 -1 -1 0 298 2 -1 -1 299 0 2 -1 300 0 0 2 301 2 0 0 302 -1 2 0 303 0 -1 2 304 0 0 -1 305 0 0 0 306 0 0 0 307 0 0 0 308 0 0 0 309 1 0 0 310 -1 1 0 311 2 -1 1 312 2 2 -1 313 -1 2 2 314 0 -1 2 315 0 0 -1 316 0 0 0 317 0 0 0 318 0 0 0 319 0 0 0 320 0 0 0 321 0 0 0 322 0 0 0 323 -2 0 0 324 -1 -2 0 325 0 -1 -2 326 0 0 -1 327 1 0 0 328 0 1 0 329 0 0 1 330 0 0 0 331 0 0 0 332 1 0 0 333 -1 1 0 334 0 -1 1 335 4 0 -1 336 -1 4 0 337 0 -1 4 338 0 0 -1 339 -1 0 0 340 0 -1 0 341 0 0 -1 342 0 0 0 343 0 0 0 344 -1 0 0 345 0 -1 0 346 -1 0 -1 347 -5 -1 0 348 1 -5 -1 349 1 1 -5 350 0 1 1 351 0 0 1 352 1 0 0 353 0 1 0 354 0 0 1 355 0 0 0 356 0 0 0 357 0 0 0 358 1 0 0 359 3 1 0 360 0 3 1 361 1 0 3 362 0 1 0 363 0 0 1 364 -1 0 0 365 0 -1 0 366 0 0 -1 367 0 0 0 368 0 0 0 369 1 0 0 370 1 1 0 371 -1 1 1 372 -1 -1 1 373 -2 -1 -1 374 0 -2 -1 375 0 0 -2 376 0 0 0 377 0 0 0 378 0 0 0 379 0 0 0 380 0 0 0 381 -1 0 0 382 -1 -1 0 383 -1 -1 -1 384 3 -1 -1 385 0 3 -1 386 2 0 3 387 0 2 0 388 0 0 2 389 0 0 0 390 0 0 0 391 0 0 0 392 1 0 0 393 1 1 0 394 -1 1 1 395 1 -1 1 396 -1 1 -1 397 0 -1 1 398 0 0 -1 399 0 0 0 400 0 0 0 401 0 0 0 402 0 0 0 403 0 0 0 404 -1 0 0 405 1 -1 0 406 0 1 -1 407 -2 0 1 408 -1 -2 0 409 -1 -1 -2 410 -2 -1 -1 411 0 -2 -1 412 0 0 -2 413 0 0 0 414 0 0 0 415 0 0 0 416 0 0 0 417 -2 0 0 418 0 -2 0 419 1 0 -2 420 -1 1 0 421 0 -1 1 422 0 0 -1 423 0 0 0 424 0 0 0 425 0 0 0 426 0 0 0 427 0 0 0 428 0 0 0 429 0 0 0 430 0 0 0 431 2 0 0 432 4 2 0 433 1 4 2 434 1 1 4 435 1 1 1 436 0 1 1 437 0 0 1 438 0 0 0 439 0 0 0 440 0 0 0 441 0 0 0 442 2 0 0 443 -1 2 0 444 -3 -1 2 445 1 -3 -1 446 0 1 -3 447 -1 0 1 448 0 -1 0 449 0 0 -1 450 0 0 0 451 0 0 0 452 0 0 0 453 2 0 0 454 -2 2 0 455 -2 -2 2 456 -1 -2 -2 457 -2 -1 -2 458 0 -2 -1 459 0 0 -2 460 0 0 0 461 0 0 0 462 0 0 0 463 0 0 0 464 0 0 0 465 -2 0 0 466 0 -2 0 467 2 0 -2 468 1 2 0 469 1 1 2 470 -1 1 1 471 0 -1 1 472 0 0 -1 473 0 0 0 474 0 0 0 475 0 0 0 476 0 0 0 477 0 0 0 478 0 0 0 479 1 0 0 480 3 1 0 481 0 3 1 482 1 0 3 483 0 1 0 484 0 0 1 485 0 0 0 486 0 0 0 487 0 0 0 488 0 0 0 489 1 0 0 490 3 1 0 491 -1 3 1 492 -4 -1 3 493 0 -4 -1 494 0 0 -4 495 0 0 0 496 0 0 0 497 0 0 0 498 0 0 0 499 0 0 0 500 0 0 0 501 -1 0 0 502 0 -1 0 503 2 0 -1 504 0 2 0 505 3 0 2 506 -1 3 0 507 0 -1 3 508 0 0 -1 509 0 0 0 510 0 0 0 511 0 0 0 512 0 0 0 513 1 0 0 514 -3 1 0 515 -4 -3 1 516 1 -4 -3 517 -1 1 -4 518 0 -1 1 519 0 0 -1 520 0 0 0 521 0 0 0 522 0 0 0 523 1 0 0 524 0 1 0 525 -1 0 1 526 0 -1 0 527 0 0 -1 528 1 0 0 529 -2 1 0 530 0 -2 1 531 0 0 -2 532 0 0 0 533 0 0 0 534 0 0 0 535 -1 0 0 536 0 -1 0 537 1 0 -1 538 0 1 0 539 3 0 1 540 -1 3 0 541 -1 -1 3 542 0 -1 -1 543 0 0 -1 544 0 0 0 545 0 0 0 546 0 0 0 547 0 0 0 548 0 0 0 549 0 0 0 550 0 0 0 551 -2 0 0 552 -1 -2 0 553 0 -1 -2 554 2 0 -1 555 0 2 0 556 0 0 2 557 0 0 0 558 0 0 0 559 0 0 0 560 0 0 0 561 0 0 0 562 5 0 0 563 5 5 0 564 1 5 5 565 4 1 5 566 -2 4 1 567 0 -2 4 568 0 0 -2 569 0 0 0 570 0 0 0 571 0 0 0 572 0 0 0 573 0 0 0 574 -3 0 0 575 -3 -3 0 576 -1 -3 -3 577 -1 -1 -3 578 1 -1 -1 579 0 1 -1 580 0 0 1 581 0 0 0 582 0 0 0 583 0 0 0 584 0 0 0 585 0 0 0 586 1 0 0 587 -4 1 0 588 -1 -4 1 589 -1 -1 -4 590 -1 -1 -1 591 0 -1 -1 592 0 0 -1 593 0 0 0 594 0 0 0 595 0 0 0 596 0 0 0 597 -1 0 0 598 -2 -1 0 599 4 -2 -1 600 5 4 -2 601 -1 5 4 602 1 -1 5 603 0 1 -1 604 0 0 1 605 0 0 0 606 0 0 0 607 0 0 0 608 0 0 0 609 1 0 0 610 -1 1 0 611 0 -1 1 612 -3 0 -1 613 2 -3 0 614 0 2 -3 615 0 0 2 616 0 0 0 617 0 0 0 618 0 0 0 619 0 0 0 620 0 0 0 621 -1 0 0 622 0 -1 0 623 0 0 -1 624 4 0 0 625 1 4 0 626 -1 1 4 627 0 -1 1 628 0 0 -1 629 0 0 0 630 0 0 0 631 0 0 0 632 0 0 0 633 1 0 0 634 1 1 0 635 -1 1 1 636 -2 -1 1 637 1 -2 -1 638 2 1 -2 639 0 2 1 640 0 0 2 641 0 0 0 642 0 0 0 643 0 0 0 644 0 0 0 645 -1 0 0 646 0 -1 0 647 0 0 -1 648 3 0 0 (1-B12)Orkanen(t-6) (1-B12)Orkanen(t-7) (1-B12)Orkanen(t-8) 131 1 0 0 132 1 1 0 133 1 1 1 134 -3 1 1 135 -1 -3 1 136 1 -1 -3 137 0 1 -1 138 0 0 1 139 0 0 0 140 0 0 0 141 0 0 0 142 0 0 0 143 -1 0 0 144 -1 -1 0 145 1 -1 -1 146 1 1 -1 147 -1 1 1 148 -2 -1 1 149 0 -2 -1 150 0 0 -2 151 0 0 0 152 0 0 0 153 0 0 0 154 0 0 0 155 0 0 0 156 -1 0 0 157 -1 -1 0 158 -2 -1 -1 159 4 -2 -1 160 2 4 -2 161 3 2 4 162 1 3 2 163 0 1 3 164 0 0 1 165 0 0 0 166 0 0 0 167 0 0 0 168 0 0 0 169 -1 0 0 170 1 -1 0 171 -6 1 -1 172 2 -6 1 173 1 2 -6 174 0 1 2 175 0 0 1 176 0 0 0 177 0 0 0 178 0 0 0 179 0 0 0 180 0 0 0 181 1 0 0 182 1 1 0 183 1 1 1 184 -3 1 1 185 -1 -3 1 186 1 -1 -3 187 0 1 -1 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0 0 0 0 558 0 0 0 1 0 0 0 559 0 0 0 0 1 0 0 560 0 0 0 0 0 1 0 561 0 0 0 0 0 0 1 562 0 0 0 0 0 0 0 563 0 0 0 0 0 0 0 564 0 0 0 0 0 0 0 565 0 0 0 0 0 0 0 566 0 0 0 0 0 0 0 567 1 0 0 0 0 0 0 568 0 1 0 0 0 0 0 569 0 0 1 0 0 0 0 570 0 0 0 1 0 0 0 571 0 0 0 0 1 0 0 572 0 0 0 0 0 1 0 573 0 0 0 0 0 0 1 574 0 0 0 0 0 0 0 575 0 0 0 0 0 0 0 576 0 0 0 0 0 0 0 577 0 0 0 0 0 0 0 578 0 0 0 0 0 0 0 579 1 0 0 0 0 0 0 580 0 1 0 0 0 0 0 581 0 0 1 0 0 0 0 582 0 0 0 1 0 0 0 583 0 0 0 0 1 0 0 584 0 0 0 0 0 1 0 585 0 0 0 0 0 0 1 586 0 0 0 0 0 0 0 587 0 0 0 0 0 0 0 588 0 0 0 0 0 0 0 589 0 0 0 0 0 0 0 590 0 0 0 0 0 0 0 591 1 0 0 0 0 0 0 592 0 1 0 0 0 0 0 593 0 0 1 0 0 0 0 594 0 0 0 1 0 0 0 595 0 0 0 0 1 0 0 596 0 0 0 0 0 1 0 597 0 0 0 0 0 0 1 598 0 0 0 0 0 0 0 599 0 0 0 0 0 0 0 600 0 0 0 0 0 0 0 601 0 0 0 0 0 0 0 602 0 0 0 0 0 0 0 603 1 0 0 0 0 0 0 604 0 1 0 0 0 0 0 605 0 0 1 0 0 0 0 606 0 0 0 1 0 0 0 607 0 0 0 0 1 0 0 608 0 0 0 0 0 1 0 609 0 0 0 0 0 0 1 610 0 0 0 0 0 0 0 611 0 0 0 0 0 0 0 612 0 0 0 0 0 0 0 613 0 0 0 0 0 0 0 614 0 0 0 0 0 0 0 615 1 0 0 0 0 0 0 616 0 1 0 0 0 0 0 617 0 0 1 0 0 0 0 618 0 0 0 1 0 0 0 619 0 0 0 0 1 0 0 620 0 0 0 0 0 1 0 621 0 0 0 0 0 0 1 622 0 0 0 0 0 0 0 623 0 0 0 0 0 0 0 624 0 0 0 0 0 0 0 625 0 0 0 0 0 0 0 626 0 0 0 0 0 0 0 627 1 0 0 0 0 0 0 628 0 1 0 0 0 0 0 629 0 0 1 0 0 0 0 630 0 0 0 1 0 0 0 631 0 0 0 0 1 0 0 632 0 0 0 0 0 1 0 633 0 0 0 0 0 0 1 634 0 0 0 0 0 0 0 635 0 0 0 0 0 0 0 636 0 0 0 0 0 0 0 637 0 0 0 0 0 0 0 638 0 0 0 0 0 0 0 639 1 0 0 0 0 0 0 640 0 1 0 0 0 0 0 641 0 0 1 0 0 0 0 642 0 0 0 1 0 0 0 643 0 0 0 0 1 0 0 644 0 0 0 0 0 1 0 645 0 0 0 0 0 0 1 646 0 0 0 0 0 0 0 647 0 0 0 0 0 0 0 648 0 0 0 0 0 0 0 > (k <- length(x[n,])) [1] 33 > head(x) (1-B12)Orkanen (1-B12)Temperatuur (1-B12)Orkanen(t-1) (1-B12)Orkanen(t-2) 131 0 -0.14 1 -1 132 0 -0.07 0 1 133 0 -0.10 0 0 134 0 0.07 0 0 135 0 -0.51 0 0 136 0 -0.25 0 0 (1-B12)Orkanen(t-3) (1-B12)Orkanen(t-4) (1-B12)Orkanen(t-5) 131 -3 1 1 132 -1 -3 1 133 1 -1 -3 134 0 1 -1 135 0 0 1 136 0 0 0 (1-B12)Orkanen(t-6) (1-B12)Orkanen(t-7) (1-B12)Orkanen(t-8) 131 1 0 0 132 1 1 0 133 1 1 1 134 -3 1 1 135 -1 -3 1 136 1 -1 -3 (1-B12)Orkanen(t-9) (1-B12)Orkanen(t-10) (1-B12)Orkanen(t-1s) 131 0 0 0 132 0 0 0 133 0 0 0 134 1 0 0 135 1 1 0 136 1 1 0 (1-B12)Orkanen(t-2s) (1-B12)Orkanen(t-3s) (1-B12)Orkanen(t-4s) 131 0 0 -1 132 0 0 -1 133 0 0 0 134 0 0 0 135 0 0 0 136 0 0 0 (1-B12)Orkanen(t-5s) (1-B12)Orkanen(t-6s) (1-B12)Orkanen(t-7s) 131 0 1 0 132 0 1 0 133 0 0 0 134 0 0 -1 135 0 0 0 136 0 0 0 (1-B12)Orkanen(t-8s) (1-B12)Orkanen(t-9s) (1-B12)Orkanen(t-10s) M1 M2 M3 M4 131 0 -1 0 1 0 0 0 132 0 0 0 0 1 0 0 133 0 0 0 0 0 1 0 134 1 0 0 0 0 0 1 135 0 0 0 0 0 0 0 136 0 0 0 0 0 0 0 M5 M6 M7 M8 M9 M10 M11 131 0 0 0 0 0 0 0 132 0 0 0 0 0 0 0 133 0 0 0 0 0 0 0 134 0 0 0 0 0 0 0 135 1 0 0 0 0 0 0 136 0 1 0 0 0 0 0 > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `(1-B12)Temperatuur` `(1-B12)Orkanen(t-1)` 0.034770 -0.053644 0.102200 `(1-B12)Orkanen(t-2)` `(1-B12)Orkanen(t-3)` `(1-B12)Orkanen(t-4)` -0.023705 -0.002138 -0.019445 `(1-B12)Orkanen(t-5)` `(1-B12)Orkanen(t-6)` `(1-B12)Orkanen(t-7)` 0.003126 -0.008202 0.008783 `(1-B12)Orkanen(t-8)` `(1-B12)Orkanen(t-9)` `(1-B12)Orkanen(t-10)` 0.018069 -0.007397 -0.054219 `(1-B12)Orkanen(t-1s)` `(1-B12)Orkanen(t-2s)` `(1-B12)Orkanen(t-3s)` -0.761711 -0.556840 -0.484030 `(1-B12)Orkanen(t-4s)` `(1-B12)Orkanen(t-5s)` `(1-B12)Orkanen(t-6s)` -0.371810 -0.215750 -0.191727 `(1-B12)Orkanen(t-7s)` `(1-B12)Orkanen(t-8s)` `(1-B12)Orkanen(t-9s)` -0.225571 -0.231220 -0.215077 `(1-B12)Orkanen(t-10s)` M1 M2 -0.068239 0.019870 -0.048887 M3 M4 M5 -0.031967 -0.037789 -0.033470 M6 M7 M8 -0.031716 -0.067092 -0.087450 M9 M10 M11 0.022729 0.083495 0.127667 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3.8141 -0.3546 -0.0384 0.1209 4.3350 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.034770 0.160229 0.217 0.828299 `(1-B12)Temperatuur` -0.053644 0.255408 -0.210 0.833731 `(1-B12)Orkanen(t-1)` 0.102200 0.036788 2.778 0.005680 ** `(1-B12)Orkanen(t-2)` -0.023705 0.036458 -0.650 0.515874 `(1-B12)Orkanen(t-3)` -0.002138 0.036913 -0.058 0.953833 `(1-B12)Orkanen(t-4)` -0.019445 0.037029 -0.525 0.599732 `(1-B12)Orkanen(t-5)` 0.003126 0.037194 0.084 0.933059 `(1-B12)Orkanen(t-6)` -0.008202 0.037014 -0.222 0.824720 `(1-B12)Orkanen(t-7)` 0.008783 0.036890 0.238 0.811916 `(1-B12)Orkanen(t-8)` 0.018069 0.036958 0.489 0.625136 `(1-B12)Orkanen(t-9)` -0.007397 0.037031 -0.200 0.841752 `(1-B12)Orkanen(t-10)` -0.054219 0.036869 -1.471 0.142046 `(1-B12)Orkanen(t-1s)` -0.761711 0.045980 -16.566 < 2e-16 *** `(1-B12)Orkanen(t-2s)` -0.556840 0.057296 -9.719 < 2e-16 *** `(1-B12)Orkanen(t-3s)` -0.484030 0.063293 -7.647 1.11e-13 *** `(1-B12)Orkanen(t-4s)` -0.371810 0.067092 -5.542 4.92e-08 *** `(1-B12)Orkanen(t-5s)` -0.215750 0.070393 -3.065 0.002298 ** `(1-B12)Orkanen(t-6s)` -0.191727 0.070462 -2.721 0.006742 ** `(1-B12)Orkanen(t-7s)` -0.225571 0.068640 -3.286 0.001089 ** `(1-B12)Orkanen(t-8s)` -0.231220 0.066964 -3.453 0.000603 *** `(1-B12)Orkanen(t-9s)` -0.215077 0.062072 -3.465 0.000577 *** `(1-B12)Orkanen(t-10s)` -0.068239 0.049146 -1.389 0.165622 M1 0.019870 0.224846 0.088 0.929618 M2 -0.048887 0.224966 -0.217 0.828060 M3 -0.031967 0.226172 -0.141 0.887662 M4 -0.037789 0.226192 -0.167 0.867387 M5 -0.033470 0.226200 -0.148 0.882430 M6 -0.031716 0.226213 -0.140 0.888558 M7 -0.067092 0.226143 -0.297 0.766839 M8 -0.087450 0.226449 -0.386 0.699534 M9 0.022729 0.226658 0.100 0.920163 M10 0.083495 0.226252 0.369 0.712261 M11 0.127667 0.225953 0.565 0.572325 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.047 on 485 degrees of freedom Multiple R-squared: 0.4083, Adjusted R-squared: 0.3692 F-statistic: 10.46 on 32 and 485 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.429372144 0.8587442888 0.5706278556 [2,] 0.306677108 0.6133542167 0.6933228916 [3,] 0.183955732 0.3679114642 0.8160442679 [4,] 0.104506178 0.2090123567 0.8954938216 [5,] 0.155299100 0.3105982007 0.8447008997 [6,] 0.126746120 0.2534922395 0.8732538803 [7,] 0.087291322 0.1745826443 0.9127086778 [8,] 0.124622808 0.2492456157 0.8753771921 [9,] 0.107318855 0.2146377092 0.8926811454 [10,] 0.630912978 0.7381740430 0.3690870215 [11,] 0.629946972 0.7401060555 0.3700530277 [12,] 0.996761683 0.0064766345 0.0032383172 [13,] 0.998181348 0.0036373042 0.0018186521 [14,] 0.997900726 0.0041985486 0.0020992743 [15,] 0.997449272 0.0051014552 0.0025507276 [16,] 0.995933794 0.0081324111 0.0040662056 [17,] 0.993735435 0.0125291308 0.0062645654 [18,] 0.990559430 0.0188811392 0.0094405696 [19,] 0.986162068 0.0276758637 0.0138379318 [20,] 0.980366755 0.0392664905 0.0196332452 [21,] 0.982989029 0.0340219423 0.0170109712 [22,] 0.984586627 0.0308267455 0.0154133727 [23,] 0.979252607 0.0414947856 0.0207473928 [24,] 0.976794134 0.0464117312 0.0232058656 [25,] 0.972701383 0.0545972348 0.0272986174 [26,] 0.966764650 0.0664707004 0.0332353502 [27,] 0.959365757 0.0812684859 0.0406342429 [28,] 0.948545297 0.1029094063 0.0514547032 [29,] 0.934735492 0.1305290154 0.0652645077 [30,] 0.917379138 0.1652417233 0.0826208617 [31,] 0.896799641 0.2064007188 0.1032003594 [32,] 0.873735503 0.2525289949 0.1262644975 [33,] 0.847016259 0.3059674821 0.1529837411 [34,] 0.825788571 0.3484228582 0.1742114291 [35,] 0.805089169 0.3898216628 0.1949108314 [36,] 0.790931649 0.4181367018 0.2090683509 [37,] 0.864037899 0.2719242020 0.1359621010 [38,] 0.955355844 0.0892883114 0.0446441557 [39,] 0.957478964 0.0850420719 0.0425210359 [40,] 0.981579841 0.0368403176 0.0184201588 [41,] 0.976972783 0.0460544337 0.0230272168 [42,] 0.970878481 0.0582430375 0.0291215187 [43,] 0.963314268 0.0733714636 0.0366857318 [44,] 0.954801340 0.0903973191 0.0451986596 [45,] 0.946935181 0.1061296372 0.0530648186 [46,] 0.935868568 0.1282628640 0.0641314320 [47,] 0.926574844 0.1468503124 0.0734251562 [48,] 0.932769872 0.1344602566 0.0672301283 [49,] 0.919138116 0.1617237688 0.0808618844 [50,] 0.933947778 0.1321044434 0.0660522217 [51,] 0.925519895 0.1489602092 0.0744801046 [52,] 0.912727824 0.1745443529 0.0872721764 [53,] 0.898390982 0.2032180365 0.1016090183 [54,] 0.881050368 0.2378992631 0.1189496315 [55,] 0.861286638 0.2774267241 0.1387133621 [56,] 0.839140009 0.3217199822 0.1608599911 [57,] 0.815171268 0.3696574640 0.1848287320 [58,] 0.791882745 0.4162345094 0.2081172547 [59,] 0.783144296 0.4337114087 0.2168557043 [60,] 0.762670191 0.4746596177 0.2373298088 [61,] 0.737505407 0.5249891865 0.2624945933 [62,] 0.750464193 0.4990716145 0.2495358073 [63,] 0.739806372 0.5203872562 0.2601936281 [64,] 0.715632405 0.5687351909 0.2843675955 [65,] 0.684497580 0.6310048395 0.3155024198 [66,] 0.651528887 0.6969422255 0.3484711127 [67,] 0.616864572 0.7662708552 0.3831354276 [68,] 0.581434503 0.8371309946 0.4185654973 [69,] 0.548927230 0.9021455408 0.4510727704 [70,] 0.515980655 0.9680386900 0.4840193450 [71,] 0.514680159 0.9706396816 0.4853198408 [72,] 0.535447003 0.9291059932 0.4645529966 [73,] 0.503732331 0.9925353373 0.4962676687 [74,] 0.713255337 0.5734893260 0.2867446630 [75,] 0.713262440 0.5734751192 0.2867375596 [76,] 0.693147492 0.6137050153 0.3068525077 [77,] 0.663565610 0.6728687797 0.3364343899 [78,] 0.633377760 0.7332444803 0.3666222402 [79,] 0.601082570 0.7978348606 0.3989174303 [80,] 0.568191449 0.8636171015 0.4318085507 [81,] 0.536845176 0.9263096487 0.4631548244 [82,] 0.506328631 0.9873427371 0.4936713685 [83,] 0.941052230 0.1178955395 0.0589477698 [84,] 0.986773046 0.0264539072 0.0132269536 [85,] 0.998934266 0.0021314680 0.0010657340 [86,] 0.999463058 0.0010738832 0.0005369416 [87,] 0.999307475 0.0013850495 0.0006925247 [88,] 0.999104621 0.0017907576 0.0008953788 [89,] 0.998868767 0.0022624662 0.0011312331 [90,] 0.998578153 0.0028436943 0.0014218471 [91,] 0.998309439 0.0033811225 0.0016905612 [92,] 0.998888591 0.0022228173 0.0011114087 [93,] 0.999017000 0.0019659991 0.0009829995 [94,] 0.999534832 0.0009303361 0.0004651681 [95,] 0.999664007 0.0006719864 0.0003359932 [96,] 0.999589910 0.0008201794 0.0004100897 [97,] 0.999468992 0.0010620156 0.0005310078 [98,] 0.999626066 0.0007478670 0.0003739335 [99,] 0.999534065 0.0009318706 0.0004659353 [100,] 0.999399171 0.0012016581 0.0006008290 [101,] 0.999217807 0.0015643852 0.0007821926 [102,] 0.998990024 0.0020199512 0.0010099756 [103,] 0.998699327 0.0026013470 0.0013006735 [104,] 0.998341619 0.0033167624 0.0016583812 [105,] 0.997919134 0.0041617322 0.0020808661 [106,] 0.997778651 0.0044426989 0.0022213495 [107,] 0.998642694 0.0027146112 0.0013573056 [108,] 0.999495889 0.0010082228 0.0005041114 [109,] 0.999734568 0.0005308631 0.0002654315 [110,] 0.999684378 0.0006312447 0.0003156223 [111,] 0.999617107 0.0007657863 0.0003828931 [112,] 0.999501082 0.0009978368 0.0004989184 [113,] 0.999352789 0.0012944210 0.0006472105 [114,] 0.999166154 0.0016676917 0.0008338459 [115,] 0.998932224 0.0021355522 0.0010677761 [116,] 0.998797060 0.0024058799 0.0012029399 [117,] 0.998979220 0.0020415590 0.0010207795 [118,] 0.998929498 0.0021410039 0.0010705019 [119,] 0.998672910 0.0026541801 0.0013270900 [120,] 0.998664876 0.0026702473 0.0013351236 [121,] 0.999034165 0.0019316697 0.0009658349 [122,] 0.999101211 0.0017975773 0.0008987886 [123,] 0.998976099 0.0020478012 0.0010239006 [124,] 0.998699170 0.0026016602 0.0013008301 [125,] 0.998356474 0.0032870521 0.0016435260 [126,] 0.997936684 0.0041266316 0.0020633158 [127,] 0.997423627 0.0051527463 0.0025763731 [128,] 0.996859691 0.0062806185 0.0031403092 [129,] 0.996150517 0.0076989650 0.0038494825 [130,] 0.996320001 0.0073599978 0.0036799989 [131,] 0.995890706 0.0082185887 0.0041092943 [132,] 0.995246385 0.0095072303 0.0047536151 [133,] 0.994751739 0.0104965220 0.0052482610 [134,] 0.997381888 0.0052362247 0.0026181124 [135,] 0.996835763 0.0063284750 0.0031642375 [136,] 0.996107671 0.0077846585 0.0038923292 [137,] 0.995220038 0.0095599240 0.0047799620 [138,] 0.994195594 0.0116088123 0.0058044062 [139,] 0.992935506 0.0141289877 0.0070644938 [140,] 0.991495666 0.0170086674 0.0085043337 [141,] 0.990955741 0.0180885172 0.0090442586 [142,] 0.989117354 0.0217652917 0.0108826459 [143,] 0.994226692 0.0115466167 0.0057733084 [144,] 0.994421879 0.0111562416 0.0055781208 [145,] 0.994374767 0.0112504653 0.0056252326 [146,] 0.995173158 0.0096536833 0.0048268417 [147,] 0.994159874 0.0116802525 0.0058401262 [148,] 0.992923897 0.0141522054 0.0070761027 [149,] 0.991433924 0.0171321510 0.0085660755 [150,] 0.989682457 0.0206350853 0.0103175426 [151,] 0.987666875 0.0246662493 0.0123331247 [152,] 0.985415430 0.0291691406 0.0145845703 [153,] 0.985230369 0.0295392613 0.0147696307 [154,] 0.982488220 0.0350235601 0.0175117801 [155,] 0.979479287 0.0410414266 0.0205207133 [156,] 0.977574763 0.0448504733 0.0224252366 [157,] 0.975073024 0.0498539529 0.0249269765 [158,] 0.979597598 0.0408048041 0.0204024020 [159,] 0.977118207 0.0457635863 0.0228817932 [160,] 0.973858197 0.0522836066 0.0261418033 [161,] 0.969404873 0.0611902542 0.0305951271 [162,] 0.964361881 0.0712762373 0.0356381187 [163,] 0.958650718 0.0826985644 0.0413492822 [164,] 0.957217527 0.0855649463 0.0427824731 [165,] 0.954388673 0.0912226545 0.0456113272 [166,] 0.948115924 0.1037681522 0.0518840761 [167,] 0.992616782 0.0147664362 0.0073832181 [168,] 0.993420157 0.0131596865 0.0065798432 [169,] 0.992325920 0.0153481594 0.0076740797 [170,] 0.993042203 0.0139155941 0.0069577970 [171,] 0.991994956 0.0160100874 0.0080050437 [172,] 0.990470277 0.0190594467 0.0095297233 [173,] 0.988606125 0.0227877510 0.0113938755 [174,] 0.986435210 0.0271295807 0.0135647903 [175,] 0.983994192 0.0320116159 0.0160058080 [176,] 0.981325939 0.0373481214 0.0186740607 [177,] 0.978317568 0.0433648639 0.0216824319 [178,] 0.976758994 0.0464820126 0.0232410063 [179,] 0.980194605 0.0396107907 0.0198053953 [180,] 0.982080013 0.0358399749 0.0179199874 [181,] 0.980448766 0.0391024679 0.0195512339 [182,] 0.982135682 0.0357286366 0.0178643183 [183,] 0.980424332 0.0391513370 0.0195756685 [184,] 0.980436033 0.0391279339 0.0195639669 [185,] 0.977057897 0.0458842052 0.0229421026 [186,] 0.973225142 0.0535497155 0.0267748577 [187,] 0.968839326 0.0623213471 0.0311606735 [188,] 0.964637648 0.0707247030 0.0353623515 [189,] 0.960112896 0.0797742074 0.0398871037 [190,] 0.954614170 0.0907716604 0.0453858302 [191,] 0.959007851 0.0819842972 0.0409921486 [192,] 0.954275442 0.0914491166 0.0457245583 [193,] 0.960248408 0.0795031843 0.0397515921 [194,] 0.956851669 0.0862966611 0.0431483305 [195,] 0.950420219 0.0991595610 0.0495797805 [196,] 0.943503976 0.1129920489 0.0564960245 [197,] 0.935617604 0.1287647926 0.0643823963 [198,] 0.926845408 0.1463091848 0.0731545924 [199,] 0.917166120 0.1656677597 0.0828338799 [200,] 0.906603465 0.1867930701 0.0933965350 [201,] 0.906835531 0.1863289370 0.0931644685 [202,] 0.907774123 0.1844517546 0.0922258773 [203,] 0.897806003 0.2043879934 0.1021939967 [204,] 0.913231102 0.1735377969 0.0867688985 [205,] 0.918740941 0.1625181172 0.0812590586 [206,] 0.909089432 0.1818211364 0.0909105682 [207,] 0.897887315 0.2042253693 0.1021126846 [208,] 0.886702423 0.2265951547 0.1132975773 [209,] 0.873421980 0.2531560404 0.1265780202 [210,] 0.859041692 0.2819166163 0.1409583081 [211,] 0.843714077 0.3125718457 0.1562859229 [212,] 0.828398465 0.3432030701 0.1716015351 [213,] 0.813940466 0.3721190679 0.1860595340 [214,] 0.797804086 0.4043918286 0.2021959143 [215,] 0.810495445 0.3790091105 0.1895045553 [216,] 0.846555195 0.3068896109 0.1534448055 [217,] 0.852239933 0.2955201348 0.1477600674 [218,] 0.880724905 0.2385501893 0.1192750946 [219,] 0.868322392 0.2633552162 0.1316776081 [220,] 0.853982960 0.2920340808 0.1460170404 [221,] 0.838255393 0.3234892138 0.1617446069 [222,] 0.821513397 0.3569732067 0.1784866033 [223,] 0.803676570 0.3926468596 0.1963234298 [224,] 0.793788515 0.4124229699 0.2062114850 [225,] 0.782590606 0.4348187878 0.2174093939 [226,] 0.791336087 0.4173278251 0.2086639125 [227,] 0.771739515 0.4565209700 0.2282604850 [228,] 0.766228438 0.4675431240 0.2337715620 [229,] 0.747545230 0.5049095394 0.2524547697 [230,] 0.756776456 0.4864470887 0.2432235444 [231,] 0.735529805 0.5289403893 0.2644701946 [232,] 0.713496662 0.5730066756 0.2865033378 [233,] 0.690390510 0.6192189791 0.3096094896 [234,] 0.666616618 0.6667667635 0.3333833817 [235,] 0.642181849 0.7156363022 0.3578181511 [236,] 0.620309030 0.7593819397 0.3796909699 [237,] 0.659291807 0.6814163865 0.3407081932 [238,] 0.648507397 0.7029852060 0.3514926030 [239,] 0.704238648 0.5915227032 0.2957613516 [240,] 0.689862971 0.6202740583 0.3101370292 [241,] 0.700510982 0.5989780352 0.2994890176 [242,] 0.701174503 0.5976509940 0.2988254970 [243,] 0.678289969 0.6434200628 0.3217100314 [244,] 0.654830075 0.6903398498 0.3451699249 [245,] 0.629948479 0.7401030417 0.3700515209 [246,] 0.604742449 0.7905151011 0.3952575506 [247,] 0.578917312 0.8421653762 0.4210826881 [248,] 0.553985794 0.8920284119 0.4460142060 [249,] 0.546940643 0.9061187132 0.4530593566 [250,] 0.531949247 0.9361015057 0.4680507528 [251,] 0.512298973 0.9754020536 0.4877010268 [252,] 0.531029680 0.9379406391 0.4689703195 [253,] 0.550720969 0.8985580612 0.4492790306 [254,] 0.533129604 0.9337407925 0.4668703962 [255,] 0.506545109 0.9869097825 0.4934548912 [256,] 0.479840134 0.9596802680 0.5201598660 [257,] 0.453334151 0.9066683015 0.5466658492 [258,] 0.426815917 0.8536318332 0.5731840834 [259,] 0.401286522 0.8025730444 0.5987134778 [260,] 0.375470948 0.7509418966 0.6245290517 [261,] 0.359127752 0.7182555034 0.6408722483 [262,] 0.360199125 0.7203982508 0.6398008746 [263,] 0.371936688 0.7438733761 0.6280633120 [264,] 0.512427064 0.9751458721 0.4875729361 [265,] 0.502125459 0.9957490811 0.4978745406 [266,] 0.491467088 0.9829341756 0.5085329122 [267,] 0.490633864 0.9812677277 0.5093661361 [268,] 0.463616991 0.9272339812 0.5363830094 [269,] 0.436897656 0.8737953115 0.5631023442 [270,] 0.410315928 0.8206318553 0.5896840724 [271,] 0.384383264 0.7687665276 0.6156167362 [272,] 0.359038116 0.7180762310 0.6409618845 [273,] 0.340221047 0.6804420945 0.6597789527 [274,] 0.365265443 0.7305308862 0.6347345569 [275,] 0.340041470 0.6800829398 0.6599585301 [276,] 0.331918176 0.6638363527 0.6680818237 [277,] 0.346172674 0.6923453473 0.6538273264 [278,] 0.322795651 0.6455913020 0.6772043490 [279,] 0.301398059 0.6027961184 0.6986019408 [280,] 0.277990344 0.5559806888 0.7220096556 [281,] 0.255754034 0.5115080678 0.7442459661 [282,] 0.234452235 0.4689044690 0.7655477655 [283,] 0.213950231 0.4279004614 0.7860497693 [284,] 0.195157482 0.3903149640 0.8048425180 [285,] 0.217930329 0.4358606585 0.7820696707 [286,] 0.223607221 0.4472144430 0.7763927785 [287,] 0.336770516 0.6735410313 0.6632294843 [288,] 0.365519202 0.7310384046 0.6344807977 [289,] 0.419585844 0.8391716885 0.5804141557 [290,] 0.396090725 0.7921814501 0.6039092750 [291,] 0.371188292 0.7423765850 0.6288117075 [292,] 0.345512240 0.6910244797 0.6544877602 [293,] 0.320209024 0.6404180483 0.6797909759 [294,] 0.295856668 0.5917133367 0.7041433316 [295,] 0.272400707 0.5448014137 0.7275992932 [296,] 0.249879802 0.4997596040 0.7501201980 [297,] 0.242377107 0.4847542136 0.7576228932 [298,] 0.232457904 0.4649158079 0.7675420961 [299,] 0.217479315 0.4349586303 0.7825206848 [300,] 0.197420194 0.3948403890 0.8025798055 [301,] 0.181937224 0.3638744482 0.8180627759 [302,] 0.167547167 0.3350943330 0.8324528335 [303,] 0.150880672 0.3017613433 0.8491193284 [304,] 0.137128438 0.2742568754 0.8628715623 [305,] 0.122237477 0.2444749542 0.8777625229 [306,] 0.108607302 0.2172146037 0.8913926982 [307,] 0.096111402 0.1922228049 0.9038885975 [308,] 0.084596739 0.1691934782 0.9154032609 [309,] 0.074706962 0.1494139237 0.9252930381 [310,] 0.068412431 0.1368248615 0.9315875692 [311,] 0.070437495 0.1408749908 0.9295625046 [312,] 0.120607647 0.2412152932 0.8793923534 [313,] 0.124646808 0.2492936157 0.8753531921 [314,] 0.118467284 0.2369345678 0.8815327161 [315,] 0.105489426 0.2109788522 0.8945105739 [316,] 0.092941396 0.1858827925 0.9070586037 [317,] 0.081508752 0.1630175035 0.9184912482 [318,] 0.071165864 0.1423317276 0.9288341362 [319,] 0.061861447 0.1237228939 0.9381385530 [320,] 0.053671578 0.1073431566 0.9463284217 [321,] 0.049164450 0.0983289009 0.9508355495 [322,] 0.067235109 0.1344702176 0.9327648912 [323,] 0.067068855 0.1341377092 0.9329311454 [324,] 0.093306705 0.1866134093 0.9066932954 [325,] 0.090747940 0.1814958793 0.9092520603 [326,] 0.079808238 0.1596164758 0.9201917621 [327,] 0.069776038 0.1395520758 0.9302239621 [328,] 0.060479600 0.1209592006 0.9395203997 [329,] 0.052124371 0.1042487412 0.9478756294 [330,] 0.044771654 0.0895433086 0.9552283457 [331,] 0.038385195 0.0767703898 0.9616148051 [332,] 0.032729121 0.0654582421 0.9672708789 [333,] 0.030426424 0.0608528484 0.9695735758 [334,] 0.033556225 0.0671124508 0.9664437746 [335,] 0.055393282 0.1107865633 0.9446067183 [336,] 0.100888048 0.2017760955 0.8991119522 [337,] 0.183876124 0.3677522477 0.8161238762 [338,] 0.201896519 0.4037930383 0.7981034809 [339,] 0.181431368 0.3628627359 0.8185686321 [340,] 0.161963577 0.3239271536 0.8380364232 [341,] 0.143946854 0.2878937073 0.8560531463 [342,] 0.127297784 0.2545955684 0.8727022158 [343,] 0.111953493 0.2239069857 0.8880465071 [344,] 0.099269510 0.1985390208 0.9007304896 [345,] 0.087673870 0.1753477403 0.9123261298 [346,] 0.099466563 0.1989331268 0.9005334366 [347,] 0.118575985 0.2371519696 0.8814240152 [348,] 0.107293200 0.2145863994 0.8927068003 [349,] 0.107279333 0.2145586669 0.8927206665 [350,] 0.102507808 0.2050156166 0.8974921917 [351,] 0.089694188 0.1793883769 0.9103058115 [352,] 0.077565248 0.1551304964 0.9224347518 [353,] 0.066725959 0.1334519189 0.9332740406 [354,] 0.057135792 0.1142715842 0.9428642079 [355,] 0.058698947 0.1173978943 0.9413010529 [356,] 0.050417945 0.1008358909 0.9495820546 [357,] 0.050454839 0.1009096777 0.9495451611 [358,] 0.045651018 0.0913020361 0.9543489819 [359,] 0.058606887 0.1172137745 0.9413931127 [360,] 0.056258768 0.1125175350 0.9437412325 [361,] 0.051918748 0.1038374951 0.9480812525 [362,] 0.044077723 0.0881554465 0.9559222768 [363,] 0.037661950 0.0753238994 0.9623380503 [364,] 0.031387647 0.0627752947 0.9686123526 [365,] 0.025995934 0.0519918679 0.9740040661 [366,] 0.021426489 0.0428529778 0.9785735111 [367,] 0.017627632 0.0352552631 0.9823723684 [368,] 0.014356619 0.0287132380 0.9856433810 [369,] 0.013372858 0.0267457156 0.9866271422 [370,] 0.012595839 0.0251916778 0.9874041611 [371,] 0.015717057 0.0314341136 0.9842829432 [372,] 0.016028790 0.0320575805 0.9839712097 [373,] 0.017571780 0.0351435598 0.9824282201 [374,] 0.014571575 0.0291431504 0.9854284248 [375,] 0.011691384 0.0233827674 0.9883086163 [376,] 0.009309604 0.0186192078 0.9906903961 [377,] 0.007388620 0.0147772405 0.9926113798 [378,] 0.005791351 0.0115827013 0.9942086494 [379,] 0.004529953 0.0090599056 0.9954700472 [380,] 0.003498916 0.0069978315 0.9965010843 [381,] 0.002796355 0.0055927091 0.9972036455 [382,] 0.002723077 0.0054461544 0.9972769228 [383,] 0.003961028 0.0079220554 0.9960389723 [384,] 0.027066740 0.0541334794 0.9729332603 [385,] 0.027253072 0.0545061435 0.9727469283 [386,] 0.025066254 0.0501325083 0.9749337458 [387,] 0.022525604 0.0450512084 0.9774743958 [388,] 0.018013920 0.0360278400 0.9819860800 [389,] 0.014625772 0.0292515435 0.9853742282 [390,] 0.011501981 0.0230039629 0.9884980185 [391,] 0.009174736 0.0183494725 0.9908252638 [392,] 0.007136686 0.0142733728 0.9928633136 [393,] 0.005770522 0.0115410434 0.9942294783 [394,] 0.019958900 0.0399178010 0.9800410995 [395,] 0.110066781 0.2201335613 0.8899332193 [396,] 0.093290688 0.1865813756 0.9067093122 [397,] 0.151813522 0.3036270449 0.8481864775 [398,] 0.139186562 0.2783731235 0.8608134382 [399,] 0.119127760 0.2382555204 0.8808722398 [400,] 0.099883250 0.1997664996 0.9001167502 [401,] 0.084212563 0.1684251251 0.9157874375 [402,] 0.069759489 0.1395189778 0.9302405111 [403,] 0.056811466 0.1136229314 0.9431885343 [404,] 0.046099994 0.0921999876 0.9539000062 [405,] 0.037166329 0.0743326586 0.9628336707 [406,] 0.037641672 0.0752833432 0.9623583284 [407,] 0.041592169 0.0831843381 0.9584078309 [408,] 0.446372170 0.8927443391 0.5536278304 [409,] 0.411508248 0.8230164961 0.5884917519 [410,] 0.389410436 0.7788208729 0.6105895635 [411,] 0.345388642 0.6907772840 0.6546113580 [412,] 0.310018857 0.6200377146 0.6899811427 [413,] 0.279165417 0.5583308349 0.7208345826 [414,] 0.247466654 0.4949333087 0.7525333457 [415,] 0.210759232 0.4215184649 0.7892407675 [416,] 0.187579963 0.3751599251 0.8124200375 [417,] 0.161957637 0.3239152739 0.8380423631 [418,] 0.287670373 0.5753407461 0.7123296269 [419,] 0.750046874 0.4999062516 0.2499531258 [420,] 0.996757672 0.0064846568 0.0032423284 [421,] 0.998694608 0.0026107843 0.0013053921 [422,] 0.998155697 0.0036886057 0.0018443029 [423,] 0.997282182 0.0054356364 0.0027178182 [424,] 0.995657984 0.0086840318 0.0043420159 [425,] 0.993669744 0.0126605125 0.0063302562 [426,] 0.991811601 0.0163767973 0.0081883987 [427,] 0.987195366 0.0256092689 0.0128046345 [428,] 0.982242673 0.0355146539 0.0177573270 [429,] 0.979497899 0.0410042016 0.0205021008 [430,] 0.983771169 0.0324576629 0.0162288314 [431,] 0.976303360 0.0473932808 0.0236966404 [432,] 0.998695852 0.0026082952 0.0013041476 [433,] 0.997661462 0.0046770759 0.0023385379 [434,] 0.995553856 0.0088922886 0.0044461443 [435,] 0.992996953 0.0140060936 0.0070030468 [436,] 0.987187632 0.0256247356 0.0128123678 [437,] 0.977345359 0.0453092827 0.0226546414 [438,] 0.961111179 0.0777776424 0.0388888212 [439,] 0.948345679 0.1033086412 0.0516543206 [440,] 0.925246359 0.1495072820 0.0747536410 [441,] 0.951231430 0.0975371407 0.0487685703 [442,] 0.933200578 0.1335988433 0.0667994216 [443,] 0.927199773 0.1456004545 0.0728002272 [444,] 0.884008597 0.2319828057 0.1159914029 [445,] 0.806970667 0.3860586664 0.1930293332 [446,] 0.868247915 0.2635041700 0.1317520850 [447,] 0.786932901 0.4261341977 0.2130670989 > postscript(file="/var/wessaorg/rcomp/tmp/162wj1450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2ryk21450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/38jwa1450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4m4is1450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/54ob91450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 518 Frequency = 1 131 132 133 134 135 -0.5651075033 -0.2101959806 -0.0347462328 -0.0090670333 0.0299112654 136 137 138 139 140 0.1163428321 -0.1544634510 0.0012978649 1.4408999107 -0.8343102441 141 142 143 144 145 -2.0950567408 -2.0255707978 0.0846800517 -0.0012001695 0.0080856453 146 147 148 149 150 -0.0382608082 -0.0686517601 -0.0653739346 -0.2046854650 -0.9264933070 151 152 153 154 155 0.1463882798 -2.1453188148 3.0350919871 0.0128228218 2.8611153368 156 157 158 159 160 0.7509599176 0.0576843297 -0.0763289644 0.1257706120 -0.0307562923 161 162 163 164 165 -0.2654614735 -0.8784856959 -0.5882866973 -0.4178466104 -3.5848120134 166 167 168 169 170 3.3145698726 2.8549885380 0.7213591775 -0.1055030606 0.0056485052 171 172 173 174 175 -0.0425181011 0.0315560212 -0.1669995262 -0.4590847233 0.1724047558 176 177 178 179 180 0.2018949223 -2.1073856012 -1.5739165303 1.5654235287 1.4542989896 181 182 183 184 185 -0.1181108782 -0.0412109889 -0.0332427167 -0.0207891795 -0.1607660395 186 187 188 189 190 -0.3497877038 -0.8726269785 -0.8878453676 -0.3516111284 -0.0601801770 191 192 193 194 195 -0.1193587869 -0.8402779998 0.1949345316 -0.0221189239 0.0095891613 196 197 198 199 200 -0.0281917674 -0.0590526054 -0.2529855576 -0.8328990601 -1.6858765590 201 202 203 204 205 -1.7730688254 -1.6473809299 4.3349779068 0.8727778401 1.8729231019 206 207 208 209 210 -0.2038677209 0.1366095384 0.0359105285 0.1560959674 -0.1291980967 211 212 213 214 215 -0.6693633534 -1.1475085435 -2.3786969180 0.7177722238 -1.1514175844 216 217 218 219 220 0.5771728691 -0.4860985665 0.2236142503 -0.1495199569 0.0125246321 221 222 223 224 225 -0.0365375406 -0.2959547207 -0.1591241862 -0.6617922049 -0.5756150418 226 227 228 229 230 -0.8202718663 2.0897003807 0.6403718906 -0.4204216173 0.0111043691 231 232 233 234 235 0.0731845455 -0.0249920397 0.0197922516 -0.2606821767 -0.2114562076 236 237 238 239 240 -0.2814520430 -0.4246557538 -0.2675782150 -2.7895310212 -0.5982194635 241 242 243 244 245 -0.0812319366 -0.0543896762 -0.1110484793 -0.0293636640 -0.1231033388 246 247 248 249 250 -0.2264824873 0.8636415131 3.9234195418 3.5723115657 3.3031211438 251 252 253 254 255 -1.3824175997 -0.6057576147 -0.0897210044 0.1106469349 -0.0065469453 256 257 258 259 260 -0.0886001596 0.9214999475 -0.1648571239 1.6636375545 0.2900541326 261 262 263 264 265 0.7680680395 0.3388162481 -1.5425283160 -0.5640720036 -0.4108409500 266 267 268 269 270 -0.0982091295 0.0141876626 0.0537612484 -0.1074882935 -0.0475588407 271 272 273 274 275 -0.2119311465 2.1305177782 2.4325750494 -1.5791836785 -0.5495167070 276 277 278 279 280 -0.6321192234 0.0208672342 -0.0064114438 0.0023539016 -0.0789016227 281 282 283 284 285 0.7567070189 1.1043440061 -0.7975873443 -0.0389593102 -1.0326455134 286 287 288 289 290 -1.7082366184 -0.8271977113 -0.5558201244 -0.0170151354 0.0056794206 291 292 293 294 295 0.0807522746 0.1180999956 -0.2565416899 -0.1980051786 1.1909268640 296 297 298 299 300 -0.9465988947 -1.0861090168 0.4776611084 -1.5470399077 -0.2196384107 301 302 303 304 305 0.0092551087 0.0028361570 -0.1429892213 -0.0385048586 -0.2165816686 306 307 308 309 310 0.8075802140 -0.1319356920 1.6201145399 0.5848940970 -1.0795695839 311 312 313 314 315 -0.7776991740 -0.0542750923 -0.0108440941 -0.0042933726 0.0336409615 316 317 318 319 320 -0.0143827233 -0.2542072382 0.8855646549 -0.0528514006 -0.6061660206 321 322 323 324 325 0.0066691572 -0.3022712126 -1.3164414241 0.6708115484 -0.4177738462 326 327 328 329 330 0.0102270171 -0.0075000125 0.0554104919 0.8790622051 -0.7173612719 331 332 333 334 335 0.2436789352 3.6790954746 -1.3371869337 0.2590867766 -1.0745767932 336 337 338 339 340 -0.4719969868 -0.0756332617 0.0535897392 0.0241894018 -0.1241169231 341 342 343 344 345 -0.2537963514 -0.0609544847 -0.8043253740 -1.6703037028 1.2148399500 346 347 348 349 350 0.7372609675 -1.2015562991 -0.6089249273 1.0480308889 -0.1273148650 351 352 353 354 355 0.0320278356 0.0771574332 -0.3013371020 -0.4536039166 0.3333971296 356 357 358 359 360 1.3090398998 0.2920570534 1.6155602158 -0.4280314933 -0.1173290386 361 362 363 364 365 -0.2401295040 0.1324414910 -0.0741575148 -0.0464805936 0.0248601975 366 367 368 369 370 0.9734694104 1.0359154086 -0.3563934956 -1.3895135209 -1.2747090705 371 372 373 374 375 -0.0159652306 -0.1421713470 -0.2213404090 -0.0506332081 -0.0029925791 376 377 378 379 380 0.0775926215 -0.1236100195 -0.3823044176 -0.2998533613 -1.4562821212 381 382 383 384 385 1.9445506864 -1.2656366468 1.9219676362 -0.3789333316 0.0456408262 386 387 388 389 390 0.0208589606 0.0945264564 -0.0770573110 0.6865619423 0.6913039106 391 392 393 394 395 -1.1464265274 -0.0580282070 0.3303916041 -0.2000440282 1.2655735550 396 397 398 399 400 0.0190659432 -0.1924955657 -0.0004151990 0.0383336729 0.0242899272 401 402 403 404 405 -0.4066543888 1.7741365367 -0.7891429160 -1.7108580796 -0.5663853252 406 407 408 409 410 -1.2082850725 -1.0979756953 0.2072938381 -0.1897891620 -0.0417446361 411 412 413 414 415 -0.0503825969 0.1037906820 -0.2043182278 -0.8918768532 -0.6022150785 416 417 418 419 420 -0.4840145859 -1.4376573880 -1.1204531703 -0.6812179608 0.0094029397 421 422 423 424 425 -0.0441686772 0.0343915429 -0.0448813120 -0.1325799712 -0.0109824595 426 427 428 429 430 -0.7112106855 -0.8538906400 1.4956455427 2.6368971530 -0.0391934380 431 432 433 434 435 0.5544961937 0.9502935616 0.0192396039 0.0404074069 0.0286460816 436 437 438 439 440 -0.0460467572 -0.1641994678 -0.3195162809 1.7293231207 0.0413273645 441 442 443 444 445 -0.9195214094 1.6576369362 0.0350221017 -0.3670790908 0.0764249513 446 447 448 449 450 0.0043624436 -0.0515509885 0.0492069935 -0.1199135500 1.5345668338 451 452 453 454 455 -1.0442814877 -2.0121097821 -1.6404090959 -1.0560813289 0.2749196725 456 457 458 459 460 -0.3226297130 -0.0709225235 -0.0618201861 0.0598835511 0.1217875270 461 462 463 464 465 -0.1421737987 -0.8238995679 -0.3555395644 0.5983324610 -0.1156128899 466 467 468 469 470 0.0037807513 -0.6076693507 0.1122202569 -0.1254285879 0.0659872074 471 472 473 474 475 -0.0313996234 -0.1375747667 0.0507346579 -0.2776370276 -0.3033457614 476 477 478 479 480 1.4190034871 2.7624094702 -0.3674358528 0.6985787415 -0.1873167702 481 482 483 484 485 -0.0135846559 0.0002271966 0.0243136617 -0.0607899744 -0.0129732121 486 487 488 489 490 0.8872384933 2.3813208035 -0.7398614499 -1.8993141242 0.1888659888 491 492 493 494 495 0.3080729651 -0.1673612161 -0.0498324966 -0.0322694042 -0.0512130988 496 497 498 499 500 0.1410419512 0.2571256484 -0.5535302842 1.5239557915 1.9574179676 501 502 503 504 505 -1.9816430042 2.9189815533 -1.3986310978 0.1994618488 -0.0164893925 506 507 508 509 510 0.0985178212 -0.0743974713 -0.0667507795 -0.1348092226 0.4254820382 511 512 513 514 515 -1.5603753761 -1.7401773732 0.3234954768 1.1992479034 -0.8683805404 516 517 518 519 520 -0.0439995015 -0.0047733233 -0.0472990836 0.1094461278 1.0724604357 521 522 523 524 525 -0.3444775587 -0.8505705630 -0.6947620481 -1.8741704212 0.7396642913 526 527 528 529 530 -1.0303586716 -0.1505680042 -0.0555322760 0.0231818227 0.0273901957 531 532 533 534 535 0.0036112891 -0.3212869412 0.1318735963 0.6839461952 -0.6613754049 536 537 538 539 540 1.5627561252 -0.9163529828 -1.2829187452 -0.2052478188 0.0572741503 541 542 543 544 545 -0.0499444194 -0.0781470475 -0.0280098861 -0.2030393860 0.0835118072 546 547 548 549 550 0.7803392236 -0.9503383438 -1.1851868812 -1.3322834904 -1.1657198210 551 552 553 554 555 1.5998121984 -0.3701027733 0.0399966386 0.0347860974 0.0571468927 556 557 558 559 560 -0.0329041721 0.0460997935 0.3097361819 4.0321668500 3.4844339070 561 562 563 564 565 -0.1884886520 2.6142382113 -1.0297939021 0.3272234395 -0.0080982091 566 567 568 569 570 0.0560104043 -0.1889414752 -0.1458617021 0.2761649247 0.3365553160 571 572 573 574 575 0.7554023321 1.3544768319 -0.7930678998 1.2326405392 0.3689498589 576 577 578 579 580 -0.1602018340 -0.0137649707 -0.0158569210 0.1048892561 -0.1226720866 581 582 583 584 585 -0.1121097103 0.1222793076 1.4792859807 -3.8141355224 -1.5247982073 586 587 588 589 590 0.2090852589 -0.4134111072 0.0213683733 -0.0123656436 -0.0291357838 591 592 593 594 595 -0.0033933022 0.0697125759 0.0906214614 -0.9027090086 -0.4980683683 596 597 598 599 600 1.0334199492 2.6635492094 -0.9925095303 0.5385366482 -0.0263395719 601 602 603 604 605 0.0905004906 0.0283346583 0.0403340064 -0.1818354527 -0.1391962210 606 607 608 609 610 0.5880274414 -1.9846240202 0.7502802898 -0.2235884127 1.7385024680 611 612 613 614 615 -0.1953138814 0.0589306858 -0.0867220834 0.0339550902 0.0035001540 616 617 618 619 620 0.1039718332 0.0184976644 -0.6579795614 -2.1753622659 -0.2041801604 621 622 623 624 625 3.7255461546 0.8075292974 -0.7364577585 0.1307163684 0.0713132362 626 627 628 629 630 0.0210635898 0.0069055941 -0.1015114060 -0.0478181160 0.4759749542 631 632 633 634 635 0.0458821969 -0.4158202463 1.1471338694 2.0204058277 1.4690902649 636 637 638 639 640 -0.1702021234 0.0297117979 0.0167138969 0.0295831373 -0.0442532650 641 642 643 644 645 0.1250486524 -0.5891130478 0.2137611778 0.5239664265 3.4953350747 646 647 648 -3.3000811280 -0.1528529097 0.0906929455 > postscript(file="/var/wessaorg/rcomp/tmp/693yp1450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 518 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.5651075033 NA 1 -0.2101959806 -0.5651075033 2 -0.0347462328 -0.2101959806 3 -0.0090670333 -0.0347462328 4 0.0299112654 -0.0090670333 5 0.1163428321 0.0299112654 6 -0.1544634510 0.1163428321 7 0.0012978649 -0.1544634510 8 1.4408999107 0.0012978649 9 -0.8343102441 1.4408999107 10 -2.0950567408 -0.8343102441 11 -2.0255707978 -2.0950567408 12 0.0846800517 -2.0255707978 13 -0.0012001695 0.0846800517 14 0.0080856453 -0.0012001695 15 -0.0382608082 0.0080856453 16 -0.0686517601 -0.0382608082 17 -0.0653739346 -0.0686517601 18 -0.2046854650 -0.0653739346 19 -0.9264933070 -0.2046854650 20 0.1463882798 -0.9264933070 21 -2.1453188148 0.1463882798 22 3.0350919871 -2.1453188148 23 0.0128228218 3.0350919871 24 2.8611153368 0.0128228218 25 0.7509599176 2.8611153368 26 0.0576843297 0.7509599176 27 -0.0763289644 0.0576843297 28 0.1257706120 -0.0763289644 29 -0.0307562923 0.1257706120 30 -0.2654614735 -0.0307562923 31 -0.8784856959 -0.2654614735 32 -0.5882866973 -0.8784856959 33 -0.4178466104 -0.5882866973 34 -3.5848120134 -0.4178466104 35 3.3145698726 -3.5848120134 36 2.8549885380 3.3145698726 37 0.7213591775 2.8549885380 38 -0.1055030606 0.7213591775 39 0.0056485052 -0.1055030606 40 -0.0425181011 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-0.6947620481 394 0.7396642913 -1.8741704212 395 -1.0303586716 0.7396642913 396 -0.1505680042 -1.0303586716 397 -0.0555322760 -0.1505680042 398 0.0231818227 -0.0555322760 399 0.0273901957 0.0231818227 400 0.0036112891 0.0273901957 401 -0.3212869412 0.0036112891 402 0.1318735963 -0.3212869412 403 0.6839461952 0.1318735963 404 -0.6613754049 0.6839461952 405 1.5627561252 -0.6613754049 406 -0.9163529828 1.5627561252 407 -1.2829187452 -0.9163529828 408 -0.2052478188 -1.2829187452 409 0.0572741503 -0.2052478188 410 -0.0499444194 0.0572741503 411 -0.0781470475 -0.0499444194 412 -0.0280098861 -0.0781470475 413 -0.2030393860 -0.0280098861 414 0.0835118072 -0.2030393860 415 0.7803392236 0.0835118072 416 -0.9503383438 0.7803392236 417 -1.1851868812 -0.9503383438 418 -1.3322834904 -1.1851868812 419 -1.1657198210 -1.3322834904 420 1.5998121984 -1.1657198210 421 -0.3701027733 1.5998121984 422 0.0399966386 -0.3701027733 423 0.0347860974 0.0399966386 424 0.0571468927 0.0347860974 425 -0.0329041721 0.0571468927 426 0.0460997935 -0.0329041721 427 0.3097361819 0.0460997935 428 4.0321668500 0.3097361819 429 3.4844339070 4.0321668500 430 -0.1884886520 3.4844339070 431 2.6142382113 -0.1884886520 432 -1.0297939021 2.6142382113 433 0.3272234395 -1.0297939021 434 -0.0080982091 0.3272234395 435 0.0560104043 -0.0080982091 436 -0.1889414752 0.0560104043 437 -0.1458617021 -0.1889414752 438 0.2761649247 -0.1458617021 439 0.3365553160 0.2761649247 440 0.7554023321 0.3365553160 441 1.3544768319 0.7554023321 442 -0.7930678998 1.3544768319 443 1.2326405392 -0.7930678998 444 0.3689498589 1.2326405392 445 -0.1602018340 0.3689498589 446 -0.0137649707 -0.1602018340 447 -0.0158569210 -0.0137649707 448 0.1048892561 -0.0158569210 449 -0.1226720866 0.1048892561 450 -0.1121097103 -0.1226720866 451 0.1222793076 -0.1121097103 452 1.4792859807 0.1222793076 453 -3.8141355224 1.4792859807 454 -1.5247982073 -3.8141355224 455 0.2090852589 -1.5247982073 456 -0.4134111072 0.2090852589 457 0.0213683733 -0.4134111072 458 -0.0123656436 0.0213683733 459 -0.0291357838 -0.0123656436 460 -0.0033933022 -0.0291357838 461 0.0697125759 -0.0033933022 462 0.0906214614 0.0697125759 463 -0.9027090086 0.0906214614 464 -0.4980683683 -0.9027090086 465 1.0334199492 -0.4980683683 466 2.6635492094 1.0334199492 467 -0.9925095303 2.6635492094 468 0.5385366482 -0.9925095303 469 -0.0263395719 0.5385366482 470 0.0905004906 -0.0263395719 471 0.0283346583 0.0905004906 472 0.0403340064 0.0283346583 473 -0.1818354527 0.0403340064 474 -0.1391962210 -0.1818354527 475 0.5880274414 -0.1391962210 476 -1.9846240202 0.5880274414 477 0.7502802898 -1.9846240202 478 -0.2235884127 0.7502802898 479 1.7385024680 -0.2235884127 480 -0.1953138814 1.7385024680 481 0.0589306858 -0.1953138814 482 -0.0867220834 0.0589306858 483 0.0339550902 -0.0867220834 484 0.0035001540 0.0339550902 485 0.1039718332 0.0035001540 486 0.0184976644 0.1039718332 487 -0.6579795614 0.0184976644 488 -2.1753622659 -0.6579795614 489 -0.2041801604 -2.1753622659 490 3.7255461546 -0.2041801604 491 0.8075292974 3.7255461546 492 -0.7364577585 0.8075292974 493 0.1307163684 -0.7364577585 494 0.0713132362 0.1307163684 495 0.0210635898 0.0713132362 496 0.0069055941 0.0210635898 497 -0.1015114060 0.0069055941 498 -0.0478181160 -0.1015114060 499 0.4759749542 -0.0478181160 500 0.0458821969 0.4759749542 501 -0.4158202463 0.0458821969 502 1.1471338694 -0.4158202463 503 2.0204058277 1.1471338694 504 1.4690902649 2.0204058277 505 -0.1702021234 1.4690902649 506 0.0297117979 -0.1702021234 507 0.0167138969 0.0297117979 508 0.0295831373 0.0167138969 509 -0.0442532650 0.0295831373 510 0.1250486524 -0.0442532650 511 -0.5891130478 0.1250486524 512 0.2137611778 -0.5891130478 513 0.5239664265 0.2137611778 514 3.4953350747 0.5239664265 515 -3.3000811280 3.4953350747 516 -0.1528529097 -3.3000811280 517 0.0906929455 -0.1528529097 518 NA 0.0906929455 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.2101959806 -0.5651075033 [2,] -0.0347462328 -0.2101959806 [3,] -0.0090670333 -0.0347462328 [4,] 0.0299112654 -0.0090670333 [5,] 0.1163428321 0.0299112654 [6,] -0.1544634510 0.1163428321 [7,] 0.0012978649 -0.1544634510 [8,] 1.4408999107 0.0012978649 [9,] -0.8343102441 1.4408999107 [10,] -2.0950567408 -0.8343102441 [11,] -2.0255707978 -2.0950567408 [12,] 0.0846800517 -2.0255707978 [13,] -0.0012001695 0.0846800517 [14,] 0.0080856453 -0.0012001695 [15,] -0.0382608082 0.0080856453 [16,] -0.0686517601 -0.0382608082 [17,] -0.0653739346 -0.0686517601 [18,] -0.2046854650 -0.0653739346 [19,] -0.9264933070 -0.2046854650 [20,] 0.1463882798 -0.9264933070 [21,] -2.1453188148 0.1463882798 [22,] 3.0350919871 -2.1453188148 [23,] 0.0128228218 3.0350919871 [24,] 2.8611153368 0.0128228218 [25,] 0.7509599176 2.8611153368 [26,] 0.0576843297 0.7509599176 [27,] -0.0763289644 0.0576843297 [28,] 0.1257706120 -0.0763289644 [29,] -0.0307562923 0.1257706120 [30,] -0.2654614735 -0.0307562923 [31,] -0.8784856959 -0.2654614735 [32,] -0.5882866973 -0.8784856959 [33,] -0.4178466104 -0.5882866973 [34,] -3.5848120134 -0.4178466104 [35,] 3.3145698726 -3.5848120134 [36,] 2.8549885380 3.3145698726 [37,] 0.7213591775 2.8549885380 [38,] -0.1055030606 0.7213591775 [39,] 0.0056485052 -0.1055030606 [40,] -0.0425181011 0.0056485052 [41,] 0.0315560212 -0.0425181011 [42,] -0.1669995262 0.0315560212 [43,] -0.4590847233 -0.1669995262 [44,] 0.1724047558 -0.4590847233 [45,] 0.2018949223 0.1724047558 [46,] -2.1073856012 0.2018949223 [47,] -1.5739165303 -2.1073856012 [48,] 1.5654235287 -1.5739165303 [49,] 1.4542989896 1.5654235287 [50,] -0.1181108782 1.4542989896 [51,] -0.0412109889 -0.1181108782 [52,] -0.0332427167 -0.0412109889 [53,] -0.0207891795 -0.0332427167 [54,] -0.1607660395 -0.0207891795 [55,] -0.3497877038 -0.1607660395 [56,] -0.8726269785 -0.3497877038 [57,] -0.8878453676 -0.8726269785 [58,] -0.3516111284 -0.8878453676 [59,] -0.0601801770 -0.3516111284 [60,] -0.1193587869 -0.0601801770 [61,] -0.8402779998 -0.1193587869 [62,] 0.1949345316 -0.8402779998 [63,] -0.0221189239 0.1949345316 [64,] 0.0095891613 -0.0221189239 [65,] -0.0281917674 0.0095891613 [66,] -0.0590526054 -0.0281917674 [67,] -0.2529855576 -0.0590526054 [68,] -0.8328990601 -0.2529855576 [69,] -1.6858765590 -0.8328990601 [70,] -1.7730688254 -1.6858765590 [71,] -1.6473809299 -1.7730688254 [72,] 4.3349779068 -1.6473809299 [73,] 0.8727778401 4.3349779068 [74,] 1.8729231019 0.8727778401 [75,] -0.2038677209 1.8729231019 [76,] 0.1366095384 -0.2038677209 [77,] 0.0359105285 0.1366095384 [78,] 0.1560959674 0.0359105285 [79,] -0.1291980967 0.1560959674 [80,] -0.6693633534 -0.1291980967 [81,] -1.1475085435 -0.6693633534 [82,] -2.3786969180 -1.1475085435 [83,] 0.7177722238 -2.3786969180 [84,] -1.1514175844 0.7177722238 [85,] 0.5771728691 -1.1514175844 [86,] -0.4860985665 0.5771728691 [87,] 0.2236142503 -0.4860985665 [88,] -0.1495199569 0.2236142503 [89,] 0.0125246321 -0.1495199569 [90,] -0.0365375406 0.0125246321 [91,] -0.2959547207 -0.0365375406 [92,] -0.1591241862 -0.2959547207 [93,] -0.6617922049 -0.1591241862 [94,] -0.5756150418 -0.6617922049 [95,] -0.8202718663 -0.5756150418 [96,] 2.0897003807 -0.8202718663 [97,] 0.6403718906 2.0897003807 [98,] -0.4204216173 0.6403718906 [99,] 0.0111043691 -0.4204216173 [100,] 0.0731845455 0.0111043691 [101,] -0.0249920397 0.0731845455 [102,] 0.0197922516 -0.0249920397 [103,] -0.2606821767 0.0197922516 [104,] -0.2114562076 -0.2606821767 [105,] -0.2814520430 -0.2114562076 [106,] -0.4246557538 -0.2814520430 [107,] -0.2675782150 -0.4246557538 [108,] -2.7895310212 -0.2675782150 [109,] -0.5982194635 -2.7895310212 [110,] -0.0812319366 -0.5982194635 [111,] -0.0543896762 -0.0812319366 [112,] -0.1110484793 -0.0543896762 [113,] -0.0293636640 -0.1110484793 [114,] -0.1231033388 -0.0293636640 [115,] -0.2264824873 -0.1231033388 [116,] 0.8636415131 -0.2264824873 [117,] 3.9234195418 0.8636415131 [118,] 3.5723115657 3.9234195418 [119,] 3.3031211438 3.5723115657 [120,] -1.3824175997 3.3031211438 [121,] -0.6057576147 -1.3824175997 [122,] -0.0897210044 -0.6057576147 [123,] 0.1106469349 -0.0897210044 [124,] -0.0065469453 0.1106469349 [125,] -0.0886001596 -0.0065469453 [126,] 0.9214999475 -0.0886001596 [127,] -0.1648571239 0.9214999475 [128,] 1.6636375545 -0.1648571239 [129,] 0.2900541326 1.6636375545 [130,] 0.7680680395 0.2900541326 [131,] 0.3388162481 0.7680680395 [132,] -1.5425283160 0.3388162481 [133,] -0.5640720036 -1.5425283160 [134,] -0.4108409500 -0.5640720036 [135,] -0.0982091295 -0.4108409500 [136,] 0.0141876626 -0.0982091295 [137,] 0.0537612484 0.0141876626 [138,] -0.1074882935 0.0537612484 [139,] -0.0475588407 -0.1074882935 [140,] -0.2119311465 -0.0475588407 [141,] 2.1305177782 -0.2119311465 [142,] 2.4325750494 2.1305177782 [143,] -1.5791836785 2.4325750494 [144,] -0.5495167070 -1.5791836785 [145,] -0.6321192234 -0.5495167070 [146,] 0.0208672342 -0.6321192234 [147,] -0.0064114438 0.0208672342 [148,] 0.0023539016 -0.0064114438 [149,] -0.0789016227 0.0023539016 [150,] 0.7567070189 -0.0789016227 [151,] 1.1043440061 0.7567070189 [152,] -0.7975873443 1.1043440061 [153,] -0.0389593102 -0.7975873443 [154,] -1.0326455134 -0.0389593102 [155,] -1.7082366184 -1.0326455134 [156,] -0.8271977113 -1.7082366184 [157,] -0.5558201244 -0.8271977113 [158,] -0.0170151354 -0.5558201244 [159,] 0.0056794206 -0.0170151354 [160,] 0.0807522746 0.0056794206 [161,] 0.1180999956 0.0807522746 [162,] -0.2565416899 0.1180999956 [163,] -0.1980051786 -0.2565416899 [164,] 1.1909268640 -0.1980051786 [165,] -0.9465988947 1.1909268640 [166,] -1.0861090168 -0.9465988947 [167,] 0.4776611084 -1.0861090168 [168,] -1.5470399077 0.4776611084 [169,] -0.2196384107 -1.5470399077 [170,] 0.0092551087 -0.2196384107 [171,] 0.0028361570 0.0092551087 [172,] -0.1429892213 0.0028361570 [173,] -0.0385048586 -0.1429892213 [174,] -0.2165816686 -0.0385048586 [175,] 0.8075802140 -0.2165816686 [176,] -0.1319356920 0.8075802140 [177,] 1.6201145399 -0.1319356920 [178,] 0.5848940970 1.6201145399 [179,] -1.0795695839 0.5848940970 [180,] -0.7776991740 -1.0795695839 [181,] -0.0542750923 -0.7776991740 [182,] -0.0108440941 -0.0542750923 [183,] -0.0042933726 -0.0108440941 [184,] 0.0336409615 -0.0042933726 [185,] -0.0143827233 0.0336409615 [186,] -0.2542072382 -0.0143827233 [187,] 0.8855646549 -0.2542072382 [188,] -0.0528514006 0.8855646549 [189,] -0.6061660206 -0.0528514006 [190,] 0.0066691572 -0.6061660206 [191,] -0.3022712126 0.0066691572 [192,] -1.3164414241 -0.3022712126 [193,] 0.6708115484 -1.3164414241 [194,] -0.4177738462 0.6708115484 [195,] 0.0102270171 -0.4177738462 [196,] -0.0075000125 0.0102270171 [197,] 0.0554104919 -0.0075000125 [198,] 0.8790622051 0.0554104919 [199,] -0.7173612719 0.8790622051 [200,] 0.2436789352 -0.7173612719 [201,] 3.6790954746 0.2436789352 [202,] -1.3371869337 3.6790954746 [203,] 0.2590867766 -1.3371869337 [204,] -1.0745767932 0.2590867766 [205,] -0.4719969868 -1.0745767932 [206,] -0.0756332617 -0.4719969868 [207,] 0.0535897392 -0.0756332617 [208,] 0.0241894018 0.0535897392 [209,] -0.1241169231 0.0241894018 [210,] -0.2537963514 -0.1241169231 [211,] -0.0609544847 -0.2537963514 [212,] -0.8043253740 -0.0609544847 [213,] -1.6703037028 -0.8043253740 [214,] 1.2148399500 -1.6703037028 [215,] 0.7372609675 1.2148399500 [216,] -1.2015562991 0.7372609675 [217,] -0.6089249273 -1.2015562991 [218,] 1.0480308889 -0.6089249273 [219,] -0.1273148650 1.0480308889 [220,] 0.0320278356 -0.1273148650 [221,] 0.0771574332 0.0320278356 [222,] -0.3013371020 0.0771574332 [223,] -0.4536039166 -0.3013371020 [224,] 0.3333971296 -0.4536039166 [225,] 1.3090398998 0.3333971296 [226,] 0.2920570534 1.3090398998 [227,] 1.6155602158 0.2920570534 [228,] -0.4280314933 1.6155602158 [229,] -0.1173290386 -0.4280314933 [230,] -0.2401295040 -0.1173290386 [231,] 0.1324414910 -0.2401295040 [232,] -0.0741575148 0.1324414910 [233,] -0.0464805936 -0.0741575148 [234,] 0.0248601975 -0.0464805936 [235,] 0.9734694104 0.0248601975 [236,] 1.0359154086 0.9734694104 [237,] -0.3563934956 1.0359154086 [238,] -1.3895135209 -0.3563934956 [239,] -1.2747090705 -1.3895135209 [240,] -0.0159652306 -1.2747090705 [241,] -0.1421713470 -0.0159652306 [242,] -0.2213404090 -0.1421713470 [243,] -0.0506332081 -0.2213404090 [244,] -0.0029925791 -0.0506332081 [245,] 0.0775926215 -0.0029925791 [246,] -0.1236100195 0.0775926215 [247,] -0.3823044176 -0.1236100195 [248,] -0.2998533613 -0.3823044176 [249,] -1.4562821212 -0.2998533613 [250,] 1.9445506864 -1.4562821212 [251,] -1.2656366468 1.9445506864 [252,] 1.9219676362 -1.2656366468 [253,] -0.3789333316 1.9219676362 [254,] 0.0456408262 -0.3789333316 [255,] 0.0208589606 0.0456408262 [256,] 0.0945264564 0.0208589606 [257,] -0.0770573110 0.0945264564 [258,] 0.6865619423 -0.0770573110 [259,] 0.6913039106 0.6865619423 [260,] -1.1464265274 0.6913039106 [261,] -0.0580282070 -1.1464265274 [262,] 0.3303916041 -0.0580282070 [263,] -0.2000440282 0.3303916041 [264,] 1.2655735550 -0.2000440282 [265,] 0.0190659432 1.2655735550 [266,] -0.1924955657 0.0190659432 [267,] -0.0004151990 -0.1924955657 [268,] 0.0383336729 -0.0004151990 [269,] 0.0242899272 0.0383336729 [270,] -0.4066543888 0.0242899272 [271,] 1.7741365367 -0.4066543888 [272,] -0.7891429160 1.7741365367 [273,] -1.7108580796 -0.7891429160 [274,] -0.5663853252 -1.7108580796 [275,] -1.2082850725 -0.5663853252 [276,] -1.0979756953 -1.2082850725 [277,] 0.2072938381 -1.0979756953 [278,] -0.1897891620 0.2072938381 [279,] -0.0417446361 -0.1897891620 [280,] -0.0503825969 -0.0417446361 [281,] 0.1037906820 -0.0503825969 [282,] -0.2043182278 0.1037906820 [283,] -0.8918768532 -0.2043182278 [284,] -0.6022150785 -0.8918768532 [285,] -0.4840145859 -0.6022150785 [286,] -1.4376573880 -0.4840145859 [287,] -1.1204531703 -1.4376573880 [288,] -0.6812179608 -1.1204531703 [289,] 0.0094029397 -0.6812179608 [290,] -0.0441686772 0.0094029397 [291,] 0.0343915429 -0.0441686772 [292,] -0.0448813120 0.0343915429 [293,] -0.1325799712 -0.0448813120 [294,] -0.0109824595 -0.1325799712 [295,] -0.7112106855 -0.0109824595 [296,] -0.8538906400 -0.7112106855 [297,] 1.4956455427 -0.8538906400 [298,] 2.6368971530 1.4956455427 [299,] -0.0391934380 2.6368971530 [300,] 0.5544961937 -0.0391934380 [301,] 0.9502935616 0.5544961937 [302,] 0.0192396039 0.9502935616 [303,] 0.0404074069 0.0192396039 [304,] 0.0286460816 0.0404074069 [305,] -0.0460467572 0.0286460816 [306,] -0.1641994678 -0.0460467572 [307,] -0.3195162809 -0.1641994678 [308,] 1.7293231207 -0.3195162809 [309,] 0.0413273645 1.7293231207 [310,] -0.9195214094 0.0413273645 [311,] 1.6576369362 -0.9195214094 [312,] 0.0350221017 1.6576369362 [313,] -0.3670790908 0.0350221017 [314,] 0.0764249513 -0.3670790908 [315,] 0.0043624436 0.0764249513 [316,] -0.0515509885 0.0043624436 [317,] 0.0492069935 -0.0515509885 [318,] -0.1199135500 0.0492069935 [319,] 1.5345668338 -0.1199135500 [320,] -1.0442814877 1.5345668338 [321,] -2.0121097821 -1.0442814877 [322,] -1.6404090959 -2.0121097821 [323,] -1.0560813289 -1.6404090959 [324,] 0.2749196725 -1.0560813289 [325,] -0.3226297130 0.2749196725 [326,] -0.0709225235 -0.3226297130 [327,] -0.0618201861 -0.0709225235 [328,] 0.0598835511 -0.0618201861 [329,] 0.1217875270 0.0598835511 [330,] -0.1421737987 0.1217875270 [331,] -0.8238995679 -0.1421737987 [332,] -0.3555395644 -0.8238995679 [333,] 0.5983324610 -0.3555395644 [334,] -0.1156128899 0.5983324610 [335,] 0.0037807513 -0.1156128899 [336,] -0.6076693507 0.0037807513 [337,] 0.1122202569 -0.6076693507 [338,] -0.1254285879 0.1122202569 [339,] 0.0659872074 -0.1254285879 [340,] -0.0313996234 0.0659872074 [341,] -0.1375747667 -0.0313996234 [342,] 0.0507346579 -0.1375747667 [343,] -0.2776370276 0.0507346579 [344,] -0.3033457614 -0.2776370276 [345,] 1.4190034871 -0.3033457614 [346,] 2.7624094702 1.4190034871 [347,] -0.3674358528 2.7624094702 [348,] 0.6985787415 -0.3674358528 [349,] -0.1873167702 0.6985787415 [350,] -0.0135846559 -0.1873167702 [351,] 0.0002271966 -0.0135846559 [352,] 0.0243136617 0.0002271966 [353,] -0.0607899744 0.0243136617 [354,] -0.0129732121 -0.0607899744 [355,] 0.8872384933 -0.0129732121 [356,] 2.3813208035 0.8872384933 [357,] -0.7398614499 2.3813208035 [358,] -1.8993141242 -0.7398614499 [359,] 0.1888659888 -1.8993141242 [360,] 0.3080729651 0.1888659888 [361,] -0.1673612161 0.3080729651 [362,] -0.0498324966 -0.1673612161 [363,] -0.0322694042 -0.0498324966 [364,] -0.0512130988 -0.0322694042 [365,] 0.1410419512 -0.0512130988 [366,] 0.2571256484 0.1410419512 [367,] -0.5535302842 0.2571256484 [368,] 1.5239557915 -0.5535302842 [369,] 1.9574179676 1.5239557915 [370,] -1.9816430042 1.9574179676 [371,] 2.9189815533 -1.9816430042 [372,] -1.3986310978 2.9189815533 [373,] 0.1994618488 -1.3986310978 [374,] -0.0164893925 0.1994618488 [375,] 0.0985178212 -0.0164893925 [376,] -0.0743974713 0.0985178212 [377,] -0.0667507795 -0.0743974713 [378,] -0.1348092226 -0.0667507795 [379,] 0.4254820382 -0.1348092226 [380,] -1.5603753761 0.4254820382 [381,] -1.7401773732 -1.5603753761 [382,] 0.3234954768 -1.7401773732 [383,] 1.1992479034 0.3234954768 [384,] -0.8683805404 1.1992479034 [385,] -0.0439995015 -0.8683805404 [386,] -0.0047733233 -0.0439995015 [387,] -0.0472990836 -0.0047733233 [388,] 0.1094461278 -0.0472990836 [389,] 1.0724604357 0.1094461278 [390,] -0.3444775587 1.0724604357 [391,] -0.8505705630 -0.3444775587 [392,] -0.6947620481 -0.8505705630 [393,] -1.8741704212 -0.6947620481 [394,] 0.7396642913 -1.8741704212 [395,] -1.0303586716 0.7396642913 [396,] -0.1505680042 -1.0303586716 [397,] -0.0555322760 -0.1505680042 [398,] 0.0231818227 -0.0555322760 [399,] 0.0273901957 0.0231818227 [400,] 0.0036112891 0.0273901957 [401,] -0.3212869412 0.0036112891 [402,] 0.1318735963 -0.3212869412 [403,] 0.6839461952 0.1318735963 [404,] -0.6613754049 0.6839461952 [405,] 1.5627561252 -0.6613754049 [406,] -0.9163529828 1.5627561252 [407,] -1.2829187452 -0.9163529828 [408,] -0.2052478188 -1.2829187452 [409,] 0.0572741503 -0.2052478188 [410,] -0.0499444194 0.0572741503 [411,] -0.0781470475 -0.0499444194 [412,] -0.0280098861 -0.0781470475 [413,] -0.2030393860 -0.0280098861 [414,] 0.0835118072 -0.2030393860 [415,] 0.7803392236 0.0835118072 [416,] -0.9503383438 0.7803392236 [417,] -1.1851868812 -0.9503383438 [418,] -1.3322834904 -1.1851868812 [419,] -1.1657198210 -1.3322834904 [420,] 1.5998121984 -1.1657198210 [421,] -0.3701027733 1.5998121984 [422,] 0.0399966386 -0.3701027733 [423,] 0.0347860974 0.0399966386 [424,] 0.0571468927 0.0347860974 [425,] -0.0329041721 0.0571468927 [426,] 0.0460997935 -0.0329041721 [427,] 0.3097361819 0.0460997935 [428,] 4.0321668500 0.3097361819 [429,] 3.4844339070 4.0321668500 [430,] -0.1884886520 3.4844339070 [431,] 2.6142382113 -0.1884886520 [432,] -1.0297939021 2.6142382113 [433,] 0.3272234395 -1.0297939021 [434,] -0.0080982091 0.3272234395 [435,] 0.0560104043 -0.0080982091 [436,] -0.1889414752 0.0560104043 [437,] -0.1458617021 -0.1889414752 [438,] 0.2761649247 -0.1458617021 [439,] 0.3365553160 0.2761649247 [440,] 0.7554023321 0.3365553160 [441,] 1.3544768319 0.7554023321 [442,] -0.7930678998 1.3544768319 [443,] 1.2326405392 -0.7930678998 [444,] 0.3689498589 1.2326405392 [445,] -0.1602018340 0.3689498589 [446,] -0.0137649707 -0.1602018340 [447,] -0.0158569210 -0.0137649707 [448,] 0.1048892561 -0.0158569210 [449,] -0.1226720866 0.1048892561 [450,] -0.1121097103 -0.1226720866 [451,] 0.1222793076 -0.1121097103 [452,] 1.4792859807 0.1222793076 [453,] -3.8141355224 1.4792859807 [454,] -1.5247982073 -3.8141355224 [455,] 0.2090852589 -1.5247982073 [456,] -0.4134111072 0.2090852589 [457,] 0.0213683733 -0.4134111072 [458,] -0.0123656436 0.0213683733 [459,] -0.0291357838 -0.0123656436 [460,] -0.0033933022 -0.0291357838 [461,] 0.0697125759 -0.0033933022 [462,] 0.0906214614 0.0697125759 [463,] -0.9027090086 0.0906214614 [464,] -0.4980683683 -0.9027090086 [465,] 1.0334199492 -0.4980683683 [466,] 2.6635492094 1.0334199492 [467,] -0.9925095303 2.6635492094 [468,] 0.5385366482 -0.9925095303 [469,] -0.0263395719 0.5385366482 [470,] 0.0905004906 -0.0263395719 [471,] 0.0283346583 0.0905004906 [472,] 0.0403340064 0.0283346583 [473,] -0.1818354527 0.0403340064 [474,] -0.1391962210 -0.1818354527 [475,] 0.5880274414 -0.1391962210 [476,] -1.9846240202 0.5880274414 [477,] 0.7502802898 -1.9846240202 [478,] -0.2235884127 0.7502802898 [479,] 1.7385024680 -0.2235884127 [480,] -0.1953138814 1.7385024680 [481,] 0.0589306858 -0.1953138814 [482,] -0.0867220834 0.0589306858 [483,] 0.0339550902 -0.0867220834 [484,] 0.0035001540 0.0339550902 [485,] 0.1039718332 0.0035001540 [486,] 0.0184976644 0.1039718332 [487,] -0.6579795614 0.0184976644 [488,] -2.1753622659 -0.6579795614 [489,] -0.2041801604 -2.1753622659 [490,] 3.7255461546 -0.2041801604 [491,] 0.8075292974 3.7255461546 [492,] -0.7364577585 0.8075292974 [493,] 0.1307163684 -0.7364577585 [494,] 0.0713132362 0.1307163684 [495,] 0.0210635898 0.0713132362 [496,] 0.0069055941 0.0210635898 [497,] -0.1015114060 0.0069055941 [498,] -0.0478181160 -0.1015114060 [499,] 0.4759749542 -0.0478181160 [500,] 0.0458821969 0.4759749542 [501,] -0.4158202463 0.0458821969 [502,] 1.1471338694 -0.4158202463 [503,] 2.0204058277 1.1471338694 [504,] 1.4690902649 2.0204058277 [505,] -0.1702021234 1.4690902649 [506,] 0.0297117979 -0.1702021234 [507,] 0.0167138969 0.0297117979 [508,] 0.0295831373 0.0167138969 [509,] -0.0442532650 0.0295831373 [510,] 0.1250486524 -0.0442532650 [511,] -0.5891130478 0.1250486524 [512,] 0.2137611778 -0.5891130478 [513,] 0.5239664265 0.2137611778 [514,] 3.4953350747 0.5239664265 [515,] -3.3000811280 3.4953350747 [516,] -0.1528529097 -3.3000811280 [517,] 0.0906929455 -0.1528529097 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.2101959806 -0.5651075033 2 -0.0347462328 -0.2101959806 3 -0.0090670333 -0.0347462328 4 0.0299112654 -0.0090670333 5 0.1163428321 0.0299112654 6 -0.1544634510 0.1163428321 7 0.0012978649 -0.1544634510 8 1.4408999107 0.0012978649 9 -0.8343102441 1.4408999107 10 -2.0950567408 -0.8343102441 11 -2.0255707978 -2.0950567408 12 0.0846800517 -2.0255707978 13 -0.0012001695 0.0846800517 14 0.0080856453 -0.0012001695 15 -0.0382608082 0.0080856453 16 -0.0686517601 -0.0382608082 17 -0.0653739346 -0.0686517601 18 -0.2046854650 -0.0653739346 19 -0.9264933070 -0.2046854650 20 0.1463882798 -0.9264933070 21 -2.1453188148 0.1463882798 22 3.0350919871 -2.1453188148 23 0.0128228218 3.0350919871 24 2.8611153368 0.0128228218 25 0.7509599176 2.8611153368 26 0.0576843297 0.7509599176 27 -0.0763289644 0.0576843297 28 0.1257706120 -0.0763289644 29 -0.0307562923 0.1257706120 30 -0.2654614735 -0.0307562923 31 -0.8784856959 -0.2654614735 32 -0.5882866973 -0.8784856959 33 -0.4178466104 -0.5882866973 34 -3.5848120134 -0.4178466104 35 3.3145698726 -3.5848120134 36 2.8549885380 3.3145698726 37 0.7213591775 2.8549885380 38 -0.1055030606 0.7213591775 39 0.0056485052 -0.1055030606 40 -0.0425181011 0.0056485052 41 0.0315560212 -0.0425181011 42 -0.1669995262 0.0315560212 43 -0.4590847233 -0.1669995262 44 0.1724047558 -0.4590847233 45 0.2018949223 0.1724047558 46 -2.1073856012 0.2018949223 47 -1.5739165303 -2.1073856012 48 1.5654235287 -1.5739165303 49 1.4542989896 1.5654235287 50 -0.1181108782 1.4542989896 51 -0.0412109889 -0.1181108782 52 -0.0332427167 -0.0412109889 53 -0.0207891795 -0.0332427167 54 -0.1607660395 -0.0207891795 55 -0.3497877038 -0.1607660395 56 -0.8726269785 -0.3497877038 57 -0.8878453676 -0.8726269785 58 -0.3516111284 -0.8878453676 59 -0.0601801770 -0.3516111284 60 -0.1193587869 -0.0601801770 61 -0.8402779998 -0.1193587869 62 0.1949345316 -0.8402779998 63 -0.0221189239 0.1949345316 64 0.0095891613 -0.0221189239 65 -0.0281917674 0.0095891613 66 -0.0590526054 -0.0281917674 67 -0.2529855576 -0.0590526054 68 -0.8328990601 -0.2529855576 69 -1.6858765590 -0.8328990601 70 -1.7730688254 -1.6858765590 71 -1.6473809299 -1.7730688254 72 4.3349779068 -1.6473809299 73 0.8727778401 4.3349779068 74 1.8729231019 0.8727778401 75 -0.2038677209 1.8729231019 76 0.1366095384 -0.2038677209 77 0.0359105285 0.1366095384 78 0.1560959674 0.0359105285 79 -0.1291980967 0.1560959674 80 -0.6693633534 -0.1291980967 81 -1.1475085435 -0.6693633534 82 -2.3786969180 -1.1475085435 83 0.7177722238 -2.3786969180 84 -1.1514175844 0.7177722238 85 0.5771728691 -1.1514175844 86 -0.4860985665 0.5771728691 87 0.2236142503 -0.4860985665 88 -0.1495199569 0.2236142503 89 0.0125246321 -0.1495199569 90 -0.0365375406 0.0125246321 91 -0.2959547207 -0.0365375406 92 -0.1591241862 -0.2959547207 93 -0.6617922049 -0.1591241862 94 -0.5756150418 -0.6617922049 95 -0.8202718663 -0.5756150418 96 2.0897003807 -0.8202718663 97 0.6403718906 2.0897003807 98 -0.4204216173 0.6403718906 99 0.0111043691 -0.4204216173 100 0.0731845455 0.0111043691 101 -0.0249920397 0.0731845455 102 0.0197922516 -0.0249920397 103 -0.2606821767 0.0197922516 104 -0.2114562076 -0.2606821767 105 -0.2814520430 -0.2114562076 106 -0.4246557538 -0.2814520430 107 -0.2675782150 -0.4246557538 108 -2.7895310212 -0.2675782150 109 -0.5982194635 -2.7895310212 110 -0.0812319366 -0.5982194635 111 -0.0543896762 -0.0812319366 112 -0.1110484793 -0.0543896762 113 -0.0293636640 -0.1110484793 114 -0.1231033388 -0.0293636640 115 -0.2264824873 -0.1231033388 116 0.8636415131 -0.2264824873 117 3.9234195418 0.8636415131 118 3.5723115657 3.9234195418 119 3.3031211438 3.5723115657 120 -1.3824175997 3.3031211438 121 -0.6057576147 -1.3824175997 122 -0.0897210044 -0.6057576147 123 0.1106469349 -0.0897210044 124 -0.0065469453 0.1106469349 125 -0.0886001596 -0.0065469453 126 0.9214999475 -0.0886001596 127 -0.1648571239 0.9214999475 128 1.6636375545 -0.1648571239 129 0.2900541326 1.6636375545 130 0.7680680395 0.2900541326 131 0.3388162481 0.7680680395 132 -1.5425283160 0.3388162481 133 -0.5640720036 -1.5425283160 134 -0.4108409500 -0.5640720036 135 -0.0982091295 -0.4108409500 136 0.0141876626 -0.0982091295 137 0.0537612484 0.0141876626 138 -0.1074882935 0.0537612484 139 -0.0475588407 -0.1074882935 140 -0.2119311465 -0.0475588407 141 2.1305177782 -0.2119311465 142 2.4325750494 2.1305177782 143 -1.5791836785 2.4325750494 144 -0.5495167070 -1.5791836785 145 -0.6321192234 -0.5495167070 146 0.0208672342 -0.6321192234 147 -0.0064114438 0.0208672342 148 0.0023539016 -0.0064114438 149 -0.0789016227 0.0023539016 150 0.7567070189 -0.0789016227 151 1.1043440061 0.7567070189 152 -0.7975873443 1.1043440061 153 -0.0389593102 -0.7975873443 154 -1.0326455134 -0.0389593102 155 -1.7082366184 -1.0326455134 156 -0.8271977113 -1.7082366184 157 -0.5558201244 -0.8271977113 158 -0.0170151354 -0.5558201244 159 0.0056794206 -0.0170151354 160 0.0807522746 0.0056794206 161 0.1180999956 0.0807522746 162 -0.2565416899 0.1180999956 163 -0.1980051786 -0.2565416899 164 1.1909268640 -0.1980051786 165 -0.9465988947 1.1909268640 166 -1.0861090168 -0.9465988947 167 0.4776611084 -1.0861090168 168 -1.5470399077 0.4776611084 169 -0.2196384107 -1.5470399077 170 0.0092551087 -0.2196384107 171 0.0028361570 0.0092551087 172 -0.1429892213 0.0028361570 173 -0.0385048586 -0.1429892213 174 -0.2165816686 -0.0385048586 175 0.8075802140 -0.2165816686 176 -0.1319356920 0.8075802140 177 1.6201145399 -0.1319356920 178 0.5848940970 1.6201145399 179 -1.0795695839 0.5848940970 180 -0.7776991740 -1.0795695839 181 -0.0542750923 -0.7776991740 182 -0.0108440941 -0.0542750923 183 -0.0042933726 -0.0108440941 184 0.0336409615 -0.0042933726 185 -0.0143827233 0.0336409615 186 -0.2542072382 -0.0143827233 187 0.8855646549 -0.2542072382 188 -0.0528514006 0.8855646549 189 -0.6061660206 -0.0528514006 190 0.0066691572 -0.6061660206 191 -0.3022712126 0.0066691572 192 -1.3164414241 -0.3022712126 193 0.6708115484 -1.3164414241 194 -0.4177738462 0.6708115484 195 0.0102270171 -0.4177738462 196 -0.0075000125 0.0102270171 197 0.0554104919 -0.0075000125 198 0.8790622051 0.0554104919 199 -0.7173612719 0.8790622051 200 0.2436789352 -0.7173612719 201 3.6790954746 0.2436789352 202 -1.3371869337 3.6790954746 203 0.2590867766 -1.3371869337 204 -1.0745767932 0.2590867766 205 -0.4719969868 -1.0745767932 206 -0.0756332617 -0.4719969868 207 0.0535897392 -0.0756332617 208 0.0241894018 0.0535897392 209 -0.1241169231 0.0241894018 210 -0.2537963514 -0.1241169231 211 -0.0609544847 -0.2537963514 212 -0.8043253740 -0.0609544847 213 -1.6703037028 -0.8043253740 214 1.2148399500 -1.6703037028 215 0.7372609675 1.2148399500 216 -1.2015562991 0.7372609675 217 -0.6089249273 -1.2015562991 218 1.0480308889 -0.6089249273 219 -0.1273148650 1.0480308889 220 0.0320278356 -0.1273148650 221 0.0771574332 0.0320278356 222 -0.3013371020 0.0771574332 223 -0.4536039166 -0.3013371020 224 0.3333971296 -0.4536039166 225 1.3090398998 0.3333971296 226 0.2920570534 1.3090398998 227 1.6155602158 0.2920570534 228 -0.4280314933 1.6155602158 229 -0.1173290386 -0.4280314933 230 -0.2401295040 -0.1173290386 231 0.1324414910 -0.2401295040 232 -0.0741575148 0.1324414910 233 -0.0464805936 -0.0741575148 234 0.0248601975 -0.0464805936 235 0.9734694104 0.0248601975 236 1.0359154086 0.9734694104 237 -0.3563934956 1.0359154086 238 -1.3895135209 -0.3563934956 239 -1.2747090705 -1.3895135209 240 -0.0159652306 -1.2747090705 241 -0.1421713470 -0.0159652306 242 -0.2213404090 -0.1421713470 243 -0.0506332081 -0.2213404090 244 -0.0029925791 -0.0506332081 245 0.0775926215 -0.0029925791 246 -0.1236100195 0.0775926215 247 -0.3823044176 -0.1236100195 248 -0.2998533613 -0.3823044176 249 -1.4562821212 -0.2998533613 250 1.9445506864 -1.4562821212 251 -1.2656366468 1.9445506864 252 1.9219676362 -1.2656366468 253 -0.3789333316 1.9219676362 254 0.0456408262 -0.3789333316 255 0.0208589606 0.0456408262 256 0.0945264564 0.0208589606 257 -0.0770573110 0.0945264564 258 0.6865619423 -0.0770573110 259 0.6913039106 0.6865619423 260 -1.1464265274 0.6913039106 261 -0.0580282070 -1.1464265274 262 0.3303916041 -0.0580282070 263 -0.2000440282 0.3303916041 264 1.2655735550 -0.2000440282 265 0.0190659432 1.2655735550 266 -0.1924955657 0.0190659432 267 -0.0004151990 -0.1924955657 268 0.0383336729 -0.0004151990 269 0.0242899272 0.0383336729 270 -0.4066543888 0.0242899272 271 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0.0273901957 0.0231818227 400 0.0036112891 0.0273901957 401 -0.3212869412 0.0036112891 402 0.1318735963 -0.3212869412 403 0.6839461952 0.1318735963 404 -0.6613754049 0.6839461952 405 1.5627561252 -0.6613754049 406 -0.9163529828 1.5627561252 407 -1.2829187452 -0.9163529828 408 -0.2052478188 -1.2829187452 409 0.0572741503 -0.2052478188 410 -0.0499444194 0.0572741503 411 -0.0781470475 -0.0499444194 412 -0.0280098861 -0.0781470475 413 -0.2030393860 -0.0280098861 414 0.0835118072 -0.2030393860 415 0.7803392236 0.0835118072 416 -0.9503383438 0.7803392236 417 -1.1851868812 -0.9503383438 418 -1.3322834904 -1.1851868812 419 -1.1657198210 -1.3322834904 420 1.5998121984 -1.1657198210 421 -0.3701027733 1.5998121984 422 0.0399966386 -0.3701027733 423 0.0347860974 0.0399966386 424 0.0571468927 0.0347860974 425 -0.0329041721 0.0571468927 426 0.0460997935 -0.0329041721 427 0.3097361819 0.0460997935 428 4.0321668500 0.3097361819 429 3.4844339070 4.0321668500 430 -0.1884886520 3.4844339070 431 2.6142382113 -0.1884886520 432 -1.0297939021 2.6142382113 433 0.3272234395 -1.0297939021 434 -0.0080982091 0.3272234395 435 0.0560104043 -0.0080982091 436 -0.1889414752 0.0560104043 437 -0.1458617021 -0.1889414752 438 0.2761649247 -0.1458617021 439 0.3365553160 0.2761649247 440 0.7554023321 0.3365553160 441 1.3544768319 0.7554023321 442 -0.7930678998 1.3544768319 443 1.2326405392 -0.7930678998 444 0.3689498589 1.2326405392 445 -0.1602018340 0.3689498589 446 -0.0137649707 -0.1602018340 447 -0.0158569210 -0.0137649707 448 0.1048892561 -0.0158569210 449 -0.1226720866 0.1048892561 450 -0.1121097103 -0.1226720866 451 0.1222793076 -0.1121097103 452 1.4792859807 0.1222793076 453 -3.8141355224 1.4792859807 454 -1.5247982073 -3.8141355224 455 0.2090852589 -1.5247982073 456 -0.4134111072 0.2090852589 457 0.0213683733 -0.4134111072 458 -0.0123656436 0.0213683733 459 -0.0291357838 -0.0123656436 460 -0.0033933022 -0.0291357838 461 0.0697125759 -0.0033933022 462 0.0906214614 0.0697125759 463 -0.9027090086 0.0906214614 464 -0.4980683683 -0.9027090086 465 1.0334199492 -0.4980683683 466 2.6635492094 1.0334199492 467 -0.9925095303 2.6635492094 468 0.5385366482 -0.9925095303 469 -0.0263395719 0.5385366482 470 0.0905004906 -0.0263395719 471 0.0283346583 0.0905004906 472 0.0403340064 0.0283346583 473 -0.1818354527 0.0403340064 474 -0.1391962210 -0.1818354527 475 0.5880274414 -0.1391962210 476 -1.9846240202 0.5880274414 477 0.7502802898 -1.9846240202 478 -0.2235884127 0.7502802898 479 1.7385024680 -0.2235884127 480 -0.1953138814 1.7385024680 481 0.0589306858 -0.1953138814 482 -0.0867220834 0.0589306858 483 0.0339550902 -0.0867220834 484 0.0035001540 0.0339550902 485 0.1039718332 0.0035001540 486 0.0184976644 0.1039718332 487 -0.6579795614 0.0184976644 488 -2.1753622659 -0.6579795614 489 -0.2041801604 -2.1753622659 490 3.7255461546 -0.2041801604 491 0.8075292974 3.7255461546 492 -0.7364577585 0.8075292974 493 0.1307163684 -0.7364577585 494 0.0713132362 0.1307163684 495 0.0210635898 0.0713132362 496 0.0069055941 0.0210635898 497 -0.1015114060 0.0069055941 498 -0.0478181160 -0.1015114060 499 0.4759749542 -0.0478181160 500 0.0458821969 0.4759749542 501 -0.4158202463 0.0458821969 502 1.1471338694 -0.4158202463 503 2.0204058277 1.1471338694 504 1.4690902649 2.0204058277 505 -0.1702021234 1.4690902649 506 0.0297117979 -0.1702021234 507 0.0167138969 0.0297117979 508 0.0295831373 0.0167138969 509 -0.0442532650 0.0295831373 510 0.1250486524 -0.0442532650 511 -0.5891130478 0.1250486524 512 0.2137611778 -0.5891130478 513 0.5239664265 0.2137611778 514 3.4953350747 0.5239664265 515 -3.3000811280 3.4953350747 516 -0.1528529097 -3.3000811280 517 0.0906929455 -0.1528529097 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/7tzpv1450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8ie581450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9ao521450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10remo1450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + 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, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, mywarning) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11go4x1450341627.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+')) + a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' ')) + a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+')) + a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' ')) + a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' ')) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12hkyk1450341627.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' ')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' ')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' ')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' ')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' ')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' ')) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13myl71450341627.tab") > if(n < 200) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a, 'Time or Index', 1, TRUE) + a<-table.element(a, 'Actuals', 1, TRUE) + a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) + a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) + a<-table.row.end(a) + for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) + a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' ')) + a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' ')) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/14ow2w1450341627.tab") + if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' ')) + a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' ')) + a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' ')) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15e9ki1450341627.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' ')) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/161mfp1450341627.tab") + } + } > > try(system("convert tmp/162wj1450341626.ps tmp/162wj1450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/2ryk21450341626.ps tmp/2ryk21450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/38jwa1450341626.ps tmp/38jwa1450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/4m4is1450341626.ps tmp/4m4is1450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/54ob91450341626.ps tmp/54ob91450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/693yp1450341626.ps tmp/693yp1450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/7tzpv1450341626.ps tmp/7tzpv1450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/8ie581450341626.ps tmp/8ie581450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/9ao521450341626.ps tmp/9ao521450341626.png",intern=TRUE)) character(0) > try(system("convert tmp/10remo1450341626.ps tmp/10remo1450341626.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.465 0.846 9.350