FSRedaCens enables to monitor several quantities in each step of the forward search
Example of use of FSRedaCens based on a starting point coming from LMS.
n=200;
p=3;
rng default
rng(123456)
X=randn(n,p);
% Uncontaminated data
y=randn(n,1)+1;
% Contaminated data
ycont=y;
cont=1:5;
ycont(cont)=ycont(cont)+5;
ycont(ycont<=0)=0;
[out]=LXS(ycont,X,'nsamp',1000);
out=FSRedaCens(ycont,X,out.bs);
fground=struct;
fground.funit=cont;
resfwdplot(out,'fground',fground)
Example of use of function FSReda using a random start and traditional t-stat monitoring.
n=200;
p=3;
rng default
rng(123456)
X=randn(n,p);
% Uncontaminated data
y=randn(n,1);
% Contaminated data
ycont=y;
ycont(1:5)=ycont(1:5)+6;
ycont(ycont<=0)=0;
out=FSRedaCens(ycont,X,0,'tstat','trad');
In the example of Kleiber and Zeileis (2008, p. 142), the number of a person's extramarital sexual inter-courses ("affairs") in the past year is regressed on the person's age, number of years married, religiousness, occupation, and won rating of the marriage. The dependent variable is left-censored at zero and not right-censored. Hence this is a standard Tobit model which can be estimated by the following lines
load affairs.mat
X=affairs{:,["age" "yearsmarried" "religiousness" "occupation" "rating"]};
y=affairs{:,"affairs"};
outLXS=LXS(y,X);
[~,sor]=sort(abs(outLXS.residuals))
out=FSRedaCens(y,X,sor(1:100));
resfwdplot(out)
Total estimated time to complete LMS: 0.05 seconds Attention: there was an exact fit. Robust estimate of s^2 is <1e-7 sor = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 456 457 462 470 471 473 478 479 480 483 484 491 495 500 504 506 507 508 515 521 524 529 530 534 536 544 546 567 576 578 580 581 597 601 460 492 513 517 522 543 554 558 561 566 569 571 575 582 586 599 600 452 453 461 467 476 481 482 501 510 520 525 539 540 563 577 584 585 589 595 454 459 463 468 469 472 477 485 486 487 489 490 498 499 503 505 509 511 512 514 516 518 519 527 528 532 533 545 549 552 557 560 562 570 573 574 583 590 591 593 596 598 455 458 464 465 466 474 475 488 493 494 496 497 502 523 526 531 535 537 538 541 542 547 548 550 551 553 555 556 559 564 565 568 572 579 587 588 592 594 m=100 m=200 m=300 m=400 m=500 m=600
rng default
rng(2)
n=300;
lambda=-0.5;
p=5;
sigma=0.1;
beta=1*ones(p,1);
X=0.2*randn(n,p);
epsilon=randn(n,1);
y=X*beta+sigma*epsilon;
y=normYJ(y,1,lambda,'inverse',true,'Jacobian',false);
sel=1:30;
y(sel)=y(sel)+1.2;
qq=quantile(y,0.3);
y(y<=qq)=qq;
left=min(y);
right=Inf;
% See function FSRfanCens on the procedure to find the correct
% transformation
yf=normYJ(y,1,lambda,'inverse',false,'Jacobian',false);
leftf=normYJ(left,1,lambda,'inverse',false,'Jacobian',false);
rightf=normYJ(right,1,lambda,'inverse',false,'Jacobian',false);
zlimits=[leftf rightf];
% Call to FSRedaCens
outLXS=LXS(yf,X);
out=FSRedaCens(yf,X,outLXS.bs,'left',leftf,'right',rightf,'init',100);
fground.funit=1:30;
resfwdplot(out,'fground',fground);
Total estimated time to complete LMS: 0.03 seconds m=100 m=200 m=300
y
— Response variable.
Vector.Response variable, specified as a vector of length n, where n is the number of observations. Each entry in y is the response for the corresponding row of X.
Missing values (NaN's) and infinite values (Inf's) are allowed, since observations (rows) with missing or infinite values will automatically be excluded from the computations.
Data Types: single| double
X
— Data matrix of explanatory variables (also called 'regressors')
of dimension (n x p-1).
Rows of X represent observations, and
columns represent variables.Missing values (NaN's) and infinite values (Inf's) are allowed, since observations (rows) with missing or infinite values will automatically be excluded from the computations.
Data Types: single| double
bsb
— list of units forming the initial
subset.
Vector or scalar.If bsb=0 (default), then the procedure starts with p units randomly chosen, else if bsb is not 0, the search will start with m0=length(bsb).
Data Types: single| double
Specify optional comma-separated pairs of Name,Value
arguments.
Name
is the argument name and Value
is the corresponding value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.
'balancedSearch',false
, 'conflev',[0.90 0.93]
, 'init',100 starts monitoring from step m=100
, 'left',1
, 'intercept',false
, 'nocheck',true
, 'right',800
, 'tstat','trad'
balancedSearch
—Balanced search.scalar logical.If Balanced search the proportion of observations in the subsets equals (as much as possible) the proportion of units in the sample. The default value of balancedSearch is true.
Example: 'balancedSearch',false
Data Types: logical
conflev
—confidence levels to be used to compute confidence interval
for the elements of and for \sigma^2.vector.The default value of conflev is [0.95 0.99] that is 95% and 99% confidence intervals are computed.
Example: 'conflev',[0.90 0.93]
Data Types: double
init
—Search initialization.scalar.It specifies the point where to initialize the search and start monitoring required diagnostics. If init is not specified it will be set equal to : p+1, if the sample size is smaller than 40;
min(3*p+1,floor(0.5*(n+p+1))), otherwise.
Example: 'init',100 starts monitoring from step m=100
Data Types: double
left
—left limit for the censored dependent variable.scalar.If set to -Inf, the dependent variable is assumed to be not left-censored; default value of left is zero (classical Tobit model).
Example: 'left',1
Data Types: double
intercept
—Indicator for constant term.true (default) | false.Indicator for the constant term (intercept) in the fit, specified as the comma-separated pair consisting of 'Intercept' and either true to include or false to remove the constant term from the model.
Example: 'intercept',false
Data Types: boolean
nocheck
—Check input arguments.boolean.If nocheck is equal to true, no check is performed on matrix y and matrix X. Notice that y and X are left unchanged. In other words the additional column of ones for the intercept is not added. As default nocheck=false. The controls on h, alpha and nsamp still remain
Example: 'nocheck',true
Data Types: boolean
right
—right limit for the censored dependent variable.scalar.If set to Inf, the dependent variable is assumed to be not right-censored; default value of right is Inf (classical Tobit model).
Example: 'right',800
Data Types: double
tstat
—the kind of t-statistics which have to be monitored.character.tstat = 'trad' implies monitoring of traditional t statistics (out.Tols). In this case the estimate of \sigma^2 at step m is based on s^2_m (notice that s^2_m<<\sigma^2 when m/n is small) tstat = 'scal' (default) implies monitoring of rescaled t statistics In this case the estimate of \sigma^2 at step m is based on s^2_m / var_{truncnorm(m/n)} where var_{truncnorm(m/n)} is the variance of the truncated normal distribution.
Example: 'tstat','trad'
Data Types: char
out
— description
StructureStructure which contains the following fields
Value | Description |
---|---|
RES |
n x (n-init+1) = matrix containing the monitoring of scaled residuals: 1st row = residual for first unit; ...; nth row = residual for nth unit. |
LEV |
(n+1) x (n-init+1) = matrix containing the monitoring of leverage: 1st row = leverage for first unit: ...; nth row = leverage for nth unit. |
BB |
n x (n-init+1) matrix containing the information about the units belonging to the subset at each step of the forward search: 1st col = indexes of the units forming subset in the initial step; ...; last column = units forming subset in the final step (all units). |
mdr |
n-init x 3 matrix which contains the monitoring of minimum deletion residual or (m+1)ordered residual at each step of the forward search: 1st col = fwd search index (from init to n-1); 2nd col = minimum deletion residual; 3rd col = (m+1)-ordered residual. Remark: these quantities are stored with sign, that is the min deletion residual is stored with negative sign if it corresponds to a negative residual. |
msr |
n-init+1 x 3 = matrix which contains the monitoring of maximum studentized residual or m-th ordered residual: 1st col = fwd search index (from init to n); 2nd col = maximum studentized residual; 3rd col = (m)-ordered studentized residual. |
nor |
(n-init+1) x 4 matrix containing the monitoring of normality test in each step of the forward search: 1st col = fwd search index (from init to n); 2nd col = Asymmetry test; 3rd col = Kurtosis test; 4th col = Normality test. |
Bols |
(n-init+1) x (p+1) matrix containing the monitoring of estimated beta coefficients in each step of the forward search. |
S2 |
(n-init+1) x 5 matrix containing the monitoring of S2 or R2, F test, in each step of the forward search: 1st col = fwd search index (from init to n); 2nd col = monitoring of S2; 3rd col = monitoring of R2; 4th col = monitoring of rescaled S2. In this case the estimated of \sigma^2 at step m is divided by the consistency factor (to make the estimate asymptotically unbiased). 5th col = monitoring of F test. Note that an asymptotic unbiased estimate of sigma2 is used. |
coo |
(n-init+1) x 3 matrix containing the monitoring of Cook or modified Cook distance in each step of the forward search: 1st col = fwd search index (from init to n); 2nd col = monitoring of Cook distance; 3rd col = monitoring of modified Cook distance. |
Tols |
(n-init+1) x (p+1) matrix containing the monitoring of estimated t-statistics (as specified in option input 'tstat'. in each step of the forward search |
Un |
(n-init) x 11 Matrix which contains the unit(s) included in the subset at each step of the fwd search. REMARK: in every step the new subset is compared with the old subset. Un contains the unit(s) present in the new subset but not in the old one. Un(1,2), for example, contains the unit included in step init+1. Un(end,2) contains the units included in the final step of the search. |
betaINT |
Confidence intervals for the elements of \beta. betaINT is a (n-init+1)-by-2*length(confint)-by-p 3D array. Each third dimension refers to an element of beta: betaINT(:,:,1) is associated with first element of beta; ...; betaINT(:,:,p) is associated with last element of beta. The first two columns contain the lower and upper confidence limits associated with conflev(1). Columns three and four contain the lower and upper confidence limits associated with conflev(2); ...; The last two columns contain the lower and upper confidence limits associated with conflev(end). For example, betaint(:,3:4,5) contain the lower and upper confidence limits for the fifth element of beta using confidence level specified in the second element of input option conflev. |
sigma2INT |
confidence interval for \sigma^2. 1st col = fwd search index; 2nd col = lower confidence limit based on conflev(1); 3rd col = upper confidence limit based on conflev(1); 4th col = lower confidence limit based on conflev(2); 5th col = upper confidence limit based on conflev(2); ... penultimate col = lower confidence limit based on conflev(end); last col = upper confidence limit based on conflev(end); |
y |
A vector with n elements that contains the response variable which has been used |
X |
Data matrix of explanatory variables which has been used (it also contains the column of ones if input option intercept was missing or equal to 1) |
class |
'FSReda'. |
Atkinson, A.C. and Riani, M. (2000), "Robust Diagnostic Regression Analysis", Springer Verlag, New York.
LXS
|
FSReda
|
FSRfanCens
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function-alpha.html |
function-cate.html |
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Functions |
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