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Data X:
421 0.458 493 0.714 515 0.715 NA 0.831 398 0.521 400 0.772 598 0.804 477 0.724 597 0.928 573 0.879 532 0.743 433 0.789 384 0.812 501 0.549 517 0.78 556 0.784 578 0.88 423 0.717 464 0.471 449 0.471 407 0.579 474 0.579 494 0.729 450 0.678 554 0.74 534 0.846 582 0.774 433 0.376 413 0.384 403 0.575 442 0.498 563 0.9 NA 0.631 NA 0.361 NA 0.365 551 0.815 592 0.71 498 0.71 580 0.483 438 0.549 524 0.758 489 0.812 483 0.819 547 0.85 568 0.861 545 0.899 NA 0.461 411 0.718 435 0.695 NA 0.695 468 0.705 471 0.679 499 0.657 NA 0.553 355 0.377 552 0.836 444 0.422 448 0.722 510 0.879 564 0.882 395 0.666 397 0.436 524 0.736 565 0.908 444 0.566 529 0.854 367 0.747 500 0.62 390 0.387 NA 0.402 468 0.632 408 0.466 466 0.615 583 0.817 545 0.89 581 0.581 516 0.678 519 0.678 390 0.639 563 0.9 485 0.885 563 0.872 453 0.872 444 0.714 541 0.887 433 0.744 479 0.75 432 0.527 NA 0.599 NA 0.886 579 0.886 385 0.81 529 0.618 404 0.618 581 0.804 488 0.764 483 0.476 323 0.402 326 0.753 NA 0.887 557 0.828 537 0.881 475 0.881 533 0.495 420 0.411 553 0.768 NA 0.692 405 0.405 578 0.823 NA 0.823 NA 0.475 576 0.759 499 0.752 NA 0.752 544 0.627 621 0.656 469 0.682 NA 0.787 515 0.612 347 0.384 441 0.517 395 0.616 NA 0.616 472 0.533 540 0.914 602 0.904 465 0.608 344 0.328 437 0.496 509 0.941 323 0.781 508 0.531 NA 0.77 411 0.679 453 0.757 NA 0.484 534 0.672 540 0.727 525 0.652 540 0.83 538 0.819 431 0.843 318 0.843 572 0.782 556 0.782 373 0.463 360 0.745 448 0.718 NA 0.69 NA 0.69 330 0.825 461 0.483 NA 0.744 509 0.744 NA 0.749 389 0.36 593 0.896 570 0.827 573 0.874 362 0.494 474 0.646 NA 0.646 576 0.868 492 0.74 427 0.74 420 0.468 NA 0.701 419 0.53 515 0.896 558 0.914 453 0.662 538 0.662 362 0.6 392 0.6 495 0.716 424 0.467 484 0.702 488 0.764 541 0.716 543 0.752 481 0.69 389 0.477 539 0.73 376 0.824 584 0.891 531 0.911 589 0.783 506 0.653 NA 0.618 490 0.618 518 0.632 367 0.497 416 0.543 493 0.473
Names of X columns:
TY2011 HDI_Value_2011
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
FALSE
TRUE
FALSE
Chart options
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R Code
library(boot) cat1 <- as.numeric(par1) cat2<- as.numeric(par2) intercept<-as.logical(par3) x <- na.omit(t(x)) rsq <- function(formula, data, indices) { d <- data[indices,] # allows boot to select sample fit <- lm(formula, data=d) return(summary(fit)$r.square) } xdf<-data.frame(na.omit(t(y))) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) xdf <- data.frame(xdf[[cat1]], xdf[[cat2]]) names(xdf)<-c('Y', 'X') if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) ) (results <- boot(data=xdf, statistic=rsq, R=1000, formula=Y~X)) sumlmxdf<-summary(lmxdf) (aov.xdf<-aov(lmxdf) ) (anova.xdf<-anova(lmxdf) ) load(file='createtable') a<-table.start() nc <- ncol(sumlmxdf$'coefficients') nr <- nrow(sumlmxdf$'coefficients') a<-table.row.start(a) a<-table.element(a,'Linear Regression Model', nc+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, lmxdf$call['formula'],nc+1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'coefficients:',1,TRUE) a<-table.element(a, ' ',nc,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) for(i in 1 : nc){ a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE) }#end header a<-table.row.end(a) for(i in 1: nr){ a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE) for(j in 1 : nc){ a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE) } a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a, '- - - ',1,TRUE) a<-table.element(a, ' ',nc,FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Std. Err. ',1,TRUE) a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, '95% CI Multiple R-sq. ',1,TRUE) a<-table.element(a, paste('[',round(boot.ci(results,type='bca')$bca[1,4], digits=3),', ', round(boot.ci(results,type='bca')$bca[1,5], digits=3), ']',sep='') ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ANOVA Statistics', 5+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) a<-table.element(a, 'Df',1,TRUE) a<-table.element(a, 'Sum Sq',1,TRUE) a<-table.element(a, 'Mean Sq',1,TRUE) a<-table.element(a, 'F value',1,TRUE) a<-table.element(a, 'Pr(>F)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, V2,1,TRUE) a<-table.element(a, anova.xdf$Df[1]) a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'F value'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residuals',1,TRUE) a<-table.element(a, anova.xdf$Df[2]) a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3)) a<-table.element(a, ' ') a<-table.element(a, ' ') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') bitmap(file='regressionplot.png') plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution') if(intercept == TRUE) abline(coef(lmxdf), col='red') if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red') dev.off() library(car) bitmap(file='residualsQQplot.png') qqPlot(resid(lmxdf), main='QQplot of Residuals of Fit') dev.off() bitmap(file='residualsplot.png') plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit') dev.off() bitmap(file='cooksDistanceLmplot.png') plot(lmxdf, which=4) dev.off()
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