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Data X:
307 0.468 487 0.716 471 0.717 NA 0.83 370 0.526 353 0.774 591 0.808 554 0.73 590 0.933 591 0.881 514 0.747 424 0.789 427 0.815 488 0.558 484 0.776 575 0.786 591 0.881 447 0.732 459 0.476 558 0.476 461 0.584 451 0.584 442 0.731 444 0.683 560 0.744 441 0.852 572 0.777 406 0.388 412 0.389 338 0.584 445 0.504 565 0.902 NA 0.636 NA 0.341 NA 0.372 574 0.822 582 0.719 499 0.711 NA 0.488 397 0.564 521 0.763 521 0.812 386 0.815 523 0.845 571 0.861 550 0.9 NA 0.467 383 0.717 429 0.7 NA 0.7 457 0.711 463 0.682 450 0.662 NA 0.556 389 0.381 564 0.84 427 0.435 NA 0.724 522 0.879 559 0.884 468 0.674 464 0.441 527 0.744 570 0.911 419 0.573 526 0.853 NA 0.744 490 0.628 382 0.392 NA 0.396 417 0.638 403 0.471 462 0.617 580 0.818 506 0.895 577 0.586 509 0.684 520 0.684 360 0.642 562 0.899 484 0.888 561 0.872 448 0.872 428 0.715 542 0.89 440 0.745 501 0.757 427 0.535 NA 0.607 NA 0.891 581 0.891 344 0.814 536 0.628 383 0.628 562 0.81 492 0.765 490 0.486 314 0.412 355 0.784 556 0.889 550 0.834 558 0.881 558 0.881 468 0.498 399 0.414 549 0.773 400 0.698 433 0.407 547 0.829 NA 0.829 354 0.487 557 0.771 496 0.756 NA 0.756 528 0.63 NA 0.663 474 0.698 477 0.789 505 0.617 369 0.393 462 0.524 347 0.624 NA 0.624 478 0.54 536 0.915 608 0.91 463 0.614 408 0.337 418 0.504 540 0.944 405 0.783 497 0.537 NA 0.775 389 0.686 480 0.765 NA 0.491 508 0.676 543 0.737 516 0.66 565 0.834 559 0.822 441 0.851 312 0.851 576 0.785 553 0.785 396 0.506 368 0.75 420 0.714 NA 0.694 NA 0.694 311 0.836 452 0.485 551 0.745 NA 0.745 NA 0.756 341 0.374 605 0.901 527 0.83 557 0.874 398 0.491 475 0.658 NA 0.658 578 0.869 477 0.75 NA 0.75 435 0.473 403 0.705 446 0.53 510 0.898 580 0.917 470 0.658 548 0.658 437 0.607 368 0.607 498 0.722 428 0.473 NA 0.705 479 0.766 497 0.721 550 0.759 472 0.698 407 0.484 542 0.734 385 0.827 590 0.892 532 0.914 587 0.79 521 0.661 NA 0.616 496 0.616 542 0.638 395 0.5 417 0.561 488 0.492
Names of X columns:
TY2013 HDI_Value_2013
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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