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Data X:
382 0.466 512 0.714 463 0.715 538 0.83 370 0.524 422 0.773 595 0.806 496 0.728 598 0.931 581 0.88 517 0.745 402 0.788 431 0.813 487 0.554 489 0.776 566 0.785 597 0.88 495 0.731 467 0.473 490 0.473 457 0.58 502 0.58 460 0.729 445 0.681 556 0.742 491 0.852 582 0.776 415 0.385 458 0.386 351 0.579 439 0.501 566 0.901 NA 0.635 NA 0.365 NA 0.37 567 0.819 588 0.715 505 0.708 NA 0.486 418 0.561 495 0.761 491 0.812 470 0.813 521 0.848 570 0.861 556 0.9 NA 0.465 450 0.716 446 0.698 NA 0.698 476 0.708 475 0.681 482 0.66 NA 0.556 371 0.38 563 0.839 434 0.429 444 0.722 517 0.879 555 0.884 457 0.67 464 0.438 571 0.741 570 0.911 427 0.571 533 0.854 366 0.743 496 0.626 386 0.391 NA 0.396 457 0.635 407 0.469 417 0.616 585 0.817 534 0.893 582 0.583 511 0.681 517 0.681 357 0.641 552 0.901 488 0.886 563 0.872 445 0.872 433 0.715 544 0.888 457 0.744 499 0.755 438 0.531 NA 0.606 NA 0.888 586 0.888 378 0.813 514 0.621 350 0.621 570 0.808 498 0.764 401 0.481 306 0.407 432 0.789 561 0.888 548 0.831 559 0.88 507 0.88 447 0.496 387 0.411 540 0.77 563 0.695 382 0.406 615 0.827 NA 0.827 420 0.485 528 0.769 504 0.755 NA 0.755 545 0.629 NA 0.657 470 0.692 NA 0.787 515 0.614 330 0.389 492 0.52 368 0.62 NA 0.62 461 0.537 540 0.915 584 0.908 454 0.611 401 0.335 440 0.5 519 0.943 381 0.781 512 0.535 NA 0.773 400 0.683 485 0.761 NA 0.49 457 0.67 539 0.734 539 0.656 554 0.833 549 0.822 397 0.85 375 0.85 570 0.782 557 0.782 356 0.502 389 0.749 413 0.715 NA 0.693 NA 0.693 301 0.833 421 0.484 NA 0.743 528 0.743 NA 0.755 345 0.368 595 0.899 576 0.829 522 0.874 433 0.489 475 0.654 NA 0.654 573 0.869 490 0.745 458 0.745 438 0.472 NA 0.702 499 0.529 503 0.897 577 0.916 458 0.662 546 0.662 474 0.603 388 0.603 498 0.72 428 0.47 NA 0.704 499 0.765 507 0.719 549 0.756 424 0.693 381 0.48 540 0.733 390 0.825 587 0.89 533 0.912 591 0.787 532 0.657 NA 0.617 483 0.617 535 0.635 378 0.499 428 0.554 489 0.484
Names of X columns:
TY2012 HDI_Value_2012
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
TRUE
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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