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Data X:
439 0.453 488 0.708 517 0.709 569 0.832 353 0.504 416 0.778 587 0.799 464 0.72 596 0.926 576 0.877 505 0.743 445 0.788 433 0.812 487 0.539 511 0.779 583 0.779 581 0.877 468 0.714 417 0.467 NA 0.467 484 0.569 500 0.569 497 0.726 427 0.672 554 0.739 439 0.844 591 0.773 421 0.367 419 0.381 461 0.571 435 0.493 568 0.896 NA 0.622 NA 0.355 NA 0.349 575 0.808 596 0.701 505 0.706 NA 0.479 402 0.565 529 0.75 504 0.806 457 0.824 527 0.848 562 0.858 558 0.898 NA 0.452 433 0.717 438 0.691 NA 0.691 470 0.701 468 0.678 524 0.652 NA 0.559 445 0.373 567 0.83 436 0.409 524 0.721 510 0.877 556 0.879 417 0.662 412 0.44 525 0.733 568 0.904 450 0.556 521 0.856 402 0.746 508 0.613 395 0.38 NA 0.401 429 0.626 417 0.462 458 0.612 568 0.817 549 0.886 578 0.57 517 0.671 518 0.671 393 0.638 561 0.899 483 0.881 555 0.869 439 0.869 444 0.712 545 0.884 447 0.744 481 0.747 434 0.522 NA 0.599 NA 0.882 576 0.882 388 0.807 539 0.614 372 0.614 557 0.809 466 0.759 NA 0.472 332 0.393 337 0.799 NA 0.882 562 0.829 561 0.881 498 0.881 489 0.494 469 0.406 536 0.766 507 0.688 386 0.398 498 0.821 NA 0.821 463 0.475 550 0.753 502 0.748 NA 0.748 531 0.627 515 0.652 476 0.671 NA 0.784 507 0.603 399 0.38 477 0.514 371 0.61 NA 0.61 462 0.527 524 0.904 604 0.903 477 0.604 390 0.323 456 0.492 529 0.939 421 0.78 503 0.526 NA 0.768 415 0.671 456 0.759 NA 0.479 459 0.669 543 0.722 534 0.651 541 0.826 538 0.816 439 0.847 476 0.847 578 0.779 553 0.779 351 0.453 420 0.747 409 0.717 NA 0.688 NA 0.688 355 0.815 462 0.483 NA 0.743 529 0.743 NA 0.763 395 0.353 595 0.894 547 0.826 552 0.873 483 0.489 473 0.638 NA 0.638 579 0.864 518 0.736 438 0.736 395 0.463 412 0.698 460 0.527 532 0.895 560 0.915 457 0.662 534 0.662 416 0.596 403 0.596 498 0.715 429 0.46 NA 0.701 478 0.764 509 0.715 547 0.738 485 0.687 411 0.472 535 0.726 387 0.824 585 0.895 533 0.908 567 0.779 523 0.648 NA 0.617 500 0.617 519 0.629 381 0.484 417 0.53 495 0.459
Names of X columns:
TY2010 HDI_Value_2010
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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