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Data X:
124252 252101 92 98956 134577 58 98073 198520 62 106816 189326 108 41449 137449 55 76173 65295 8 177551 439387 134 22807 33186 1 126938 178368 64 61680 186657 77 72117 261949 86 79738 191051 93 57793 138866 44 91677 296878 106 64631 192648 63 106385 333462 160 161961 243571 104 112669 263451 86 114029 155679 93 124550 227053 119 105416 240028 107 72875 388549 86 81964 156540 50 104880 148421 92 76302 177732 123 96740 191441 81 93071 249893 93 78912 236812 113 35224 142329 52 90694 259667 113 125369 231625 112 80849 176062 44 104434 286683 123 65702 87485 38 108179 322865 111 63583 247082 77 95066 346011 92 62486 191653 74 31081 114673 33 94584 284224 105 87408 284195 108 68966 155363 66 88766 177306 69 57139 144571 62 90586 140319 50 109249 405267 91 33032 78800 20 96056 201970 101 146648 302674 129 80613 164733 93 87026 194221 89 5950 24188 8 131106 342263 79 32551 65029 21 31701 101097 30 91072 246088 86 159803 273108 116 143950 282220 106 112368 273495 127 82124 214872 75 144068 335121 138 162627 267171 114 55062 187938 55 95329 229512 67 105612 209798 45 62853 201345 88 125976 163833 67 79146 204250 75 108461 197813 114 99971 132955 123 77826 216092 86 22618 73566 22 84892 213198 67 92059 181713 77 77993 148698 105 104155 300103 119 109840 251437 88 238712 197295 78 67486 158163 112 68007 155529 66 48194 132672 58 134796 377205 132 38692 145905 30 93587 223701 100 56622 80953 49 15986 130805 26 113402 135082 67 97967 300805 57 74844 271806 95 136051 150949 139 50548 225805 73 112215 197389 134 59591 156583 37 59938 222599 98 137639 261601 58 143372 178489 78 138599 200657 88 174110 259084 142 135062 313075 127 175681 346933 139 130307 246440 108 139141 252444 128 44244 159965 62 43750 43287 13 48029 172239 89 95216 183738 83 92288 227681 116 94588 260464 157 197426 106288 28 151244 109632 83 139206 268905 72 106271 266805 134 1168 23623 12 71764 152474 106 25162 61857 23 45635 144889 83 101817 346600 126 855 21054 4 100174 224051 71 14116 31414 18 85008 261043 98 124254 197819 66 105793 154984 44 117129 112933 29 8773 38214 16 94747 158671 56 107549 302148 112 97392 177918 46 126893 350552 129 118850 275578 139 234853 368746 136 74783 172464 66 66089 94381 42 95684 243875 70 139537 382487 97 144253 114525 49 153824 335681 113 63995 147989 55 84891 216638 100 61263 192862 80 106221 184818 29 113587 336707 95 113864 215836 114 37238 173260 41 119906 271773 128 135096 130908 142 151611 204009 88 144645 245514 147 0 1 0 6023 14688 4 0 98 0 0 455 0 0 0 0 0 0 0 77457 195765 56 62464 326038 121 0 0 0 0 203 0 1644 7199 7 6179 46660 12 3926 17547 0 42087 107465 37 0 969 0 87656 173102 47
Names of X columns:
GrootteC TimeRFC BloggedC
Response : Variable 1
Factor : Variable 2
Factor : Variable 3
Include Intercept Term ?
TRUE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
cat1 <- as.numeric(par1) # cat2<- as.numeric(par2) # cat3 <- as.numeric(par3) intercept<-as.logical(par4) x <- t(x) x1<-as.numeric(x[,cat1]) f1<-as.character(x[,cat2]) f2 <- as.character(x[,cat3]) xdf<-data.frame(x1,f1, f2) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) (V3 <-dimnames(y)[[1]][cat3]) mynames<- c(V1, V2, V3) xdf2<-xdf names(xdf2)<-mynames names(xdf)<-c('R', 'A', 'B') mynames <- c(V1, V2, V3) if(intercept == FALSE)eval (substitute(lmout<-lm(xdf$R ~ xdf$A * xdf$B- 1, data = xdf), list(xdf=quote(xdf2),R=mynames[1],A=mynames[2],B=mynames[3]) ))else eval(substitute(lmout<-lm(xdf$R ~ xdf$A * xdf$B, data = xdf), list(xdf=quote(xdf2),R=mynames[1],A=mynames[2],B=mynames[3]) )) oldnames<-names(lmout$coeff) newnames<-gsub('xdf2$', '', oldnames) (names(lmout$coeff)<-newnames) (names(lmout$coefficients)<-newnames) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ANOVA Model', length(lmout$coefficients)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) callstr<-gsub('xdf2$', '',as.character(lmout$call$formula)) callstr<-paste(callstr[2], callstr[1], callstr[3]) a<-table.element(a,callstr ,length(lmout$coefficients)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'names',,TRUE) for(i in 1:length(lmout$coefficients)){ a<-table.element(a, names(lmout$coefficients[i]),,FALSE) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'means',,TRUE) for(i in 1:length(lmout$coefficients)){ a<-table.element(a, signif(lmout$coefficients[i], digits=5),,FALSE) } a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') (aov.xdf<-aov(lmout) ) (anova.xdf<-anova(lmout) ) rownames(anova.xdf)<-gsub('xdf2$','',rownames(anova.xdf)) 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, ' ',,TRUE) a<-table.element(a, 'Df',,FALSE) a<-table.element(a, 'Sum Sq',,FALSE) a<-table.element(a, 'Mean Sq',,FALSE) a<-table.element(a, 'F value',,FALSE) a<-table.element(a, 'Pr(>F)',,FALSE) a<-table.row.end(a) for(i in 1 : length(rownames(anova.xdf))-1){ a<-table.row.start(a) a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE) a<-table.element(a, anova.xdf$Df[1],,FALSE) a<-table.element(a, signif(anova.xdf$'Sum Sq'[i], digits=5),,FALSE) a<-table.element(a, signif(anova.xdf$'Mean Sq'[i], digits=5),,FALSE) a<-table.element(a, signif(anova.xdf$'F value'[i], digits=5),,FALSE) a<-table.element(a, signif(anova.xdf$'Pr(>F)'[i], digits=5),,FALSE) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a, 'Residuals',,TRUE) a<-table.element(a, anova.xdf$'Df'[i+1],,FALSE) a<-table.element(a, signif(anova.xdf$'Sum Sq'[i+1], digits=5),,FALSE) a<-table.element(a, signif(anova.xdf$'Mean Sq'[i+1], digits=5),,FALSE) a<-table.element(a, ' ',,FALSE) a<-table.element(a, ' ',,FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') bitmap(file='anovaplot.png') boxplot(R ~ A + B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups') dev.off() bitmap(file='designplot.png') xdf2 <- xdf # to preserve xdf make copy for function names(xdf2) <- c(V1, V2, V3) plot.design(xdf2, main='Design Plot of Group Means') dev.off() bitmap(file='interactionplot.png') interaction.plot(xdf$A, xdf$B, xdf$R, xlab=V2, ylab=V1, trace.label=V3, main='Possible Interactions Between Anova Groups') dev.off() if(intercept==TRUE){ thsd<-TukeyHSD(aov.xdf) names(thsd) <- c(V2, V3, paste(V2, ':', V3, sep='')) bitmap(file='TukeyHSDPlot.png') layout(matrix(c(0,0,1,2,1,2,0,0,3,3,3,3), 2,6)) plot(thsd, las=1) dev.off() } if(intercept==TRUE){ ntables<-length(names(thsd)) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tukey Honest Significant Difference Comparisons', 5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ', 1, TRUE) for(i in 1:4){ a<-table.element(a,colnames(thsd[[1]])[i], 1, TRUE) } a<-table.row.end(a) for(nt in 1:ntables){ for(i in 1:length(rownames(thsd[[nt]]))){ a<-table.row.start(a) a<-table.element(a,rownames(thsd[[nt]])[i], 1, TRUE) for(j in 1:4){ a<-table.element(a,signif(thsd[[nt]][i,j], digits=5), 1, FALSE) } a<-table.row.end(a) } } # end nt a<-table.end(a) table.save(a,file='hsdtable.tab') }#end if hsd tables if(intercept==FALSE){ a<-table.start() a<-table.row.start(a) a<-table.element(a,'TukeyHSD Message', 1,TRUE) a<-table.row.end(a) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Must Include Intercept to use Tukey Test ', 1, FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab') } library(car) lt.lmxdf<-levene.test(lmout) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Levenes Test for Homogeneity of Variance', 4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ', 1, TRUE) for (i in 1:3){ a<-table.element(a,names(lt.lmxdf)[i], 1, FALSE) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Group', 1, TRUE) for (i in 1:3){ a<-table.element(a,signif(lt.lmxdf[[i]][1], digits=5), 1, FALSE) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ', 1, TRUE) a<-table.element(a,lt.lmxdf[[1]][2], 1, FALSE) a<-table.element(a,' ', 1, FALSE) a<-table.element(a,' ', 1, FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab')
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