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Data X:
87.28 255 87.28 280.2 87.09 299.9 86.92 339.2 87.59 374.2 90.72 393.5 90.69 389.2 90.3 381.7 89.55 375.2 88.94 369 88.41 357.4 87.82 352.1 87.07 346.5 86.82 342.9 86.4 340.3 86.02 328.3 85.66 322.9 85.32 314.3 85 308.9 84.67 294 83.94 285.6 82.83 281.2 81.95 280.3 81.19 278.8 80.48 274.5 78.86 270.4 69.47 263.4 68.77 259.9 70.06 258 73.95 262.7 75.8 284.7 77.79 311.3 81.57 322.1 83.07 327 84.34 331.3 85.1 333.3 85.25 321.4 84.26 327 83.63 320 86.44 314.7 85.3 316.7 84.1 314.4 83.36 321.3 82.48 318.2 81.58 307.2 80.47 301.3 79.34 287.5 82.13 277.7 81.69 274.4 80.7 258.8 79.88 253.3 79.16 251 78.38 248.4 77.42 249.5 76.47 246.1 75.46 244.5 74.48 243.6 78.27 244 80.7 240.8 79.91 249.8 78.75 248 77.78 259.4 81.14 260.5 81.08 260.8 80.03 261.3 78.91 259.5 78.01 256.6 76.9 257.9 75.97 256.5 81.93 254.2 80.27 253.3 78.67 253.8 77.42 255.5 76.16 257.1 74.7 257.3 76.39 253.2 76.04 252.8 74.65 252 73.29 250.7 71.79 252.2 74.39 250 74.91 251 74.54 253.4 73.08 251.2 72.75 255.6 71.32 261.1 70.38 258.9 70.35 259.9 70.01 261.2 69.36 264.7 67.77 267.1 69.26 266.4 69.8 267.7 68.38 268.6 67.62 267.5 68.39 268.5 66.95 268.5 65.21 270.5 66.64 270.9 63.45 270.1 60.66 269.3 62.34 269.8 60.32 270.1 58.64 264.9 60.46 263.7 58.59 264.8 61.87 263.7 61.85 255.9 67.44 276.2 77.06 360.1 91.74 380.5 93.15 373.7 94.15 369.8 93.11 366.6 91.51 359.3 89.96 345.8 88.16 326.2 86.98 324.5 88.03 328.1 86.24 327.5 84.65 324.4 83.23 316.5 81.7 310.9 80.25 301.5 78.8 291.7 77.51 290.4 76.2 287.4 75.04 277.7 74 281.6 75.49 288 77.14 276 76.15 272.9 76.27 283 78.19 283.3 76.49 276.8 77.31 284.5 76.65 282.7 74.99 281.2 73.51 287.4 72.07 283.1 70.59 284 71.96 285.5 76.29 289.2 74.86 292.5 74.93 296.4 71.9 305.2 71.01 303.9 77.47 311.5 75.78 316.3 76.6 316.7 76.07 322.5 74.57 317.1 73.02 309.8 72.65 303.8 73.16 290.3 71.53 293.7 69.78 291.7 67.98 296.5 69.96 289.1 72.16 288.5 70.47 293.8 68.86 297.7 67.37 305.4 65.87 302.7 72.16 302.5 71.34 303 69.93 294.5 68.44 294.1 67.16 294.5 66.01 297.1 67.25 289.4 70.91 292.4 69.75 287.9 68.59 286.6 67.48 280.5 66.31 272.4 64.81 269.2 66.58 270.6 65.97 267.3 64.7 262.5 64.7 266.8 60.94 268.8 59.08 263.1 58.42 261.2 57.77 266 57.11 262.5 53.31 265.2 49.96 261.3 49.4 253.7 48.84 249.2 48.3 239.1 47.74 236.4 47.24 235.2 46.76 245.2 46.29 246.2 48.9 247.7 49.23 251.4 48.53 253.3 48.03 254.8 54.34 250 53.79 249.3 53.24 241.5 52.96 243.3 52.17 248 51.7 253 58.55 252.9 78.2 251.5 77.03 251.6 76.19 253.5 77.15 259.8 75.87 334.1 95.47 448 109.67 445.8 112.28 445 112.01 448.2 107.93 438.2 105.96 439.8 105.06 423.4 102.98 410.8 102.2 408.4 105.23 406.7 101.85 405.9 99.89 402.7 96.23 405.1 94.76 399.6 91.51 386.5 91.63 381.4 91.54 375.2 85.23 357.7 87.83 359 87.38 355 84.44 352.7 85.19 344.4 84.03 343.8 86.73 338 102.52 339 104.45 333.3 106.98 334.4 107.02 328.3 99.26 330.7 94.45 330 113.44 331.6 157.33 351.2 147.38 389.4 171.89 410.9 171.95 442.8 132.71 462.8 126.02 466.9 121.18 461.7 115.45 439.2 110.48 430.3 117.85 416.1 117.63 402.5 124.65 397.3 109.59 403.3 111.27 395.9 99.78 387.8 98.21 378.6 99.2 377.1 97.97 370.4 89.55 362 87.91 350.3 93.34 348.2 94.42 344.6 93.2 343.5 90.29 342.8 91.46 347.6 89.98 346.6 88.35 349.5 88.41 342.1 82.44 342 79.89 342.8 75.69 339.3 75.66 348.2 84.5 333.7 96.73 334.7 87.48 354 82.39 367.7 83.48 363.3 79.31 358.4 78.16 353.1 72.77 343.1 72.45 344.6 68.46 344.4 67.62 333.9 68.76 331.7 70.07 324.3 68.55 321.2 65.3 322.4 58.96 321.7 59.17 320.5 62.37 312.8 66.28 309.7 55.62 315.6 55.23 309.7 55.85 304.6 56.75 302.5 50.89 301.5 53.88 298.8 52.95 291.3 55.08 293.6 53.61 294.6 58.78 285.9 61.85 297.6 55.91 301.1 53.32 293.8 46.41 297.7 44.57 292.9 50 292.1 50 287.2 53.36 288.2 46.23 283.8 50.45 299.9 49.07 292.4 45.85 293.3 48.45 300.8 49.96 293.7 46.53 293.1 50.51 294.4 47.58 292.1 48.05 291.9 46.84 282.5 47.67 277.9 49.16 287.5 55.54 289.2 55.82 285.6 58.22 293.2 56.19 290.8 57.77 283.1 63.19 275 54.76 287.8 55.74 287.8 62.54 287.4 61.39 284 69.6 277.8 79.23 277.6 80 304.9 93.68 294 107.63 300.9 100.18 324 97.3 332.9 90.45 341.6 80.64 333.4 80.58 348.2 75.82 344.7 85.59 344.7 89.35 329.3 89.42 323.5 104.73 323.2 95.32 317.4 89.27 330.1 90.44 329.2 86.97 334.9 79.98 315.8 81.22 315.4 87.35 319.6 83.64 317.3 82.22 313.8 94.4 315.8 102.18 311.3
Names of X columns:
Colombia USA
Factor 1
Factor 2
Type of test to use
(?)
Pearson Chi-Squared
Exact Pearson Chi-Squared by Simulation
McNemar Chi-Squared
Fisher Exact Test
Chart options
Title:
R Code
library(vcd) cat1 <- as.numeric(par1) # cat2<- as.numeric(par2) # simulate.p.value=FALSE if (par3 == 'Exact Pearson Chi-Squared by Simulation') simulate.p.value=TRUE x <- t(x) (z <- array(unlist(x),dim=c(length(x[,1]),length(x[1,])))) (table1 <- table(z[,cat1],z[,cat2])) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) bitmap(file='pic1.png') assoc(ftable(z[,cat1],z[,cat2],row.vars=1,dnn=c(V1,V2)),shade=T) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tabulation of Results',ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste(V1,' x ', V2),ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ', 1,TRUE) for(nc in 1:ncol(table1)){ a<-table.element(a, colnames(table1)[nc], 1, TRUE) } a<-table.row.end(a) for(nr in 1:nrow(table1) ){ a<-table.element(a, rownames(table1)[nr], 1, TRUE) for(nc in 1:ncol(table1) ){ a<-table.element(a, table1[nr, nc], 1, FALSE) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') (cst<-chisq.test(table1, simulate.p.value=simulate.p.value) ) if (par3 == 'McNemar Chi-Squared') { (cst <- mcnemar.test(table1)) } if (par3=='Fisher Exact Test') { (cst <- fisher.test(table1)) } if ((par3 != 'McNemar Chi-Squared') & (par3 != 'Fisher Exact Test')) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tabulation of Expected Results',ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste(V1,' x ', V2),ncol(table1)+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ', 1,TRUE) for(nc in 1:ncol(table1)){ a<-table.element(a, colnames(table1)[nc], 1, TRUE) } a<-table.row.end(a) for(nr in 1:nrow(table1) ){ a<-table.element(a, rownames(table1)[nr], 1, TRUE) for(nc in 1:ncol(table1) ){ a<-table.element(a, round(cst$expected[nr, nc], digits=2), 1, FALSE) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab') } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Statistical Results',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, cst$method, 2,TRUE) a<-table.row.end(a) a<-table.row.start(a) if (par3=='Pearson Chi-Squared') a<-table.element(a, 'Pearson Chi Square Statistic', 1, TRUE) if (par3=='Exact Pearson Chi-Squared by Simulation') a<-table.element(a, 'Exact Pearson Chi Square Statistic', 1, TRUE) if (par3=='McNemar Chi-Squared') a<-table.element(a, 'McNemar Chi Square Statistic', 1, TRUE) if (par3=='Fisher Exact Test') a<-table.element(a, 'Odds Ratio', 1, TRUE) if (par3=='Fisher Exact Test') { if ((ncol(table1) == 2) & (nrow(table1) == 2)) { a<-table.element(a, round(cst$estimate, digits=2), 1,FALSE) } else { a<-table.element(a, '--', 1,FALSE) } } else { a<-table.element(a, round(cst$statistic, digits=2), 1,FALSE) } a<-table.row.end(a) if(!simulate.p.value){ if(par3!='Fisher Exact Test') { a<-table.row.start(a) a<-table.element(a, 'Degrees of Freedom', 1, TRUE) a<-table.element(a, cst$parameter, 1,FALSE) a<-table.row.end(a) } } a<-table.row.start(a) a<-table.element(a, 'P value', 1, TRUE) a<-table.element(a, round(cst$p.value, digits=2), 1,FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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