Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
145 233 130 250 NA NA 120 236 NA NA 140 192 NA NA 120 263 172 199 150 168 140 239 NA NA 130 266 110 211 NA NA NA NA NA NA NA NA 150 247 NA NA 135 234 130 233 140 226 150 243 140 199 NA NA 150 212 110 175 NA NA 130 197 NA NA 120 177 130 219 125 273 125 213 NA NA NA NA 150 232 NA NA NA NA NA NA 130 245 104 208 NA NA 140 321 120 325 140 235 138 257 NA NA NA NA NA NA 120 302 130 231 NA NA NA NA 134 201 122 222 115 260 118 182 NA NA NA NA 108 309 118 186 135 203 140 211 NA NA 100 222 NA NA 120 220 NA NA 120 258 94 227 130 204 140 261 NA NA NA NA 125 245 140 221 128 205 105 240 112 250 128 308 NA NA 152 298 NA NA NA NA 118 277 101 197 NA NA NA NA 124 255 132 207 138 223 NA NA NA NA 142 226 NA NA 108 233 130 315 130 246 148 244 178 270 NA NA 120 240 129 196 NA NA 160 234 NA NA NA NA NA NA NA NA 150 126 NA NA 110 211 130 262 NA NA 130 214 120 193 NA NA NA NA NA NA 138 271 NA NA NA NA NA NA NA NA 112 204 NA NA NA NA NA NA NA NA NA NA 120 295 110 235 NA NA NA NA NA NA 128 208 110 201 128 263 NA NA 115 303 NA NA NA NA NA NA 156 245 NA NA NA NA 120 226 130 180 160 228 NA NA 170 227 NA NA NA NA NA NA 130 253 122 192 125 220 130 221 120 240 NA NA 120 157 138 175 138 175 160 286 120 229 NA NA 130 254 140 203 130 256 110 229 120 284 132 224 130 206 110 167 117 230 140 335 120 177 150 276 132 353 NA NA NA NA 112 230 150 243 112 290 130 253 124 266 140 233 110 172 NA NA 128 216 120 188 145 282 140 185 170 326 150 231 125 254 120 267 110 248 110 197 125 258 150 270 180 274 NA NA 128 255 110 239 NA NA 120 188 140 177 128 229 120 260 118 219 NA NA 125 249 NA NA NA NA 130 330 135 254 130 256 NA NA 140 217 138 282 NA NA 110 239 145 174 120 281 120 198 170 288 125 309 108 243 165 289 160 289 120 246 130 322 140 299 125 300 140 293 125 304 126 282 160 269 NA NA 145 212 152 274 132 184 124 274 NA NA 160 246 192 283 140 254 140 298 132 247 NA NA 100 299 160 273 142 309 128 259 144 200 NA NA 120 231 NA NA 112 230 123 282 NA NA 110 206 112 212 NA NA 118 149 122 286 130 283 120 249 134 234 120 237 100 234 110 275 125 212 146 218 124 261 NA NA 138 166 136 315 128 204 126 218 152 223 140 207 140 311 134 204 154 232 110 335 NA NA 148 203 114 318 NA NA 152 212 120 169 140 187 NA NA 164 176 NA NA 110 264 144 193 130 131 NA NA
Names of X columns:
bloodpressureMale cholesterolMale
Type of Correlation
pearson
spearman
kendall
Chart options
Title:
R Code
panel.tau <- function(x, y, digits=2, prefix='', cex.cor) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) rr <- cor.test(x, y, method=par1) r <- round(rr$p.value,2) txt <- format(c(r, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep='') if(missing(cex.cor)) cex <- 0.5/strwidth(txt) text(0.5, 0.5, txt, cex = cex) } panel.hist <- function(x, ...) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) print(n) a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') ncorrs <- (n*n -n)/2 mycorrs <- array(0, dim=c(10,3)) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'Pearson r',1,TRUE) a<-table.element(a,'Spearman rho',1,TRUE) a<-table.element(a,'Kendall tau',1,TRUE) a<-table.row.end(a) cor.test(y[1,],y[2,],method=par1) for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='') a<-table.element(a,dum,header=TRUE) rp <- cor.test(y[i,],y[j,],method='pearson') a<-table.element(a,round(rp$estimate,4)) rs <- cor.test(y[i,],y[j,],method='spearman') a<-table.element(a,round(rs$estimate,4)) rk <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,round(rk$estimate,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=T) a<-table.element(a,paste('(',round(rp$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rs$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rk$p.value,4),')',sep='')) a<-table.row.end(a) for (iii in 1:10) { iiid100 <- iii / 100 if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1 if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1 if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1 } } } a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Type I error',1,TRUE) a<-table.element(a,'Pearson r',1,TRUE) a<-table.element(a,'Spearman rho',1,TRUE) a<-table.element(a,'Kendall tau',1,TRUE) a<-table.row.end(a) for (iii in 1:10) { iiid100 <- iii / 100 a<-table.row.start(a) a<-table.element(a,round(iiid100,2),header=T) a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2)) a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2)) a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation