Created
November 30, 2011 16:55
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raw scores vs difference scores
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| #see file `dput_of_a.R` below this file for the data object `a` | |
| #load helpful libraries | |
| library(MASS) | |
| library(plyr) | |
| library(reshape) | |
| library(lme4) | |
| ######## | |
| # Method 1: Model and sample, then collapse | |
| ######## | |
| #fit a model to the raw data & include cue | |
| fit1 = lmer( | |
| data = a | |
| , formula = value ~ (1|id) + cue*s1*s2*r1*r2 | |
| ) | |
| #get the model's estimates for the means & covariances | |
| f1 = fixef(fit1) | |
| v1 = vcov(fit1) | |
| #sample the model | |
| set.seed(1) | |
| samples1 = mvrnorm(1e3,f1,v1) | |
| #create a model matrix for converting the samples to condition values | |
| m1 = model.matrix( | |
| object = terms(value~cue*s1*s2*r1*r2) | |
| , data = expand.grid( | |
| cue = unique(a$cue) | |
| , s1 = unique(a$s1) | |
| , s2 = unique(a$s2) | |
| , r1 = unique(a$r1) | |
| , r2 = unique(a$r2) | |
| , value = 0 | |
| ) | |
| ) | |
| #convert the samples to condition values | |
| mat1 = matrix(NA,nrow=2*2*2*2*3,ncol=1e3) | |
| for(i in 1:1e3){ | |
| mat1[,i] <- m1%*%samples1[i,] | |
| } | |
| #reshape the samples and add condition labels | |
| data1 = expand.grid( | |
| cue = unique(a$cue) | |
| , s1 = unique(a$s1) | |
| , s2 = unique(a$s2) | |
| , r1 = unique(a$r1) | |
| , r2 = unique(a$r2) | |
| ) | |
| data1 = cbind(data1,as.data.frame(mat1)) | |
| data1 = melt( | |
| data = data1 | |
| , id.vars = c('cue','s1','s2','r1','r2') | |
| , variable_name = 'iteration' | |
| ) | |
| #collapse cue to a difference score within each sample | |
| cuing1 = ddply( | |
| .data = data1 | |
| , .variables = .(iteration,s1,s2,r1,r2) | |
| , .fun = function(x){ | |
| data.frame( | |
| value = x$value[x$cue=='uncued']-x$value[x$cue=='cued'] | |
| ) | |
| } | |
| , .progress = 'text' | |
| ) | |
| #compute 95% CI's for cuing | |
| cuing1_stats = ddply( | |
| .data = cuing1 | |
| , .variables = .(s1,s2,r1,r2) | |
| , .fun = function(x){ | |
| data.frame( | |
| lo = quantile(x$value,.025) | |
| , hi = quantile(x$value,.975) | |
| ) | |
| } | |
| ) | |
| cuing1_stats$sig = (cuing1_stats$lo>0)|(cuing1_stats$hi<0) | |
| mean(cuing1_stats$sig) | |
| ######## | |
| # Method 2: Collapse, then Model and sample | |
| ######## | |
| #collapse cue to a difference score | |
| b = ddply( | |
| .data = a | |
| , .variables = .(id,s1,s2,r1,r2) | |
| , .fun = function(x){ | |
| data.frame( | |
| value = x$value[x$cue=='cued']-x$value[x$cue=='uncued'] | |
| ) | |
| } | |
| ) | |
| #fit a model to the collapsed data | |
| fit2 = lmer( | |
| data = b | |
| , formula = value ~ (1|id) + s1*s2*r1*r2 | |
| ) | |
| #get the model's estimates for the means & covariances | |
| f2 = fixef(fit2) | |
| v2 = vcov(fit2) | |
| #sample the model | |
| set.seed(1) | |
| samples2 = mvrnorm(1e3,f2,v2) | |
| #create a model matrix for converting the samples to condition values | |
| m2 = model.matrix( | |
| object = terms(value~s1*s2*r1*r2) | |
| , data = expand.grid( | |
| s1 = unique(a$s1) | |
| , s2 = unique(a$s2) | |
| , r1 = unique(a$r1) | |
| , r2 = unique(a$r2) | |
| , value = 0 | |
| ) | |
| ) | |
| #convert the samples to condition values | |
| mat2 = matrix(NA,nrow=2*2*2*3,ncol=1e3) | |
| for(i in 1:1e3){ | |
| mat2[,i] <- m2%*%samples2[i,] | |
| } | |
| #reshape the samples and add condition labels | |
| data2 = expand.grid( | |
| s1 = unique(a$s1) | |
| , s2 = unique(a$s2) | |
| , r1 = unique(a$r1) | |
| , r2 = unique(a$r2) | |
| ) | |
| data2 = cbind(data2,as.data.frame(mat2)) | |
| data2 = melt( | |
| data = data2 | |
| , id.vars = c('s1','s2','r1','r2') | |
| , variable_name = 'iteration' | |
| ) | |
| #compute 95% CI's for cuing | |
| cuing2_stats = ddply( | |
| .data = data2 | |
| , .variables = .(s1,s2,r1,r2) | |
| , .fun = function(x){ | |
| data.frame( | |
| lo = quantile(x$value,.025) | |
| , hi = quantile(x$value,.975) | |
| ) | |
| } | |
| ) | |
| cuing2_stats$sig = (cuing2_stats$lo>0)|(cuing2_stats$hi<0) | |
| mean(cuing2_stats$sig) | |
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| a = structure(list(id = structure(c(11L, 3L, 1L, 17L, 15L, 16L, 7L, | |
| 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, | |
| 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, | |
| 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, | |
| 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, | |
| 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, | |
| 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, | |
| 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, | |
| 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, | |
| 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, | |
| 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, | |
| 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, | |
| 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, | |
| 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, | |
| 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, | |
| 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, | |
| 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, | |
| 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, | |
| 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, | |
| 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, | |
| 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, | |
| 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, | |
| 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, | |
| 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, | |
| 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, | |
| 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, | |
| 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, | |
| 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, | |
| 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, | |
| 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, | |
| 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, | |
| 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, | |
| 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, | |
| 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, | |
| 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, | |
| 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, | |
| 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, | |
| 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, | |
| 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, | |
| 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, | |
| 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, | |
| 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, | |
| 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, | |
| 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, | |
| 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, | |
| 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, | |
| 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, | |
| 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, | |
| 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, | |
| 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, | |
| 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, | |
| 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, | |
| 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, | |
| 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, | |
| 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, | |
| 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, 2L, 12L, 13L, | |
| 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, 7L, 4L, 5L, | |
| 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L, 11L, 3L, 1L, 17L, 15L, 16L, | |
| 7L, 4L, 5L, 2L, 12L, 13L, 10L, 14L, 8L, 9L, 6L), .Label = c("AA", | |
| "AL", "CS", "EM", "IG", "JS", "JV", "LR", "MF", "NS", "PC", "SC", | |
| "SH", "SM", "SS", "TF", "VA"), class = "factor"), block = c(1, | |
| 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 3, 6, 5, 2, 4, | |
| 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 6, 2, 4, 3, 1, 6, 5, 2, 4, | |
| 3, 1, 6, 5, 2, 4, 3, 1, 2, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, | |
| 3, 1, 6, 5, 5, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, | |
| 4, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 1, 5, 3, 4, | |
| 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 3, 6, 5, 2, 4, 3, 1, 6, | |
| 5, 2, 4, 3, 1, 6, 5, 2, 4, 6, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, | |
| 5, 2, 4, 3, 1, 2, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, | |
| 5, 5, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 4, 1, 2, | |
| 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 1, 5, 3, 4, 6, 1, 2, | |
| 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 3, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, | |
| 3, 1, 6, 5, 2, 4, 6, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, | |
| 3, 1, 2, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 5, 4, | |
| 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 4, 1, 2, 5, 3, 4, | |
| 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 1, 5, 3, 4, 6, 1, 2, 5, 3, 4, | |
| 6, 1, 2, 5, 3, 4, 6, 3, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, | |
| 5, 2, 4, 6, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 2, | |
| 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 5, 4, 6, 1, 2, | |
| 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 4, 1, 2, 5, 3, 4, 6, 1, 2, | |
| 5, 3, 4, 6, 1, 2, 5, 3, 1, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, | |
| 5, 3, 4, 6, 3, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, | |
| 6, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 2, 3, 1, 6, | |
| 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 5, 4, 6, 1, 2, 5, 3, 4, | |
| 6, 1, 2, 5, 3, 4, 6, 1, 2, 4, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, | |
| 6, 1, 2, 5, 3, 1, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, | |
| 6, 3, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 6, 2, 4, | |
| 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 2, 3, 1, 6, 5, 2, 4, | |
| 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 5, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, | |
| 5, 3, 4, 6, 1, 2, 4, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, | |
| 5, 3, 1, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 3, 6, | |
| 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 6, 2, 4, 3, 1, 6, | |
| 5, 2, 4, 3, 1, 6, 5, 2, 4, 3, 1, 2, 3, 1, 6, 5, 2, 4, 3, 1, 6, | |
| 5, 2, 4, 3, 1, 6, 5, 5, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, | |
| 6, 1, 2, 4, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 1, | |
| 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 3, 6, 5, 2, 4, | |
| 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, 6, 2, 4, 3, 1, 6, 5, 2, 4, | |
| 3, 1, 6, 5, 2, 4, 3, 1, 2, 3, 1, 6, 5, 2, 4, 3, 1, 6, 5, 2, 4, | |
| 3, 1, 6, 5, 5, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, | |
| 4, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3, 4, 6, 1, 2, 5, 3), r1 = structure(c(1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, | |
| 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("IGN", | |
| "MAN", "SAC"), class = "factor"), r2 = structure(c(1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("MAN", | |
| "SAC"), class = "factor"), s1 = structure(c(2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("endo", | |
| "exo"), class = "factor"), s2 = structure(c(2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("endo", | |
| "exo"), class = "factor"), cue = structure(c(1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, | |
| 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, | |
| 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("cued", | |
| "uncued"), class = "factor"), value = c(282.29, 306.165, 350.79, | |
| 310.75, 244.96, 316.665, 262.71, 228.29, 252.335, 260, 271.54, | |
| 371.5, 258.415, 205.75, 300.92, 234.25, 239.5, 236.04, 280.67, | |
| 254.795, 264.33, 228.665, 261.295, 259.375, 226.29, 189.795, | |
| 262.625, 280.585, 341.67, 266.75, 195.71, 276.83, 224.865, 157, | |
| 249.295, 324.42, 297.09, 269.585, 385.835, 276.835, 267.205, | |
| 244.83, 279.585, 276.5, 286.085, 333.25, 299.665, 281.955, 388.375, | |
| 237.42, 284, 154.58, 302.71, 278.29, 158.25, 150.375, 174.125, | |
| 188, 146.865, 164.33, 247.83, 190.67, 187.125, 165.25, 160.245, | |
| 264.79, 157.54, 158.75, 165.315, 172.835, 184.205, 227.585, 143.625, | |
| 162.405, 190.295, 139.085, 165.005, 261.605, 211.25, 169.375, | |
| 173.25, 237.42, 251.75, 167.665, 157, 214.955, 172.075, 222.915, | |
| 127.96, 232.335, 173.825, 163, 149.835, 174.25, 144.185, 197.875, | |
| 209.57, 167.79, 143.5, 361.045, 135.75, 168.04, 247.415, 292.335, | |
| 271.56, 294.5, 253.915, 307.875, 227.585, 237.835, 229.625, 271.25, | |
| 215.125, 325.375, 240.635, 195.955, 290.335, 186.955, 235.695, | |
| 216.415, 284, 238.625, 226.665, 221.08, 248.705, 231.335, 195.25, | |
| 190.83, 237.705, 239.96, 275.665, 236.875, 185.5, 250.335, 161.465, | |
| 172.01, 266.5, 337.125, 291.04, 236.33, 360.71, 222.835, 256.295, | |
| 277.5, 250.305, 256.5, 255.675, 285.46, 259.875, 264.17, 401.165, | |
| 199.445, 316.46, 150.25, 281.455, 221.71, 158.955, 148.5, 166.775, | |
| 162.82, 128.18, 175.25, 213.83, 144.915, 212.135, 134.545, 155.545, | |
| 255.875, 147.835, 146.75, 210.98, 164, 161.265, 153.89, 140.29, | |
| 181.265, 161.55, 139.1, 167.83, 313.25, 167.045, 152.71, 134.54, | |
| 199.445, 234.455, 135.97, 172.01, 180.665, 142.31, 143.345, 125.96, | |
| 164.05, 156.335, 153.375, 139.665, 139.79, 137.05, 153.625, 149.125, | |
| 148.165, 165.08, 358.415, 157.085, 177.17, 336.09, 350.705, 357.46, | |
| 330.42, 303.17, 372.435, 287.59, 257, 270.92, 327.075, 318.54, | |
| 340.08, 324.59, 243.72, 331.465, 256.835, 251.5, 272.56, 321.375, | |
| 278.135, 309.36, 291.495, 334.045, 335.29, 227.715, 213.46, 288.95, | |
| 306.98, 331.365, 325.545, 237.68, 311.17, 284.095, 264.33, 302.715, | |
| 397.685, 318.515, 327, 411.46, 303.55, 333.735, 290.61, 305.875, | |
| 302.875, 303.29, 340.845, 334.475, 295, 399.82, 276.035, 314.25, | |
| 232.31, 266.92, 284.545, 186.5, 164.21, 247.175, 267.265, 223.86, | |
| 244.335, 258.385, 268.375, 242.25, 251.435, 233.335, 361.97, | |
| 221.29, 183.125, 287.865, 178.725, 203.5, 225.01, 177.45, 236.875, | |
| 263.045, 185.46, 185.115, 262.125, 229.36, 205.08, 245.945, 276.035, | |
| 260.455, 340.085, 264.33, 251.845, 229.1, 277.19, 188.085, 249.83, | |
| 252.86, 238.725, 220.625, 210.04, 205.73, 257.665, 245.89, 226.255, | |
| 216.125, 369, 211.125, 199.295, 318.515, 344.915, 324.475, 305.21, | |
| 306.5, 360.59, 316.795, 281.935, 275.79, 304.79, 311.785, 339.82, | |
| 348.26, 252.455, 310.87, 233.955, 280.47, 313.52, 328, 294.165, | |
| 289.435, 297.29, 279.955, 348.04, 254.21, 238.24, 280.125, 298.835, | |
| 337.345, 304.54, 252.58, 261.38, 240, 184.085, 290.835, 359.875, | |
| 319.035, 262.545, 368.635, 279.585, 285.03, 280.485, 307.21, | |
| 266.04, 264.75, 291.71, 290.295, 290.25, 365.085, 228.045, 288.885, | |
| 241.27, 271.045, 253.525, 201.835, 183.125, 210.62, 221.895, | |
| 218.17, 208.08, 218.54, 219.295, 291.64, 211, 207.165, 319.295, | |
| 218.08, 207.29, 225.215, 173.17, 177.29, 170.86, 184.085, 229.96, | |
| 227.725, 182.785, 171.75, 272.81, 202.835, 191.2, 209.835, 228.045, | |
| 241.625, 248.045, 184.085, 228.29, 235.045, 191.5, 180.585, 219.3, | |
| 230.71, 235.6, 205.985, 195.96, 182.715, 202.165, 217.57, 183.915, | |
| 247.485, 351.33, 180.455, 221.24, 273.96, 286.79, 292.075, 295.5, | |
| 257.125, 315.585, 240.67, 247.29, 250, 290.96, 263.29, 330.42, | |
| 311.045, 214.955, 300.29, 210.31, 238.79, 252.375, 298.75, 261.415, | |
| 296.335, 232.375, 299.91, 270.365, 231.21, 234.45, 297.665, 286.375, | |
| 367.625, 297.455, 221.92, 293.75, 227.295, 159.615, 256.5, 295.5, | |
| 301.3, 306.79, 363.665, 282.46, 288.455, 280.46, 302.79, 294.5, | |
| 283.33, 335.5, 285.71, 286.335, 388.33, 236.79, 290.5, 160.42, | |
| 283.415, 216.295, 150.085, 152.625, 226.37, 173.04, 157.125, | |
| 165.7, 261.79, 174.875, 182.795, 158.54, 154.005, 236.875, 149.545, | |
| 169.75, 222.14, 151.25, 179.46, 230.55, 149.75, 172.85, 165.555, | |
| 133.54, 141.71, 371.165, 177.29, 186.335, 186.545, 236.79, 266.33, | |
| 136.75, 159.615, 209.84, 172.2, 219.725, 148.625, 235.365, 199.46, | |
| 159.17, 176.915, 205.58, 131.035, 211.375, 221.125, 172.46, 178.335, | |
| 370.085, 152.335, 224.11, 250.335, 282.165, 265.9, 284.125, 245.415, | |
| 285.77, 217.125, 227.625, 249.375, 269.83, 231.415, 317.25, 305.625, | |
| 204.79, 292.125, 174.94, 228.845, 222.295, 280.75, 243.625, 239.625, | |
| 212.875, 238, 252.205, 213.54, 199.125, 266.5, 249.545, 275.67, | |
| 235.33, 196.935, 252, 215.21, 157.865, 282.625, 317.455, 299.27, | |
| 269.42, 343.54, 234.21, 274.42, 278.75, 265.915, 268.21, 242.875, | |
| 299.545, 259.955, 281.42, 386.21, 198.665, 247.04, 165.705, 257.29, | |
| 198.82, 215.585, 150.25, 157.165, 174.095, 146.89, 165.79, 245.335, | |
| 151.585, 170.085, 148.09, 157.795, 225.96, 164.54, 158.96, 159.98, | |
| 146.085, 174.46, 173.925, 122.455, 160.54, 157.835, 131.71, 156.875, | |
| 405.2, 171.295, 148.415, 132.585, 198.665, 232.625, 144.435, | |
| 157.865, 181.17, 175.015, 210.665, 148.75, 173.585, 178.625, | |
| 167, 138.28, 189.75, 131.775, 167.665, 167.125, 171.455, 144.25, | |
| 319.33, 140.5, 160.015, 345.58, 334.915, 323.25, 328.92, 274.25, | |
| 370.285, 311.37, 268.46, 245.125, 308.79, 313.96, 307.335, 351.555, | |
| 242.58, 320.375, 255.835, 260.725, 271.915, 305, 278.91, 271.18, | |
| 273.29, 310.47, 295.245, 234.46, 221.525, 268.83, 295.375, 285.665, | |
| 314.455, 238.65, 284.79, 255.935, 199.085, 299.71, 388.335, 332.515, | |
| 348.375, 436.25, 331.7, 330.625, 287.335, 359.665, 297.96, 287.21, | |
| 339.335, 343.425, 310.915, 413.465, 294.545, 352.275, 217.795, | |
| 265, 284.045, 190.265, 168.415, 234.725, 238.57, 198.72, 215.04, | |
| 244.22, 248.555, 222.58, 219.14, 217.42, 295.19, 190.79, 189.295, | |
| 246.445, 184.755, 233.3, 245.285, 191.04, 199.415, 268.01, 195.46, | |
| 210.585, 320.335, 242.25, 212.3, 272.635, 294.545, 292.955, 311.715, | |
| 199.085, 283.325, 197.34, 297.355, 226.84, 236.625, 276.8, 234.165, | |
| 232.5, 217.26, 188.21, 344.085, 242, 243.165, 202.33, 381.04, | |
| 196.33, 194.625, 312.93, 336.835, 345.5, 308.92, 328.96, 374.79, | |
| 286.585, 258.25, 280.125, 313.17, 314.29, 341.955, 249.19, 270.445, | |
| 316.75, 215.315, 265.91, 320.71, 330.65, 290.155, 295.415, 309.625, | |
| 297.03, 290.535, 267.785, 260.165, 294.375, 344.985, 366.77, | |
| 281.985, 245.665, 268.595, 242.25, 193.235, 298.04, 374.125, | |
| 330.63, 290.125, 407.625, 238.03, 300.335, 272.325, 303.125, | |
| 293.96, 324.95, 337.83, 281.5, 318.015, 395.42, 252.205, 299.425, | |
| 231.265, 261.46, 270.16, 190.335, 177, 211.705, 238.35, 236.09, | |
| 214.25, 277.015, 208.295, 253.535, 228.905, 212, 306.085, 226.415, | |
| 187.705, 228.365, 186.42, 215.8, 201.375, 189.705, 206.875, 219.06, | |
| 208.22, 195.18, 227.135, 230.835, 221.665, 239.625, 252.205, | |
| 248.915, 287.89, 193.235, 220.495, 207.65, 261.915, 177.61, 222.915, | |
| 226.125, 221.28, 207, 187.25, 163.15, 233.67, 248.04, 186.79, | |
| 255.425, 349.67, 223.125, 213.13)), .Names = c("id", "block", | |
| "r1", "r2", "s1", "s2", "cue", "value"), row.names = c(1L, 3L, | |
| 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, | |
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| 858L, 859L, 860L, 861L, 862L, 863L, 864L), class = "data.frame") |
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