Fatma Ell
2018-Dec-02 10:50 UTC
[R] Does the correlations of component makes the correlation of one phenomena ?
Hi,
I have the following dataset Mesures. It contains test which is a given
context, Space is portion of this following context test. For each test we
have twelve Space and an empirical measure of a behavior Behavior_empirical and
a mesure of simulated behavior Behavior_simulated.
Mesures=structure(list(test = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), Space = c(1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L), Behavior_empirical = c(3.02040816326531, 7.95918367346939,
10.6162790697674, 4.64150943396226, 1.86538461538462, 1.125,
1.01020408163265, 1.2093023255814, 0.292452830188679, 0, 0, 0, 0,
1.3265306122449, 0, 3.09433962264151, 0, 1.6875, 2.02040816326531,
1.2093023255814, 1.75471698113208, 1.79347826086957,
0.243589743589744, 0, 0.377551020408163, 1.98979591836735,
6.75581395348837, 6.18867924528302, 7.46153846153846, 0.75, 0, 0,
0.292452830188679, 0, 0, 0, 0, 1.3265306122449, 1.93023255813953,
10.8301886792453, 3.73076923076923, 0, 2.69387755102041,
0.604651162790698, 1.75471698113208, 0, 0, 0, 1.51020408163265,
2.6530612244898, 3.86046511627907, 1.54716981132075, 1.86538461538462,
1.875, 2.35714285714286, 1.2093023255814, 0.292452830188679, 0, 0,
0.823529411764706, 6.79591836734694, 15.2551020408163,
5.7906976744186, 1.54716981132075, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.773584905660377, 0, 0, 0.673469387755102, 1.81395348837209,
1.75471698113208, 2.51086956521739, 3.10576923076923,
3.70588235294118, 3.77551020408163, 9.28571428571428,
3.86046511627907, 1.54716981132075, 0, 0, 0, 0, 1.4622641509434, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.673469387755102, 0, 0.292452830188679,
4.30434782608696, 1.09615384615385, 5.76470588235294, 0, 0,
1.93023255813953, 4.64150943396226, 3.73076923076923, 2.625,
0.673469387755102, 0.604651162790698, 0, 0, 0, 0), Behavior_simulated
= c(18, 61, 129, 198, 128, 57, 44, 80, 36, 8, 0, 0, 0, 0, 0, 49, 50,
194, 211, 353, 352, 214, 120, 15, 10, 74, 145, 224, 158, 99, 26, 19,
7, 2, 0, 0, 180, 89, 47, 36, 34, 56, 51, 65, 44, 4, 0, 0, 116, 133,
131, 103, 74, 132, 75, 44, 0, 0, 0, 0, 532, 165, 18, 5, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 6, 47, 164, 193, 185, 91, 239, 219, 168,
83, 1, 14, 45, 136, 129, 89, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 17, 92,
280, 273, 0, 6, 25, 108, 129, 285, 171, 181, 39, 2, 0, 0)), .Names
c("test", "Space", "Behavior_empirical",
"Behavior_simulated"),
row.names = c(NA, 120L), class = "data.frame")
For each test we study correlation between Behavior_empirical
Behavior_simulatedelation
Correlation <- character()for(i in 1:10){Mes=Mesures[(Mesures$test==i),]
co=data.frame(test=i,value=cor(Mes$Behavior_empirical,Mes$Behavior_simulated))Correlation
<- rbind(Correlation, as.data.frame(co))
i=i+1}
which give us for each test many good correlation values :
test value1 1 0.55086832 2 0.43690913 3
0.90498064 4 -0.10627145 5 0.84101656 6 0.55608257 7
0.80880348 8 0.77212329 9 0.708862410 10 0.5116938
Now , we want to conclude that, if the we have good values of
Behavior_simulated for each test. It could build the final distribution
which is the sum of Behavior_simulated and then compare with the sum of
Behavior_empirical.
Mesures_aggregated<- Mesures %>% group_by(Space) %>%
summarize(Sum_Behavior_empirical=sum(Behavior_empirical),Sum_Behavior_simulated=sum(Behavior_simulated))
I may think that my final correlation result should be good. But it is not
the case
> cor(Mesures_aggregated$
Sum_Behavior_empirical,Mesures_aggregated$Sum_Behavior_simulated)[1] 0.07710804
Is correlation could be a result of correlations of the component of one
phenomena ? and How to evaluate the contribution of each component test in
building the 'Sum`?
Thanks a lot for your help.
Lenny
[[alternative HTML version deleted]]
David L Carlson
2018-Dec-03 00:43 UTC
[R] Does the correlations of component makes the correlation of one phenomena ?
This is really a statistics question rather than an R question, but you did provide reproducible data. You have some moderate correlations for some of the tests, but they are all different relationships. You used a combination of base R and dplyr code, but I'll just stick with base R:> Mesures.split <- split(Mesures, Mesures$test) > Corrs <- sapply(Mesures.split, function(x) cor(x[, 3], x[, 4])) > options(digits=3) > Corrs1 2 3 4 5 6 7 8 9 10 0.551 0.437 0.905 -0.106 0.841 0.556 0.809 0.772 0.709 0.512> sapply(Mesures.split, function(x) coef(lm(x[, 3]~x[, 4])))1 2 3 4 5 6 7 (Intercept) 0.6875 0.6530 -0.2597 2.24313 0.3498 1.4436 0.4103 x[, 4] 0.0309 0.0034 0.0353 -0.00668 0.0171 0.0168 0.0137 8 9 10 (Intercept) -0.7379 0.2929 0.48115 x[, 4] 0.0255 0.0129 0.00891 This gives you the intercept and slope for the regression lines for each test. Notice that they vary considerably. The slope value for predicting behavior from simulated varies from -0.007 to .031. When you average over space you effectively eliminate the correlations at the test level:> Mesures_aggregated <- aggregate(Mesures[, 3:4], by=list(Mesures$Space), sum) > cor(Mesures_aggregated[, 2:3])[1, 2][1] 0.0771 If you sum predicted values for empirical behavior using the 10 regression equations and compare that to the summed empirical value, things work out better.> pred <- rowSums(sapply(Mesures.split, function(x) predict(lm(x[, 3]~x[, 4])))) > cor(Mesures_aggregated[, 2], pred)[1] 0.776 Without knowing where the simulated values come from, especially if they are completely independent of the empirical values, I can't say if this approach is wise. --------------------------------------- David L. Carlson Department of Anthropology Texas A&M University -----Original Message----- From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Fatma Ell Sent: Sunday, December 2, 2018 4:50 AM To: r-help at r-project.org Subject: [R] Does the correlations of component makes the correlation of one phenomena ? Hi, I have the following dataset Mesures. It contains test which is a given context, Space is portion of this following context test. For each test we have twelve Space and an empirical measure of a behavior Behavior_empirical and a mesure of simulated behavior Behavior_simulated. Mesures=structure(list(test = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), Space = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L), Behavior_empirical = c(3.02040816326531, 7.95918367346939, 10.6162790697674, 4.64150943396226, 1.86538461538462, 1.125, 1.01020408163265, 1.2093023255814, 0.292452830188679, 0, 0, 0, 0, 1.3265306122449, 0, 3.09433962264151, 0, 1.6875, 2.02040816326531, 1.2093023255814, 1.75471698113208, 1.79347826086957, 0.243589743589744, 0, 0.377551020408163, 1.98979591836735, 6.75581395348837, 6.18867924528302, 7.46153846153846, 0.75, 0, 0, 0.292452830188679, 0, 0, 0, 0, 1.3265306122449, 1.93023255813953, 10.8301886792453, 3.73076923076923, 0, 2.69387755102041, 0.604651162790698, 1.75471698113208, 0, 0, 0, 1.51020408163265, 2.6530612244898, 3.86046511627907, 1.54716981132075, 1.86538461538462, 1.875, 2.35714285714286, 1.2093023255814, 0.292452830188679, 0, 0, 0.823529411764706, 6.79591836734694, 15.2551020408163, 5.7906976744186, 1.54716981132075, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.773584905660377, 0, 0, 0.673469387755102, 1.81395348837209, 1.75471698113208, 2.51086956521739, 3.10576923076923, 3.70588235294118, 3.77551020408163, 9.28571428571428, 3.86046511627907, 1.54716981132075, 0, 0, 0, 0, 1.4622641509434, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.673469387755102, 0, 0.292452830188679, 4.30434782608696, 1.09615384615385, 5.76470588235294, 0, 0, 1.93023255813953, 4.64150943396226, 3.73076923076923, 2.625, 0.673469387755102, 0.604651162790698, 0, 0, 0, 0), Behavior_simulated = c(18, 61, 129, 198, 128, 57, 44, 80, 36, 8, 0, 0, 0, 0, 0, 49, 50, 194, 211, 353, 352, 214, 120, 15, 10, 74, 145, 224, 158, 99, 26, 19, 7, 2, 0, 0, 180, 89, 47, 36, 34, 56, 51, 65, 44, 4, 0, 0, 116, 133, 131, 103, 74, 132, 75, 44, 0, 0, 0, 0, 532, 165, 18, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 6, 47, 164, 193, 185, 91, 239, 219, 168, 83, 1, 14, 45, 136, 129, 89, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 17, 92, 280, 273, 0, 6, 25, 108, 129, 285, 171, 181, 39, 2, 0, 0)), .Names c("test", "Space", "Behavior_empirical", "Behavior_simulated"), row.names = c(NA, 120L), class = "data.frame") For each test we study correlation between Behavior_empirical Behavior_simulatedelation Correlation <- character()for(i in 1:10){Mes=Mesures[(Mesures$test==i),] co=data.frame(test=i,value=cor(Mes$Behavior_empirical,Mes$Behavior_simulated))Correlation <- rbind(Correlation, as.data.frame(co)) i=i+1} which give us for each test many good correlation values : test value1 1 0.55086832 2 0.43690913 3 0.90498064 4 -0.10627145 5 0.84101656 6 0.55608257 7 0.80880348 8 0.77212329 9 0.708862410 10 0.5116938 Now , we want to conclude that, if the we have good values of Behavior_simulated for each test. It could build the final distribution which is the sum of Behavior_simulated and then compare with the sum of Behavior_empirical. Mesures_aggregated<- Mesures %>% group_by(Space) %>% summarize(Sum_Behavior_empirical=sum(Behavior_empirical),Sum_Behavior_simulated=sum(Behavior_simulated)) I may think that my final correlation result should be good. But it is not the case> cor(Mesures_aggregated$ Sum_Behavior_empirical,Mesures_aggregated$Sum_Behavior_simulated)[1] 0.07710804Is correlation could be a result of correlations of the component of one phenomena ? and How to evaluate the contribution of each component test in building the 'Sum`? Thanks a lot for your help. Lenny [[alternative HTML version deleted]] ______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Fatma Ell
2018-Dec-11 10:02 UTC
[R] Does the correlations of component makes the correlation of one phenomena ?
Thanks a lot David for this extended answer The aim is to say: if simulated vs emprical correlate one by one, the sum of both should correlate also I want to be sure that I understood correctly: What you have done 1) building the model ( the fittingness) according empirical vs simulated value and predict value from this model 2) compare predicted value of the fittingness model with the sum of empirical value, isnt ? Thanks a lot Le lundi 3 d?cembre 2018, David L Carlson <dcarlson at tamu.edu> a ?crit :> This is really a statistics question rather than an R question, but you > did provide reproducible data. You have some moderate correlations for some > of the tests, but they are all different relationships. You used a > combination of base R and dplyr code, but I'll just stick with base R: > > > Mesures.split <- split(Mesures, Mesures$test) > > Corrs <- sapply(Mesures.split, function(x) cor(x[, 3], x[, 4])) > > options(digits=3) > > Corrs > 1 2 3 4 5 6 7 8 9 10 > 0.551 0.437 0.905 -0.106 0.841 0.556 0.809 0.772 0.709 0.512 > > > sapply(Mesures.split, function(x) coef(lm(x[, 3]~x[, 4]))) > 1 2 3 4 5 6 7 > (Intercept) 0.6875 0.6530 -0.2597 2.24313 0.3498 1.4436 0.4103 > x[, 4] 0.0309 0.0034 0.0353 -0.00668 0.0171 0.0168 0.0137 > 8 9 10 > (Intercept) -0.7379 0.2929 0.48115 > x[, 4] 0.0255 0.0129 0.00891 > > This gives you the intercept and slope for the regression lines for each > test. Notice that they vary considerably. The slope value for predicting > behavior from simulated varies from -0.007 to .031. When you average over > space you effectively eliminate the correlations at the test level: > > > Mesures_aggregated <- aggregate(Mesures[, 3:4], by=list(Mesures$Space), > sum) > > cor(Mesures_aggregated[, 2:3])[1, 2] > [1] 0.0771 > > If you sum predicted values for empirical behavior using the 10 regression > equations and compare that to the summed empirical value, things work out > better. > > > pred <- rowSums(sapply(Mesures.split, function(x) predict(lm(x[, 3]~x[, > 4])))) > > cor(Mesures_aggregated[, 2], pred) > [1] 0.776 > > Without knowing where the simulated values come from, especially if they > are completely independent of the empirical values, I can't say if this > approach is wise. > > --------------------------------------- > David L. Carlson > Department of Anthropology > Texas A&M University > > > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Fatma Ell > Sent: Sunday, December 2, 2018 4:50 AM > To: r-help at r-project.org > Subject: [R] Does the correlations of component makes the correlation of > one phenomena ? > > Hi, > > I have the following dataset Mesures. It contains test which is a given > context, Space is portion of this following context test. For each test we > have twelve Space and an empirical measure of a behavior > Behavior_empirical and > a mesure of simulated behavior Behavior_simulated. > > Mesures=structure(list(test = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, > 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, > 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, > 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, > 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, > 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), Space = c(1L, 2L, 3L, > 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, > 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, > 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, > 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, > 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, > 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, > 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, > 11L, 12L), Behavior_empirical = c(3.02040816326531, 7.95918367346939, > 10.6162790697674, 4.64150943396226, 1.86538461538462, 1.125, > 1.01020408163265, 1.2093023255814, 0.292452830188679, 0, 0, 0, 0, > 1.3265306122449, 0, 3.09433962264151, 0, 1.6875, 2.02040816326531, > 1.2093023255814, 1.75471698113208, 1.79347826086957, > 0.243589743589744, 0, 0.377551020408163, 1.98979591836735, > 6.75581395348837, 6.18867924528302, 7.46153846153846, 0.75, 0, 0, > 0.292452830188679, 0, 0, 0, 0, 1.3265306122449, 1.93023255813953, > 10.8301886792453, 3.73076923076923, 0, 2.69387755102041, > 0.604651162790698, 1.75471698113208, 0, 0, 0, 1.51020408163265, > 2.6530612244898, 3.86046511627907, 1.54716981132075, 1.86538461538462, > 1.875, 2.35714285714286, 1.2093023255814, 0.292452830188679, 0, 0, > 0.823529411764706, 6.79591836734694, 15.2551020408163, > 5.7906976744186, 1.54716981132075, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, > 0.773584905660377, 0, 0, 0.673469387755102, 1.81395348837209, > 1.75471698113208, 2.51086956521739, 3.10576923076923, > 3.70588235294118, 3.77551020408163, 9.28571428571428, > 3.86046511627907, 1.54716981132075, 0, 0, 0, 0, 1.4622641509434, 0, 0, > 0, 0, 0, 0, 0, 0, 0, 0.673469387755102, 0, 0.292452830188679, > 4.30434782608696, 1.09615384615385, 5.76470588235294, 0, 0, > 1.93023255813953, 4.64150943396226, 3.73076923076923, 2.625, > 0.673469387755102, 0.604651162790698, 0, 0, 0, 0), Behavior_simulated > = c(18, 61, 129, 198, 128, 57, 44, 80, 36, 8, 0, 0, 0, 0, 0, 49, 50, > 194, 211, 353, 352, 214, 120, 15, 10, 74, 145, 224, 158, 99, 26, 19, > 7, 2, 0, 0, 180, 89, 47, 36, 34, 56, 51, 65, 44, 4, 0, 0, 116, 133, > 131, 103, 74, 132, 75, 44, 0, 0, 0, 0, 532, 165, 18, 5, 0, 0, 0, 0, 0, > 0, 0, 0, 0, 0, 0, 1, 0, 0, 6, 47, 164, 193, 185, 91, 239, 219, 168, > 83, 1, 14, 45, 136, 129, 89, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 17, 92, > 280, 273, 0, 6, 25, 108, 129, 285, 171, 181, 39, 2, 0, 0)), .Names > c("test", "Space", "Behavior_empirical", "Behavior_simulated"), > row.names = c(NA, 120L), class = "data.frame") > > For each test we study correlation between Behavior_empirical > Behavior_simulatedelation > > Correlation <- character()for(i in 1:10){Mes=Mesures[(Mesures$test==i),] > co=data.frame(test=i,value=cor(Mes$Behavior_empirical, > Mes$Behavior_simulated))Correlation > <- rbind(Correlation, as.data.frame(co)) > i=i+1} > > which give us for each test many good correlation values : > > test value1 1 0.55086832 2 0.43690913 3 > 0.90498064 4 -0.10627145 5 0.84101656 6 0.55608257 7 > 0.80880348 8 0.77212329 9 0.708862410 10 0.5116938 > > Now , we want to conclude that, if the we have good values of > Behavior_simulated for each test. It could build the final distribution > which is the sum of Behavior_simulated and then compare with the sum of > Behavior_empirical. > > Mesures_aggregated<- Mesures %>% group_by(Space) %>% > summarize(Sum_Behavior_empirical=sum(Behavior_empirical),Sum_Behavior_ > simulated=sum(Behavior_simulated)) > > I may think that my final correlation result should be good. But it is not > the case > > > cor(Mesures_aggregated$ Sum_Behavior_empirical,Mesures_aggregated$Sum_Behavior_simulated)[1] > 0.07710804 > > Is correlation could be a result of correlations of the component of one > phenomena ? and How to evaluate the contribution of each component test in > building the 'Sum`? > > > Thanks a lot for your help. > > > Lenny > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]