search for: mycor

...= (0.9614, 1.0398) The MIC is not inside the confidence intervals ! Is there something wrong in the R code ? Here is the reproducible example : ########## C=c(2,4,5,6,3,4,5,7,8,7,6,5,6,7,7,8,5,4,3,2) D=c(3,5,4,6,7,2,3,1,2,4,5,4,6,4,5,4,3,2,8,9) library(minerva) mine(C,D)$MIC library(boot) myCor <- function(data, index){ mine(data[index, ])$MIC } results=boot(data = cbind(C,D), statistic = myCor, R = 2000) boot.ci(results,type="all") ########## ________________________________ De : Rui Barradas <ruipbarradas at sapo.pt> roject.org> Envoy? le : Dimanche 10 d?c...

Dear R-Experts, Here below is my R code (reproducible example) to calculate the confidence intervals around the spearman coefficient. ########## C=c(2,4,5,6,3,4,5,7,8,7,6,5,6,7,7,8,5,4,3,2) D=c(3,5,4,6,7,2,3,1,2,4,5,4,6,4,5,4,3,2,8,9) cor(C,D,method= "spearman") library(boot) myCor=function(data,index){ cor(data[index, ])[1,2] } results=boot(data=cbind(C,D),statistic=myCor, R=2000) boot.ci(results,type="all") ########## Now, I would like to calculate the CIs around the MIC (Maximal information coefficient). The MIC can be calculated thanks to the library(minerva)....

You need: myCor <- function(data, index){ mine(data[index, ])$MIC[1, 2] } results=boot(data = cbind(C,D), statistic = myCor, R = 2000) boot.ci(results,type="all") Look at the differences between: mine(C, D) and mine(cbind(C, D)) The first returns a value, the second returns a symmetric matrix....

Hello, First of all, when I tried to use function mic I got an error. mic(cbind(C, D)) Error in mic(cbind(C, D)) : could not find function "mic" So I've changed your function myCor and all went well, with a warning relative to BCa intervals. myCor <- function(data, index){ mine(data[index, ])$MIC } results=boot(data = cbind(C,D), statistic = myCor, R = 2000) boot.ci(results,type="all") Hope this helps, Rui Barradas On 12/10/2017 3:19 PM, varin sacha vi...

I'd like to paste a superstring with a number in an object. Thanks for any help. Murray mycor <- cor(1:10,1:10) plot(1:10,1:10) text(8,2,paste(expression(R^2)," = ",mycor)) [[alternative HTML version deleted]]

Hello, I have a data set with 15 variables, and use "pairs" to plot the scatterplot of this data set. Then I want to plot some circles on the small pictures with high correlation(e.g. > 0.9). First, I use "cor" to obtain the corresponding correlation matrix (x) for this scatterplot. Second, use "seq(along = x)[x > 0.9]" to find the positions of the small

...ariables declaration attach(basetraits) pdias=as.numeric(pdias) longindex=as.numeric(longindex) light=as.numeric(light) humid=as.numeric(humid) basic=as.numeric(basic) azot=as.numeric(azot) durflow=as.numeric(durflow) height=as.ordered(height) spread=as.ordered(spread) begflow=as.ordered(begflow) mycor=as.ordered(mycor) piq=as.factor(piq) lign=as.factor(lign) grain=as.factor(grain) ros=as.factor(ros) semiros=as.factor(semiros) leafy=as.factor(leafy) autopoll=as.factor(autopoll) geito=as.factor(geito) insect=as.factor(insect) wind=as.factor(wind) suman=as.factor(suman) winan=as.factor(winan) monoc...

Hello, I am using function princomp from the package psych. I have my principle component object mypc: mypc <- princomp(covmat=mycor) plot(mypc) # shows me a screeplot Question: how could I actually see the values displayed in the screeplot. I don't mean on the graph - I just want to know the actual value for each component (e.g., 10, 3.2, 1.8, etc.) I need to know how much variance, in total, a certain number of comonen...