I am trying to generate a table output of a correlation matrix. Specifically, I am using a for loop in order to identify a correlation between all data in columns 4:40 to column 1. While the results of the table are decent, it does not identify what is being compared to what. In checking attributes of cor.test,I find that data.name is being given as x[1] and y[1] which is not good enough to trace back which columns is being compared to what. Here is my code:
input <- read.delim(file="InputData.txt", header=TRUE)
x<-input[,41, drop=FALSE]
y=input[,4:40]
corr.values <- vector("list", 37)
for (i in 1:length(y) ){
corr.values[[i]] <- cor.test(x[[1]], y[[i]], method="pearson")
}
lres <- sapply(corr.values, `[`, c("statistic","p.value","estimate","method", "data.name"))
lres<-t(lres)
write.table(lres, file="output.xls", sep="\t",row.names=TRUE)
The output file looks like this:
statistic p.value estimate method data.name
1 -2.030111981 0.042938137 -0.095687495 Pearson's product-moment correlation x[[1]] and y[[i]]
2 -2.795786248 0.005400938 -0.131239287 Pearson's product-moment correlation x[[1]] and y[[i]]
3 -2.099114632 0.036368337 -0.098908573 Pearson's product-moment correlation x[[1]] and y[[i]]
4 -1.920649487 0.055413178 -0.090571599 Pearson's product-moment correlation x[[1]] and y[[i]]
5 -1.981326962 0.048168291 -0.093408365 Pearson's product-moment correlation x[[1]] and y[[i]]
6 -2.80390736 0.00526909 -0.131613912 Pearson's product-moment correlation x[[1]] and y[[i]]
7 -1.265138839 0.206482153 -0.059798855 Pearson's product-moment correlation x[[1]] and y[[i]]
8 -2.861448156 0.004415411 -0.134266636 Pearson's product-moment correlation x[[1]] and y[[i]]
9 -2.103403363 0.035990039 -0.099108672 Pearson's product-moment correlation x[[1]] and y[[i]]
10 -3.610094985 0.000340807 -0.168498786 Pearson's product-moment correlation x[[1]] and y[[i]]
Clearly, this is not perfect as rows are numbered and can't tell which correlation is to what. Is there a way to fix this? I tried many solutions but none worked.I know that the trick must be in editing the data.name attribute however I couldn't figure out how to do that.
Here's a way to return a data frame with all the cor.test results that also includes the names of the variables for which each correlation was calculated: We create a function to extract the relevant results of cor.test then use mapply to apply the function to each pair of variables for which we want the correlations. mapply returns a list, so we use do.call(rbind, ...) to turn it into a data frame.
# Function to extract correlation coefficient and p-values
corrFunc <- function(var1, var2, data) {
result = cor.test(data[,var1], data[,var2])
data.frame(var1, var2, result[c("estimate","p.value","statistic","method")],
stringsAsFactors=FALSE)
}
## Pairs of variables for which we want correlations
vars = data.frame(v1=names(mtcars)[1], v2=names(mtcars)[-1])
# Apply corrFunc to all rows of vars
corrs = do.call(rbind, mapply(corrFunc, vars[,1], vars[,2], MoreArgs=list(data=mtcars),
SIMPLIFY=FALSE))
var1 var2 estimate p.value statistic method
cor mpg cyl -0.8475514 9.380327e-10 -8.747152 Pearson's product-moment correlation
cor1 mpg disp -0.7761684 1.787835e-07 -6.742389 Pearson's product-moment correlation
cor2 mpg hp 0.4186840 1.708199e-02 2.525213 Pearson's product-moment correlation
cor3 mpg drat 0.6811719 1.776240e-05 5.096042 Pearson's product-moment correlation
cor4 mpg wt 0.4802848 5.400948e-03 2.999191 Pearson's product-moment correlation
cor5 mpg qsec 0.6640389 3.415937e-05 4.864385 Pearson's product-moment correlation
cor6 mpg vs 0.5998324 2.850207e-04 4.106127 Pearson's product-moment correlation
cor7 mpg am 1.0000000 0.000000e+00 Inf Pearson's product-moment correlation
cor8 mpg gear -0.8676594 1.293959e-10 -9.559044 Pearson's product-moment correlation
cor9 mpg carb -0.8521620 6.112687e-10 -8.919699 Pearson's product-moment correlation
来源：https://stackoverflow.com/questions/34326906/extracting-and-formatting-results-of-cor-test-on-multiple-pairs-of-columns