BioProgram - 23 days ago 4x

R Question

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`

`x[1]`

`y[1]`

`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`

Answer

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
```

Source (Stackoverflow)

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