Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for rcorr tidy(x, diagonal = FALSE, ...)
x | An |
---|---|
diagonal | Logical indicating whether or not to include diagonal
elements of the correlation matrix, or the correlation of a column with
itself. For the elements, |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Suppose the original data has columns A and B. In the correlation
matrix from rcorr
there may be entries for both the cor(A, B)
and
cor(B, A)
. Only one of these pairs will ever be present in the tidy
output.
A tibble::tibble()
with columns:
Name or index of the first column being described.
Name or index of the second column being described.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
Number of observations used to compute the correlation
#> #>#>#> #>#>#> #>#>#> #>#>#> #>#>#> #>mat <- replicate(52, rnorm(100)) # add some NAs mat[sample(length(mat), 2000)] <- NA # also column names colnames(mat) <- c(LETTERS, letters) rc <- rcorr(mat) td <- tidy(rc) td#> # A tibble: 1,326 x 5 #> column1 column2 estimate n p.value #> <chr> <chr> <dbl> <int> <dbl> #> 1 B A 0.0670 36 0.698 #> 2 C A 0.196 38 0.239 #> 3 C B 0.172 37 0.309 #> 4 D A -0.0397 36 0.818 #> 5 D B -0.00762 31 0.968 #> 6 D C 0.250 42 0.110 #> 7 E A 0.224 36 0.189 #> 8 E B 0.0700 35 0.690 #> 9 E C 0.211 46 0.159 #> 10 E D 0.147 40 0.364 #> # … with 1,316 more rows