rowCors: Rowwise Correlation with a Vector
In scrime: Analysis of High-Dimensional Categorical Data Such as SNP Data

Description Usage Arguments Value Author(s) References See Also Examples

Computes Pearson's correlation coefficient of a vector with each row of a matrix.

1	rowCors(X, y, trendStat = FALSE, use.n = NULL)

`X`	a numeric matrix in which each row represents a variable and each column an observation.
`y`	a numeric vector of length `ncol(X)`.
`trendStat`	instead of the correlation coefficients should the values of the statistic for a test of linear trend based on this coefficient be returned? If `TRUE`, then it is assumed that all variables in `X` and the variable represented by `y` are ordinal, and the values in `X` and `y` represent scores for the different levels.
`use.n`	should the squared values of the correlation coefficient be multiplied by `ncol(X)`? Ignored if `trendStat = FALSE`. If `FALSE`, the squared values are multiplied by `ncol(X) - 1`. By default, the squared values are multiplied by `ncol(X)` if `y` shows two levels, leading to the Cochran-Armitage test of trend. Otherwise, they are multiplied by `ncol(X) - 1`.

A vector containing the rowwise values of Pearson's correlation coefficient (if trendStat = FALSE or the rowwise values of the trend statistics (if trendStat = TRUE.

Holger Schwender, holger.schwender@udo.edu

Agresti, A.\ (2002). Categorical Data Analysis. Wiley, Hoboken, NJ. 2nd Edition.

rowTrendStats, rowCATTs, rowMsquares

## Not run: 
# Generate a random matrix containing 10 continuous variables
# and a vector representing a continuous variable.

mat <- matrix(runif(200, 0, 20), 10)
y <- sample(runif(20, 0, 20))

# The correlations between y and each of row of mat are
# computed by

rowCors(mat, y)

# Generate a random binary vector and a matrix consisting
# of 10 ordinal variables with levels 0, 1, 2, where these 
# values can be interpreted as scores for the differ 
# categories.

mat <- matrix(sample(0:2, 500, TRUE), 10)
y <- sample(0:1, 50, TRUE)

# The values of the Cochran-Armitage trend statistic are
# computed by

rowCors(mat, y, trendStat = TRUE)

# If the values of the general test of linear trend described
# on page 87 of Agresti (2002) should be computed, then call

rowCors(mat, y, trendStat = TRUE, use.n = FALSE) 


## End(Not run)