Calculates correlation between two matrices columns or auto-correlation between a matrix columns.
tgs_cor(
x,
y = NULL,
pairwise.complete.obs = FALSE,
spearman = FALSE,
tidy = FALSE,
threshold = 0
)
tgs_cor_knn(
x,
y,
knn,
pairwise.complete.obs = FALSE,
spearman = FALSE,
threshold = 0
)numeric matrix
numeric matrix
see below
if 'TRUE' Spearman correlation is computed, otherwise Pearson
if 'TRUE' data is outputed in tidy format
absolute threshold above which values are outputed in tidy format
the number of highest correlations returned per column
'tgs_cor_knn' or 'tgs_cor' with 'tidy=TRUE' return a data frame,
where each row represents correlation between two pairs of columns from 'x'
and 'y' (or two columns of 'x' itself if 'y==NULL'). 'tgs_cor' with the
'tidy=FALSE' returns a matrix of correlation values, where val[X,Y]
represents the correlation between columns X and Y of the input matrices (if
'y' is not 'NULL') or the correlation between columns X and Y of 'x' (if 'y'
is 'NULL').
'tgs_cor' is very similar to 'stats::cor'. Unlike the latter it uses all available CPU cores to compute the correlation in a much faster way. The basic implementation of 'pairwise.complete.obs' is also more efficient giving overall great run-time advantage.
Unlike 'stats::cor' 'tgs_cor' implements only two modes of treating data containing NA, which are equivalent to 'use="everything"' and 'use="pairwise.complete.obs". Please refer the documentation of this function for more details.
'tgs_cor(x, y, spearman = FALSE)' is equivalent to 'cor(x, y, method = "pearson")' 'tgs_cor(x, y, spearman = TRUE)' is equivalent to 'cor(x, y, method = "spearman")' 'tgs_cor(x, y, pairwise.complete.obs = TRUE, spearman = TRUE)' is equivalent to 'cor(x, y, use = "pairwise.complete.obs", method = "spearman")' 'tgs_cor(x, y, pairwise.complete.obs = TRUE, spearman = FALSE)' is equivalent to 'cor(x, y, use = "pairwise.complete.obs", method = "pearson")'
'tgs_cor' can output its result in "tidy" format: a data frame with three columns named 'col1', 'col2' and 'cor'. Only the correlation values which abs are equal or above the 'threshold' are reported. For auto-correlation (i.e. when 'y=NULL') a pair of columns numbered X and Y is reported only if X < Y.
'tgs_cor_knn' works similarly to 'tgs_cor'. Unlike the latter it returns only the highest 'knn' correlations for each column in 'x'. The result of 'tgs_cor_knn' is always outputed in "tidy" format.
One of the reasons to opt 'tgs_cor_knn' over a pair of calls to 'tgs_cor' and 'tgs_knn' is the reduced memory consumption of the former. For auto-correlation case (i.e. 'y=NULL') given that the number of columns NC exceeds the number of rows NR, then 'tgs_cor' memory consumption becomes a factor of NCxNC. In contrast 'tgs_cor_knn' would consume in the similar scenario a factor of max(NCxNR,NCxKNN). Similarly 'tgs_cor(x,y)' would consume memory as a factor of NCXxNCY, wherever 'tgs_cor_knn(x,y,knn)' would reduce that to max((NCX+NCY)xNR,NCXxKNN).
# \donttest{
# Note: all the available CPU cores might be used
set.seed(seed = 0)
rows <- 100
cols <- 1000
vals <- sample(1:(rows * cols / 2), rows * cols, replace = TRUE)
m <- matrix(vals, nrow = rows, ncol = cols)
m[sample(1:(rows * cols), rows * cols / 1000)] <- NA
r1 <- tgs_cor(m, spearman = FALSE)
r2 <- tgs_cor(m, pairwise.complete.obs = TRUE, spearman = TRUE)
r3 <- tgs_cor_knn(m, NULL, 5, spearman = FALSE)
# }