CircosHeatmap-aardio/dist/lib/r-library/matrixStats/benchmarking/colRowVars.md.rsp

<%@include file="includes/setup.md.rsp"%>

<%@string colname="colVars"%>
<%@string rowname="rowVars"%>
<%@meta title="${colname}() and ${rowname}() benchmarks"%>
<%@meta author="Henrik Bengtsson"%>
<%@meta date="2015-01-06"%>

<%@include file="${header}"%>

<%@string test_genefilter="TRUE"%>

<%@ifeq test_genefilter="TRUE"%>
<%
use("genefilter", how = "load")
genefilter_rowVars <- genefilter::rowVars
genefilter_colVars <- function(x, ...) genefilter_rowVars(t(x), ...)
%>
<%@endif%>

# <%@meta name="title"%>

This report benchmark the performance of <%=colname%>() and <%=rowname%>() against alternative methods.

## Alternative methods

* apply() + var()
* colVarColMeans() and rowVarColMeans()
<%@ifeq test_genefilter="TRUE"%>
* genefilter::rowVars(t(.)) and genefilter::rowVars()
<%@endif%>

where

```r
<%=withCapture({
colVarColMeans <- function(x, na.rm = TRUE) {
  if (na.rm) {
    n <- colSums(!is.na(x))
  } else {
    n <- nrow(x)
  }
  var <- colMeans(x*x, na.rm = na.rm) - (colMeans(x, na.rm = na.rm))^2
  var * n/(n-1)
}
})%>
```

and

```r
<%=withCapture({
rowVarRowMeans <- function(x, na.rm = TRUE) {
  if (na.rm) {
    n <- rowSums(!is.na(x))
  } else {
    n <- ncol(x)
  }
  mu <- rowMeans(x, na.rm = na.rm)
  var <- rowMeans(x*x, na.rm = na.rm) - mu^2
  var * (n/(n-1))
}
})%>
```


<% for (mode in c("integer", "double")) { %>

## Data type "<%=mode%>"

### Data
```r
<%=withCapture({
<%@include file="R/random-matrices.R"%>
data <- rmatrices(mode = mode)
})%>
```

### Results

<% for (dataLabel in names(data)) { %>
<% mprintf("%s: %s\n", mode, dataLabel)  %>
#### <%=dataLabel%> <%=mode%> matrix

```r
<%=withCapture({
X <- data[[.dataLabel.]]
gc()

colStats <- microbenchmark(
 colVars        = colVars(X, na.rm = FALSE),
 colVarsCenter  = colVars(X, center = colMeans(X, na.rm = FALSE), na.rm = FALSE),
 colVarColMeans = colVarColMeans(X, na.rm = FALSE),
 "apply+var"    = apply(X, MARGIN = 2L, FUN = var, na.rm = FALSE),
<%@ifeq test_genefilter="TRUE"%>
 "genefilter::rowVars(t(.))" = genefilter_colVars(X, na.rm = FALSE),
<%@endif%>
 unit = "ms"
)

X <- t(X)
gc()

rowStats <- microbenchmark(
 rowVars        = rowVars(X, na.rm = FALSE),
 rowVarsCenter  = rowVars(X, center = rowMeans(X, na.rm = FALSE), na.rm = FALSE),
 rowVarRowMeans = rowVarRowMeans(X, na.rm = FALSE),
"apply+var"     = apply(X, MARGIN = 1L, FUN = var, na.rm = FALSE),
<%@ifeq test_genefilter="TRUE"%>
 "genefilter::rowVars" = genefilter_rowVars(X, na.rm = FALSE),
<%@endif%>
 unit = "ms"
)
})%>
```

<% crBenchmarkResults(colStats, rowStats, tags=c(mode, dataLabel)) %>

<% } # for (dataLabel ...) %>

<% } # for (mode ...) %>


<%@include file="${footer}"%>


<%---------------------------------------------------------------------------
HISTORY:
2015-01-06
o Now benchmarking 'genefilter' functions too.
2014-11-23
o Now benchmarking rowVars() instead of rowSds() since the latter uses
  the former.
2014-06-09
o Created.
---------------------------------------------------------------------------%>