# SpatRaster PCA with princomp

`princomp.Rd`

Compute principal components for SpatRaster layers. This method can use all values to compute the principal components, even for very large rasters. This is because it computes the covariance matrix by processing the data in chunks, if necessary, using `layerCor`

. The population covariance is used (not the sample, with `n-1`

denominator, covariance).

Alternatively, you can specify `maxcell`

or sample raster values to a data.frame to speed up calculations for very large rasters (see the examples below).

See `prcomp`

for an alternative method that has higher numerical accuracy, but is slower, and for very large rasters can only be accomplished with a sample since all values must be read into memory.

## Usage

```
# S4 method for class 'SpatRaster'
princomp(x, cor=FALSE, fix_sign=TRUE, use="pairwise.complete.obs", maxcell=Inf)
```

## Arguments

- x
SpatRaster

- cor
logical. If

`FALSE`

, the covariance matrix is used. Otherwise the correlation matrix is used- fix_sign
logical. If

`TRUE`

, the signs of the loadings and scores are chosen so that the first element of each loading is non-negative- use
character. To decide how to handle missing values. This must be (an abbreviation of) one of the strings "everything", "complete.obs", "pairwise.complete.obs", or "masked.complete". With "pairwise.complete.obs", the covariance between a pair of layers is computed for all cells that are not

`NA`

in that pair. Therefore, it may be that the (number of) cells used varies between pairs. The benefit of this approach is that all available data is used. Use "complete.obs", if you want to only use the values from cells that are not`NA`

in any of the layers. By using "masked.complete" you indicate that all layers have NA values in the same cells- maxcell
positive integer. The maximum number of cells to be used. If this is smaller than ncell(x), a regular sample of

`x`

is used

## Examples

```
f <- system.file("ex/logo.tif", package = "terra")
r <- rast(f)
pca <- princomp(r)
x <- predict(r, pca)
# use "index" to get a subset of the components
p <- predict(r, pca, index=1:2)
### use princomp directly
pca2 <- princomp(values(r), fix_sign = TRUE)
p2 <- predict(r, pca2)
### may need to use sampling with a large raster
### here with prcomp instead of princomp
sr <- spatSample(r, 100000, "regular")
pca3 <- prcomp(sr)
p3 <- predict(r, pca3)
```