eofPred: EOF prediction

In marchtaylor/sinkr: Collection of functions with emphasis in multivariate data analysis

EOF prediction

Description

The eofPred function predics the principal component loadings of a new dataset with an eof object

Usage

eofPred(EOF, newdata = NULL, pcs = NULL)

Arguments

EOF	An object resulting from the function eof
newdata	new data to project onto eofs
pcs	The principal components (PCs) to use in the reconstruction (defaults to the full set of PCs: pcs=seq(ncol(EOF$u)))

Value

A matrix of principal component loadings (as in EOF$A)

Examples

### Non-gappy example
# Data
set.seed(1)
ptrain <- 0.5 # portion of data to use for training set
tmp <- sample(nrow(iris), nrow(iris)*ptrain)
train <- iris[tmp,1:4] # training set
valid <- iris[-tmp,1:4] # validation set

# EOF analysis
Efull <- eof(iris[,1:4], centered=TRUE, scaled=TRUE) # EOF of full data
Etrain <- eof(train, centered=TRUE, scaled=TRUE) # EOF of training data

# Null model
Efull.n.sig <- eofNull(iris[,1:4], centered=TRUE, scaled=TRUE, nperm = 99)$n.sig
Etrain.n.sig <- eofNull(train, centered=TRUE, scaled=TRUE, nperm = 99)$n.sig

# Predict PCs of validation set
pred <- eofPred(Etrain, newdata=valid)
# plot against full data-derived PCs
SIGN <- diag(sign(diag(cor(Efull$A[-tmp,], pred)))) # correction for differing sign
matplot(Efull$A[-tmp,] %*% SIGN, pred)
abline(0,1, col=8)
legend("topleft", legend=paste("PC", seq(ncol(pred))), 
       col=seq(ncol(pred)), lty=0, pch=1)

# Predict PCs of full set
pred <- eofPred(Efull, newdata=iris[,1:4])
# plot against full data-derived PCs (should be equal)
SIGN <- diag(sign(diag(cor(Efull$A, pred)))) # correction for differing sign
matplot(Efull$A %*% SIGN, pred)
abline(0,1, col=8)
legend("topleft", legend=paste("PC", seq(ncol(pred))), 
       col=seq(ncol(pred)), lty=0, pch=1)


### gappy example
# Data
set.seed(1) # 
ptrain <- 0.5 # portion of data to use for training set
pgap <- 0.2 # portion of data that are gaps (e.g. NaNs)
irisg <- as.matrix(iris[,1:4])
irisg[sample(length(irisg), length(irisg)*pgap)] <- NaN
tmp <- sample(nrow(irisg), nrow(irisg)*ptrain)
traing <- irisg[tmp,] # training set
validg <- irisg[-tmp,] # validation set

# EOF analysis
Efullg <- eof(irisg, centered=TRUE, scaled=TRUE, recursive=TRUE) # EOF of full data
Etraing <- eof(traing, centered=TRUE, scaled=TRUE, recursive=TRUE) # EOF of training data

# EOF Null model
EfullgNull <- eofNull(irisg, centered=TRUE, scaled=TRUE, recursive=TRUE, nperm=99)
EtraingNull <- eofNull(traing, centered=TRUE, scaled=TRUE, recursive=TRUE, nperm=99)

# Predict PCs of validation set
pred <- eofPred(Etrain, newdata=validg)
# plot against full data-derived PCs
SIGN <- diag(sign(diag(cor(Efullg$A[-tmp,], pred)))) # correction for differing sign
matplot(Efullg$A[-tmp,] %*% SIGN, pred)
abline(0,1, col=8)
legend("topleft", legend=paste("PC", seq(ncol(pred))), 
       col=seq(ncol(pred)), lty=0, pch=1)

# Reconstruction and measurement of error against non-gappy data
usePCs <- seq(1) # Efull.n.sig = 1
Rg <- eofRecon(Etrain, pcs=seq(usePCs), newpcs=pred)
plot( c(as.matrix(iris[-tmp,1:4])), c(Rg) )
abline(0,1, col=8)
rmse <- sqrt( mean( ( c(as.matrix(iris[-tmp,1:4])) - c(Rg)   )^2, na.rm=TRUE) )
rmse

marchtaylor/sinkr documentation built on July 4, 2022, 5:48 p.m.