# compute_rms: Compute the root mean-squared error of a PCA projection In HGray384/pcaNet: Probabilistic principal components analysis - covariance estimation and network reconstruction

## Description

Root mean-squared error is the square root of the element-wise error's mean. This is a useful quantity to display during parameter estimation in `pca_updates` since it is a measure of how well the PCA projection is fitting the data.

## Usage

 `1` ```compute_rms(X, A, S, M, ndata, verbose = TRUE) ```

## Arguments

 `X` `matrix` – the data matrix with variables in rows and observations in columns. `A` `matrix` – initialised loadings matrix with observed variables in rows and latent variables in columns. `S` `matrix` – initialised factor scores matrix with latent variables in rows and observations in columns. `M` `matrix` – logical matrix whose values indicate whether the corresponding entry in `X` is observed. `ndata` `numerical` – the total number of observed values. `verbose` `logical` – whether extra output should be displayed.

## Value

A `list` of length 2:

errMx

`matrix` – matrix of element-wise differences (errors) between the observed data and the PCA projection.

rms

`numerical` – root mean-squared error of the PCA projection.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```p <- 20 n <- 7 set.seed(10045) X <- matrix(rnorm(p*n), p, n) miss.inds <- sample(1:(p*n), (p*n)/4) X[miss.inds] <- NA M <- !is.na(X) Nobs_i <- rowSums(M) Mu <- rowSums(X, na.rm = TRUE) / Nobs_i update_bias <- TRUE Xcent <- subtractMu(Mu=Mu, X=X, M=M, p=p, n=n, update_bias=update_bias, verbose=TRUE) init.model <- initParms(p=p, n=n, ncomp=2, verbose = TRUE) compute_rms(X=X, A=init.model\$A, S=init.model\$S, M=M, ndata=sum(Nobs_i), verbose=TRUE) ```

HGray384/pcaNet documentation built on Oct. 5, 2019, 3:36 p.m.