# compute_rrmse: Compute RRMSE in a time series estimate In davidearn/fastbeta: Fast Estimation of Time-Varying Transmission Rates

## Description

`compute_rrmse()` computes the relative root mean square error (RRMSE) in a time series estimate of a known time-varying quantity.

## Usage

 `1` ```compute_rrmse(x, y, na_rm = TRUE) ```

## Arguments

 `x` Numeric vector. A time series. `y` Numeric vector with length equal to `length(x)`. A time series estimate of `x`. `na_rm` Logical. If `TRUE`, then missing values (`NA`, `NaN`, `Inf`) are ignored.

## Value

Let `cc` ("complete cases") be the vector of indices `i` such that neither `x[i]` nor `y[i]` is missing. If `length(cc) = 0` or if `length(cc) < length(x)` and `na_rm = FALSE`, then `NA`. Otherwise, the RRMSE in `y` given by `sqrt(sum((x[cc] - y[cc])^2) / length(cc)) / mean(x[cc])`.

## Examples

 ```1 2 3``` ```x <- rep(c(0, 1, 0, -1), 100) y <- x + rnorm(x, 0, 0.01) compute_rrmse(x, y) # RRMSE in `y` ```

davidearn/fastbeta documentation built on June 14, 2020, 3:11 p.m.