SimDesign (version 0.7)

RMSE: Compute the (normalized) root mean square error

Description

Computes the average deviation (root mean square error; also known as the root mean square deviation) of a sample estimate from the parameter value. Accepts estimate and parameter values, as well as estimate values which are in deviation form.

Usage

RMSE(estimate, parameter = NULL, type = "RMSE")

Arguments

estimate

a numeric vector or matrix/data.frame of parameter estimates. If a vector, the length is equal to the number of replications. If a matrix/data.frame, the number of rows must equal the number

parameter

a numeric scalar/vector indicating the fixed parameter values. If a single value is supplied and estimate is a matrix/data.frame then the value will be recycled for each column. If NULL then

type

type of deviation to compute. Can be 'RMSE' (default) for the root mean square-error, 'NRMSE' for the normalized RMSE (RMSE / (max(estimate) - min(estimate))), 'NRMSE_SD' for the normalized RMSE with the standard dev

Value

• returns a numeric vector indicating the overall average deviation in the estimates

See Also

bias

MAE

Examples

pop <- 1
samp <- rnorm(100, 1, sd = 0.5)
RMSE(samp, pop)

dev <- samp - pop
RMSE(dev)

RMSE(samp, pop, type = 'NRMSE')
RMSE(dev, type = 'NRMSE')
RMSE(dev, pop, type = 'NRMSE_SD')
RMSE(samp, pop, type = 'CV')

# matrix input
mat <- cbind(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))
RMSE(mat, parameter = 2)

# same, but with data.frame
df <- data.frame(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))
RMSE(df, parameter = c(2,2))