rmse — Compute root mean squared error between two arrays.
rmse(F,A) computes sqrt(mean(abs(F-A).^2)). MATLAB-compatible implicit expansion, dimension selection, "all", missing-value flags, and "Weights",W for single-dimension reductions are supported.
Syntax
E = rmse(X, Y)
E = rmse(X, Y, "Weights", W)
E = rmse(X, Y, dim)
E = rmse(X, Y, dim, nanflag)
E = rmse(X, Y, dim, nanflag, "Weights", W)Inputs
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
X | Any | Yes | — | Input data array. |
Y | Any | Yes | — | Second input data array. |
optionName | StringScalar | No | "Weights" | Name-value option name. |
W | NumericArray | No | — | Observation weights. |
dim | Any | No | — | Dimension, dimension vector, or "all" selector. |
options | Any | Variadic | — | Optional dimension and missing-value arguments. |
Returns
| Name | Type | Description |
|---|---|---|
Y | NumericArray | Computed statistic. |
Errors
| Identifier | When | Message |
|---|---|---|
RunMat:rmse:InvalidArgument | Inputs, flags, dimensions, or options are malformed. | descriptive statistics: invalid argument |
RunMat:rmse:Internal | Internal tensor conversion or allocation fails. | descriptive statistics: internal error |
How rmse works
FandAmay be scalars or compatible-size numeric or complex arrays.- NaNs are included by default.
"omitnan"and"omitmissing"skip NaN residuals and NaN weights in each slice."Weights",Waccepts scalar weights, full-size weights, or a vector matching the reduction dimension.- Weights are rejected with
"all"or vector-dimension reductions. - Weights must be nonnegative; zero total weight returns
NaN.
Examples
Root mean squared error
e = rmse(predicted, actual)Weighted column-wise error
e = rmse(F, A, 1, "Weights", W)Using rmse with coding agents
Open a RunMat example with live inputs, then ask the agent to explain how rmse changes the result.
Run a small rmse example, explain the result, then change one input and compare the output.
FAQ
How are weights normalized?⌄
Weighted RMSE uses sqrt(sum(W.*E.^2)/sum(W)) over each reduction slice.
Related Stats functions
Summary
binocdf · boxplot · cdf · cdfplot · chi2cdf · corr · corrcoef · corrcov · cov · cov2corr · dummyvar · ecdf · filloutliers · fitdist · geomean · grpstats · harmmean · icdf · isoutlier · kstest · kurtosis · lsline · mad · mode · nanmax · normalize · normcdf · norminv · normpdf · onehotdecode · onehotencode · pdf · prctile · quantile · refline · skewness · tabulate · tcdf · tiedrank · tinv · tpdf · ttest2 · wblinv
Ml
bayesopt · classify · confusionmat · crossvalind · cvpartition · fitclinear · fitctree · fitlm · kmeans · knnsearch · lasso · lassoglm · linkage · lscov · mnrfit · optimizableVariable · pdist · pdist2 · perfcurve · predict · regress · ridge · squareform · test · training · tsne
Random
binornd · bootstrp · datasample · dividerand · exprnd · gamrnd · lhsdesign · mvnrnd · normrnd · random · randsample · rng · trnd · unidrnd · unifrnd · wblrnd
Hist
Open-source implementation
Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how rmse is executed, line by line, in Rust.
- View the source for rmse in Rust on GitHub
- Learn how the RunMat runtime works
- Found a bug? Open an issue with a minimal reproduction.
About RunMat
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