Error estimates of continuous predictions
aa_predobs.RdError estimates of continuous predictions. Estimates of the regression line between observed and predicted values are obtained from standardized major axis regression (SMA), but coefficients from ordinary least squares regression are also provided for reference. Further the mean square error is partitioned into a random (PLP) and systematic component (PLA) following Correndo et al. (2021a). The code for the error decomposition is taken from Correndo et al. (2021b).
Value
A list or data.frame of error estimates and statistics comparing predicted (p) and observed (o) values:
bias: Estimated bias is \((\bar{p} - \bar{o})\)
varratio: Variance ratio is \(s_p \div s_o\)
mse: Mean square error (MSE)
rmse: Root mean square error (RMSE)
rrmse: Relative RMSE as \(RMSE \div \bar{o}\)
mlp: Mean Lack of Precision (Correndo et al., 2021)
mla: Mean Lack of Accuracy (Correndo et al., 2021)
rmlp: Root Mean Lack of Precision is \(\sqrt{MLP}\)
rmla: Root Mean Lack of Accuracy is \(\sqrt{MLA}\)
plp: Proportion Lack of Precision is \(MLP \div MSE\)
pla: Proportion Lack of Accuracy is \(MLA \div MSE\)
sma_intercept: intercept of standardized major axis regression
sma_slope: slope of standardized major axis regression
ols_intercept: intercept of ordinary least squares regression
ols_slope: slope of ordinary least squares regression
r_squared: Coefficient of determination between predictions and observations
References
Correndo, A.A., Hefley, T.J., Holzworth, D.P., & Ciampitti, I.A., 2021a. Revisiting linear regression to test agreement in continuous predicted-observed datasets. Agricultural Systems, 192
Correndo, A.A., Hefley, T., Holzworth, D., Ciampitti, I.A., 2021b. R-Code Tutorial: Revisiting linear regression to test agreement in continuous predicted-observed datasets. Harvard Dataverse V3. https://doi.org/10.7910/DVN/EJS4M0.