PMC

Correlation analysis between true value and predicted value of soybean plant phenotypic information: (A) the correlation analysis diagram between the true value and the predicted value of plant height, (B) the correlation analysis diagram between the true value and the predicted value of pitch number, (C) the correlation analysis diagram between the true value and the predicted value of internodal length, (D) the correlation analysis diagram between the true value and the predicted value of main stem length, (E) the correlation analysis diagram between the true value and the predicted value of stem curvature, and (F) the correlation analysis diagram between the true value and the predicted value of branching angle.

Confusion Matrix. The example shown is for a binary classifier. A confusion matrix can, however, be extended to have more than two classes. TP = true positive (true condition correctly identified as true e.g. predicted Yes and true condition is positive). TN = true negative (negative condition correctly identified as negative e.g. predicted No and true condition is negative). FP = false positive (predicted value is positive but true condition is negative—also known as ‘Type I error’). FN = false negative (predicted value is negative but true condition is positive—also known a ‘Type II error’). True Positive Rate (TPR) or Sensitivity (SN) = how often the classifier correctly predicts a positive condition when the condition is positive. False Positive Rate (FPR) = when the condition is negative, how often the classifier incorrectly predicts a positive condition. True Negative Rate (TNR) or Specificity (SP) = how often the classifier correctly predicts a negative condition when the condition is negative. False Negative Rate (FNR) = when the condition is positive, how often the classifier incorrectly predicts a negative condition. Misclassification or Error Rate = how often the classifier is incorrect. Precision or Positive Predictive Value (PPV) = how often is the prediction correct when the classifier predicts a positive condition. Negative Predictive Value (NPV) = how often is the prediction correct when the classifier predicts a negative condition. Accuracy (ACC) = how often the classifier is correct. Prevalence = how often the positive condition occurs in the sample.

The performance of the proposed predictive ADRC compensation system for seeker lag and time delay: (a) True value, estimation, and original value of LOS angle; (b) Errors between true value and estimation of LOS angle; (c) True value, estimation, and original value of LOS angular rate; (d) Errors between true value and estimation of LOS angular rate.

Comparison of DI and direction circular variance measures for simulated data. We created 100 simulations of tuning curves for each of 21 underlying “true” tuning curves, ranging from DI = 0 to DI = 1, some curves shown in (A). Each trial had 50% noise added. (B) Percentiles of the empirically determined DI for the 100 simulations at each underlying “true” DI value. Note that for cells with 0 true selectivity, the empirical DI values range from about 0 to about 0.5. (C) Percentiles of the empirically determined 1-DirCirVar index for each of the underlying “true” DI values. Note that when “true” DI is low, the 1-DirCirVar is always low. The index 1-DirCirVar increases as “true” OI increases but the range of values remains narrower than the corresponding range of empirical DI values in (B). (D) The inverse of (B); given we observed an empirical DI value of x, what is the range of possible “true” DI values that produced x in our simulations? An empirical DI of 0 could have arisen from cells with “true” DI values ranging from about 0 to 0.5, and an empirical DI of 0.5 could have arisen from cells with a “true” DI ranging from about 0.1 to 0.7. (E) The inverse of (C). A 1-DirCirVar of 0 could have arisen from a “true” DI ranging from 0 to about 0.4, and 1-DirCirVar of 0.25 could have arisen from a “true” DI ranging from about 0.1 to 0.7. The range of possible underlying “true” DI values is narrower when 1-DirCirVar is used as a readout as compared to DI.