Contingency ( or by or ) how well one category (test positive) predicts another category (true positive), used for sensitivity and specificity, and chi square. 13. We already know whether each subject was really in 1 or 2 (true values) and we need a second column of categorical values that uses our threshold to determine whether the subject was probably in group 1 or 2 (test values) based on their TFAT36 value. Add a column named PredictGroup. Under column properties, select formula. Using functions under conditional and comparison, enter the statement If TFAT36≥threshold, then 2, else 1. 14. Make a contingency plot of Group (Y) and Predict Group (X). This is literally called a of “Table Confusion” in JMP and a Classification table in medical literature. Actual Group normal disease normal True Predicted Negatives Negatives Group Disease False True Positives Positives 15. Click on the hotspot and check only Count. Count gives the number of cases in each category (TP, FP, TN, FN). Use the equations for sensitivity and specificity 16. Go back to the hot spot and check col%. The TP box shows sensitivity, and the TN box shows specificity. 17. One last thing. What if instead of having all of the individual PSA or log PSA values for the diseas and normal groups, you were only told the mean and standard deviation for each group, and told that the distribution is normal. Do you have enough information to calculate sensitivity and specificity for a given threshold? 18. The Z score describes the distance between any value on a distribution and the population mean in units of the standard deviation. Z is negative when the value is below the mean, positive when above. (Image used with permission, CreativeCommons) 19. Determine the Z for each group (get two Z scores) using threshold (t) 39.3 for both. How can you get the mean (μ) and standard deviation(σ) for the groups?
Contingency ( or by or ) how well one category (test positive) predicts another category (true positive), used for sensitivity and specificity, and chi square. 13. We already know whether each subject was really in 1 or 2 (true values) and we need a second column of categorical values that uses our threshold to determine whether the subject was probably in group 1 or 2 (test values) based on their TFAT36 value. Add a column named PredictGroup. Under column properties, select formula. Using functions under conditional and comparison, enter the statement If TFAT36≥threshold, then 2, else 1. 14. Make a contingency plot of Group (Y) and Predict Group (X). This is literally called a “Table of Confusion” in JMP and a Classification table in medical literature. Actual Group normal disease Predicted Group normal True Negatives Negatives Disease False Positives True Positives 15. Click on the hotspot and check only Count. Count gives the number of cases in each category (TP, FP, TN, FN). Use the equations for sensitivity and specificity 16. Go back to the hot spot and check col%. The TP box shows sensitivity, and the TN box shows specificity. 17. One last thing. What if instead of having all of the individual PSA or log PSA values for the diseas and normal groups, you were only told the mean and standard deviation for each group, and told that the distribution is normal. Do you have enough information to calculate sensitivity and specificity for a given threshold? 18. The Z score describes the distance between any value on a distribution and the population mean in units of the standard deviation. Z is negative when the value is below the mean, positive when above. (Image used with permission, CreativeCommons) 19. Determine the Z for each group (get two Z scores) using threshold (t) 39.3 for both. How can you get the mean (μ) and standard deviation(σ) for the groups?
JMP%20Session%20Labs%201,2,3.docx