Search results “Roc curve interpretation”

NOTE: At 8:24 in this video there is a typo and I incorrectly labeled the True Negatives. It should read (and say), "True Negatives are correctly classified as _not_ obese". I will correct this as soon as YouTube allows me to.
ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matrices into a single, easy to interpret graph. This video walks you through how to create and interpret ROC graphs step-by-step. We then show how the AUC can be used to compare classification methods and, lastly, we talk about what to do when your data isn't as warm and fuzzy as it should be.
NOTE: This video assumes you already know about
Confusion Matrices...
https://youtu.be/Kdsp6soqA7o
...Sensitivity and Specificity...
https://youtu.be/sunUKFXMHGk
...and the example I work through is based on Logistic Regression, so it would help to understand the basics of that as well:
https://youtu.be/yIYKR4sgzI8
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a cool StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer

Views: 56428
StatQuest with Josh Starmer

In this video I describe how ROC curves are constructed and how to interpret them

Views: 1382
Terry Shaneyfelt

An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others).
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- Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf
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Views: 306994
Data School

Produced for BST 230 at the University of Kentucky for educational purposes.
BMJ Article: http://dx.doi.org/10.1136/bmj.327.7417.716

Views: 30635
Jennifer Daddysman

I am trying my best to simplify this concept, it took me a while, if you like the video please give it a thumb up
Thank you

Views: 16493
Crushed USMLE Questions

This video demonstrates how to calculate and interpret a Receiver Operator Characteristic (ROC) Curve in SPSS. Evaluating sensitivity and specificity to inform selection of cutoff values is reviewed.

Views: 66615
Dr. Todd Grande

Currell: Scientific Data Analysis. SPSS analysis for Fig 8.27 http://ukcatalogue.oup.com/product/9780198712541.do
© Oxford University Press

Views: 22530
Oxford Academic (Oxford University Press)

In a typical diagnostic test analysis, an individual is given a score with the intent that the score will be useful in predicting whether the individual has or does not have the condition of interest. Based on a (hopefully large) number of individuals for which the score and condition is known, researchers may use ROC curve analysis to determine the ability of the score to classify or predict the condition. The analysis may also be used to determine the optimal cutoff value (or optimal decision threshold).
For a given cutoff value, a positive or negative diagnosis is made for each unit by comparing the measurement to the cutoff value. If the measurement is less (or greater, as the case may be) than the cutoff, the predicted condition is negative. Otherwise, the predicted condition is positive. However, the predicted condition doesn’t necessarily match the true condition of the individual. There are four possible outcomes: true positive, true negative, false positive, false negative.
For a given cutoff value, each individual falls into only one of the four outcomes. When all of the individuals are assigned to the four outcomes for a given cutoff, a count for each outcome is produced.
Various rates can be used to describe a classification table.
Some of the more commonly used rates are the true positive rate, or sensitivity, the true negative rate, or specificity, the false positive rate, the positive predictive value, the proportion correctly classified, or accuracy, and the sensitivity plus specificity.
Each of the rates are calculated for a given table, based on a single cutoff value. An ROC curve plots the true positive rate (or sensitivity) against the false positive rate for all possible cutoff values. The ROC curve gives a visual representation of how well the diagnostic test performs across all false positive rates. Better diagnostic tests are those with ROC curves that reach closer to the top left corner, since they better maintain a true positive rate. The diagonal line serves as a reference line since it is the ROC curve of a diagnostic test that randomly classifies the condition.
The area under the ROC curve provides a numeric representation of the overall performance of the diagnostic test.
NCSS also provides the capability to produce a smooth estimate of the ROC curve, called the bi-Normal estimation ROC curve.
To produce an ROC curve in NCSS, two columns of data are needed: a condition column, representing the known condition of each individual, and a score column, giving the score for each individual for the diagnostic test.
The ‘One ROC Curve and Cutoff Analysis’ procedure can be opened from the menus. In this example, the Condition Variable is assigned the Condition column, and a positive condition is assigned the value of one.
The Score is the Criterion Variable.
Since, in this example, higher scores are more likely to imply a positive condition, the Criterion Direction is set to ‘Higher values indicate a Positive Condition’.
We’ll leave checked the set of standard reports.
The Run button is pressed to generate the report.
The first several numeric tables show a variety of summary statistics for each of the cutoff values. Each statistic is defined in the Definitions section below the report.
The Area Under Curve Analysis report gives a statistical test comparing the area under the curve to the value 0.5. The small P-value indicates a significant difference from 0.5. The report also gives the 95% confidence interval for the estimated area under the curve.
Finally, the ROC curve itself is shown. It is seen to be moderately away from the 45 degree line and seems to indicate a decent separation from random classification.
If we wish to determine the optimal cutoff value for this diagnostic test, two common indices to consider are the accuracy, which is the proportion correctly classified, and the sensitivity plus specificity, which is the true positive rate plus the true negative rate.
Both of these indices point to seven as the optimal cutoff value, or optimal decision threshold.

Views: 3056
NCSS Statistical Software

Sensitivity, specificity, tradeoffs and ROC curves. With a little bit of radar thrown in there for fun.

Views: 127062
Rahul Patwari

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 14784
Noureddin Sadawi

Views: 3595
R studio Finance

This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue student Jianxin Sun. The complete slecture is posted at
https://www.projectrhea.org/rhea/index.php/ROC_curve_analysis_slecture_ECE662_Spring0214_Sun
To view other slectures on the same topic, go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin
For more information about slectures, go to http://slectures.projectrhea.org

Views: 1403
Project Rhea

Review: prediction success table. Sensitivity vs. Specificity. What is the ROC curve, and how is it used to evaluate model performance? Advantages of evaluating based on ROC. How to utilize the Area Under Curve (AUC).
http://www.salford-systems.com

Views: 45540
Salford Systems

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 47212
Noureddin Sadawi

This Video talks about how to decide a value of threshold to convert Probabilities into classes in a Classification Problem.
This video is part of a Self Paced course on Mydatacafe. Please visit www.mydatacafe.com if you want to enroll into any of our courses. Subscribe for more sch free Videos on Data Science.

Views: 832
MyDataCafe

In this video you will learn plotting ROC curve while doing Logistic Regression in SAS. You will also learn how to interpret a ROC Curve
For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected]
Find all free videos & study packs available with us here:
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SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop

Views: 8097
Analytics University

This video is part of an online course, Model Building and Validation. Check out the course here: https://www.udacity.com/course/ud919.

Views: 3812
Udacity

This tutorial walks you through, step-by-step, how to draw ROC curves and calculate AUC in R. We start with basic ROC graph, learn how to extract thresholds for decision making, calculate AUC and partial AUC and how to layer multiple ROC curves on the same graph.
You can get a copy of the code from the StatQuest website, here:
http://statquest.org/2018/12/17/roc-and-auc-in-r/
NOTE: This StatQuest builds on the example in the original ROC and AUC StatQuest:
https://youtu.be/xugjARegisk
Also, if you're curious, here are some links to StatQuests about...
...Logistic Regression
https://youtu.be/yIYKR4sgzI8
...and Random Forests...
https://youtu.be/J4Wdy0Wc_xQ
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a cool StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer

Views: 10671
StatQuest with Josh Starmer

Determing the accuracy of a diagnostic-evaluative test in predicting a dichotomous outcome. For methods to determine a cut-off score for the diagnosis of the outcome, please see ROC Curve Part 2 (http://www.youtube.com/watch?v=WO8Re7YqnP0).
The following resource can be used to determine sample sizes for ROC analysis: Hanley JA, & McNeil BJ. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143(1), 29-36.

Views: 132851
TheRMUoHP Biostatistics Resource Channel

This videio will cover:
* what is a receiver operator curve.
* how to interpret a receiver operating characteristic curve.
* how to perform the calculations with Excel.
* how to graph the results with Excel.
Excel's pivot table tool is used to create a frequency distribution table. Another way to create the table is to use Excel's histogram tool. A video on how to create ROC curves using the histogram function has been posted at https://youtu.be/-rfzhtLOYq8.

Views: 25272
Stokes Baker

There are 2 test I can use to see if this patient has got cancer, which one is best? How do I know? How can I compare them?? These were just some of the thoughts going through the candidates mind as his stared at the paper in the academic viva in national selection!
If only they'd listened to Rob Radcliffe, who is on hand to explain how you do just that using receiver operating characteristic curves, a really easy way to compare the performance of tests and probably the most useful to medicine thing that had its origin in WW II radar technology.
Starting with a review of sensitivity and specificity (see http://schoolofsurgery.podomatic.com/entry/2014-05-02T00_31_49-07_00 for full revision) Rob shows how sensitivity and specificity vary with the cut off point for a test and demonstrates the best test you can design and the worst and shows you how to construct a ROC curve. Real life examples are discussed and how to compare test visually from their curves, and how this can be qualified (and so compared statistically to find the best performing test) using Area Under the Curve (AUC) is also explained.
This is the clearest explanation you will find anywhere for this commonly used comparison (check out the Wikipedia page on this if you don't believe me). Is is essential to know as ROC curve feature often in medical literature and often in exams and academic interviews.
Rob Radcliffe was a maths teacher in a former life and is now a trainee in Urology in the East Midlands, UK

Views: 8089
school of surgery

ROC Curve (Receiver Operating Characteristic Curve) and Random Oversampling Examples (ROSE Package) Analysis in R
1. Example Data Set LoanAnalysis.csv
https://drive.google.com/open?id=1a6VBAvhoprYFayIVpsaMNCK4CLSQK35y
2. Analysis Code
https://drive.google.com/open?id=1888o-tjgOkmAcpYfooqA8-GUOLrDSij5
3. Data Partition Analysis in R Lecture Video
https://www.youtube.com/watch?v=UFaZvynajtI
4. Logistic Regression Analysis in R Lecture Video
https://www.youtube.com/watch?v=eScK5w5JcHI
5. Decision Tree Analysis in R Example Tutorial Video
https://www.youtube.com/watch?v=bJC5S_ViRCo

Views: 14468
The Data Science Show

ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matrices into a single, easy to interpret graph. This video walks you through how to create and interpret ROC graphs step-by-step.

Views: 2497
Bhavesh Bhatt

Provides easy to apply example obtaining ROC curve and AUC using R.
Data: https://goo.gl/VoHhyh
Machine Learning videos: https://goo.gl/WHHqWP
Includes an example with,
- logistic regression model
- confusion matrix
- misclassification rate
- rocr package
- accuracy versus cutoff curve
- identifying best cutoff values for best accuracy
- roc curve
- true positive rate (tpr) or sensitivity
- false positive rate (fpr) or '1-specificity'
- area under curve (auc)
roc curve is an important model evaluation tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 44675
Bharatendra Rai

This tutorial demonstrates how to produce a single ROC curve for a single classifier. It also demonstrates how to get the Area under ROC curve or (AUC). ROC curves are cost-sensitive measures to evaluate classifier performance. However, it is not a good mesure of model goodness if the dataset is imbalanced (highly skewed class distributions are present).
LinkedIn: http://www.linkedin.com/pub/rushdi-shams/3b/83b/9b3

Views: 79926
Rushdi Shams

In a ROC curve, we plot ‘True Positives‘ on Y-axis and ‘True Negatives‘ on X-axis. The average number of mistakes made while predicting the number of true positive values defines ROC(Receiver operating characteristic). How to make 0% mistake while identifying the positives, where AUC value nears to 1. AUC (Area under curve) is related to ROC. A detailed explanation is provided about ROC & AUC. Watch the video for more information on the topic.
Data Scientists take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize. But worry not we are here to the rescue and teach you how to be a data scientist, more importantly, upgrade your analytic skills to tackle any problem in the field of data science. Join us on "statinfer.com" for becoming a "scientist in data science"
Our "Machine Learning" course is now available on Udemy
https://www.udemy.com/machine-learning-made-easy-beginner-to-advance-using-r/
Part 1 – Introduction to R Programming.
This is the part where you will learn basic of R programming and familiarize yourself with R environment. Be able to import, export, explore, clean and prepare the data for advance modeling. Understand the underlying statistics of data and how to report/document the insights.
Part 2 – Machine Learning using R
Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it. Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.
Data science YouTube playlist.
https://www.youtube.com/statinferanalytics
Facebook link:-
(Visit our facebook page we are sharing data science videos)
https://www.facebook.com/aboutanalytics/

Views: 1373
Statinfer Analytics

Lecture 8-15 at https://vimeo.com/ondemand/logisticmodel/, available for paid subscription
In this video we cover the basics of Receiver Operating Curves (ROC) curves. The explanation shows how to calculate Sensitivity, 1-Specificity and plot a curve using excel.
We then cover the area under curve (AUC) of the ROC curve as a measure of the predictive power of the model and the apply that to both training and validation datasets and compare against each other to test stability of the model.

Views: 16021
Learn Analytics

Determining a cut-off score for a diagnostic test using a ROC curve.

Views: 66922
TheRMUoHP Biostatistics Resource Channel

An introduction to the calculation and use of ROC Curves and Area Under the Curve to accompany "Childhood forecasting of a segment of the adult population characterized by economic burden", Caspi, Houts, Belsky, Harrington, Hogan, Ramrakha, Poulton, & Moffitt (under review).

Views: 13190
moffittcaspi group

In this video you will learn the theory about ROC curve. ROC curve is used to assess predictive power of a Logistic Regression Model (any binary model for that matter)
For all our videos, visit our video gallery : http://analyticuniversity.com/
Contact : [email protected]

Views: 8505
Analytics University

The video describes how to analyze data from a recognition memory experiment to create a Receiver Operating Characteristic (ROC) curve, which indicates how well the person is able to distinguish things they studied from things they didn't study. We don't get too far into the theory here, this really will just let you see how to do the simple calculations that let you create the ROC curve! (this is part I where we set up the problem, in part II we actually plot the ROC)

Views: 57845
Sean Polyn

ROC Curve - Sensitivity, Specificity
Gülin Zeynep Öztaş

Views: 1812
Prof Dr Sabri Erdem

Views: 23557
Stata Learner

Correlation analysis, ROC curve, Likelihood ratios
Attached documents https://app.box.com/s/x49s8lodpu15wgzsr1dl0popblf0u3by

Views: 1268
Mohamed Saadeldin

Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer.
This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines.
Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.

Views: 555
GeoEngineerings School

In this video you will learn how to use ROC curves to select the best fit model out of a range of model.
Visit : http://analyticuniversity.com/

Views: 1411
Analytics University

Machine Learning #51 ROC Curve
Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm
Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE)
Best Programming Courses @ https://goo.gl/MVVDXR
Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if
MATLAB Tutorials @ https://goo.gl/EiPgCF

Views: 1521
Xoviabcs

Sometimes one needs to calculate the area under a curve in your research, Here’s how you can do it simply in GraphPad Prism.
©2018 James Clark, KCL

Views: 2639
Dory Video

In this video you will learn about the different performance matrix used for model evaludation such as Receiver Operating Charateristics, Confusion matrix, Accuracy. This is used very well in evauating classfication models like deicision tree, Logistic regression, SVM
ANalytics Study Pack : https://analyticuniversity.com
Analytics University on Twitter : https://twitter.com/AnalyticsUniver
Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Logistic Regression in R: https://goo.gl/S7DkRy
Logistic Regression in SAS: https://goo.gl/S7DkRy
Logistic Regression Theory: https://goo.gl/PbGv1h
Time Series Theory : https://goo.gl/54vaDk
Time ARIMA Model in R : https://goo.gl/UcPNWx
Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx
Data Science Case Study : https://goo.gl/KzY5Iu
Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA

Views: 18476
Big Edu

MIT 15.071 The Analytics Edge, Spring 2017
View the complete course: https://ocw.mit.edu/15-071S17
Instructor: Allison O'Hair
Receiver Operator Characteristic (ROC) curves can help you decide which threshold value is the best depending.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu

Views: 760
MIT OpenCourseWare

Tutorial for MedCalc: 教學
MedCalc 授權經銷商
SoftHome International ; Software for Science
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Views: 5141
全傑

Here you will learn how to fit a decision tree model in R and how to do predictions and get the probabilities for each classes and then how to plot a ROC curve in R.
This channel includes machine learning algorithms and implementation of machine learning algorithms in R like random forest algorithm in R,neural networks algorithms in R,decision tree in R and so on.Please do subscribe and like this channel for more videos on advances topics like deeplearning,graph theory,etc.

Views: 7073
Data Science by Arpan Gupta IIT,Roorkee

This is a companion movie to the chapter on Receiver-Operator curves in "Interactive Mathematics for Laboratory Medicine" by Prof. T.S. Pillay. Available here: https://itunes.apple.com/us/book/interactive-mathematics-for/id1038925720?mt=11

Views: 58896
Kzn Elearning

ROC curves produced from different classifiers are a good means to compare classifier performances. This session demonstrates the use of Knowledge-flow environment of Weka to generate multiple ROC curves for more than one classifiers.
Tutorial 28 shows how to generate a single ROC curve for a single classifier using Weka Explorer. The tutorial can be found at http://www.youtube.com/watch?v=j97h_-b0gvw

Views: 23908
Rushdi Shams

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2jDsrGS].
Our goal in this video would be to understand logistic regression, evaluation metrics of binary classification problems, and interpretation of the ROC curve.
• Explain the concept behind logistic regression
• Understand the evaluation metrics and interpretation of the ROC curve
• Implement in R
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

Views: 2139
Packt Video

In this Intellipaat's ROC curve in logistic expression video you will learn about ROC curves in R which is used for understanding the trade-off between the specificity and sensitivity of the binary classifier. You will learn to do threshold evaluation and find out the area under curve. This video also includes understanding the roc curve in logistic regression.
You will learn in this data science tutorial video that if you want to turn the real valued scores into a yes or no prediction then you need to set a threshold. All the scores which are above the threshold are positive and those that are below the threshold are negative. The various threshold values will give the different levels of specificity and sensitivity.
Intellipaat Data Science Course:- https://intellipaat.com/data-scientist-course-training/
Interested to learn more about Data Science? Please check similar blogs here:- https://goo.gl/94cLeV
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This ROC curve in R programming language tutorial helps you to learn following topics:
00:22 Threshold Evaluation
00:45 ROC Curve
1:14 Area Under Curve
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Why should you watch this ROC curve in R video?
Intellipaat data science tutorial video that includes the various aspects of data science, r programming and more will help you understand the details of how r programming is used in data science domain. You will learn about the various terminologies involved like what is ROC curve, ROC curve in logistic regression, threshold evaluation, area under curve and more about the roc curve explained in this video on ROC curve.
If you want to learn data science with r this video on ROC curve from Intellipaat will help you be on your journey to becoming a data scientist. This video on learning about the ROC curve, threshold evaluation, area under curve and more is part of the data science tutorial.
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#ROCCurve #ROCCurveInLogisticRegression #ROCCurveInR
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Trigger Types. A DML trigger is fired by a DML statement, a DDL trigger is fired by a DDL statement, a DELETE trigger is fired by a DELETE statement, and so on. An INSTEAD OF trigger is a DML trigger that is defined on a view (not a table). The database fires the INSTEAD OF trigger instead of executing the triggering DML statement. For more information, see Modifying Complex Views (INSTEAD OF Triggers). A system trigger is defined on a schema or the database. A trigger defined on a schema fires for each event associated with the owner of the schema (the current user). A trigger defined on a database fires for each event associated with all users. A simple trigger can fire at exactly one of the following timing points : Before the triggering statement executes. After the triggering statement executes. Before each row that the triggering statement affects. After each row that the triggering statement affects. A compound trigger can fire at more than one timing point. Compound triggers make it easier to program an approach where you want the actions you implement for the various timing points to share common data. For more information, see Compound Triggers. Trigger States.