# Calculate Area Under A Curve In Excel

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Calculate Area Under A Curve In Excel – An area chart is based on a line chart, with the area between the line and the x-axis colored to illustrate volume. In this post, we’ll explore how to create a standard area chart, as well as a stacked area chart, in Excel.

Don’t forget though, you can easily create a free area chart using our free area chart generator!

## Calculate Area Under A Curve In Excel

The easiest way to create a pie chart in Excel is to first set up the data as a table. The first column should contain the labels and the second column the values. To create a stacked range table where the values ​​are divided into subgroups, create a column for each of the subgroups. In this example, we’ll use a dataset showing annual building permits by region for single-unit housing in the United States from 2001 to 2016. This dataset shows the total permits issued (in thousands) for each region depending on the year. The first column contains the y-axis label (year) and a column for each region. The values ​​in the table represent the number of authorizations.

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To create an area chart using the data above, highlight the data range (cells A1:B28 in the above example) and choose Insert > Chart, select the drop-down menu Line Charts group and select the second option Area Chart 2 -D. .

This area chart is created from the selected data. You can change the zone table properties by first selecting the zone table and accessing the Table option that appears at the top of the menu toolbar. From here you can change the design and format properties of the chart. In this example, I added a chart title and changed the font size for the legend and axis.

To create the chart above, I started with the data and then turned it into a pie chart. In Excel, you can first create the table object and provide the data to populate the table. To do this, first select Area Chart from the Insert > Charts menu to select one of the area chart options. This will create a table object with empty areas on the sheet. Next, choose Table Tools > Design > Select Data. This opens the Select Data Source dialog box. Note that the chart object must be selected for the Chart Tools menu to appear.

For the Table dataset, select B1:E28 from the example dataset above. In the Horizontal Axis Labels (Category) section, click the Edit button and select cells A2:A28 and click OK. Excel will create the same chart that was created above. Again, you can change the design and formatting of the chart using the Chart Tools menu described above.

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Ready to move beyond Excel? There are other ways to create views that offer more advanced options and flexibility. Instead, learn how to create your own area chart!« Because It’s Friday: Intransitive Dice | Main | Tutorial: Building a Rental Prediction Service with SQL Server R Services »

Receiver operating characteristic (ROC) curves are a popular way to visualize the tradeoff between sensitivity and specificity of a binary classifier. In a previous post, I described a simple “turtle eye” in these plots: a classifier is used to rank cases in order from most to least likely to be positive, with a turtle-like logo on this series of cases. Turtle believes that all of his past cases have tested positive. Depending on their actual class, they are either false positives (FP) or true positives (TP); this is equivalent to adjusting a score threshold. When the turtle passes a TP, it takes a step up on the y-axis, and when it passes a FP, it takes a step to the right on the x-axis. The size of the steps is inversely proportional to the amount of positive (in the y-direction) or negative (in the x-direction) current, so the path always ends at coordinates (1, 1). The result is a plot of the true positive rate (TPR or sensitivity) versus the false positive rate (FPR or 1 – specificity), which is all an ROC curve is.

Calculating the area under the curve is one way to summarize it into a single value; This measurement is so common that if scientists say “area under the curve” or “AUC” you can generally assume they mean an ROC curve unless otherwise stated.

Probably the simplest and most intuitive metric for classification performance is accuracy. Unfortunately, there are circumstances where simple precision doesn’t work well. For example, with a disease that affects only 1 in a million people, a completely bogus screening test that always reports “negative” will be 99.9999% accurate. Unlike accuracy, ROC curves are insensitive to class imbalance; The false screening test would have an AUC of 0.5, which is like having no test at all.

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In this post I will work through the geometry exercise to calculate the area and develop a short vectorized function that uses this approach. Then we’ll look at another way of looking at AUC that leads to a probabilistic interpretation.

Keep the ersatz notes; these should normally be assigned by a classification, but here we have only assigned numbers so that the descending order of the grades matches the order given in the category labels. Scores 9 and 10, one representing a positive case and the other a negative case, are replaced by means so that the data are related without otherwise disturbing the order.

To draw an ROC curve, we will need to calculate the true positive and false positive rates. In the previous article we did this using the cumulative sum of positive (or negative) sets of classified binary labels. But here we will use

Function returns an object with draw methods and other facilities, but for our purposes all we want from it is the vector of TPR and FPR values. TPR is the same as sensitivity and FPR is 1 – specificity (see “confusion matrix” in Wikipedia). Unfortunately, there

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The function reports these values ​​sorted in ascending order; We want to start from the bottom left corner, so we reversed the order. In accordance with

Function in the pROC package, our simulation category, and the prediction data give an AUC of 0.825; we will compare other attempts to calculate AUC with this value.

If the ROC curve were a perfect step function, we could find the area under it by adding a series of vertical bars with a width equal to the space between the points on the FPR axis and a height equal to the height of the step on the axis. TPR. Because real ROC curves may also include portions that represent ranges of non-squared values ​​and scores, we need to adjust the area for these segments. In the figure below we use green bars to represent the area under the steps. Adjustments for the range of tidal values ​​will be shown as blue rectangles; half the area of ​​each of these blue rectangles under an inclined segment of the curve.

The function to draw polygons in R takes vectors of x and y values; we will start by defining a

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Functions that use simpler and more specialized syntax; takes the x and y coordinates for the bottom left corner of the rectangle and a height and width. It sets some default display options and passes any other parameters we might specify (like color).

. Since this results in a position vector that is shorter than the original data, we pad each difference vector with a trailing zero:

For this figure, we will draw the ROC curve last to place it on top of the other elements, so we start by drawing an empty graph (

) that spans from 0 to 1 on each axis. Since the data set contains exactly ten positive and ten negative cases, all TPR and FPR values ​​will be multiples of 1/10, and the ROC curve points will fall on a regularly spaced grid. We draw the grid using light blue horizontal and vertical lines that are separated by a tenth of a unit. Now we can pass the values ​​we calculated above to the rectangle function, using

## Area Under Curve (auc) Calculation By Excel?

) to iterate over all cases and draw all green and blue rectangles. Finally, we draw the ROC curve (that is, we plot TPR versus FPR) above everything in red.

The area under the red curve is the entire green area plus half of the blue area. To add an area we only care about the height and width of each rectangle, not its (x,y) position. The heights of the green rectangles, which all start at 0, are in the TPR column and their widths in the dFPR column, so the total area of ​​all green rectangles is the dot product of TPR and dFPR. Note that the vector approach calculates a rectangle for each data point, even when the height or width is zero (in which case it doesn’t hurt to add them). Similarly, the height and width of the blue rectangles (if any) are in the dTPR and dFPR columns, so the total areas are dot products.

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