The verb gauge, which refers to measuring or estimating, also has a variant gage. This variant appears to show up primarily in informal sources, though not often. Gauge is by far the preferred spelling in general usage for both the noun and the verb; we encourage you use it.

GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods.

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To mark the 20th anniversary of the U.S. decision to invade Iraq, GAGE convened students and faculty at the Foundation to engage in a discussion with eminent American historian Melvyn P. Leffler about his new book, Confronting Saddam Hussein: George W. Bush and the Invasion of Iraq. Leffler, an emeritus professor of history at UVA, discussed what drove Bush to invade Iraq and how fear, hubris, and power influenced his decision.

Data Set 2 must include exactly NUMGAGE lines (or records) of data. If NUMGAGE > 1, it is permissible to interleaf in Data Set 2 records for stream gaging stations (according to the format specified in the documentation for the Stream Package) with records for gages on lakes. Data lines (records) within Data Set 2 can be listed in any arbitrary order.

Note 3: Data Set 2 must include exactly NUMGAGE lines (records) of data. If NUMGAGE > 1, it is permissible to interleaf the Item 2 lines for stream gaging stations with lines for lake gages. Data lines (records) within Item 2 can be listed in any arbitrary order.

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In this tutorial, you will associate precipitation gage data with a gage weights precipitation method in a Meteorologic Model in order to model the May 1996 precipitation event in the Mahoning Creek watershed. The precipitation gages have already been added to the HEC-HMS model. For more information on creating precipitation gage time-series, see Creating Time Series Data. The watershed has been represented using a single subbasin for the entire drainage area above the Punxsutawney stream gage.

Step 2. Expand the Basin Models node of the Watershed Explorer Tree and select the May1996 Basin Model. You will see that this model contains two elements, a Subbasin and a Sink. You should see in the Desktop map a display that looks like the watershed above. Next, expand the Time-Series Data node of the tree and you will see that there are two types of time-series data in the model. This tutorial is concerned with the precipitation data time series. Expand that Precipitation Gages sub-node of the tree and you should see three precipitation gages.

These three gages are located around the watershed and represent different observations of similar storm events. Due to the inherent variation of precipitation in space and time, these three gages are likely to observe different values of precipitation despite not being very far apart. The map below shows the location of these three stations relative to the watershed.

Step 3. Expand the gages in the Watershed Explorer to see that there is a time window associated with each of them, corresponding to the May 1996 event. You can click on the time window and then select the Graph tab in the Component Editor to see a graph of each of the gage's data for this event.

The precipitation gages are hourly. For this size of basin, 1-hour precipitation should reasonably capture the peak rainfall intensity and therefore peak runoff. If the watershed was smaller, a finer timestep might be required.

Step 9. You will see that two additional items appear beneath the May 1996 Gage Weights Met Model in the Watershed Explorer: an entry called Precipitation Gages, and then the subbasin element MahoningCreek. Expand the MahoningCreek node to see the option to set the gage weights, and then select it.

Step 10. We need to tell the Met Model which precipitation gages we want to use. Switch over to the Selections tab of the Component Editor. Here you should see the three precipitation time-series gages: DUJP, MFFP and PNXP. Enable all three of them in the Met Model by toggling the Use Gage option over to Yes. Then, save your project.

Step 11. Switch to the Weights tab of the Component Editor. Now you should see a table where the first column has the three gages you just enabled. The other two columns contain the parameters for this precipitation method, the Depth Weight and the Time Weight.

The depth weight controls how much each gage contributes to the total estimate for precipitation depth for a storm event. Generally this is estimated using area-average weighting, for example using Thiessen polygons. The influence of each gage on the spatial estimate of rainfall can be estimated by constructing Thiessen polygons, and then estimating the percentage of the watershed area covered by each gage's polygon, as below.

These depth weight values can be estimated using GIS by intersecting the Thiessen polygons with the watershed boundary. The estimated values serve as a starting point for estimating the contribution of each gage to the basin-averaged precipitation depth and may be adjusted during the calibration process. When there are many gages, the weight for each becomes less impactful on the result.

The time weight controls how much each gage contributes to the time pattern used for the storm event. In general, it is best to choose one gage located closest to the center of the watershed and give it a time weight of 1 (and the other gages 0).

Select a gage that has a fine time step so that the temporal resolution of the precipitation pattern can be well-defined. A daily gage may be available near the watershed centroid, but this will not represent sub-daily intensities. A nearby gage that is not ideally located, but has hourly or 15-minute data, will be better.

When calibrating a gage weights model, it can be beneficial to test which of the gages being used gets the time weight of 1. This can affect timing of rainfall and the shape of the overall hyetograph, and using the best gage possible for timing can improve calibration. 0852c4b9a8

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