## Sas kaplan meier survival analysis klein,difference between ford ka edge and zetec 3dr,what causes cardiogenic pulmonary edema,surviving high school book pdf zusammenf?gen - PDF 2016

Published 26.04.2014 | admin

Feel free to take a look around, meet the Waverunners, and see how the foundation is being set in place, by building a softball powerhouse in Indiana! The interaction term of age with ndrugtx is not significant and will not be included in the model. One solution is to include the time-dependent variable for the non-proportional predictors.

There was a recent comment on the original 'Unbox Your Box Plots', where a user wants to see the original data for the box, but only label the outliers. As noted in the comment, labeling all the scatter markers and turning on the outlier display is not ideal. The process above is a bit tedious, so I figured I could use the power of SAS to create the graph I need as shown below.

I created this graph by downloading the 2-year stock data for Face Book (FB) from the NASDAQ site.

I applaud the creativity of the user, who has clearly taken to heart the lesson that multiple plot statements can often be used creatively to build the graph you may want. While we have discussed AxisTables in earlier articles, it seems worthwhile to review the subject. Note, since the CLASS option is used with the xAxisTable, the statistical values for the three levels of "Origin" are displayed stacked under each category on the x-axis. With SAS 9.40M3, the CLASSDISPLAY option was added to allow the display of the class values in the clustered arrangement as shown on the right. The benefit of this option is that multiple statistics can be displayed with such grouped plot statements.

Finally, the user wanted to add vertical divider lines (column border) to separate the column of values. Note, this visualization does not use a scatter plot, as was the case with the polar graph in the previous article. Note in the bar chart on the right, the bars are actually plotted using the High-Low plot, with Type=bar.

Using the equation shown above, we transform the (R, Theta) coordinates to the (x, y) coordinates. In the same data set, data is generated for the 16 radial grid lines, with values 0-315, the angle around the circle.

In the previous article for Polar Graph, I had used GTL to display the circular grid lines using the EllipseParm statement. The directions are displayed using the N-S-E-W arrows, so understanding the information is easier. In the data step for the graph, x and y are computed from R and Theta using the following formula. A simpler method would be to just plot the Wind Speed and and Wind Direction on the Y and X axes of a rectangular XY plot as suggested at the start of the article.

Instead, we can also make the same graph using GTL, which does support the ELLIPSEPARM statement that can be used to draw the circular grids. Note, I have used the option ASPECT=1.0 to ensure that the display area is circular regardless of the size or aspect of the graph itself.

While we have achieved one goal (inserting additional information into the graph), there are too many bars with small defect counts cluttering up the graph. In the code above, I have used the HBAR statement with the CATEGORYORDER=RESPDESC which creates this graph with the descending order of the bars. The full detailed data is provided to the procedure and the HBAR is computing the number of defects by product and arranging them by descending statistic (frequency). To create the graph shown on the right, I have to first compute the defect count for each product using PROC MEANS.

One shortcoming for the graph above is that it does not scale well for moderately large data.

An alternate way which is relatively easy to build to view the same data is shown on the right. As you can see, this graph scales very well for all kinds of data, with small or large observation counts and for different number of categories on the x-axis. A "Violin Graph" can be created instead using the same data by using the BAND statement instead of HIGLOW. Often we see bar charts showing revenues or other related measures by a classifier using a visual of a stack of coins. I thought this would be a good exercise to see how we can do this using the SGPLOT procedure. I searched the web for some appropriate images of coins, anything with a perspective image of a coin that can be used to create a stack.

The data for the graph is very simple as show on the right below the graph, and the program is shown below. Now, to create the graph of the pile of coins, we need to render each coin in the stack individually, using a SCATTER plot where the marker symbol is built from the image of the coin. At the 2013 SAS Global Forum, I presented a paper titled "Make a Good Graph" which reviewed some of the features that make for a good graph. Recently, a question was posted on the SAS Communities site asking how to create the graph shown on the right using SAS. Assuming the purpose of the graph is to better understand a company's sales vis-a-vis a peer, the second question becomes relevant. The visual shown above would not be the best one to facilitate accurate magnitude comparisons.

The clustered bar chart on the right provides a better visual for magnitude comparisons of sales by region between company and its peer.

Linear distance from common baseline along with proximity of items to be compared create a better graph. The advent of the AXISTABLE statement with SAS 9.4, has made it considerably easier to create graphs that include statistics aligned with x-axis values (Survival Plot) or with the y-axis (Forest Plot). It contains a wide range of statistical tests including many handy features not found in programs such as SPSS or SAS -- for example, easy analysis from summary data (as well as from raw data), nonparametric multiple comparisons, APA standard analysis write-up suggestions and more. These are used because I made this into a macro that will download the data, process it, and create multiple graphs given the stock name and symbol.

We pride ourselves on hard work, dedication, and improvement; while enjoying the game of fastpitch softball. Hundreds of hours of working with consultants and researchers went in to creating the interface as well as creating understandable examples and program output. Our goal as a team is to develop as softball players and build character within ourselves as well as represent our communities as responsible and classy individuals. We have been established in the area for several years and have helped to teach young ladies the skills needed to become champions on and off the field.

There was a recent comment on the original 'Unbox Your Box Plots', where a user wants to see the original data for the box, but only label the outliers. As noted in the comment, labeling all the scatter markers and turning on the outlier display is not ideal. The process above is a bit tedious, so I figured I could use the power of SAS to create the graph I need as shown below.

I created this graph by downloading the 2-year stock data for Face Book (FB) from the NASDAQ site.

I applaud the creativity of the user, who has clearly taken to heart the lesson that multiple plot statements can often be used creatively to build the graph you may want. While we have discussed AxisTables in earlier articles, it seems worthwhile to review the subject. Note, since the CLASS option is used with the xAxisTable, the statistical values for the three levels of "Origin" are displayed stacked under each category on the x-axis. With SAS 9.40M3, the CLASSDISPLAY option was added to allow the display of the class values in the clustered arrangement as shown on the right. The benefit of this option is that multiple statistics can be displayed with such grouped plot statements.

Finally, the user wanted to add vertical divider lines (column border) to separate the column of values. Note, this visualization does not use a scatter plot, as was the case with the polar graph in the previous article. Note in the bar chart on the right, the bars are actually plotted using the High-Low plot, with Type=bar.

Using the equation shown above, we transform the (R, Theta) coordinates to the (x, y) coordinates. In the same data set, data is generated for the 16 radial grid lines, with values 0-315, the angle around the circle.

In the previous article for Polar Graph, I had used GTL to display the circular grid lines using the EllipseParm statement. The directions are displayed using the N-S-E-W arrows, so understanding the information is easier. In the data step for the graph, x and y are computed from R and Theta using the following formula. A simpler method would be to just plot the Wind Speed and and Wind Direction on the Y and X axes of a rectangular XY plot as suggested at the start of the article.

Instead, we can also make the same graph using GTL, which does support the ELLIPSEPARM statement that can be used to draw the circular grids. Note, I have used the option ASPECT=1.0 to ensure that the display area is circular regardless of the size or aspect of the graph itself.

While we have achieved one goal (inserting additional information into the graph), there are too many bars with small defect counts cluttering up the graph. In the code above, I have used the HBAR statement with the CATEGORYORDER=RESPDESC which creates this graph with the descending order of the bars. The full detailed data is provided to the procedure and the HBAR is computing the number of defects by product and arranging them by descending statistic (frequency). To create the graph shown on the right, I have to first compute the defect count for each product using PROC MEANS.

One shortcoming for the graph above is that it does not scale well for moderately large data.

An alternate way which is relatively easy to build to view the same data is shown on the right. As you can see, this graph scales very well for all kinds of data, with small or large observation counts and for different number of categories on the x-axis. A "Violin Graph" can be created instead using the same data by using the BAND statement instead of HIGLOW. Often we see bar charts showing revenues or other related measures by a classifier using a visual of a stack of coins. I thought this would be a good exercise to see how we can do this using the SGPLOT procedure. I searched the web for some appropriate images of coins, anything with a perspective image of a coin that can be used to create a stack.

The data for the graph is very simple as show on the right below the graph, and the program is shown below. Now, to create the graph of the pile of coins, we need to render each coin in the stack individually, using a SCATTER plot where the marker symbol is built from the image of the coin. At the 2013 SAS Global Forum, I presented a paper titled "Make a Good Graph" which reviewed some of the features that make for a good graph. Recently, a question was posted on the SAS Communities site asking how to create the graph shown on the right using SAS. Assuming the purpose of the graph is to better understand a company's sales vis-a-vis a peer, the second question becomes relevant. The visual shown above would not be the best one to facilitate accurate magnitude comparisons.

The clustered bar chart on the right provides a better visual for magnitude comparisons of sales by region between company and its peer.

Linear distance from common baseline along with proximity of items to be compared create a better graph. The advent of the AXISTABLE statement with SAS 9.4, has made it considerably easier to create graphs that include statistics aligned with x-axis values (Survival Plot) or with the y-axis (Forest Plot). It contains a wide range of statistical tests including many handy features not found in programs such as SPSS or SAS -- for example, easy analysis from summary data (as well as from raw data), nonparametric multiple comparisons, APA standard analysis write-up suggestions and more. These are used because I made this into a macro that will download the data, process it, and create multiple graphs given the stock name and symbol.

We pride ourselves on hard work, dedication, and improvement; while enjoying the game of fastpitch softball. Hundreds of hours of working with consultants and researchers went in to creating the interface as well as creating understandable examples and program output. Our goal as a team is to develop as softball players and build character within ourselves as well as represent our communities as responsible and classy individuals. We have been established in the area for several years and have helped to teach young ladies the skills needed to become champions on and off the field.

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