By Molenberghs G., Thijs H., Jansen I.

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13). The pioneers of exploratory data analysis came up with several recipes to describe such smooth relationships without making many parametric assumptions or being unduly influenced by outliers. One of these recipes, robust locally weighted regression, is incorporated in R as lowess: book February 27, 2006 51 700 EXPLORATORY DATA ANALYSIS AND GRAPHICS 400 300 100 200 Depth 500 600 lowess spline (default) spline (df=4) regression quad. 13 Lines through data: earthquake depth vs. longitude in eastern cluster book February 27, 2006 52 CHAPTER 2 it essentially does linear or quadratic regression on local subsets of the data to produce a smooth curve.

If you later save data sets or functions in R’s own format (using save() to save and load() to load them), you will be able to exchange them freely across platforms. Many people have their data in Excel spreadsheets. csv (comma-separated values) file. If your data are in some more exotic form (within a GIS or database system), you’ll have to figure out how to extract them from that particular system into a text file. There are ways of making more direct connections between R and databases/GIS systems, but they’re beyond the scope of this book.

Avail) I’ll actually use a variant of barplot(), barplot2() (from the gplots package) to plot these values with standard errors (I find it mildly embarrassing that R does not supply error-bar plotting as a built-in function, but you can use the barplot2() (gplots package) and plotCI() (gplots or plotrix package) functions. u) limits. Another alternative would be a box plot or box-and-whisker plot, which would actually usually be preferable to a barplot since it displays more information (median, interquartile ranges, and outliers) and because the information it shows (median rather than mean, IQR rather than standard deviation) is more robust to peculiarities in distributions.

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Analyzing incomplete longitudinal clinical trial data by Molenberghs G., Thijs H., Jansen I.

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