By Geoff Der, Brian S. Everitt
Easily Use SAS to provide Your Graphics
Diagrams, plots, and different varieties of photographs are vital elements in approximately all levels of statistical research, from the preliminary review of the knowledge to the choice of applicable statistical types to the prognosis of the selected versions after they were suited to the information. Harnessing the total photos services of SAS, A instruction manual of Statistical pix utilizing SAS ODS covers crucial graphical equipment wanted in each statistician’s toolkit. It explains how you can enforce the tools utilizing SAS 9.4.
The instruction manual indicates easy methods to use SAS to create many sorts of statistical pictures for exploring info and diagnosing equipped versions. It makes use of SAS’s more recent ODS portraits all through as the program bargains an a variety of benefits, together with ease of use, top of the range of effects, constant visual appeal, and handy semiautomatic graphs from the statistical tactics.
Each bankruptcy offers graphically with a number of units of instance facts from a large choice of parts, equivalent to epidemiology, drugs, and psychology. those examples illustrate using photograph monitors to provide an summary of knowledge, to signify attainable hypotheses for checking out new information, and to interpret geared up statistical types. The SAS courses and information units can be found online.
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Extra resources for A handbook of statistical graphics using SAS ODS
2) −∞ Essentially, such kernel estimators sum a series of ‘bumps’ placed at each of the observations. The kernel function determines the shape of the bumps while h determines their width. 11. 5. In general, the choice of the shape of the kernel function is not usually of great importance. In contrast, the choice of bandwidth can be critical. There are situations in which it is satisfactory to choose the bandwidth relatively subjectively to achieve a ‘smooth’ estimate. More formal methods are however available.
Another commonly used bar chart shows the percentage breakdown within groups. 5; format age age2grp. 5 Bar chart showing percentage breakdown of age. The pctlevel=group option on the proc statement specifies that the percentages are to be calculated across groups within a category so that the category sums to 100. As mentioned earlier the default for vbar / hbar is groupdisplay=stack so this option has been omitted. The barwidth option has been used to reduce the width of the bars. Values for this option range from 0 to 1 and give the proportion of the overall width occupied by the bars, the remainder being the space between the bars.
Proc format; value age2grp low-50='younger' 50<-high='older'; value $sex 'F'='Female' 'M'='Male'; run; proc sgplot data=bodycomp; vbar sex / response=pctfat stat=mean limitstat=stderr limits=upper group=age groupdisplay=cluster; format age age2grp. ; run; We first create two formats, one for a median split of age and the other to give better labelling of sex. On the vbar statement sex defines the category axis; the response and stat options are as in the previous example but this time the standard error is used for the limits and only the upper limit is to be displayed.