By Brian Everitt
Each bankruptcy will include easy statistical concept, easy examples of S-PLUS code, extra advanced examples of S-PLUS code, and workouts. All facts units should be taken from actual clinical investigations and may be made to be had, if attainable, on a website. All examples will comprise vast graphical research to spotlight one of many best beneficial properties of S-PLUS. The ebook may supplement Venables and Ripley (VR). besides the fact that, there's a ways much less concerning the info of S-PLUS and doubtless much less technical descriptions of innovations. The booklet concentrates completely on clinical info units attempting to reveal the pliability of S-PLUS and its large benefits, fairly for utilized scientific statisticians.
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Extra resources for Analyzing Medical Data Using S-PLUS
Which graphs and which summary statistics are most appropriate will largely depend on the type of observations and measurements that have been recorded. In this chapter, we shall illustrate the possibilities using a number of data sets containing continuous or categorical variables. 1 shows the heights in centimeters of a sample of 351 elderly women, randomly selected from the community in a study of osteoporosis. 2 gives the survival times of 43 patients suffering from chronic granulocytic leukaemia, measured in days from time of diagnosis.
Scatterplot with regression line. 5. 6. Plotting parameters Parameter type="p"/"l"/"h"/"s"/"n", etc. axes=T/F main sub xlab,ylab xlim,ylim=c(min,max) pch=1/2/3, etc. or pCh=I+" /". ", etc. Ity=1/2/3, etc. 8. Three scatterplots. splits the graphics window into two panels, layed out into one row and two columns. Subsequent graph commands will fill out the panels one by one in lexicographical order. 8. (As mentioned in the previous section, the plot () function may also be used to produce 'default' plots for most analyses, by using an object returned from an analysis as the argument.
These commands can also be used to combine two data frames into a single data frame. Additional vectors (variables) can be added to a data frame using the data. frame () function: > datal <- data. frame (data, age2 = data$age-2) Longitudinal data can be viewed as multivariate data, in which the measurement occasions are different variables, or as univariate hierarchical data, in which the measurement occasions are different observations. Here, we show an example of how we can convert from the multivariate (wide) representation to the univariate (long) one.