By Brian Everitt
Each bankruptcy will encompass simple statistical idea, easy examples of S-PLUS code, extra complicated examples of S-PLUS code, and workouts. All facts units might be taken from real clinical investigations and should be made to be had, if attainable, on an internet site. All examples will comprise vast graphical research to focus on one of many best positive aspects of S-PLUS. The ebook may supplement Venables and Ripley (VR). even if, there's some distance much less concerning the information of S-PLUS and possibly much less technical descriptions of concepts. The booklet concentrates exclusively on clinical information units attempting to reveal the pliability of S-PLUS and its large merits, fairly for utilized scientific statisticians.
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Additional info for Analyzing Medical Data Using S-PLUS
So to get the required three means, > means <- apply(data[, c(3, 4, 5)], 2, mean) > means height weight age 69 150 33 We might be more interested in the medians than in the means > medians <- apply(data[, c(3, 4, 5)], 2, median) > medians height weight age 70 140 33 Variances could be obtained similarly using the var 0 function. 5 Standard deviations are now obtained as 22 1. 455767 The var 0 function when applied to a matrix rather than a vector gives the resulting variance-covariance matrix. 0 Similarly, the cor 0 function can be used to find the correlation matrix of the three variables.
4 The following is a hypothetical data set giving sex, ages, weights and heights of five individuals. Read the data into a data frame, Data. 35 36 1. 5 Find the covariance and correlation matrices of Age, Weight, and Height in Data. 6 Find the mean weight of the women in Data. 1. 7 Produce a diagram containing the boxplots of Age, Weight and Height, and scatterplots of Age v Weight, Age v Height and Weight v Height. 8 Attach the Data data frame and then produce the same graphic as in 17 using the G UI.
Perhaps the most common class of density estimators is of the form j(x) = ~ ~ K(X-Xi) nh~ (1) h i=l where h is known as window width or bandwidth and K is known as the kernel function, and is such that I: (2) K(u)du = 1 • 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. " .. 1 ~.. j. U ..... l • In general, the choice of the shape of the kernel function is not usually of great importance.
Analyzing Medical Data Using S-PLUS by Brian Everitt