R statistical software

Analysing Napping and sorted Napping data using the R statistical software... 

The GLM approach assuming a negative binomial error stracture was used, and modeling was done in the R statistical software [RCO 13], Several statistical measures were used to assess the goodness of fit of the models. 

B. P. Butler, M. G. Cox, S. L. R. Ellison, and W. A. Hardcastle, Statistics Software Qualification (Reference Data Sets), The Royal Society of Chemistry, ISBN 0-85404-422-1. 

For practical computation the software environment R is used. R is a powerful statistical software tool, it is freeware and can be downloaded at http //cran.r-project. org. Throughout the book we will present relevant R commands, and in Appendix 3 a brief introduction to R is given. An R-package chemometrics has been established it contains most of the data sets used in the examples and a number of newly written functions mentioned in this book. 

F.K. Uno, H.L. Ralston, R.I. Jennrich and P.F. Sampson, Test problems from the pharmacokinetic literature requiring fitting models defined by differential equations, Technical Report No. 61. BMDP Statistical Software, Los Angeles, 1979. 

Use such statistical software as Minitab, SigmaXL, and IMP to calculate control limits for Xbar/R charts. If you re curious or want to perform your own variable Process Behavior Chart calculations, see resources at the end of this technique. 

To demonstrate the robnstness of Principal Component Analysis (PCA) classification and identification methods when applied to the IR spectra of different spore types, transmission FTIR spectral libraries were generated for three different spore strains (BG, BA, and BS) and classification models were developed based upon Mahalanobis Distance by PCA with Residnals (MD/PCA/R) statistical methods using PLSplns IQ (Thermo Electron) chemometric software. Figme 5 shows a representative IR spectrum of a BG spore sample recorded in transmission, which in many respects is representative of all spore types dne to their nearly identical compositions at the molecnlar level. 

Fortunately, statistical software cuts down on the tedious computatiOTis. Let us now look at several standard ways to add or subtract x, predictor variables based on this F-test strategy. Later, we discuss other methods, including those that use R. The first method we examine adds to the basic model new x, predictor variables and tests the contribution of each one. The second method tests the significance of each x, in the model and then adds... 

By creating a correlation matrix of the x, predictor variables, one can often observe directly if any of the xpcj predictor variables are correlated. Values in the correlation matrix of 0.90 and up flag potential correlation problems, but they probably are not severe until r > 0.95. If there are only two Xj predictor variables, the correlation matrix is very direct just read the x,- vs. Xj row column = r Xj, the correlation between the two XjXj variables. When there are more than two x,- variables, then partial correlation analysis is of more use, because the other x,- variables are in the model. Nevertheless, the correlation matrix of the x,- variables is a good place to do a quick number scan, particularly if it is already printed out via the statistical software. For example, using the data from Table 6.1A, given in Example 4.2 (bioreactor problem). Table 6.2 presents the r Xj correlation matrix of the x, variables. 

Depending on the source of the data, care may be required in the application of these various techniques. Fortunately, many of these techniques are already implemented in a variety of statistical software programs, such as R, so that data can be quickly and appropriately transformed. More effort can then be spent in order to answer the fundamental biological questions of interest. 

Statistical tests can be conducted by usual statistical software or any software developed for large biological data. In particular, many usefid R packages for... 

In addition to these ranking functions, we can also consider using various functions available in external statistical software packages such as R, Excel, SAS, and so on. In that case, the first method can be used, or it is possible to implement a wrapper function that has the prototype as in the second method and performs a filtering operation. 

Pfaff, B. Var, svar and svec models Implementation within r package vars. Journal of Statistical Software, 27(4) 1-32, 2008. 

Mevik B.H. Wehrens R. 2007. The pis Package Principal Component and Partial Least Squares Regression in R [J], Journal of Statistical Software, 18(2) l-24. 

Ardia, D., Hoogerheide, L. F. Dijk, ff. K. v. 2009. Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation The R Package KAMii. Journal of Statistical Software, 29(3) 1-32. 

In recent years, the free statistical software R (Ihaka and Gentleman, 1996 R Development Core Team, 2010) and specially developed sensory function packages have gained in popularity. The described data table structure is suited for data... 

The analysis was performed using the statistical software R, the FactoMineR package, an R package dedicated to exploratory multivariate analysis, and the SensoMineR package, an R package dedicated to sensory data analysis. 

---