CRAN: Manuals edited by the R Development Core Team. The following manuals for R were created on Debian Linux and may differ from the manuals for Mac or Windows on platform-specific pages, but most parts will be identical for all platforms. The correct version of the manuals for each platform are part of the respective R installations. The manuals change with R, hence we provide versions for the most recent released R version (R-release), a very current version for the patched release version (R-patched) and finally a version for the forthcoming R version that is still in development (R-devel). Here they can be downloaded as PDF files, EPUB files, or directly browsed as HTML: Translations of manuals into other languages than English are available from the contributed documentation section (only a few translations are available). The LaTeX or Texinfo sources of the latest version of these documents are contained in every R source distribution (in the subdirectory doc/manual of the extracted archive).
Multivariate analysis of variance Multivariate analysis of variance or multiple analysis of variance (MANOVA) is a statistical test procedure for comparing multivariate (population) means of several groups. Unlike univariate ANOVA, it uses the variance-covariance between variables in testing the statistical significance of the mean differences. It is a generalized form of univariate analysis of variance (ANOVA). It is used when there are two or more dependent variables. It helps to answer: 1. do changes in the independent variable(s) have significant effects on the dependent variables?; 2. what are the interactions among the dependent variables? Where sums of squares appear in univariate analysis of variance, in multivariate analysis of variance certain positive-definite matrices appear. Analogous to ANOVA, MANOVA is based on the product of model variance matrix, and inverse of the error variance matrix, , or . implies that the product The most common[3][4] statistics are summaries based on the roots (or eigenvalues) of the
Starting data analysis/wrangling with R: Things I wish I'd been told October 14, 2014, [MD] R is a very powerful open source environment for data analysis, statistics and graphing, with thousands of packages available. After my previous blog post about likert-scales and metadata in R, a few of my colleagues mentioned that they were learning R through a Coursera course on data analysis. I have been working quite intensively with R for the last half year, and thought I'd try to document and share a few tricks, and things I wish I'd have known when I started out. I don't pretend to be a statistics whiz – I don't have a strong background in math, and much of my training in statistics was of the social science "click here, then here in SPSS" kind, using flowcharts to divine which tests to run, given the kinds of variables you wanted to compare. So here are some of my suggestions and "lessons learnt", in no particular order. RStudio is an great open source integrated development environment for R. There are lot's of R textbooks and documentation out there.
StatNotes: Topics in Multivariate Analysis, from North Carolina State University Looking for Statnotes? StatNotes, viewed by millions of visitors for the last decade, has now been converted to e-books in Adobe Reader and Kindle Reader format, under the auspices of Statistical Associates Publishers. The e-book format serves many purposes: readers may cite sources by title, publisher, year, and (in Adobe Reader format) page number; e-books may be downloaded to PCs, Ipads, smartphones, and other devices for reference convenience; and intellectual property is protected against piracy, which had become epidemic. Click here to go to the new Statnotes website at . Or you may use the Google search box below to search the website, which contains free e-books and web pages with overview summaries and tables of contents. Or you may click on a specific topic below to view the specific overview/table of contents page.
Cookbook for R The R Project for Statistical Computing Basic Widget Methods Widget (class) [#] Widget implementation class. This class is used as a mixin by all widget classes. after(delay_ms, callback=None, *args) [#] Registers an alarm callback that is called after a given time. This method registers a callback function that will be called after a given number of milliseconds. The callback is only called once for each call to this method. class App: def __init__(self, master): self.master = master self.poll() def poll(self): ... do something ... self.master.after(100, self.poll) after_cancel to cancel the callback. You can also omit the callback. delay_ms Delay, in milliseconds. callback The callback. *args Optional arguments that are passed to the callback. Returns: An alarm identifier. after_cancel(id) [#] Cancels an alarm callback. id Alarm identifier. after_idle(callback, *args) [#] Registers a callback that is called when the system is idle. bbox(column=None, row=None, col2=None, row2=None) [#] The bbox method. column row col2 row2 bell(displayof=0) [#] displayof cget(key) [#]
Library Tcl8.6.1/Tk8.6.1 Documentation > Tcl C API, version 8.6.1 Tcl/Tk Applications | Tcl Commands | Tk Commands | [incr Tcl] Package Commands | SQLite Package Commands | TDBC Package Commands | tdbc::mysql Package Commands | tdbc::odbc Package Commands | tdbc::sqlite3 Package Commands | Thread Package Commands | Tcl C API | Tk C API | [incr Tcl] Package C API | TDBC Package C API Copyright © 1989-1994 The Regents of the University of California Copyright © 1992-1999 Karl Lehenbauer and Mark Diekhans Copyright © 1994-1998 Sun Microsystems, Inc Copyright © 1997-2000 Ajuba Solutions Copyright © 1998-2000 Scriptics Corporation Copyright © 2001 ActiveState Corporation Copyright © 2001 ActiveState Tool Corp Copyright © 2001 Vincent Darley Copyright © 2001-2002 Kevin B.
If you have to use circles… Stats Chat is an interesting kiwi site—managed by the Department of Statistics of the University of Auckland—that centers around the use and presentation of statistics in the media. This week there was an interesting discussion on one of those infographics that make you cringe: I understand the newspaper’s need to grab our attention, as well as the designer’s esthetical considerations, but they have to follow avoiding misleading the reader and providing at least a ball-park idea of the importance of whatever issue is under discussion. Clearly, as pointed out in the discussion, a line chart would convey the message with a minimum of ink; however, the designer may still want to use circles, and here we could go back to a faceted version of the always maligned pie chart. Faceted pie charts using ggplot2 The code reads the data, reshapes it and plots it using pretty much the explanation for pie charts in the ggplot2 documentation. # Location and libraries require(reshape) require(ggplot2) crazy
How To Disassemble Laptop (Dell Latitude E4300) › How To It has been awhile since I actually disassembled a laptop or even written an article. Fortunately, when I was back in hometown for new year, my brother who trusted my ability to disassemble laptop had offered me the opportunity to clean his Dell Latitude E4300. discount real viagra Kept telling my husband how good these were. This guide will explain detailed step by step way on how to dismantle to clean a Dell Latitude E4300 notebook. Advices taken here are taken at your own risk. NOTE: Even though Dell said disassembling laptop yourself would lose your warranty, but I haven’t void anybody’s warranty in any possible way yet. Tools You Need For Dismantling and Cleaning Philips Screwdriver (Size #0 is great)Flat Blade Screwdriver (usually a test pen)A brush (about 1 inch or 2 cm wide would do it, not too big) It pays to go to your nearest hardware store and get a well fit Phillips screwdriver because you don’t want to spoil the screws in your laptop. Before You Start Getting excited?