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	Provedor de dados AgEcon (3) Scientia Agricola (1) Autor Cox, Nicholas J. (1) Linhart, Jean Marie (1) Mexia,João Tiago (1) Pereira,Dulce Gamito Santinhos (1) Rodrigues,Paulo Canas (1) Royston, Patrick (1) Mais... Palavra-chave Missing values (4) Research Methods/ Statistical Methods (3) AMMI models (1) Binary (1) Binary variables (1) Categorical variables (1) Data Editor (1) Dates (1) Decode (1) Denormalized number (1) Destring (1) Double precision (1) Durum wheat (1) Encode (1) Format (1) Genotype by environment interaction (1) Hexadecimal (1) IEEE 754 (1) Ice (1) Identifiers (1) Mais... Tipo do documento Journal Article (2) Article (1) Info:eu-repo/semantics/article (1) Ano 2011 (1) 2008 (1) 2005 (1) 2002 (1) País United States (3) Brazil (1) Idioma Inglês (4)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 4 Primeira ... 1 ... Última A comparison between Joint Regression Analysis and the Additive Main and Multiplicative Interaction model: the robustness with increasing amounts of missing data Scientia Agricola Rodrigues,Paulo Canas; Pereira,Dulce Gamito Santinhos; Mexia,João Tiago. This paper joins the main properties of joint regression analysis (JRA), a model based on the Finlay-Wilkinson regression to analyse multi-environment trials, and of the additive main effects and multiplicative interaction (AMMI) model. The study compares JRA and AMMI with particular focus on robustness with increasing amounts of randomly selected missing data. The application is made using a data set from a breeding program of durum wheat (Triticum turgidum L., Durum Group) conducted in Portugal. The results of the two models result in similar dominant cultivars (JRA) and winner of mega-environments (AMMI) for the same environments. However, JRA had more stable results with the increase in the incidence rates of missing values. Tipo: Info:eu-repo/semantics/article Palavras-chave: AMMI models; Genotype by environment interaction; Joint regression analysis; Missing values; Durum wheat. Ano: 2011 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162011000600012 Mata Matters: Overflow, underflow and the IEEE floating–point format AgEcon Linhart, Jean Marie. Mata is Stata’s matrix language. The Mata Matters column shows how Mata can be used interactively to solve problems and as a programming language to add new features to Stata. In this quarter’s column, we investigate underflow and overflow and then delve into the details of how floating-point numbers are stored in the IEEE 754 floating-point standard. We show how to test for overflow and underflow. We demonstrate how to use the %21x format to see underflow and the %16H, %16L, %8H, and %8L formats for displaying the byte content of doubles and floats. Tipo: Article Palavras-chave: Underflow; Overflow; Denormalized number; Normalized number; Subnormal number; Double precision; Missing values; IEEE 754; Format; Binary; Hexadecimal; Research Methods/ Statistical Methods. Ano: 2008 URL: http://purl.umn.edu/122590 Multiple imputation of missing values: Update of ice AgEcon Royston, Patrick. Royston (2004) introduced mvis, an implementation for Stata of MICE, a method of multiple multivariate imputation of missing values under missing-at-random (MAR) assumptions. In a second article, Royston (2005) described ice, an upgrade incorporating various improvements and changes to the software based on personal experience, discussion with colleagues, and user requests. This article describes an update to ice. The changes are less substantial but nevertheless important enough to warrant a brief explanation. The major modification is that the default method of imputing missing values in ice is now by sampling from the posterior predictive distribution rather than by predicted mean matching. The ice system comprises five ado-files: ice, micombine,... Tipo: Journal Article Palavras-chave: Ice; Uvis; Multiple imputation; Missing values; Update; Research Methods/ Statistical Methods. Ano: 2005 URL: http://purl.umn.edu/117543 Speaking Stata: On numbers and strings AgEcon Cox, Nicholas J.. The great divide among data types in Stata is between numeric and string variables. Most of the time, which kind you want to use for particular variables is clear and unproblematic, but surprisingly often, users face difficulties in making the right decision or need to convert variables from one kind to another. The main problems that may arise and their possible solutions are surveyed with reference both to official Stata and to user-written programs. Tipo: Journal Article Palavras-chave: Binary variables; Categorical variables; Data Editor; Dates; Decode; Destring; Encode; Identifiers; Missing values; Numeric variables; Spreadsheets; String functions; String variables; Tostring; Value labels; Research Methods/ Statistical Methods. Ano: 2002 URL: http://purl.umn.edu/116018 Registros recuperados: 4 Primeira ... 1 ... Última

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