STATAMETA-ANALYSISREFERENCE MANUAL

Syntax - Stata

2cluster kmeans and kmedians— Kmeans and kmedians cluster analysis Options Main k(#) is required and indicates that # groups are to be formed by the cluster analysis.

  Syntax

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2cluster dendrogram— Dendrograms for hierarchical cluster analysis The height of the vertical lines and the range of the (dis)similarity axis give visual clues about the

Syntax Description - Stata

2substr()— Extract substring Diagnostics In substr(s, b, l) and substr(s, b), if b describes a position before the beginning of the string or after the end, "" is returned.

  Descriptions, Stata, Syntax, Syntax description

Title stata.com generate — Create or change …

Title stata.com generate — Create or change contents of variable SyntaxMenuDescriptionOptions Remarks and examplesMethods and formulasReferencesAlso see Syntax Create new variable generate type newvar

  Change, Content, Variable, Create, Stata, Generate, Generate create or change, Generate create or change contents of variable

SyntaxDescriptionRemarks and examplesAlso see

macro— Macro definition and manipulation 3 Macro extended functions related to matrices rownamesjcolnamesjrowfullnamesjcolfullnames matname roweqjcoleq

  Syntaxdescriptionremarks and examplesalso see, Syntaxdescriptionremarks, Examplesalso

Title stata.com graph box — Box plots

graph box— Box plots 3 Menu Graphics > Box plot Syntax graph box yvars if in weight, options graph hbox yvars if in weight, options where yvars is a varlist options Description

  Plot, Box plot

Title stata

area options — Options for specifying the look of special areas 3 Also see [G-2] graph dot — Dot charts (summary statistics)

  Title, Stata, Title stata

Title stata.com putexcel — Export results to an Excel …

putexcel— Export results to an Excel file 3 export options Description Main overwritefmt overwrite existing cell formatting when exporting new content

  Putexcel

destring — Convert string variables to numeric ... - …

Title stata.com destring — Convert string variables to numeric variables and vice versa SyntaxMenuDescription Options for destringOptions for tostringRemarks and …

  Title, Variable, Stata, Numeric, String, Convert, Title stata, Destring convert string variables to numeric, Destring

Title stata.com gettoken — Low-level parsing

Title stata.com gettoken — Low-level parsing SyntaxDescriptionOptionsRemarks and examplesAlso see Syntax gettoken emname1 emname2: emname3, parse("pchars") quotes qed(lmacname) match(lmacname) bind

  Title, Stata, Title stata

Time Series Analysis and Its Applications: With R Examples ...

NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in ... models, and (multivariate) regression with autocorrelated errors in Chapter 6 have been expanded. In this edition, we use standard R functions as much as ... 1.1 Introduction The analysis of experimental data that have been observed at di ...

  Analysis, Introduction, Brief, Multivariate

An Introduction to Partial Least Squares Regression

The next section gives a brief overview of how PLS works, relating it to other multivariate techniques such as principal components regression and maximumre-dundancy analysis. An extended chemometric exam-ple is presented that demonstrates how PLS models are evaluated and how their components are inter-preted. A final section discusses ...

  Analysis, Introduction, Tesla, Square, Brief, Partial, Multivariate, Partial least squares

An Introduction to Categorical Data Analysis - Web …

12 A Historical Tour of Categorical Data Analysis * 325 Appendix: Software for Categorical Data Analysis 331 A.1 R for Categorical Data Analysis 331 A.2 SAS for Categorical Data Analysis 332 A.3 Stata for Categorical Data Analysis 342 A.4 SPSS for Categorical Data Analysis 346 Brief Solutions to Odd-Numbered Exercises 349 Bibliography 363 ...

  Analysis, Introduction, Data, Brief, Categorical, An introduction to categorical data analysis

Vector Autoregressive Models for Multivariate Time Series

Multivariate Time Series 11.1 Introduction The vector autoregression (VAR) model is one of the most successful, flexi-ble, and easy to use models for the analysis of multivariate time series. It is a natural extension of the univariate autoregressive model to dynamic mul-tivariate time series. The VAR model has proven to be especially useful for

  Analysis, Introduction, Multivariate, Mul tivariate, Tivariate

An Introduction in Structural Equation Modeling

Introduction Structural Equation Modeling 3 spatial visperc cubes lozenges wordmean paragraph sentence e_v e_c e_l e_p e_s e_w verbal 1 1 1 1 1 1 1 1 Figure 1. Confirmatory Factor analysis; Holzinger and Swineford data. Factor analysis assumes that the covariances between a set of observed variables can be explained by a smaller number of ...

  Analysis, Introduction, Modeling, Structural, Equations, Structural equation modeling, Introduction structural equation modeling

Gaussian Processes for Regression: A Quick Introduction

A Quick Introduction M. Ebden, August 2008 Prerequisite reading: Gaussian Processes for Regression 1 OVERVIEW As mentioned in the previous document, GPs can be applied to problems other than regression. For example, if the output …

  Introduction