Syntax - Stata

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

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

www.stata.com

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

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

Multinomial Response Models - Princeton University

6.2.4 Maximum Likelihood Estimation Estimation of the parameters of this model by maximum likelihood proceeds by maximization of the multinomial likelihood (6.2) with the probabilities ˇ ij viewed as functions of the jand parameters in Equation 6.3. This usu-ally requires numerical procedures, and Fisher scoring or Newton-Raphson

  Maximum, Multinomial, Likelihood, Maximum likelihood, Multinomial likelihood

CHAPTER N-gram Language Models

estimate probabilities is called maximum likelihood estimation or MLE. We get maximum likelihood estimation the MLE estimate for the parameters of an n-gram model by getting counts from a normalize corpus, and normalizing the counts so that they lie between 0 and 1.1 For example, to compute a particular bigram probability of a word w n given a ...

  Language, Model, Maximum, Gram, Likelihood, Maximum likelihood, N gram language models

Introduction to Generalized Linear Models

The estimates ^ have the usual properties of maximum likelihood estimators. In particular, ^ is asymptotically N ( ;i 1) where i( ) = 1 X T WX Standard errors for the j may therefore be calculated as the square roots of the diagonal elements of cov^( ^ ) = (X T WX^ ) 1 in which (X T WX^ ) 1 is a by-product of the nal IWLS iteration.

  Introduction, Linear, Model, Maximum, Estimates, Generalized, Likelihood, Maximum likelihood, Introduction to generalized linear models

Factor Analysis

Maximum likelihood method (MLE) " Goal: maximize the likelihood of producing the observed corr matrix " Assumption: distribution of variables (Y and F) is multivariate normal " Objective function: det(R MLE- ηI)=0, where R MLE=U-1(R-U2)U-1=U-1R LSU-1, and U2 is diag(1-h2) " Iterative fitting algorithm similar to LS approach

  Maximum, Likelihood, Maximum likelihood

Maximum Likelihood (ML), Expectation Maximization (EM)

Find maximum likelihood estimates of µ 1, µ 2 ! EM basic idea: if x(i) were known " two easy-to-solve separate ML problems ! EM iterates over ! E-step: For i=1,…,m fill in missing data x(i) according to what is most likely given the current model µ ! M-step: run ML for completed data, which gives new model µ

  Maximum, Maximization, Estimates, Expectations, Likelihood, Maximum likelihood, Expectation maximization, Maximum likelihood estimates