


Registros recuperados: 63  

 

 

 

 

 


Cox, Nicholas J.. 
Tablelike graphs can be interesting, useful, and even mildly innovative. This column outlines some Stata techniques for producing such graphs. graph dot is likely to be the most underappreciated command among all existing commands. Using by() with various choices is a good way to mimic a categorical axis in many graph commands. When graph bar or graph dot is not flexible enough to do what you want, moving to the more flexible twoway is usually advisable. labmask and seqvar are introduced as new commands useful for preparing axis labels and axis positions for categorical variables. Applications of these ideas to, e.g., confidence interval plots lies ahead. 
Tipo: Article 
Palavraschave: Labmask; Seqvar; Tables; Graphs; Dot charts; Research Methods/ Statistical Methods. 
Ano: 2008 
URL: http://purl.umn.edu/122591 
 


Cox, Nicholas J.. 
Ronald Aylmer Fisher suggested transforming correlations by using the inverse hyperbolic tangent, or atanh function, a device often called Fisher’s z transformation. This article reviews that function and its inverse, the hyperbolic tangent, or tanh function, with discussions of their definitions and behavior, their use in statistical inference with correlations, and how to apply them in Stata. Examples show the use of Stata and Mata in calculator style. New commands corrci and corrcii are also presented for correlation confidence intervals. The results of using bootstrapping to produce confidence intervals for correlations are also compared. Various historical comments are sprinkled throughout. 
Tipo: Article 
Palavraschave: Corrci; Corrcii; Correlation; Confidence intervals; Fisher's z; Transformation; Bootstrap; Mata; Research Methods/ Statistical Methods. 
Ano: 2008 
URL: http://purl.umn.edu/122603 
 


Cox, Nicholas J.. 
Counting panels, and more generally groups, is sometimes possible in Stata through a reduction command (e.g., collapse, contract, statsby) that produces a smaller dataset or through a tabulation command. Yet there are also many problems, especially with irregular sets of observations for varying times, that do not yield easily to this approach. This column focuses on techniques for answering such questions while maintaining the same data structure. Especially useful are the Stata commands by: and egen and indicator variables constructed for the purpose. With by: we often exploit the fact that subscripts are defined within group, not within dataset. egen functions are often used to produce grouplevel statistics. Tagging each group just once ensures that... 
Tipo: Article 
Palavraschave: Data management; Panels; Research Methods/ Statistical Methods. 
Ano: 2007 
URL: http://purl.umn.edu/119295 
 


Cox, Nicholas J.. 
Density probability plots show two guesses at the density function of a continuous variable, given a data sample. The first guess is the density function of a specified distribution (e.g., normal, exponential, gamma, etc.) with appropriate parameter values plugged in. The second guess is the same density function evaluated at quantiles corresponding to plotting positions associated with the sample’s order statistics. If the specified distribution fits well, the two guesses will be close. Such plots, suggested by Jones and Daly in 1995, are explained and discussed with examples from simulated and real data. Comparisons are made with histograms, kernel density estimation, and quantile–quantile plots. 
Tipo: Journal Article 
Palavraschave: Density probability plots; Distributions; Histograms; Kernel density estimation; Quantile–quantile plots; Statistical graphics; Research Methods/ Statistical Methods. 
Ano: 2005 
URL: http://purl.umn.edu/117517 
 


Cox, Nicholas J.; Longton, Gary M.. 
Distinct observations are those different with respect to one or more variables, considered either individually or jointly. Distinctness is thus a key aspect of the similarity or difference of observations. It is sometimes confounded with uniqueness. Counting the number of distinct observations may be required at any point from initial data cleaning or checking to subsequent statistical analysis. We review how far existing commands in official Stata offer solutions to this issue, and we show how to answer questions about distinct observations from first principles by using the by prefix and the egen command. The new distinct command is offered as a convenience tool. 
Tipo: Article 
Palavraschave: Distinct; By; Egen; Distinctness; Uniqueness; Data management; Research Methods/ Statistical Methods. 
Ano: 2008 
URL: http://purl.umn.edu/122622 
 

 

 

 

 


Cox, Nicholas J.. 
Time series showing seasonality—marked variation with time of year—are of interest to many scientists, including climatologists, other environmental scientists, epidemiologists, and economists. The usual graphs plotting response variables against time, or even time of year, are not always the most effective at showing the fine structure of seasonality. I survey various modifications of the usual graphs and other kinds of graphs with a range of examples. Although I introduce here two new Stata commands, cycleplot and sliceplot, I emphasize exploiting standard functions, data management commands, and graph options to get the graphs desired. 
Tipo: Journal Article 
Palavraschave: Cycleplot; Sliceplot; Seasonality; Time series; Graphics; Cycle plot; Rotation; State space; Incidence plots; Folding; Repeating; Research Methods/ Statistical Methods. 
Ano: 2006 
URL: http://purl.umn.edu/117590 
 


Cox, Nicholas J.. 
Three commands in official Stata, foreach, forvalues, and for, provide structures for cycling through lists of values (variable names, numbers, arbitrary text) and repeating commands using members of those lists in turn. All these commands may be used interactively, and none is restricted to use in Stata programs. They are explained and compared in some detail with a variety of examples. In addition, a selfcontained exposition is given on local macros, understanding of which is needed for use of foreach and forvalues. 
Tipo: Journal Article 
Palavraschave: Foreach; Forvalues; For; Lists; Local macros; Substitution first; Research Methods/ Statistical Methods. 
Ano: 2002 
URL: http://purl.umn.edu/115962 
 

 

 


Cox, Nicholas J.. 
Spells in time series (and more generally in any kind of onedimensional series) may be defined as sequences of observations that are homogeneous in some sense. For example, a categorical variable may remain in the same state, or values of a measured variable may satisfy the same true–false condition. Devices for working with spells in Stata include marking the start of each spell with indicator variables and tagging spells with integer codes. Panel data are easy to handle with the by: prefix. Some kinds of spell identification require two passes through the data, as when only spells of some minimum length are of interest or short gaps are tolerable within spells. Many questions concerning spells are easy to answer given careful use of by: and appropriate... 
Tipo: Article 
Palavraschave: Spells; Runs; Time series; Data management; Research Methods/ Statistical Methods. 
Ano: 2007 
URL: http://purl.umn.edu/119273 
 
Registros recuperados: 63  


