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Registros recuperados: 63
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A brief history of Stata on its 20th anniversary AgEcon
Cox, Nicholas J..
Tipo: Journal Article Palavras-chave: Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117491
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A multivariable scatterplot smoother AgEcon
Royston, Patrick; Cox, Nicholas J..
We present an extension of Sasieni, Royston, and Cox’s bivariate smoother running to the multivariable context. The software aims to provide a picture of the relation between a response variable and each of several continuous predictors simultaneously. This may be a valuable tool in exploratory data analysis, before constructing a more formal multiple regression model.
Tipo: Journal Article Palavras-chave: Mrunning; Running; Scatterplot smoothing; Multivariable regression analysis; Running line; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117529
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A note on the concordance correlation coefficient AgEcon
Steichen, Thomas J.; Cox, Nicholas J..
Program concord implements L. I. Lin’s concordance correlation coefficient (Lin 1989), as well as the limits-of-agreement procedure (Bland and Altman 1986). Recently, Lin (2000) issued an erratum reporting a number of typographical errors in his seminal 1989 paper. Further, changes in Stata Version 7 required modification of concord. This note reports the effect of the errors and provides a corrected and updated program.
Tipo: Journal Article Palavras-chave: Concordance correlation; Graphics; Measurement comparison; Limits-of-agreement; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/115960
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Depending on conditions: a tutorial on the cond() function AgEcon
Kantor, David; Cox, Nicholas J..
This is a tutorial on the cond() function, giving explanations and examples and assessing its advantages and limitations.
Tipo: Journal Article Palavras-chave: Cond(); Functions; If command; If qualifier; Generate; Replace; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117530
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Do-it-yourself shuffling and the number of runs under randomness AgEcon
Smeeton, Nigel; Cox, Nicholas J..
A common class of problem in statistical science is estimating, as a benchmark, the probability of some event under randomness. For example, in a sequence of events in which several outcomes are possible and the length of the sequence and number of outcomes of each type known, the number of runs gives an indication of whether the outcomes are random, clustered, or alternating. This note explains and illustrates a simple method of random shuffling that is often useful. We show how the conditional probability distribution of the number of runs may be derived easily in Stata, thus yielding p-values for testing the null hypothesis that the type of outcome is random. We also compare our direct approach with that using the simulate command.
Tipo: Journal Article Palavras-chave: Alternation; Categorical data; Clustering; Conditional distribution; Forvalues; P-value; Permutation; Run; Sequence; Simulate; Simulation; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116095
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Review of Statistical Evaluation of Measurement Errors by Dunn AgEcon
Cox, Nicholas J..
This article reviews Statistical Evaluation of Measurement Errors by Dunn.
Tipo: Journal Article Palavras-chave: Measurement errors; Linear models; Mixed models; Gllamm; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116280
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Speaking Stata: Between tables and graphs AgEcon
Cox, Nicholas J..
Table-like 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 under-appreciated 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 Palavras-chave: Labmask; Seqvar; Tables; Graphs; Dot charts; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/122591
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Speaking Stata: Correlation with confidence, or Fisher's z revisited AgEcon
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 Palavras-chave: Corrci; Corrcii; Correlation; Confidence intervals; Fisher's z; Transformation; Bootstrap; Mata; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/122603
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Speaking Stata: Counting groups, especially panels AgEcon
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 group-level statistics. Tagging each group just once ensures that...
Tipo: Article Palavras-chave: Data management; Panels; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119295
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Speaking Stata: Density probability plots AgEcon
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 Palavras-chave: 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
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Speaking Stata: Distinct observations AgEcon
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 Palavras-chave: Distinct; By; Egen; Distinctness; Uniqueness; Data management; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/122622
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Speaking Stata: Graphing agreement and disagreement AgEcon
Cox, Nicholas J..
Many statistical problems involve comparison and, in particular, the assessment of agreement or disagreement between data measured on identical scales. Some commonly used plots are often ineffective in assessing the fine structure of such data, especially scatterplots of highly correlated variables and plots of values measured “before” and “after” using tilted line segments. Valuable alternatives are available using horizontal reference patterns, changes plotted as parallel lines, and parallel coordinates plots. The quantities of interest (usually differences on some scale) should be shown as directly as possible, and the responses of given individuals should be identified as easily as possible.
Tipo: Journal Article Palavras-chave: Graphics; Comparison; Agreement; Paired data; Panel data; Scatterplot; Difference-mean plot; Bland–Altman plot; Parallel lines plot; Parallel coordinates plot; Pairplot; Parplot; Linkplot; Tukey; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116252
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Speaking Stata: Graphing categorical and compositional data AgEcon
Cox, Nicholas J..
A variety of graphs have been devised for categorical and compositional data, ranging from widely familiar to more unusual displays. Both official Stata commands and user-written programs are available. After a stacking trick for binary responses is explained, bar charts and related displays for cross-tabulations are discussed in detail. Tips and tricks are introduced for plotting cumulative distributions of graded (ordinal) data. Triangular plots are explained for three-way compositions, such as three proportions or percentages.
Tipo: Journal Article Palavras-chave: Graphics; Categorical data; Binary data; Nominal data; Ordinal data; Grades; Compositional data; Cross-tabulations; Bar charts; Cumulative distributions; Logit scale; Catplot; Tabplot; Tableplot; Distplot; Mylabels; Triplot; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116238
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Speaking Stata: Graphing distributions AgEcon
Cox, Nicholas J..
Graphing univariate distributions is central to both statistical graphics, in general, and Stata’s graphics, in particular. Now that Stata 8 is out, a review of official and user-written commands is timely. The emphasis here is on going beyond what is obviously and readily available, with pointers to minor and major trickery and various user-written commands. For plotting histogram-like displays, kernel-density estimates and plots based on distribution functions or quantile functions, a large variety of choices is now available to the researcher.
Tipo: Journal Article Palavras-chave: Graphics; Histogram; Spikeplot; Dotplot; Onewayplot; Kdensity; Distplot; Qplot; Skewplot; Bin width; Rug; Density function; Kernel estimation; Transformations; Logarithmic scale; Root scale; Intensity function; Distribution function; Quantile function; Skewness; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116213
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Speaking Stata: Graphing model diagnostics AgEcon
Cox, Nicholas J..
Plotting diagnostic information calculated from residuals and fitted values is a long-standard method for assessing models and seeking ways of improving them. This column focuses on the statistical mainstream defined by regression models for continuous responses, treated in a broad sense to include (for example) generalized linear models. After some comments on the history of such ideas (and even their anthropology and psychology), the commands available in official Stata are reviewed, and a modeldiag package is introduced. A detailed example on fuelwood yield from fallow areas in Nigeria illustrates a variety of general points and specific tips.
Tipo: Journal Article Palavras-chave: Modeldiag; Anovaplot; Indexplot; Ofrtplot; Ovfplot; Qfrplot; Racplot; Rdplot; Regplot; Rhetplot; Rvfplot2; Rvlrplot; Rvpplot2; Graphics; Diagnostics; Regression; Generalized linear models; Analysis of variance; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116273
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Speaking Stata: Graphs for all seasons AgEcon
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 Palavras-chave: 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
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Speaking Stata: How to face lists with fortitude AgEcon
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 self-contained exposition is given on local macros, understanding of which is needed for use of foreach and forvalues.
Tipo: Journal Article Palavras-chave: Foreach; Forvalues; For; Lists; Local macros; Substitution first; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/115962
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Speaking Stata: How to move step by: step AgEcon
Cox, Nicholas J..
The by varlist: construct is reviewed, showing how it can be used to tackle a variety of problems with group structure. These range from simply doing some calculations for each of several groups of observations to doing more advanced manipulations making use of the fact that with this construct, subscripts and the built-ins _n and _N are all interpreted within groups. A fairly complete tutorial on numerical evaluation of true and false conditions is included.
Tipo: Journal Article Palavras-chave: By; Sorting; Subscripts; True and false; _n; _N; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/115953
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Speaking Stata: How to repeat yourself without going mad AgEcon
Cox, Nicholas J..
This column will focus on how to improve your fluency in Stata. Over the next issues we will look at Stata problems of intermediate size which turn out to be soluble with a few command lines. As an introduction, systematic ways of repeating the same or similar operations are surveyed to give one overview of the territory to be covered.
Tipo: Journal Article Palavras-chave: Append; By; Collapse; Contract; Do-files; Egen; For; Foreach; Forvalues; Log files; Merge; Naming conventions; Programs; Repetition; Reshape; Statsby; Subset or group structure; Tabulations; Research Methods/ Statistical Methods.
Ano: 2001 URL: http://purl.umn.edu/115938
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Speaking Stata: Identifying spells AgEcon
Cox, Nicholas J..
Spells in time series (and more generally in any kind of one-dimensional 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 Palavras-chave: Spells; Runs; Time series; Data management; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119273
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