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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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Review of A Visual Guide to Stata Graphics by Mitchell AgEcon
Kohler, Ulrich.
This article reviews A Visual Guide to Stata Graphics by Mitchell.
Tipo: Journal Article Palavras-chave: Graphics; Stata; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116276
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Review of A Visual Guide to Stata Graphics, Second Edition by Michael N. Mitchell AgEcon
Merryman, Scott.
This article reviews A Visual Guide to Stata Graphics, Second Edition by Michael N. Mitchell.
Tipo: Article Palavras-chave: Graphics; Stata texts; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/122604
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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: On structure and shape: the case of multiple responses AgEcon
Cox, Nicholas J.; Kohler, Ulrich.
A frequent problem in data management is that datasets may not arrive in the best structure for many analyses, so that it may be necessary to restructure the data in some way. The particular case of multiple response data is discussed at length, with special attention to different possible structures; the generation of new variables holding the data in different form; valuable inbuilt string and egen functions; using foreach and forvalues to loop over lists; and the use of the reshape command. Tabulations and graphics for such data are also reviewed briefly.
Tipo: Journal Article Palavras-chave: Composite variables; Concatenation; Egen; Foreach; Forvalues; Graphics; Indicator variables; Multiple responses; Reshape; Split; String functions; Tabulations; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116035
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Speaking Stata: Spineplots and their kin AgEcon
Cox, Nicholas J..
The term spineplot has been applied over the last decade or so to a type of bar chart used particularly for showing frequencies, proportions, or percentages of two cross-classified categorical variables. The principle is that the areas of rectangular tiles are proportional to the frequencies in the cells of a contingency table. Often both coarse and fine structure are easy to see, including departures from independence. The main idea has, in fact, been rediscovered repeatedly over at least the last 130 years. In its most general form, it has been widely publicized under the name mosaic plots. This column introduces, discusses, and exemplifies a Stata implementation of spineplots. It is noted that a restriction to two variables is more apparent than real,...
Tipo: Article Palavras-chave: Spineplots; Mosaic plots; Bar charts; Graphics; Categorical data; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/120931
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Speaking Stata: Turning over a new leaf AgEcon
Cox, Nicholas J..
Stem-and-leaf displays have been widely taught since John W. Tukey publicized them energetically in the 1970s. They remain useful for many distributions of small or modest size, especially for showing fine structure such as digit preference. Stata’s implementation stem produces typed text displays and has some inevitable limitations, especially for comparison of two or more displays. One can re-create stem-and-leaf displays with a few basic Stata commands as scatterplots of stem variable versus position on line with leaves shown as marker labels. Comparison of displays then becomes easy and natural using scatter, by(). Back-to-back presentation of paired displays is also possible. I discuss variants on standard stem-and-leaf displays in which each distinct...
Tipo: Article Palavras-chave: Stemplot; Stem-and-leaf; Graphics; Distributions; Digit preference; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119285
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