Calculate Proportionate Reduction Error
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PRE
Proportional Reduction In Error Lambda
measures. Proportional Reduction of Error (PRE) The proportional reduction in error stata concept that underlies the definition and interpretation of several measures
Proportional Reduction In Error Statistics
of association, PRE measures are derived by comparing the errors made in predicting the dependent while ignoring the proportional reduction in error definition independent variable with errors made when making predictions that use information about the independent variable. E1 = errors of prediction made when the independent variable is ignored E2 = errors of prediction made when the prediction proportional reduction in error formula is based on the independent variable "All PRE measures are based on comparing predictive error levels that result from each of two methods of prediction" (Frankfort-Nachmias and Leon-Guerrero 2011:366). Table 12.1 on page 366 of the textbook helps us to understand this. The independent variable is number of children; the dependent variable is support for abortion. Content on this page requires a newer version of Adobe Flash Player. Two of the most commonly used PRE measures of association are lambda (λ) and gamma (γ). Two PRE Measures: Lambda and Gamma Lambda λ Appropriate for: Nominal Variables Gamma γ Appropriate for: Ordinal and Dichotomous Nominal Variables
of making observations which are possibly subject to errors of all types. Such measures quantify how much having the observations available has reduced the loss (cost) of
Proportional Reduction Of Error Example
the uncertainty about the intended quantity compared with not having those observations. a simple way to calculate proportionate reduction in error is by Proportional reduction in error is a more restrictive framework widely used in statistics, in which the general loss function
Proportionate Reduction In Error Is Conceptually
is replaced by a more direct measure of error such as the mean square error. Examples are the coefficient of determination and Goodman and Kruskal's lambda.[1] The concept of proportional https://learn.bu.edu/bbcswebdav/pid-826908-dt-content-rid-2073693_1/courses/13sprgmetcj702_ol/week05/metcj702_W05S03T02_proportional.html reduction in loss was proposed by Bruce Cooil and Roland T. Rust in their 1994 paper. Many commonly used reliability measures for quantitative data (such as continuous data in an experimental design) are PRL measures, including Cronbach's alpha and measures proposed by Ben J. Winer (1971). It also provides a general way of developing measures for the reliability of qualitative https://en.wikipedia.org/wiki/Proportional_reduction_in_loss data. For example, this framework provides several possible measures that are applicable when a researcher wants to assess the consensus between judges who are asked to code a number of items into mutually exclusive qualitative categories (Cooil and Rust, 1995). Measures of this latter type have been proposed by several researchers, including Perrault and Leigh (1989). References[edit] ^ Upton G., Cook, I. (2006) Oxford Dictionary of Statistics, OUP. ISBN 978-0-19-954145-4 Cooil, B., and Rust, R. T. (1994), "Reliability and Expected Loss: A Unifying Principle," Psychometrika, 59, 203-216. (available here) Cooil, B., and Rust, R. T. (1995), "General Estimators for the Reliability of Qualitative Data," Psychometrika, 60, 199-220. (available here) Rust, R. T., and Cooil, B. (1994), "Reliability Measures for Qualitative Data: Theory and Implications," Journal of Marketing Research, 31(1), 1-14. (available here) Winer, B.J. (1971), Statistical Principles in Experimental Design. New York: McGraw-Hill. Perreault, W.D. and Leigh, L.E. (1989), “Reliability of Nominal Data Based on Qualitative Judgments,” Journal of Marketing Research, 26, 135-148 Retrieved from "https://en.wikipedia.org/w/index.php?title=Proportional_reduction_in_loss&oldid=735653331" Categories: Comparison of assessments Navigation menu Personal tools Not logged inTalkContributionsCreate accountLog in Namespac
sim=FALSE, R=2500) Arguments mod1 A model of class glm (with family binomial), polr or multinom for which (e)PRE will be calculated. mod2 A model of the same class http://artax.karlin.mff.cuni.cz/r-help/library/DAMisc/html/pre.html as mod1 against which proportional reduction in error will be measured. If NULL, the null model will be used. sim A logical argument indicating whether a parametric bootstrap should be used to calculate confidence bounds for (e)PRE. See Details for more information. R Number of bootstrap samples to be drawn if sim=TRUE. Details Proportional reduction in reduction in error is calculated as a function of correct and incorrect predictions (and the probabilities of correct and incorrect predictions for ePRE). When sim=TRUE, a parametric bootstrap will be used that draws from the multivariate normal distribution centered at the coefficient estimates from the model and using the estimated variance-covariance matrix of the estimators as Sigma. This matrix reduction in error is used to form R versions of XB and predictions are made for each of the R different versions of XB. Confidence intervals can then be created from the bootstrap sampled (e)PRE values. Value An object of class pre, which is a list with the following elements: pre The proportional reduction in error epre The expected proportional reduction in error m1form The formula for model 1 m2form The formula for model 2 pcp The percent correctly predicted by model 1 pmc The percent correctly predicted by model 2 epcp The expected percent correctly predicted by model 1 epmc The expected percent correctly predicted by model 2 pre.sim A vector of bootstrapped PRE values if sim=TRUE epre.sim A vector of bootstrapped ePRE values if sim=TRUE Author(s) Dave Armstrong (UW-Milwaukee, Department of Political Science) References Herron, M. 1999. Postestimation Uncertainty in Limited Dependent Variable Models. Political Analysis 8(1): 83–98. Examples data(france) left.mod <- glm(voteleft ~ male + age + retnat + poly(lrself, 2), data=france, family=binomial) pre(left.mod) [Package DAMisc version 1.1 Index]