Given a nonnegative matrix as well as row and column marginals the ipf procedure generates a sequence of matrices, called the ipf sequence, by alternately fitting rows and columns to match their respective marginals. The mission i am trying to find a way to do iterative proportional fitting in r. On the effective implementation of the iterative proportional. In this article, i introduce the ipfraking package, which implements weightcalibration procedures known as iterative proportional fitting, or raking, of complex survey weights. A short proof is given of the necessary and sufficient conditions for the convergence of the iterative proportional fitting procedure. Pdf convergence of the iterative proportional fitting procedure. Ipf is a procedure for adjusting a table of data cells such that they add up. The iterative proportional fitting procedure ipfp, also known as biproportional fitting in statistics, ras algorithm in economics, raking in survey statistics, and matrix ranking or matrix scaling in computer science is an iterative algorithm for estimating cell values of a contingency table such that the marginal totals remain fixed and the.
Fitting table iso 8015 jis b 0401 iterative proportional fitting for a twodimensional table. The iterative proportional fitting procedure ipfp is a commonly used algorithm for maximum. Once a survey is conducted it is common for the researcher to adjust the survey weights to match known population values. Calibrating survey data using iterative proportional fitting. Putting iterative proportional fitting on the researchers desk. The iterative process is repeated until the difference between the sample margins and the known population margins is smaller than a specified tolerance value or the specified. The data sources for creating synthetic population are 2000 census summary tape file 3 stf3 and census public use microdata sample pums data. A fast algorithm for iterative proportional fitting in log. This module may be installed from within stata by typing ssc install ipf.
Iterative proportional fitting and population dynamics using sas himanshu joshi, houstongalveston area council, houston, tx dmitry messen, houstongalveston area council, houston, tx abstract for doing small area socioeconomic forecast metropolitan planning organizations mpos often need demographic data at individual person level. A fact from iterative proportional fitting appeared on wikipedia s main page in the did you know. Its convergence and statistical properties have been investigated since. Design of iterative proportional fitting procedure for possibility distributions jir ina vejnarova laboratory for intelligent systems, prague, czech republic abstract we design an iterative proportional tting procedure parameterized by a continuous tnorm for computation of multidimensional possibility distri. Iterative proportional fitting ipf refers to an iterative algorithm whose origins date back to research on tra c networks in the 1930s. Calibrating survey data using iterative proportional. Excel can use iteration to calculate the solutions to simultaneous equations which refer to one another in a circular way. Iterative proportional fitting is an algorithm used is many different fields such as economics or social sciences, to alter results in such a way that aggregates along one or several dimensions match known marginals or aggregates along these same dimensions. The purpose for which i use ipf is to allocated individuals to zones. Not sure if this is the same but the function is called ipf and was covered by one of the stbs.
Ipf is a wellestablished technique with the theoretical and practical considerations behind the method thoroughly explored and reported. This process was first introduced by edwards deming. Primary amongst these for urban modelling has been its use in static spatial microsimulation to generate small area microdata individual level data allocated to administrative zones. As we can see, the line graph depicts sql server proportional fill algorithm when populating data files in file group pf. In order for the procedure to work the sum of the target row totals must equal the sum of the target column totals. Oct 15, 20 a short proof is given of the necessary and sufficient conditions for the convergence of the iterative proportional fitting procedure. The package can handle a large number of control variables and trim the weights in various ways. Using iterative proportional fitting technique to improve estimates for rdd phone surveys office of surveillance, epidemiology, and laboratory services.
Iterative proportional fitting wikipedia republished wiki 2. The iterative proportional fitting procedure ipfp, also known as biproportional fitting in statistics, ras algorithm in economics, raking in survey statistics, and matrix ranking or matrix scaling in computer science is an iterative algorithm for estimating cell values of a contingency table such that the marginal totals remain fixed and the estimated table decomposes into an outer product. Its convergence and statistical properties have been investigated since then by several authors and by several different methods. Unfortunately, this book cant be printed from the openbook.
Citeseerx document details isaac councill, lee giles, pradeep teregowda. Iterative proportional fitting procedure ipfp was originally designed by deming and stephan 3 in 1940 for adjustment of frequencies in contingency tables. The input consists of a nonnegative matrix and of positive target marginals for row sums and for column sums. Biproportional scaling of matrices and the iterative. Package ipfp august 29, 2016 type package title fast implementation of the iterative proportional fitting procedure in c version 1. Iterative proportional fitting improving information for. Among these is a new proposal which is based on the application of sparse matrix techniques to the model matrix, and which exploits the special structure of hierarchical loglinear models.
The input consists of a nonnegative matrix and of positive. The asymptotic behavior of the iterative proportional fitting procedure ipf procedure is analyzed comprehensively. Iterative proportional fitting ipf ipf procedure is carried out to produce synthetic population. Oct 27, 2012 the asymptotic behavior of the iterative proportional fitting procedure ipf procedure is analyzed comprehensively. The famous iterative proportional fitting procedure is known to be. Symmetric iterative proportional fitting sven kurras department of computer science, university of hamburg, germany sven. It also provides diagnostic tools for the weights it creates. Ponatinib versus bosutinib in 3rdline chronic phase. Iterative proportional fitting ipf, also known as biproportional fitting, raking or the ras algorithm, is an established procedure used in a variety of applications across the social sciences. Accumulation points of the iterative proportional fitting. Ipf stands for iterative proportional fitting, and is sometimes referred to as raking. Williamson 2007 also represents good practice, with a manual for running. Matrices and iterative procedures real statistics using excel.
This technique is usually done when you know the true population values that your survey should match. We also describe some related topics, such as determinants and solution of simultaneous linear equations, as well as iterative procedures, such as newtons method and iterative proportional fitting procedure ipfp. The iterative proportional fitting procedure ipfp was introduced in 1940 by deming and stephan to estimate cell probabilities in contingency tables subject to certain marginal constraints. Due to its widespread use, simplicity and scalefree nature, sae is often preferred.
Iterative proportional fitting how is iterative proportional fitting abbreviated. Primary amongst these for urban modelling has been its use in static spatial microsimulation to generate small area microdata a individual level data allocated to administrative. Iterative proportional fitting and population dynamics. Objective to compare efficacy, progression free survival pfs and overall survival os among thirdline cpcml patients receiving ponatinib versus bosutinib with. The classical use of iterative proportional fitting is to adjust frequencies to conform to new marginal totals. The module is made available under terms of the gpl v3. Iterative proportional fitting ipf is a widely used method for spatial mi. As such studies are unlikely, we indirectly compared efficacy in the two trials using iterative proportional fitting ipf to adjust for differences in baseline characteristics. Use the iterative proportional fitting procedure to find the expected values for the data in example 1 of independence testing assuming that a persons level of schooling is independent of their parents wealth. He suggested that iterative proportional fitting ipf, a procedure originally developed to combine the information from two or more datasets bishop et al.
Fits and ppt video online download 1 chapter tolerances and fits. Design of iterative proportional fitting procedure for. Ps poststratification, ipf iterative proportional fitting difference between ps and ipf using 2010 bfss states that have cell phone sample size less than 250 were excluded. Later, ipfp was applied to several problems in different domains. It is also known as raking and can be seen as a subset of entropy maximisation. Stata module to create adjustment weights for surveys. Iterative proportional fitting is a way of adjusting internal cells in a multidimensional matrix to optimise fit. Citeseerx putting iterative proportional fitting on the. Fitting procedure and to discuss its basic properties. Pdf iterative proportional fitting theoretical synthesis and. Matrices and iterative procedures real statistics using. Figure 1 start of iterative proportional fitting procedure. Compute weights for each control variable independently and multiply all weights together.
A weighting methodology known as iterative proportional fitting or raking was used in 2017 to allow for the incorporation of cell phone data and to improve the accuracy of prevalence estimates based on mibrfs data. Iterative proportional fitting ipf, also known as biproportional fitting, raking. Evaluating the performance of iterative proportional fitting. Evaluating the performance of iterative proportional fitting for. The simplicity of the algorithm and its relation to the theory of loglinear models make it a useful tool, especially for the analysis of. Get me started in iterative proportional fitting, the professional geographer, 68. Iterative proportional fitting wikipedia republished. Last updated about 7 years ago hide comments share hide toolbars. Generating a synthetic population of individuals in households. Sep 09, 2019 a read is counted each time someone views a publication summary such as the title, abstract, and list of authors, clicks on a figure, or views or downloads the fulltext. Maximum entropy retrieval using iterative proportional fitting. This process is known as iterative proportional fitting ipf or also known as raking.
Putting iterative proportional fitting on the researchers. Using iterative proportional fitting technique to improve. It was rediscovered in other elds, in several variants, and in a large variety of di erent names for example as sheleikhovskiis method, kruithofs algorithm, furness method, sinkhornknopp algorithm, or ras. Use the iterative proportional fitting procedure to find the expected values for the data in example 1 of independence testing assuming that a persons level of schooling is. Iterative proportional fitting for a twodimensional table. If you need to print pages from this book, we recommend downloading it as a pdf.
To get these maximum likelihood estimates the iterative proportional fitting procedure may be used 2,5. Ian, i wrote a function to do iterative proportional fitting for loglinear modelling. Finally, the last group of considered measures is proposed by. Evaluating the performance of iterative proportional. For example, if you would like to solve the two simultaneous equations. The iterative proportional fitting procedure is an iterative algorithm for estimating cell values of a.
Iterative proportional fitting procedure ipfp real statistics using. The iterative process is repeated until the difference between the sample margins and the known population margins is smaller than a specified tolerance value or the specified maximum number of iterations is obtained. Iterative proportional fitting ipf, also known as biproportional fitting, a. Iterative proportional fitting ipf is a mathematical procedure originally developed to combine the information from two or more datasets. This example shows a very simple ipf algorithm than can be used to adjust survey weights. Iterative proportional fitting sam roweis february 11, 2004 undirected models in directed models, each node plus its parents form a clique, and. Adads 2mba c arnegiep4ellon univ pittsburgh pa dept of.
Iterative proportional ftting let us describe how the maximum likelihood estimate of relative frequencies for a given graph g v, e is obtained with the help of the iterative proportional fitting procedure. Stata module to perform loglinear modelling using iterative proportional fitting, statistical software components s438901, boston college department of economics, revised 22 jul 2009. The iterative proportional fitting procedure ipfp, also known as biproportional fitting in statistics, ras algorithm in economics and matrix ranking or matrix scaling in computer science is an iterative algorithm for estimating cell values of a contingency table such that the marginal totals remain fixed and the estimated table decomposes into an outer product. The iterative proportional fitting procedure ipfp is a commonly used algorithm for maximum likelihood estimation in loglinear models. Iterative proportional fitting iterative proportional tting ipf, also known as raking, is a very useful tool once a survey has been conducted. Iterative proportional fitting and population dynamics using sas. Pdf convergence of the iterative proportional fitting. The pums file has 5% sample from long form census records. Article pdf available in the annals of statistics 234 august. You supply a table that contains new margins and a table that contains old frequencies.
Pdf the iterative proportional fitting procedure ipfp was. Specifically, the performance of the iterative proportional fitting ipf procedure used with a null base matrix is examined on the basis of a field experiment in which true od flows are observed. E7 contains the target row totals and the range a8. Figure 2 iterative proportional fitting procedure for example 1 the final result of the procedure is found in the lower righthand corner of figure 2. Iterative proportional fitting statistical research.
We prove that the ipf sequence has at most two accumulation. Attribution license, meaning that anyone is free to share. Pdf iterative proportional fitting ipf is described formally and. In this paper, the performance of a simple procedure for routelevel od flow determination requiring only boarding and alighting data is investigated. Iterative proportional fitting procedure to determine bus. The proportional fill algorithm is not about distributing the equal amount of data across data files, it is about writing the proportional amount in the file to each file according to the free space within the file group. Iterative information set reduction box matching algorithm. Use the ipf subroutine to perform this kind of analysis. How to use the iterative proportional fitting procedure ipfp to solve problems of independence testing. Iterative proportional fitting procedure ipfp real. In this normalization process, differences in sample sizes used to generate the matrices are eliminated and, therefore, individual cell values within the matrix are directly comparable. After the sequence, control variables may not have similar distribution of that in population.