Stata module to create adjustment weights for surveys. Mander i wrote a function to do iterative proportional fitting for loglinear modelling. Stephan 1940 iterative proportional fitting algorithm which fits hierarchical models of any dimensions. The package can handle a large number of control variables and trim the weights in various ways. Its convergence and statistical properties have been investigated since then by several authors and by several different methods. Putting iterative proportional fitting on the researchers desk. Once a survey is conducted it is common for the researcher to adjust the survey weights to match known population values. I am trying to understand iterative proportional fitting in python using pandas dataframes across a 2 dimensional matrix using ipfn.
The module is made available under terms of the gpl v3. It is also known as raking and can be seen as a subset of entropy maximisation. Iterative proportional fitting sam roweis february 11, 2004 undirected models in directed models, each node plus its parents form a clique, and. In these situations it is often possible to synthesize the information using a combination of tables provided by ctpp 2000 and an iterative proportional fit ipf process. Convergence of symmetric iterative proportional fitting. Ipfp is defined as iterative proportional fitting procedure somewhat frequently. Iterative proportional fitting procedure ipfp real statistics using. 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. Not sure if this is the same but the function is called ipf and was covered by one of the stbs. Iterative information set reduction box matching algorithm.
This process is known as iterative proportional fitting ipf or also known as raking. This limit is the relativeentropy nearest solution to w among all matrices of prescribed row marginals r and column marginals c. 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. This process was first introduced by edwards deming. Pdf iterative proportional fitting theoretical synthesis and. E7 contains the target row totals and the range a8. You supply a table that contains new margins and a table that contains old frequencies. One method for accomplishing this goal is known as iterative proportional fitting or raking. The mission i am trying to find a way to do iterative proportional fitting in r. Iterative model in software engineering advantages. 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. Figure 3 iterative proportional fitting procedure for example 2. Calibrating survey data using iterative proportional. 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.
The nonlinear iterative curve fitting is more difficult to do for the computer, anyway and more prone to error, but its necessary if you need to track shifts in peak position or width or to decompose a complex overlapping peak signal into fundamental components knowing only their shape. Its convergence and statistical properties have been investigated since. Ipf allows one to find a matrix s, close to an input matrix t, but such that the row sums of s are r, and the column sums of s are c. During software development, more than one iteration of the software development cycle may be in progress at the same time. Iterative proportional fitting is a way of adjusting internal cells in a multidimensional matrix to optimise fit. Using iterative proportional fitting technique to improve. 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. The asymptotic behavior of the iterative proportional fitting procedure ipf procedure is analyzed comprehensively. Computeraided civil and infrastructure engineering, published online ahead of. How to use the iterative proportional fitting procedure ipfp to solve problems of independence testing. Requirements changes, again and again, can cause over budget and over time.
Oct 15, 20 a short proof is given of the necessary and sufficient conditions for the convergence of the iterative proportional fitting procedure. Population synthesis for microsimulating travel behavior. Iterative proportional fitting ipf is a mathematical procedure originally developed to combine the information from two or more datasets. We saw that in some cases a nonlinear situation can be converted into a linear one by a coordinate transformation, but this is possible only in some special cases, it may restrict the. Iterative proportional fitting ipf ipf procedure is carried out to produce synthetic population.
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. Pdf putting iterative proportional fitting on the researchers desk. A strategy to reduce storage requirement in cases in which a goodfitting and parsimonious model is not available is proposed. Use the ipf subroutine to perform this kind of analysis. Putting iterative proportional fitting on the researchers. Though i did pieces of the development for the ipf functions, the heavy lifting the bulk.
Behavioral risk factor surveillance system brfss fact. Evaluating the performance of iterative proportional fitting for spatial microsimulation. Timmermans, 2005 since its introduction in the mid1980s jones et al. A strategy to reduce storage requirement in cases in which a good fitting and parsimonious model is not available is proposed. I have found these examples, however i am finding them difficul. Accumulation points of the iterative proportional fitting. Last updated about 7 years ago hide comments share hide toolbars. Pdf iterative proportional fitting ipf is a mathematical procedure. 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 pums file has 5% sample from long form census records. Iterative proportional fitting procedure ipfp real. Iterative proportional fitting statistical research. The linear least squares curve fitting described in curve fitting a is simple and fast, but it is limited to situations where the dependent variable can be modeled as a polynomial with linear coefficients. This module may be installed from within stata by typing ssc install ipf. Symmetric iterative proportional fitting sven kurras department of computer science, university of hamburg, germany sven. Behavioral risk factor surveillance system brfss fact sheet.
Using iterative proportional fitting technique to improve estimates for rdd phone surveys office of surveillance, epidemiology, and laboratory services. Alaska department of labor and workforce development. In order for the procedure to work the sum of the target row totals must equal the sum of the target column totals. Introduction growing interest in activitybased travel demand forecasting has been observed guo and bhat, 2007. The data sources for creating synthetic population are 2000 census summary tape file 3 stf3 and census public use microdata sample pums data. Stata module to perform loglinear modelling using iterative proportional fitting, statistical software components s438901, boston college department of economics, revised 22 jul 2009. Ian, i wrote a function to do iterative proportional fitting for loglinear modelling. Iterative proportional fitting procedure to determine bus. The process of raking iterative proportional fitting o nce brfss data are collected, statistical procedures are undertaken to make sure the data are representative of the population for each state andor local area. The input consists of a nonnegative matrix and of positive target marginals for row sums and for column sums. Iterative proportional fitting improving information for.
Ipf is used to revise tables of data where the information is incomplete, inaccurate, outdated, or a sample. How is iterative proportional fitting procedure abbreviated. Ipf stands for iterative proportional fitting, and is sometimes. The main challenge is how to represent a three dimensional table in two dimensional space.
Evaluating the performance of iterative proportional fitting for. Ipfp stands for iterative proportional fitting procedure. The iterative proportional fitting ipf technique is widely used to create synthetic spatial microdata from a variety of aggregate data sources, such as census or administrative records. Figure 1 start of iterative proportional fitting procedure. Citeseerx putting iterative proportional fitting on the. Hierarchical models for threedimensional cases are presented, along with a description of the w.
Design can be changed again and again due to nonperfect requirements. Iterative proportional fitting ipf, also known as biproportional fitting, a. 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. Evaluating the performance of iterative proportional. The iterative model is a particular implementation of a software development life cycle sdlc that focuses on an initial, simplified implementation, which then progressively gains more complexity and a broader feature set until the final system is complete.
A contingency approach to estimating record selectivities. Biproportional scaling of matrices and the iterative. Iterative proportional fitting ipf table rounding process with appropriate marginal control is a key factor. I am quering a database for some paramaters which depend on a attribute called count. These results agree with those found in figure 1 of independence testing. 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. Jul 31, 2002 ian, i wrote a function to do iterative proportional fitting for loglinear modelling. The classical use of iterative proportional fitting is to adjust frequencies to conform to new marginal totals.
Nels tomlinson handed down much of any knowledge and computer code that i have on ipf. This volume, second in the series, provides essential background material for policy analysts, researchers, s. Dec 15, 2016 the iterative model is a particular implementation of a software development life cycle sdlc that focuses on an initial, simplified implementation, which then progressively gains more complexity and a broader feature set until the final system is complete. A fact from iterative proportional fitting appeared on wikipedia s main page in the did you know. Iterative proportional fitting and population dynamics using sas. Iterative proportional fitting ipf, also known as biproportional fitting, raking or the. Its useful in a range of tasks i use it in traffic matrix problems, but is often used in statistics for examining independence assumptions in contingency tables. Iterative model in software engineering advantages disadvantages. 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. The input consists of a nonnegative matrix and of positive. The iterative proportional fitting procedure is an iterative algorithm for estimating cell values of a. To date, the conventional approach to synthesizing the baseyear population has been based on the iterative proportional fitting procedure. Two issues associated with this conventional approach are discussed. Population synthesis using iterative proportional fitting ipf.
Contrary to what several studies have reported, in this study appropriately rounded ipf outperforms simulated annealing sa. Combining sample and census data in small area estimates. Iterative proportional fitting for a twodimensional table. Population synthesis using iterative proportional fitting. Iterative proportional fitting ipf generates from an input matrix w a sequence of matrices that converges, under certain conditions, to a specific limit matrix w. 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. This example shows a very simple ipf algorithm than can be used to adjust survey weights. Ian watson does anyone know if there is a stata ado to do iterative proportional fitting for calculating sampling weights. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Iterative proportional fitting if one performs a statistical match in order to determine multivariate frequency counts for a variety of variables that do not coexist on. Since the collection of such detailed data for the whole population is too expensive, if not infeasible, population synthesis has been proposed to predict the data and produce them synthetically based on a sample. Activitybased travel demand modeling requires the detailed socioeconomic data of the study area population. Ipf is a wellestablished technique with the theoretical and practical considerations behind the method thoroughly explored and reported.
Project completion date prediction is a problem because the software is changing again and again in iteration. When discussing the iterative method, the concept of incremental development will also often be used liberally and interchangeably. In this paper, the performance of a simple procedure for routelevel od flow determination requiring only boarding and alighting data is investigated. Comparison of iterative proportional fitting and simulated. This technique is usually done when you know the true population values that your survey should match. Pdf iterative proportional fitting ipf is described formally and historically.
Iterative and incremental development is a combination of both iterative design or iterative method and incremental build model for development. Ipfp iterative proportional fitting procedure acronymfinder. The iterative proportional fitting procedure is an iterative algorithm for estimating cell values of a contingency table such that the marginal totals remain fixed and. The purpose for which i use ipf is to allocated individuals to zones. Iterative proportional fit ipf exercise ctpp 2000 provides a large number of tables, but there are times when a table of interest for a particular analysis does not exist. Iterative proportional fitting and population dynamics. If unique mles do not exist, ipfp converges toward the so called extended mles by design haberman 1974, but convergence may be. Iterative proportional fitting iterative proportional tting ipf, also known as raking, is a very useful tool once a survey has been conducted. It also provides diagnostic tools for the weights it creates. Abstract established technique robin lovelace, mark birkin. Package ipfp august 29, 2016 type package title fast implementation of the iterative proportional fitting procedure in c version 1.
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