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Ipf is used to revise tables of data where the information is incomplete, inaccurate, outdated, or a sample.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