Floating Point Overflow Error
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the Z3, included floating-point arithmetic (replica on display at Deutsches Museum in Munich). In computing, floating point is the formulaic representation that approximates a real number so as to support a trade-off between range and precision.
Floating Point Overflow Example
A number is, in general, represented approximately to a fixed number of significant digits
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(the significand) and scaled using an exponent in some fixed base; the base for the scaling is normally two, ten, or error floating point overflow error termination due to floating point exception sixteen. A number that can be represented exactly is of the following form: significand × base exponent , {\displaystyle {\text{significand}}\times {\text{base}}^{\text{exponent}},} where significand ∈ Z, base is an integer ≥ 2, and exponent ∈ Z. https://communities.sas.com/t5/Base-SAS-Programming/ERROR-Floating-Point-Overflow/td-p/216936 For example: 1.2345 = 12345 ⏟ significand × 10 ⏟ base − 4 ⏞ exponent {\displaystyle 1.2345=\underbrace {12345} _{\text{significand}}\times \underbrace {10} _{\text{base}}\!\!\!\!\!\!^{\overbrace {-4} ^{\text{exponent}}}} The term floating point refers to the fact that a number's radix point (decimal point, or, more commonly in computers, binary point) can "float"; that is, it can be placed anywhere relative to the significant digits of the number. This position is indicated as the exponent https://en.wikipedia.org/wiki/Floating_point component, and thus the floating-point representation can be thought of as a kind of scientific notation. A floating-point system can be used to represent, with a fixed number of digits, numbers of different orders of magnitude: e.g. the distance between galaxies or the diameter of an atomic nucleus can be expressed with the same unit of length. The result of this dynamic range is that the numbers that can be represented are not uniformly spaced; the difference between two consecutive representable numbers grows with the chosen scale.[1] Over the years, a variety of floating-point representations have been used in computers. However, since the 1990s, the most commonly encountered representation is that defined by the IEEE 754 Standard. The speed of floating-point operations, commonly measured in terms of FLOPS, is an important characteristic of a computer system, especially for applications that involve intensive mathematical calculations. Contents 1 Overview 1.1 Floating-point numbers 1.2 Alternatives to floating-point numbers 1.3 History 2 Range of floating-point numbers 3 IEEE 754: floating point in modern computers 3.1 Internal representation 3.1.1 Piecewise linear approximation to exponential and logarithm 3.2 Special values 3.2.1 Signed zero 3.2.2 Subnormal numbers 3.2.3 Infinities 3.2.4 NaNs 3.2.5 IEEE 754 design rationale 4 Representable numbers, conversion and rounding 4.1 Rounding mode
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this http://stats.stackexchange.com/questions/24614/floating-point-overflow-while-computing-the-kaplan-meier-estimator-in-sas site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it floating point works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Floating Point Overflow while computing the Kaplan-Meier estimator in SAS up vote 3 down vote favorite I try to estimate survival curves based on a Kaplan-Meier estimator using proc lifetest. However, SAS outputs an error message which I do not manage to circumvent. Can you floating point overflow help me? The data set is available here. My code filename work_di "TO BE COMPLETED"; data data; infile work_di("data.txt") dlm="09"x firstobs=2; input id centre time event x $; run; proc lifetest data=data method=KM plots=none; time time * event(0); by centre x; ods output ProductLimitEstimates = surv; run; Error message NOTE: The LOGLOG transform is used to compute the confidence limits for the quartiles of the survivor distribution. To suppress using this transform, specify CONFTYPE=LINEAR in the PROC LIFETEST statement. ERROR: Floating Point Overflow. NOTE: The data set WORK.SURV has 77 observations and 10 variables. ERROR: Termination due to Floating Point Exception NOTE: The SAS System stopped processing this step because of errors. NOTE: PROCEDURE LIFETEST used (Total process time): real time 0.09 seconds cpu time 0.03 seconds survival sas kaplan-meier share|improve this question asked Mar 14 '12 at 7:28 ocram 11.4k23758 add a comment| 1 Answer 1 active oldest votes up vote 3 down vote accepted There are quite a few 1e+308 as time to event in your dataset (variable time). I'd say that your problem is due to that. The MEANS Procedure Analysis Variable : time N Mean Std Dev Minimum Maximum ------------------------
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