Logistic Regression Singular Matrix Error
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Singular Covariance Matrix Problem
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How To Fix Near Singular Matrix
What correlation makes a matrix singular and what are implications of singularity or near-singularity? up vote 32 down vote favorite 37 I am doing some calculations on different matrices (mainly in logistic regression) and I commonly get the error "Matrix is singular", where I have to go back and remove the correlated variables. My question here is what would you consider a "highly" correlated matrix? Is there a threshold value of correlation to represent this what does near singular matrix mean in eviews word? Like if a variable was 0.97 correlated to another one, is this a "high" enough to make a matrix singular? Apologies if the question is very basic, I wasn't able to find any references talking about this issue (a hint towards any reference would be a big plus!). regression correlation matrix multicollinearity singular share|improve this question edited Sep 25 '13 at 16:59 ttnphns 25.9k560137 asked Sep 24 '13 at 10:55 Error404 3561415 2 Tip: search our site for VIF and correlation. –whuber♦ Sep 24 '13 at 13:18 Will definitely have a look. Cheers. –Error404 Sep 24 '13 at 13:26 2 @ttnphns has provided an outstanding explanation below (no surprise there, this seems to be his specialty). For a simple example of a situation where you can get a singular data matrix, it might help to read my answer here: qualitative-variable-coding-in-regression-leads-to-singularities. –gung Sep 24 '13 at 13:36 Indeed he did!! Actually saved me hours of reading with confusion. Thanks for your example @gung. That was very helpful guys. –Error404 Sep 24 '13 at 13:45 add a comment| 1 Answer 1 active oldest votes up vote 47 down vote accepted What is singular matrix? A square matrix is singular, that is, its determinant is zero, if it contains rows or columns which are proportionally interrelated; in other word
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Near Singular Matrix Error. Regressors May Be Perfectly Collinear
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Singular Matrix Error In Eviews
you might have Meta Discuss the workings and policies of this site About Us eviews near singular matrix error. regressors may be perfectly collinear. Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs http://stats.stackexchange.com/questions/70899/what-correlation-makes-a-matrix-singular-and-what-are-implications-of-singularit Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Calculate logistic regression in python up vote http://stackoverflow.com/questions/17105154/calculate-logistic-regression-in-python 5 down vote favorite 5 I tried to calculate logical regression. I have the data as csv file. it looks like node_id,second_major,gender,major_index,year,dorm,high_school,student_fac 0,0,2,257,2007,111,2849,1 1,0,2,271,2005,0,51195,2 2,0,2,269,2007,0,21462,1 3,269,1,245,2008,111,2597,1 .......................... This is my coding. import pandas as pd import statsmodels.api as sm import pylab as pl import numpy as np df = pd.read_csv("Reed98.csv") print df.describe() dummy_ranks = pd.get_dummies(df['second_major'], prefix='second_major') cols_to_keep = ['second_major', 'dorm', 'high_school'] data = df[cols_to_keep].join(dummy_ranks.ix[:, 'year':]) train_cols = data.columns[1:] # Index([gre, gpa, prestige_2, prestige_3, prestige_4], dtype=object) logit = sm.Logit(data['second_major'], data[train_cols]) result = logit.fit() print result.summary() When I run the coding in python I got an error: Traceback (most recent call last): File "D:\project\logisticregression.py", line 24, in
Calculators Downloads Apps, Software & UpdatesGuidebooks Activities All ActivitiesMath NspiredScience NspiredBuilding Concepts in MathematicsTI MathPublisher Connections Professional Development For Your SchoolPD in Your AreaT³ International ConferencesOnline LearningT³ CommunityTI MathForward Solutions Common Core State StandardsTEKS ResourcesScience ToolsStudent and Parent ResourcesFunding & ResearchLua Scripting in TI-Nspire™Test Preparation Tools Support Where to buy https://epsstore.ti.com/OA_HTML/csksxvm.jsp?nSetId=93563 Site: US and Canada Knowledge Base Home > Product Usage > TI-Nspire™ Family > https://books.google.com/books?id=eNpjlIVzsVgC&pg=PA58&lpg=PA58&dq=logistic+regression+singular+matrix+error&source=bl&ots=mOj2ONs878&sig=RBtb09ySZMJVHcl7XC8HGjYqzxk&hl=en&sa=X&ved=0ahUKEwi8pYmOjeHPAhVl0YMKHdsdAggQ6AEIU TI-Nspire™ Handheld in TI-84 Plus Mode > Matrices > > Solution 11935 Solution 11935: Causes of a Singular Matrix Error on a TI-83 Family, TI-84 Plus Family, Or TI-Nspire™ Handheld In TI-84 Plus Mode. Type Question and Answer Last Updated 06-AUG-2010 14:02:53 What causes a Singular Matrix error message on a TI-83 family, TI-84 singular matrix Plus family, or TI-Nspire handheld in TI-84 Plus mode? Themessage "ERR:SINGULAR MAT"may be the result of one of two possible things. The firstreason is an attempt to take the inverse of a matrixfor which thedeterminant is zero. A second possible cause for this error message is if the coordinates provided are only from a single cycle of the sine wave. A minimum of two points from twocycles of the sine wave singular matrix error are needed to generate an answer.Example: Matrix A, shown below, has a determinant of 0. The determinant is shown by using the det command. Herewe attempt to find the inverse, shown below, of Matrix A. This results in the Singular Matrix error. This error also occurs if a calculation (typically a regression) produces a singular matrix. For more information on the algorithim used for calculating the Sine Regression please see Solution 11576: Algorithm Used for Calculating the Sine Regression.Please see the TI-83 family and TI-84 Plus family guidebooks for additional information. Note: TI-Nspire in TI-84 Plus mode users may refer to the TI-84 Plus family guidebook.
Product TI-83 TI-83 PLUS TI-83 PLUS SILVER TI-83 PLUS VSC TI-83 VSC TI-83+ SILVER VSC TI-84 PLUS TI-84 PLUS SILVER TI-84+ SILVER VSC TI-NSPIRE Related Categories Home > TI-Nspire™ Family of Products > TI-Nspire™ Handheld in TI-84 Plus Mode > Matrices Home > Product Usage > TI-83 Family, TI-84 Family, or TI-Nspire™ Handheld in TI-84 Plus Mode > Matrices Home > Product Usage > TI-Nspire™ Family > TI-Nspire™ Handheld in TI-84 Plus Mode > Matrices Home > TI-84 Plus Family > Matrices Home > Product Usage Home > Product Usage > TI-83 Family, TI-84 Family, or TI-Nspire™ Handheld in TI-84 Plusvan GoogleInloggenVerborgen veldenBoekenbooks.google.nl - In this dissertation, we developed a Bayesian approach to classification problems. Classification problems range from recognizing handwritten text to predicting whether an individual will develop a certain disease. To address classification problems, various methods (for example, Classification Trees,...https://books.google.nl/books/about/Bayesian_Classification_Using_Bayesian_A.html?hl=nl&id=eNpjlIVzsVgC&utm_source=gb-gplus-shareBayesian Classification Using Bayesian Additive and Regression TreesMijn bibliotheekHelpGeavanceerd zoeken naar boekenGedrukt boek aanschaffenGeen eBoek beschikbaarProQuestZoeken in een bibliotheekAlle verkopers»Boeken kopen Google PlayBrowse door 's werelds grootste eBoekenwinkel en begin vandaag nog met lezen op internet, je tablet, telefoon of eReader.Ga nu naar Google Play »Bayesian Classification Using Bayesian Additive and Regression TreesProQuest, 2008 - 209 pagina's 0 Recensieshttps://books.google.nl/books/about/Bayesian_Classification_Using_Bayesian_A.html?hl=nl&id=eNpjlIVzsVgCIn this dissertation, we developed a Bayesian approach to classification problems. Classification problems range from recognizing handwritten text to predicting whether an individual will develop a certain disease. To address classification problems, various methods (for example, Classification Trees, Artificial Neural Networks, Support Vector machines) have been developed. However, still there are problems where classification error rate is 10% or higher; in addition, applications of classifiers have shown that estimating class membership probabilities, and their uncertainty, is very important aspect of classification problems. In this dissertation, we addressed the above issues by developing a new classifier: CBART. The classifier is based on Bayesian Additive and Regression trees (BART); we used latent variables to extend BART to binary and multiclass ordered classification problems. Our investigation has shown that CBART provides error rates and AUC (Area Under the Curve) comparable with those of benchmark classifiers. The benefits of CBART are that it provides class membership probabilities and their distributions; hence, using CBART, we have a measure of the u