similar to: interpreting "not defined because of singularities" in lm

Hello, I am trying to run a linear regression analysis on my data set. For some reason most variables are removed due to singularities. My linear regression looks this way (I am using only partial data, which is selected by flags): fm<-lm(log(cplex6.time..sec..[flags]) ~ cplex6.cities[flags] + log(1/features.meanOver.frust[flags]) + log(1/features.meanOver.minDist[flags]) + [...]

Hello, I'm doing an lm(y1~x1), no NAs in them, both of length 283. I get out however and 'NA' for the estimate of x1 and summary gives: Residuals: Min 1Q Median 3Q Max -0.1998309 -0.0447269 -0.0006252 0.0390933 0.3141687 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) -0.021291 0.003994 -5.331 2.01e-07 *** x1

glm(A~B+C+D+E+F,family = binomial(link = "logit"),data=tre,na.action=na.omit) Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels however, glm(A~B+C+D+E,family = binomial(link = "logit"),data=tre,na.action=na.omit) runs fine glm(A~B+C+D+F,family = binomial(link =

Hello, I have a data set which I am using to find a model with the most significant parameters included and most importantly, the p-values. The full model is of the form: sad[,1]~b_1 sad[,2]+b_2 sad[,3]+b_3 sad[,4]+b_4 sad[,5]+b_5 sad[,6]+b_6 sad[,7]+b_7 sad[,8]+b_8 sad[,9]+b_9 sad[,10], where the 9 variables on the right hand side are all indicator variables. The thing I don't understand

With the following example using contr.sum for both factors, > dd <- data.frame(a = gl(3,4), b = gl(4,1,12)) # balanced 2-way > model.matrix(~ a * b, dd, contrasts = list(a="contr.sum", b="contr.sum")) (Intercept) a1 a2 b1 b2 b3 a1:b1 a2:b1 a1:b2 a2:b2 a1:b3 a2:b3 1 1 1 0 1 0 0 1 0 0 0 0 0 2 1 1 0 0 1 0

Hi, I am trying to find out a collinearity in explanatory variables with alias(). I creat a dataframe: dat <- ds[,sapply(ds,nlevels)>=2] dat$Y <- Response Explanatory variables are factor and response is continuous random variable. When I run a regression, I have the following error: fit <- aov( Y ~ . , data = dat) Error in "contrasts<-"(`*tmp*`, value =

Hi- I'm wrestling with some of my data apparently not being called into a GLM or an LM. I'm looking at factors affecting fish annual catch rates (ie. CPUE) over 30 years. Two of the factors I'm using are sea surface temperature and sea surface temperature anomaly. A small sample of my data is below: CPUE Year Vessel_ID Base_Port Boat_Lgth Planing SST Anomaly 0.127

I am using a nested model in R and the lm output shows 47 not defined because of singularities and I have no idea why. Any help on why this is happening or how to fix this problem would be very much appreciated. Below is the output I received from R. Thanks and happy holidays! Call: lm(formula = Dist ~ Treatment/SiteL/Territory) Residuals: Min 1Q Median 3Q Max -6.646 -1.443

The lm() function in R seems to handle the inversion of singular X'X matrices (where there is collinearity between regression inputs) in a way where one of the inputs is dropped and this also seems to be the default behavior in SAS (please let me know if i'm wrong about this). In some other packages (i.e. octave ols() function) the pseudo inverse is computed where singular values less

Dear list, I want to perform a logistic regression analysis with multiple categorical predictors (i.e., a logit) on some data where there is a very definite relationship between one predicator and the response/independent variable. The problem I have is that in such a case the p value goes very high (while I as a naive newbie would expect it to crash towards 0). I'll illustrate my problem

Hi all, I believe this is a bug in the model.matrix function. I'd like a second opinion before filing a bug report. If I have a nested covariate B with multiple values for just one level of A, I can not get a non-singular design matrix out of model.matrix > df <- data.frame(A = factor(c("a", "a", "x", "x"), levels = c("x",

Hi, I am analyzing a repeated-measures Greco-Latin Square with the aov command. I am using aov to calculate the MSs and then picking by hand the appropriate neumerator and denominator terms for the F tests. The data are the following: responseFinger mapping.code Subject.n index middle ring little ---------------------------------------------------------------------------- 1 1

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I got the report Coefficients: (1 not defined because of singularities) while trying to get my model's coefficients from lm() What shall I do to avoid it and get the one missing coefficient? Thank you. lukas - -- Lukas Kubin lukas.kubin at permonik.com phone: 00420603836180 -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.0.5 (GNU/Linux)

R friends, In a matrix of 1s and 0s, I'm getting a singularity error. Any helpful ideas? lm(formula = activity ~ metaF + metaCl + metaBr + metaI + metaMe + paraF + paraCl + paraBr + paraI + paraMe) Residuals: Min 1Q Median 3Q Max -4.573e-01 -7.884e-02 3.469e-17 6.616e-02 2.427e-01 Coefficients: (1 not defined because of singularities)

This command cdmoutcome<- glm(log(value)~factor(year) > +log(gdppcpppconst)+log(gdppcpppconstAII) > +log(co2eemisspc)+log(co2eemisspcAII) > +log(dist) > +fdiboth > +odapartnertohost > +corrupt > +log(infraindex) > +litrate > +africa >

I am using RW 1.2.3. on an IBM PC 300GL. Using the data bp.dat which accompanies Helen Brown and Robin Prescott 1999 Applied Mixed Models in Medicine. Statistics in Practice. John Wiley & Sons, Inc., New York, NY, USA which is also found at www.med.ed.ac.uk/phs/mixed. The data file was opened and initialized with > dat <- read.table("bp.dat") >

Hi, I was wondering if there is a way, or function in R to find confounders. For istance, > a = sample( c(1:3), size=10,replace=T) > X1 = factor( c('A','B','C')[a] ) > X2 = factor( c('Aa','Bb','Cc')[a] ) > Xmat = data.frame(X1,X2,rnorm(10),rnorm(10)) > dimnames(Xmat)[[2]] = c('z1','z2','z3','y') Now,

Is this a bug? seasonal.dummies <- function(x, contr=NULL) { # takes a time series and returns a matrix of seasonal dummies for # x. This is almost cycle(x), we only have to make it into a factor # and add suitable level names. # return a matrix which includes a constant! # level names here assumes frequency is 12! cyc <- factor( cycle(x), labels=c("ene","feb",

Hi R-Helpers: I?m trying to fit the same linear model to a bunch of variables in a data frame, so I was trying to adapt the codes John Fox, Spencer Graves and Peter Dalgaard proposed and discused yesterday on this e-mail list: for (y in df[, 3:5]) { mod = lm(y ~ Trt*Dose, data = x, contrasts = list(Trt = contr.sum, Dose = contr.sum)) Anova(mod, type = "III") } ## by John Fox or for

Many thanks to all who helped with my question last week about how to take the output of lm() and turn it into code that can be run on systems without R (using SQL, C, etc). This is a summary of the answers, caveats, and a solution including a little Perl script I wrote to do this. Brian Ripley pointed out an important caveat to this whole process - the model.matrix could contain R functions that