Displaying 20 results from an estimated 800 matches similar to: "lm, coefficient 'not defined because of singularities'? What does this mean?"
2011 Dec 05
1
Summary coefficients give NA values because of singularities
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
2009 Mar 30
1
interpreting "not defined because of singularities" in lm
I run lm to fit an OLS model where one of the covariates is a factor with 30 levels. I use contr.treatment() to set the base level of the factor, so when I run lm() no coefficients are estimated for that level. But in addition (and regardless of which level I choose to be the base), lm also gives a vector of NA coefficients for another level of my factor.
The output says that these coefficients
2003 May 30
2
Coefficients: (20 not defined because of singularities)
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]) +
[...]
2013 May 02
1
Package survey: singularities in linear regression models
Hello,
I want to specify a linear regression model in which the metric outcome
is predicted by two factors and their interaction. glm() computes
effects for each factor level and the levels of the interaction. In the
case of singularities glm() displays "NA" for the corresponding
coefficients. However, svyglm() aborts with an error message. Is there a
possibility that svyglm()
2006 Apr 19
1
Singularities in glm()
Hello,
i have the following model,
poi1<-glm(F~S+T+L+C,family=poisson,x=T)
where F,S,T,L are metric and C is a factor variable with the levels "0",
"1", "2", "3", "4", "5" and "6"
if i do summary(poi1), i get the following
Call:
glm(formula = F ~ S + T + L + C, family = poisson, x = T)
Deviance Residuals:
Min
2006 Nov 26
1
GLM and LM singularities
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
2011 Dec 24
1
Nested model - "singularities not defined"
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
2002 Apr 09
1
how to deal with singularities in lm()
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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
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2012 Jun 08
2
Percent of a given subset
How would I find the Percent of FuelTypeNum within the Band given
AvailableMW?
example:
type 1 is 1% of PB0
type 2 is 54% of PB0
type 4 is 4% of PB0
type 5 is 42% of PB0
Note: the Bands and fuel types are not always constant.
Data:
FuelTypeNum Bands AvailableMW AvailableMWNewFormat
1 PB0 185319 185.319
2 PB0 18352000 18352
4 PB0 1338785 1338.785
5 PB0 14189756 14189.756
2 PB1
2009 Feb 26
4
Singularity in a regression?
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)
2012 Aug 06
1
How to convert data to 'normal' if they are in the form of standard scientific notations?
Dear R users
I read two csv data files into R and called them Tem1 and Tem5.
For the first column, data in Tem1 has 13 digits where in Tem5 there are 14
digits for each observation.
Originally there are 'numerical' as can be seen in my code below. But how
can I display/convert them using other form rather than scientific
notations which seems a standard/default?
I want them to be in
2010 Aug 12
3
Regression Error: Otherwise good variable causes singularity. Why?
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
>
2008 Aug 28
1
Singularity?
Hi all,
When using lm to model a response with 8 explanatory variables, one of
the variables is not defined due to "singularities". I have checked the
csv file from which the data come, there are no na's in the dataset,
etc. What should I be looking for in this variable to correct the
problem?
Thanks for any help.
Robin Williams
Met Office summer intern - Health Forecasting
2009 Sep 09
2
ggplot2: mixing colour and linetype in geom_line
Hi all,
I try to represent a multiple curve graphic where the x-axis is the
temperature and the different y-axes are the different X (X22,X43,X44...)
some X corresponds to the same molecule (22 and 44 are for CO2 for instance)
so I use the same colour for them.
I wanna mix the linetype with the colour to be able to visually see the
difference between X43 and X45
The best I have done up to now
2004 Sep 27
1
Funny behaviour of coef() and vcov() if X is singular
coef() and vcov() have different dimensions if a model contains alised
parameters
as the following example illustrates.
A search on "alised" gave noting as far as I could see.
Is this a known bug?
Bendix C
----------------------
Bendix Carstensen
Senior Statistician
Steno Diabetes Center
Niels Steensens Vej 2
DK-2820 Gentofte
Denmark
tel: +45 44 43 87 38
mob: +45 30 75 87 38
fax: +45
2005 Oct 19
1
nlme Singularity in backsolve at level 0, block 1
Hi,
I am hoping some one can help with this.
I am using nlme to fit a random coefficients model. It ran for hours before returning
Error: Singularity in backsolve at level 0, block 1
The model is
> plavix.nlme<-nlme(PLX_NRX~loglike(PLX_NRX,PD4_42D,GAT_34D,VIS_42D,MSL_42D,SPE_ROL,XM2_DUM,THX_DUM,b0,b1,b2,b3,b4,b5,b6,b7,alpha),
+ data=data,
+ fixed=list(b0 +
2004 Jul 28
2
Simulation from a model fitted by survreg.
Dear list,
I would like to simulate individual survival times from a model that has been fitted using the survreg procedure (library survival). Output shown below.
My plan is to extract the shape and scale arguments for use with rweibull() since my error terms are assumed to be Weibull, but it does not make any sense. The mean survival time is easy to predict, but I would like to simulate
2010 Oct 19
3
scatter.smooth() fitted by loess
Hi there,
I would like to draw a scatter plot and fit a smooth line by loess.
Below is the data.
However, the curve line started from 0, which my "resid" list doesn't
consist of 0 value.
It returned some warnings which I don't know if this is the reason
affecting such problem. Here I also attached the warning messages.
Please let me know if there is a solution to fix this. Thank
2008 Dec 05
1
lme4, error in mer_finalize(ans)
Using lmer() on my data results in an error. The problem,
I think, is my model specification. However, lm() works
ok.
I recreated this error with a more simple dataset. (See
code below.)
# word and letter recognition data
# two within factors:
# word length: 4, 5, 6 letters
# letter position: 1-4 (in 4-letter words), 1-5 (in
5-letter words), 1-6 (in 6-letter words)
# one dependent variable:
#
2009 Apr 11
2
who happenly read these two paper Mohsen Pourahmadi (biometrika1999, 2000)
http://biomet.oxfordjournals.org/cgi/reprint/86/3/677 biometrika1999
http://biomet.oxfordjournals.org/cgi/reprint/94/4/1006 biometrika2000
Hi All:
I just want to try some luck.
I am currenly working on my project,one part of my project is to
reanalysis the kenward cattle data by using the method in Mohsen's paper,but
I found I really can get the same or close output as he did,so,any