I have run the following model using lmerTest and using lme4:
model2 = lmer(log(RT)~Group*A*B*C+(1|item)+(1+A+B+C|subject),data=dt)
Using lmerTest I get the following error when typing the summary() command:
> summary(model1)
Error in `colnames<-`(`*tmp*`, value = c("Estimate", "Std. Error", "df", :
length of 'dimnames' [2] not equal to array extent
I saw this has already been an issue for other users and that one user was able to bypass the issue running lsmeans().
When I tried lsmeans, I got the error:
Error in asMethod(object) : not a positive definite matrix.
I did not see any NAs when looking into the covariance matrix.
Note that I am able to run this model if I simply inverse the contrasts in the Group factor.
I have difficulties understanding why this is the case.
When I run the same model using lme4 and not lmerTest, I am able to get all the outputs of summary() but no p-values (as expected). pvals.fnc is discontinued in lme4 and I have not found an alternative yet. Plus it would be nice to have the p-values estimated in the same way for model2 as for the other models for which I was successfully able to use lmerTest.
Does anyone know what I should do at this point? Any help would be much appreciated!
If A or B or C are factors then you might get errors - such models are not yet supported by the lmerTest package (we will put the warning message together with the restrictions for such models in the help page)
Related
I am fitting a Spatial Error Model using the errorsarlm() function in the spdep library.
The Breusch-Pagan test for spatial models, calculated using the bptest.sarlm() function, suggest the presence of heteroskedasticity.
A natural next step would be to get the robust standard error estimates and update the p-values. In the documentation of the bptest.sarlm() function says the following:
"It is also technically possible to make heteroskedasticity corrections to standard error estimates by using the “lm.target” component of sarlm objects - using functions in the lmtest and sandwich packages."
and the following code (as reference) is presented:
lm.target <- lm(error.col$tary ~ error.col$tarX - 1)
if (require(lmtest) && require(sandwich)) {
print(coeftest(lm.target, vcov=vcovHC(lm.target, type="HC0"), df=Inf))}
where error.col is the spatial error model estimated.
Now, I can easily adapt the code to my problem and get the robust standard errors.
Nevertheless, I was wondering:
What exactly is the “lm.target” component of sarlm objects? I can not find any mention to it in the spdep documentation.
What exactly are $tary and $tarX? Again, it does not seem to be mentioned on the documentation.
Why documentation says it is "technically possible to make heteroskedasticity corrections"? Does it mean that proposed approach is not really recommended to overcome issues of heteroskedasticity?
I report this issue on github and had a response by Roger Bivand:
No, the approach is not recommended at all. Either use sphet or a Bayesian approach giving the marginal posterior distribution. I'll drop the confusing documentation. tary is $y - \rho W y$ and similarly for tarX in the spatial error model case. Note that tary etc. only occur in spdep in documentation for localmoran.exact() and localmoran.sad(); were you using out of date package versions?
I have to find multiple objects of one type in an image and for this purpose, I am using OpenCV (Python). First I started with Template matching which doesn't work well if the orientation of the image is changed.
So, I used the method described here. But, unfortunately, it is not working as expected and also, everytime I rerun my code, the output (i.e. the bounding box around the object detected) keeps changing.
Here is my output:
Output(Run 1)
Output(Run 2)
Output(Run 3)
Output(Run 4)
Output(Run 5)
Output(Run 6)
I have tweaked the values and I am still not getting the desired output. Why is it that 2/5 objects of the same type is detected, but not the rest?
I have been thinking about ways to fix it for weeks now. I also tried bf.knnMatch instead of flann.knnMatch and SURF/SIFT instead of ORB, but still no luck.
Do you guys have any idea on how can I fix this? Any suggestions will be appreciated.
Note: I have not made any changes in the code given in the link
I have defined a generalised linear model as follows:
glm(formula = ParticleCount ~ ParticlePresent + AlgaePresent +
ParticleTypeSize + ParticlePresent:ParticleTypeSize + AlgaePresent:ParticleTypeSize,
family = poisson(link = "log"), data = PCB)
and I have the below significant interactions
Df Deviance AIC LRT Pr(>Chi)
<none> 666.94 1013.8
ParticlePresent:ParticleTypeSize 6 680.59 1015.4 13.649 0.033818 *
AlgaePresent:ParticleTypeSize 6 687.26 1022.1 20.320 0.002428 **
I am trying to proceed with a posthoc test (Tukey) to compare the interaction of ParticleTypeSize using the lsmeans package. However, I get the following message as soon as I proceed:
library(lsmeans)
leastsquare=lsmeans(glm.particle3,~ParticleTypeSize,adjust ="tukey")
Error in `contrasts<-`(`*tmp*`, value = contrasts.arg[[nn]]) :
contrasts apply only to factors
I've checked whether ParticleTypeSize is a valid factor by applying:
l<-sapply(PCB,function(x)is.factor(x))
l
Sample AlgaePresent ParticlePresent ParticleTypeSize
TRUE FALSE FALSE TRUE
ParticleCount
FALSE
I'm stumped and unsure as to how I can rectify this error message. Any help would be much appreciated!
That error happens when the variable you specify is not a factor. You tested and found that it is, so that's a mystery and all I can guess is that the data changed since you fit the model. So try re-fitting the model with the present dataset.
All that said, I question what you are trying to do. First, you have ParticleTypeSize interacting with two other predictors, which means it is probably not advisable to look at marginal means (lsmeans) for that factor. The fact that there are interactions means that the pattern of those means changes depending on the values of the other variables.
Second, are AlgaePresent and ParticlePresent really numeric variables? By their names, they seem like they ought to be factors. If they are really indicators (0 and 1), that's OK, but it is still cleaner to code them as factors if you are using functions like lsmeans where factors and covariates are treated in distinctly different ways.
BTW, the lsmeans package is being deprecated, and new developments are occurring in its successor, the emmeans package.
Could someone point me to examples on how to configure the encogmodel with selectmethod? This is an overloaded method with the first one providing just taking inputs as dataset and method. The second one however allows the following:
dataset
methodtype
methodArgs
trainingType
trainingArgs
I am unable to get this working as the following error appears "Layer can't have zero neurons, Unknown architecture element:". Any help is appreciated. thank you.
Also, some insight on how to dump the weights in this approach? When the model is built via building the network (BasicNetwork), it is possible to dump the weights as network.flat approach. In this encogmodel driven approach, how do we dump the weights, gradients etc? thank you
There are three examples for EncogModel, you can find them here:
If that does not help, let me know more specifically what you are trying to do, or provide some code that is not working, and I update this to a more specific answer.
The weights can be directly accessed by BasicNetwork.dumpWeights, BasicNetwork.dumpWeightsVerbose(), or more directly with BasicNetwork.getWeight
everyone. I'm pretty new to R. I've been trying to educate myself about this issue, but I've continued to run into road blocks.
I have a data set with two categorical, independent variables (habitat (1,2,3) and site (1,2,3,4,5). My response variables are the presence or absence of AFLP loci. I have 96 loci, and I want to determine which, if any, of these loci are significantly associated with habitat (site is a random effect). Each of the loci can be assumed to be independent from the others.
As far as relevancy to other researchers, this should be a problem that people trying to analyze molecular data with GLM or LME will begin to run into more.
Here is my code:
##Independent variables
Site=AFLP$Site ##AFLP is my data file
Habitat=AFLP$Habitat
##Dependent variable
Loci=AFLP[,4:99]
##Establishing matrix of variables
mydata <- cbind(Site, Habitat, Loci)
##glm
model1 <- glm(Loci ~ (1|Site)+Habitat, data=mydata, family="binomial")
I get this error:
Error in model.frame.default(formula = Loci ~ (1 | Site) + Habitat, data = mydata, :
invalid type (list) for variable 'Loci'
I know this error is associated with the data type of Loci; however, I've tried a bunch of things and still can't figure out how to correctly address the issue.
My problem seems to be similar to the ones in the below links, but again, I haven't been able to figure out how to apply this information to my data set.
http://stackoverflow.com/questions/18067519/using-r-to-do-a-regression-with-multiple-dependent-and-multiple-independent-vari
https://stats.stackexchange.com/questions/26585/how-to-do-a-generalized-linear-model-with-multiple-dependent-variables-in-r
Thank you in advance. If this turns out to have a simple answer, I apologize for taking up space. I have been Googling and trying to educate myself, and I haven't made any head-way.