I'm running a logit model using the zelig package in R.
m3 <- zelig (newdata$bes.Turnout ~ civicduty + newdata$bes.CivicDuty2+newdata$bes.Age, model="logit", data=newdata)
newdata$bes.Turnout is binary, the rest are numerical.
I try to set the values of x.
mcos<- median(newdata$bes.CivicDuty2)
civicmean<-mean(civicduty)
mage<-mean(newdata$bes.Age)
test<-setx(m3, civicduty=civicmean, newdata$bes.CivicDuty2=mcos, newdata$bes.Age=mage)
I get the following error message:
Error: unexpected '=' in "test<-setx(m3, civicduty=civicmean, newdata$bes.CivicDuty2="
I have tried the same code for the survival data in Zelig, and here the setx worked.
Any ideas what I am doing wrong and how to fix it?
That call to setx doesn't make sense to me. My best guess is that you want something like
test<-setx(m3,
civicduty = mean(newdata$civicduty),
bes.CivicDuty2 = mean(newdata$bes.CivicDuty2,
bes.Age = mean(newdata$bes.Age))
Related
I am running a negative binomial regression on my dataset using the glm.nb() function.
My model looks something like this:
m_nb= glm.nb(Error_Count ~ TotalWL + Auto_frac +PHONE+JUSTIF_weight + MESSAGE_OTHER_count + Hour+
I(Auto_frac^2)+I(TotalWL^2), data = df)
When I ran it with a dataset of 10,000, the model is able to run, however, when I ran it with a larger dataset (60,000), I got this error:
`Error: no valid set of coefficients has been found: please supply starting values`
I then tried to give it some start values, but still throw the same error
m_nb= glm.nb(Error_Count ~ TotalWL + Auto_frac +PHONE+JUSTIF_weight + MESSAGE_OTHER_count + Hour+
I(Auto_frac^2)+I(TotalWL^2), data = df, start = c(0.02, 0.3,0.2,3,43, 4,13,0.04, 100))
Error: cannot find valid starting values: please specify some
But the model still doesn't converge. How should I set the starting value?
I also tried the same model with the fenebin() function in the fixest pacakage and the model works. However, I need the glm package, since the fixest package does not provide the standard error (S.E.) in the predict().
Thank you.
I have discovered some heteroscedasticity in my model that I would like to compensate for with more robust standard errors. I have tried to use the Huber-White robust standard errors from the merDeriv package in R but I beleive these only work for a GLMM with a binomial distribution. Is there a way I could achieve the same thing for a Negative Binomial distribition?
Model:
library(lme4)
model <- glmer.nb(Jobs ~ 1 + Month + Year + (1|Region), data = df)
Huber-White robust standard errors:
library(merDeriv)
bread.glmerMod(model)
Error:
Error in vcov.lmerMod(object, full = full) : estfun.lmerMod() only works for lmer() models.
Thank you for any help!
This looks like a bug in the package, as far as I can tell (the bread.glmerMod function was calling estfun.lmerMod rather than estfun.glmerMod; there's a broader question here about the design of the generic functions, but never mind ...)
You should be able to install a fixed version from my fork via remotes::install_github("bbolker/merDeriv"), then reload the package and try again.
Alternately, download the tarball, change vcov.lmerMod to vcov.glmerMod in the last line of R/bread.glmerMod.R, and re-install the package ...
Try something like this:
library(lme4)
model <- glmer.nb(Jobs ~ 1 + Month + Year + (1|Region), data = df)
cov <- vcovHC(model, type = "HC1", sandwich = T)
se <- sqrt(diag(cov_m1))
(Can't confirm if it works since this there isn't a reproducible example)
I am trying to implement ordinal logistic regression on my dataset in r. I use the function 'polr' for this, but cannot seem to find a lot of information regarding its implementation.
The following errors are the ones I'm stuck on:
> dat.polr <- polr(as.factor(relevance)~allterms+idf.title, data=dat.one)
Warning message:
In polr(as.factor(relevance) ~ allterms + idf.title + idf.desc + :
design appears to be rank-deficient, so dropping some coefs
> dat.pred <- predict(dat.polr,dat.test,type="class")
Error in X %*% object$coefficients : non-conformable arguments
I want to train my model to guess the relevance of a new dataset. dat.one is the dataset I'm using to train the data, dat.test is the dataset I'm using to test the data. I believe that the predict variable's error is caused by the warning in polr. However, I have no clue how to resolve this. Any help would be appreciated :)
I'm trying to use the gamsel R package to fit a sparse generalized additive model, and I can't seem to get it to work on real data. When I run on synthetic data as described in the package documentation, everything works well:
library(gamsel)
data=gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
gamsel.out=gamsel(X,y,bases=bases)
But when I run on real data, I get the following error:
library(gamsel)
X = as.matrix(read.csv("X.csv"),header=FALSE)
y = as.matrix(read.csv("y.csv"),header=FALSE)
gam_fit = gamsel(X,y)
Error in if (abs((df - current.df)/df) < 1e-04 | iterations == 1)
return(list(lambda = lambda, : missing value where TRUE/FALSE
needed
You can access sample data files that will reproduce this result here. Any thoughts about how to fix this error?
I'm building a segmented regression model using R's Segmented package.
I was able to create the model but have trouble using the predict.segmented function. It always throws an error saying "subscript out of bounds"
This is the exact error message:
Error in newdata[[nameZ[i]]] : subscript out of bounds
Traceback just gives this:
1: predict.segmented(seg_model, xtest)
I created a simple case that gives the same error:
require(segmented)
x = c(1:90, 991:1000)
y = c((x[1:10]/2), (x[11:100]*2))
lm_model = lm(y~x)
seg_model = segmented(lm_model, seg.Z=~x, psi=list(x=NA),
control=seg.control(display=FALSE, K=1, random=TRUE))
xtest = c(1:1000)
predict.segmented(seg_model, xtest)
I am starting to think this could be a bug. I'm new to R and not sure how to debug this either. Any help is appreciated!
You are using predict.segemented incorrectly. Like nearly all the predict() functions, your newdata parameter should be a data.frame, not a vector. Also, it needs to have names that match the variables used in your regression. Try
predict.segmented(seg_model, data.frame(x=xtest))
instead. When using a function for the first time, be sure the read the help page (?predict.segmented) to know what the function expects for each of the parameters.