similar to: OT: (quasi-?) separation in a logistic GLM

Hi all, I have run a (glm) analysis where the dependent variable is the gender (family=binomial) and the predictors are percentages. I get a warning saying "fitted probabilities numerically 0 or 1 occurred" that is indicating that quasi-separation or separation is occurring. This makes sense given that one of these predictors have a very influential effect that is depending on a

Hi, I am currently working on some data and feel that NMDS would return an excellent result. With my current data set however I have been experiencing some problems and cannot carry out metaMDS. I have tried with a few smaller data sets which I created for practice sake and this has worked fine. I think it is the set up of my data set that is causing me trouble. I have 18 columns and 18 rows,

H ello R-experts, I want to do ordination plots using vegan metaMDS. I have a where many cells have zero values. Data structure: X[1:10,1:14] Height.1 Height.2 Height.3 Height.4 Height.5 Height.6 Height.7 Height.8 Height.9 Height.10 Height.11 Height.12 Height.13 D30I1A 46 0 0 0 0 0 0 0 0 0 39 0 98 D30I1B

Dear all, I am encountering some issues with my data and need some help. I am trying to run glm analysis with a presence/absence variable as response variable and several explanatory variable (time, location, presence/absence data, abundance data). First I tried to use the glm() function, however I was having 2 warnings concerning glm.fit () : # 1: glm.fit: algorithm did not converge # 2:

i'm working on some distance matrices and i was wondering if there is a way to export the matrices from R to excel. OG thanks [[alternative HTML version deleted]]

Hello, everybody, I hope somebody could help me with a dist() function. I have a data frame of size 2*4087 (col*row), where col corresponds to the treatment and rows are species, values are Hellinger distances, I should reconstruct a distance matrix with a dist() function. I know that "euclidean" method should be used. When I type: dist(dframe,"euclidean") it gives me a

Hi, I need to build ROC curve in R, can you please provide data steps / code or guide me through it. Thanks and Regards Rithesh M Mohan [[alternative HTML version deleted]]

Dear List, With regard to the question I previously raised, here is the result I obtained right now, brglm() does help, but there are two situations: 1) Classifiers with extremely high AIC (over 200), no perfect separation, coefficients converge. in this case, using brglm() does help! It stabilize the AIC, and the classification power is better. Code and output: (need to install package:

Hello everybody. I am trying to cluster circular data (data points which are angles), thus i can not use the "dist" function in "mclust" to generate my distance matrix, I am using the function " Dij = 0.5*( 1 - cos(theta_i - theta_j)). The thing is "hclust" will not accept this distance matrix, i tried to put it in a data frame, but again i get an error message

Before re-inventing the wheel I would like to ask: does anyone know about an optimizer in R which can reliably identify which value of X (Xopt) leads to Y (Yopt) closest to Ytarget in Y <- MonotoneStepFun(X) optionally with the restriction that Yopt <= Ytarget (at least if any Y <= Ytarget, otherwise any Yopt > Ytarget would be the preferred answer) If none is known, I will write

I''m running brglm with binomial loguistic regression. The perhaps multicollinearity-related feature(s) are: (1) the k IVs are all binary categorical, coded as 0 or 1; (2) each row of the IVs contains exactly C (< k) 1''s; (3) k IVs, there are n * k unique rows; (4) when brglm is run, at least 1 IV is reported as involving a singularity. I''ve tried recoding the n

I ran logistic regression models last week using glm (...,family=binomial) and got a set of results. Since then I have loaded the Epi package for ROC analysis. Now when I run those same models I get completely different results, with most being: Warning message: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,

Hello, In the documentation for agnes in the package 'cluster', it says that NAs are allowed, and sure enough it works for a small example like : > m <- matrix(c( 1, 1, 1, 2, 1, NA, 1, 1, 1, 2, 2, 2), nrow = 3, byrow = TRUE) > agnes(m) Call: agnes(x = m) Agglomerative coefficient: 0.1614168 Order of objects: [1] 1 2 3 Height (summary): Min. 1st Qu. Median Mean 3rd

UseRs, I have a dataframe with 2547 rows and several hundred columns in R 3.1.3. I am trying to run a small logistic regression with a subset of the data. know_fin ~ comp_grp2+age+gender+education+employment+income+ideol+home_lot+home+county > str(knowf3) 'data.frame': 2033 obs. of 18 variables: $ userid : Factor w/ 2542 levels

Michael, Thank you for the suggestion. I will take your advice and look more critically at the covariates. John On 7/27/2017 8:08 AM, Michael Friendly wrote: > Rather than go to a penalized GLM, you might be better off > investigating the sources of quasi-perfect separation and simplifying > the model to avoid or reduce it. In your data set you have several > factors with large

Hi all, As you can see from below, the result is strange... I would imagined that the bb result should be much higher and close to 1, any way to improve the fit? Any other classification methods? Thank you! data=data.frame(y=rep(c(0, 1), times=100), x=1:200) aa=glm(y~x, data=data, family=binomial(link="logit")) newdata=data.frame(x=6, y=100) bb=predict(aa, newdata=newdata,

Rather than go to a penalized GLM, you might be better off investigating the sources of quasi-perfect separation and simplifying the model to avoid or reduce it. In your data set you have several factors with large number of levels, making the data sparse for all their combinations. Like multicolinearity, near perfect separation is a data problem, and is often better solved by careful

Hello R-user community! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) Presently I try to run the function metaMDS (vegan) using an existing dissimilarity-matrix. As I would like to start with this matrix I thought I could just give the matrix using the x= -argument Test<-metaMDS(x=Dist.Gower) Fehler in inherits(comm, "dist") :

Dear list useRs, I have to generate a random set of coordinates (x,y) in [-1 ; 1]^2 for say, N points. At each of these points is drawn a circle (later on, an ellipse) of random size, as in: > N <- 100 > > positions <- matrix(rnorm(2 * N, mean = 0 , sd= 0.5), nrow=N) > sizes<-rnorm(N, mean = 0 , sd= 1) > plot(positions,type="p",cex=sizes) My problem is to

The name conflicts in Epi and ROC packages (2 'ROC' functions are the problem) cause the following code to work once, but not twice: library(MASS); data(cats); x = cats[,2] y = ifelse(cats[,1]=='F',0,1) library(Epi); ROC(x,y,grid=0)$AUC library(ROC); AUC(rocdemo.sca(y, x, dxrule.sca)) What is the standard way of resolving name conflicts? Ask maintainers to resolve