similar to: puzzling results from logistic regression

Dear List, Apologies for this off-topic post but it is R-related in the sense that I am trying to understand what R is telling me with the data to hand. ROC curves have recently been used to determine a dissimilarity threshold for identifying whether two samples are from the same "type" or not. Given the bashing that ROC curves get whenever anyone asks about them on this list (and

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

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:

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:

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

Consider the following example (based on an example in Pat Altham's GLM notes) pyears <- scan() 18793 52407 10673 43248 5710 28612 2585 12663 1462 5317 deaths <- scan() 2 32 12 104 28 206 28 186 31 102 Smoke <- gl(2,1,10,labels=c("No","Yes")) Age <- gl(5,2,10,labels=c("35-44","45-54","55-64","65-74","75-84"),

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

Hi! I'm having some issues on both conceptual and technical levels for selecting the right combination of variables for this model I'm working on. The basic, all inclusive form looks like lm(mic ~ B * D * S * U * V * ICU) Where mic, U, V, and ICU are numeric values and B D and S are factors with about 16, 16 and 2 levels respectively. In short, there's a ton of actual explanatory

hello i have a mixed effect model which gives slope and intercept terms for 6 groups (diagnosis (3 levels) by risk group(2 levels)). the fixed part of the model is -- brain volume ~ Diagnosis + Risk Group + (Risk Group * age : Diagnosis) - 1 thus allowing risk group age/slope terms to vary within diagnosis and omitting a nonsignificant diagnosis by risk group intercept (age was centered)

Hi all, I hope that there might be some statistician out there to help me for a possible explanation for the following simple question. Y1~ lm(y~ t1 + t2 + t3 + t4 + t5,data=temp) # oridnary linear model library(gam) Y2~ gam(y~ lo(t1) +lo(t2) +lo(t3) +lo(t4) +lo(t5),data=temp) # additive model In the first model t1, t2 and t3 found to be significant,. However, in the second model (using

Hello, Sorry to be a bit longwinded, but I've struggled quite a bit with the following over the last few days. I've read all entries related to spatial autocorrelation in R help and haven't found what I'm after. If it's okay, I'm going to first describe my general understanding of the process by which a mixed model can account for correlated errors. If possible, please

Hi all, I am trying to play around with the randomForest function for classification. I know its performance is great. I am currently using the default options. It has many options. How do I further tweak the options so that I can make its performance even better? What are the options that are mostly used? Thanks a lot! M [[alternative HTML version deleted]]

Dear R people, I notice that the confidence intervals of a very small sample (e.g. n=6) derived from the one-sample wilcox.test are just the maximum and minimum values of the sample. This only occurs when the required confidence level is higher than 0.93. Example: > sample <- c(1.22, 0.89, 1.14, 0.98, 1.37, 1.06) > summary(sample) Min. 1st Qu. Median Mean 3rd Qu. Max.

Hi all, I am looking for recommendations/pointers about best report generator you think that are currently available? i.e. the package that can help turn console output into nice-looking neat report to send to bosses? thanks a lot! [[alternative HTML version deleted]]

Hi all, What parameter do I normally change in the rpart function? How do I set the "cp" option? Is there a way to read off error rate directly from the "rpart" function for training data; I imagine for testing data I have to apply a "predict", but for training data I guess the error count would be somewhere existing once the "rpart" function is

Dear R-helpers, I'm stuck with a little problem that surely has an easy solution but I can't think of a way to solve it. I'd really appreciate any help you can offer me! I'll provide a small example. Given a dataframe data.txt that looks like this: ID freq Var Var_mean Ratio_mean Var_median Ratio_median Var_sum Ratio_min Var_max Ratio_max Var_min

Is there a data/variable explorer in R? Hi all, I am inspecting a complex variable which has lists inside lists... Is there a data-explorer that can help me view the structure and content of such variables? Thank you! [[alternative HTML version deleted]]

head, tail and fix commands don't really work well if I have large matrix/array for which I would like to be able to scroll up and dow, left and right ... Could anybody please help me? Thanks [[alternative HTML version deleted]]

Dear R forum I have a data.frame df = data.frame(currency_type = c("EURO_o_n", "EURO_o_n", "EURO_1w", "EURO_1w", "USD_o_n", "USD_o_n", "USD_1w", "USD_1w"), rates = c(0.47, 0.475, 0.461, 0.464, 1.21, 1.19, 1.41, 1.43))   currency_type     rates 1      EURO_o_n   0.470 2      EURO_o_n   0.475 3       EURO_1w   0.461

Hi all, I tended to use rbind, or cbind to force a vector be be deemed as a column or row vector. This is very important if I want to do things like u' * A * u, where u' is a row vector and u is a column vector, regardless of what originall format the "u" is... I want to recast it to column vector or row vector... How can I do that?