similar to: NA as a result of using GLM

A time series text with a title that seems designed to hide its wide scope is: Forecasting with Exponential Smoothing The State Space Approach Hyndman, R.J., Koehler, A.B., Ord, J.K., Snyder, R.D. Springer 2009. This book is actually an excellent overview of time series theory, ARIMA as well as state space. It is of course, in part, a manual for the forecast and other packages in what has been

Hello list, I am trying to apply the paired t.test between diseased and not diseased patients to identify genes that are more expressed in the one situation under the other. In order to retrieve the genes that are more expressed in the positive disease state I do: p.values<-c() for(i in 1:length(Significant[,1])){ p.values[i]<-try(t.test(positive[i,],negative[i,],alternative

Hello all! I have the following problem with the %in% command: 1) I have a data frame that consists of functions (rows) and genes (columns). The whole has been loaded with the "read.delim" command because of gene-duplications between the different rows. 2) Now, there is another data frame that contains all the genes (only the genes and without duplicates) from all the functions of

Hi, maybe someone can help me with this: I have a matrix of genes and values: GeneName Value Abc1 10 Abc2 11 Bbc1 -5 Bbc31 2 Ccd 5 Ccd -2 Ccd 7 Dda 5 Dda 10 ..... ..... Zzz3 -1 I would like to

Dear R-Help List, I'm trying to simulate data from a conditional distribution, and haven't been able to modify my existing code to do so. I searched the archives, but didn't find any previous post that matched my question. n=10000 pop = data.frame(W1 = rbinom(n, 1, .2), W2 = runif(n, min = 3, max = 8), W3 = rnorm(n, mean=0, sd=2)) pop = transform(pop, A = rbinom(n, 1,

Hi all! My question is why, and what I can do about that I sometimes, but not always, get warning-messages like nlminb returned message singular convergence (7) in: LMEopt(x = mer, value = cv) or IRLS iterations for PQL did not converge when trying to fit a model looking like this: lmer<-(cbind(Diseased,Healthy)~Fungus+(1|Family)+(1|Fungus:Family), family="binomial") to four

Dear R-list members, I would like to pose a question about the use and results of the glm() function for logistic regression calculations. The question is based on an example provided on p. 229 in P. Dalgaard, Introductory Statistics with R, 2nd. edition, Springer, 2008. By means of this example, I was trying to practice the different ways of entering data in glm(). In his book, Dalgaard

Thanks for the advice. My question is more on how to do this? Let me use a biology gene analysis example to illustrate: In biology, there are always some house keeping genes which differ little even at pathological conditions. We know that at different batches, there are external factors affect the measurements. For example, overall signal intensity might be different due to lab reagents. A

Hi R help list I'm simulating logistic regression data with a specified odds ratio (beta) and have a problem/unexpected behaviour that occurs. The datasets includes a lognormal exposure and diseased and healthy subjects. Here is my loop: ors <- vector() for(i in 1:200){ # First, I create a vector with a lognormally distributed exposure: n <- 10000 # number of study subjects

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

My laboratory is measuring the abundance of various proteins in the blood from either healthy individuals or from individuals with various diseases. I would like to determine which proteins, if any, have significantly different abundances between the healthy and diseased individuals. Currently, one of my colleagues is performing an ANOVA on each protein with MS Excel. I would like to analyze

Dear RUsers, I am new to R. I am learning how to use R. I am a PC user and run R on windows. I would appreciate if some one could guide me on a few questions I have: 1) I have 4 cel files (2 replicates for NORM and Disease resp). I have been able to run siggenes on this dataset where I have 4 labels in the class file groupsnhi.cl op-> (0,0,1,1) and my data has been read into datrmanhi after

Dear all, i searched for some classification methods and I have no glue if i took the right once. My problem: I have a matrix with 17000 rows and 33 colums (genes and patients). The patients are grouped into 3 diseases. No I want to classify the patients and for sure i want to know which rows are more helpful for the classification than others. I tried SVM and random forest. Do you think this

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

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

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

Dear Consultant I've done linear regression successfully on R a few times before. But this time it keeps telling me:- "Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases" The model is:- fm1 <- lm(TS.CM ~ AGE + SEX + HFE.Y.01 + TFC2B.01 + HFE.Y.01*TFC2B.01, data = IRONresults, subset = DIAG2.1D == 0) summary (fm1) TS.CM is a

Help! I am unsure if I can analyze data from the following experiment. Fish were placed in a tank at (t=0) Measurements of Carbon Dioxide were taken each day for 120 days (t=0,...120) A few fish were then randomly pulled out of the tank at different days, killed and examined for the presence of a disease T= time of examination in days from start (i.e. 85th day), E = 0/1 for nonevent/event My

Dear Bill, Thanks for pointing this difference out -- I was unaware of it. I think that the difference occurs in model.matrix.default(), which coerces character variables but not logical variables to factors. Later it treats both factors and logical variables as "factors" in that it applies contrasts to both, but unused factor levels are dropped while an unused logical level is not. I