similar to: package quantreg behaviour in weights in function rq,

I've searched the Web with Google and do not find what might cause this particular error from an invocation of cenboxplot: cenboxplot(cu.t$quant, cu.t$ceneq1, cu.t$era, range=1.5, main='Total Recoverable Copper', ylab='Concentration (mg/L)', xlab='Time Period') Error in if (n > 0) (1L:n - a)/(n + 1 - 2 * a) else numeric() : argument is of length zero I do

Dear all R-users, I wanted to fit a Garch(1,1) model to a dataset by: >garch1 = garch(na.omit(dat)) But I got a warning message while executing, which is: >Warning message: >NaNs produced in: sqrt(pred$e) The garch parameters that I got are: > garch1 Call: garch(x = na.omit(dat)) Coefficient(s): a0 a1 b1 1.212e-04 1.001e+00 1.111e-14 Can any one

Hi, I am new to time-dependent Cox model to estimate time dependent hazard ratios. Let me use aml dataset from survival package: > aml3<-survSplit(aml2,cut=c(5,10,20),end="time",start="start", event="status",episode="i") If I want to esimate hazard ratio for each of the time intervals 0-5, 5-10, 10-20 and >=20, would the following calculate

Dear all, I am using the bigglm package to fit a few GLM's to a large dataset (3 million rows, 6 columns). While trying to fit a Poisson GLM I noticed that the coefficient estimates were very different from what I obtained when estimating the model on a smaller dataset using glm(), I wrote a very basic toy example to compare the results of bigglm() against a glm() call. Consider the

Dear Thomas, The head of my dataset > head(wsuv) parcel sp time censo treatment species 1 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 2 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 3 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 4 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1

Dear R helpers, I know R language at a preliminary level. This is my first post to this R forum. I have recently learned the use of function and have been successful in writing few on my own. However I am not able to figure out how to apply the function to multiple sets of data. # MY QUERY Suppose I am having following data.frame df = data.frame(k = c(1:8), ratings = c("A",

Hi Sir, Thanks for making package available to us. I am facing few problems if you can give some hints: Problem-1: The model summary and residual deviance matched (in the mail below) but I didn't understand why AIC is still different. > AIC(m1) [1] 532965 > AIC(m1big_longer) [1] 101442.9 Problem-2: chunksize argument is there in bigglm but not in biglm, consequently,

Dear R-help list members, I am hoping to get you help in reproducing a problem I am having That is only reproducible on a large-memory machine. Whenever I run the following lines, get a segfault listed below: *** caught segfault *** address 0x7f092cc46e40, cause 'invalid permissions' Traceback: 1: La.svd(x, nu, nv) 2: svd(X) 3: ginv(bigmatrix) Here is the code that I run:

HI, GUYS, I used the following codes to run SVM and get prediction on new data set hh. dim(all_h) [1] 2034 24 dim(hh) # it contains all the variables besides the variables in all_h data set. [1] 640 415 require(e1071) svm.tune<-tune(svm, as.factor(out) ~ ., data=all_h, ranges=list(gamma=2^(-5:5), cost=2^(-5:5)))# find the best parameters. bestg<-svm.tune$best.parameters[[1]]

Hi, I'm analyzing my data using GEE, which looks like below: > interact <- geeglm(L ~ O + A + O:A, + data = data1, id = id, + family = binomial, corstr = "ar1") > summary(interact) Call: geeglm(formula = lateral ~ ontask + attachment + ontask:attachment, family = binomial, data = firstgroupnowalking, id = id, corstr = "ar1") Coefficients:

Hi all, I wanted to have a seasonality study like whether a particular month has significant effect as compared to others. Here is my data : 0.10499 0 0 1 0 0 0 0 0 0 0 0 0.00259 0 0 0 1 0 0 0 0 0 0 0 -0.06015 0 0 0 0 1 0 0 0 0 0 0 0.10721 0 0 0 0 0 1 0 0 0 0 0 0.03597 0 0 0 0 0 0 1 0 0 0 0 0.10584 0 0 0 0 0 0 0 1 0 0 0 0.02063 0 0 0 0 0 0 0 0 1 0 0 -0.03509 0 0 0 0 0 0 0 0 0 1 0 -0.03485 0 0 0

Dear Colleague I am trying to figure out how to use R to do OLS restricted VECM regression. However, there are some notation I cannot understand. Please tell me what is 'ect', 'sd' and 'LRM.dl1 in the following practice: #OLS retricted VECM regression data(denmark) sjd <- denmark[, c("LRM", "LRY", "IBO", "IDE")] sjd.vecm<-

Hi, I am new in statistics and i manage to make the linear model analysis but i have some difficulties in explaining the results. Can someone help me explalinig the output of my linear model analysis ? My data are with 2 variables habitat (e,s) and treatment (a,c,p) with multiple trials within. Thank you in advance Call: lm(formula = a$wild ~ a$habitat/a$treatment/a$trial) Residuals: Min

I have fitted a number of models with receipt of social assictance (toim1) during a year (values 0 or 1) with a number of covariates. The data include sampling weights which I use in the models. Using the exact same data, glm() under 1.0.1 and 1.1.0 give different results in many (but not all) of the models. I have re-installed 1.0.1 to check this and I found now mention in the NEWS file that

Hi, I ran a small data set from a factorial experiment through R, Minitab and JMP... the result from R is significantly different from what Minitab or JMP give... The data set is at the following link: http://www.personal.psu.edu/nug107/Uploads/2x3_16repsANOVA.txt The first 5 columns are the factors and the next three are responses. In particular, for the response beta11MSE, two of the

Hello Again R Folks: I?m trying to clean up some code. Suppose I have an object like this: > str(test) List of 2 $ G:List of 2 ..$ cls:'data.frame': 101 obs. of 2 variables: .. ..$ V1: num [1:101] -0.0019 -0.0019 -0.00189 -0.00188 -0.00186 ... .. ..$ V2: num [1:101] 0.000206 0.000247 0.000288 0.000329 0.000371 ... ..$ rob:'data.frame': 101 obs. of 2

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

I have a situation where no params are being sent when the form is submitted. After the rendering this is the html that I get... <form action="/stats/new" method="post"> Height: <input id="stat_height_ft" name="stat[height_ft]" size="5" type="text" />ft </p> Weight: <input id="stat_weight_lbs"

I'll try to be more clear, but will have to give much more info. Here we go.... As part of my Masters in Public Health I writing a dissertation reviewing the mortality trend of internally displaced persons in camp settings in Sub Saharan Africa. I have 50 surveys with - crude mortality rate (cmr), CMR lower confidence interval(cmrlci), CMR upper confidence interval(cmruci), - recall period

Hello, I have a big dataframe with *NO* na's (9 columns, 293380 rows). # doing memory.limit(size = 1000000000) cor(x) #gives Error in cor(x) : missing observations in cov/cor In addition: Warning message: NAs introduced by coercion #I found the obvious workaround: COR <- matrix(rep(0, 81),9,9) for (i in 1:9) for (j in 1:9) {if (i>j) COR[i,j] <- cor (x[,i],x[,j])} #which works fine,