similar to: Selecting/Accessing the last vector in a list of a list of data.frames

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

Hello, I am a new R user and I am trying to estimate some generalized linear models (glm). I am trying to compare a model with a gaussian distribution and an identity link function, and a poisson model with a log link function. My problem is that while the gaussian model has significantly lower (i.e. "better") AIC (Akaike Information Criterion) most of the coefficients are not

I'll address two issues. The first is today's error message and the other is change management for contributed packages on CRAN. TODAY'S ERROR MESSAGE I switched from the 0.995-1 versions of lme4 and Matrix to those referenced in the subject line this afternoon. Prior to using these packages on anything else, I applied them to code that 'worked' (provided numerical results

Dear friends, I installed NUT and followed all suggested procedures to verify if my ups is supported. No driver recognized my ups, and the genericups tests get wrong. Here I send some informations about it: The ups model is APC BZ1200-BR I use Ubuntu, and the lsusb results Bus 003 Device 003: ID 0403:6001 Future Technology Devices International, Ltd FT232 USB-Serial (UART) IC for ups. The

#is there a way to get NA in the table of descriptive statistics instead of the function stopping Thank you in advance #data x.f <- structure(list(Site = structure(c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("BC", "HC", "RM119", "RM148", "RM179", "RM185",

Dear all, I am using the nlsLM function to fit a Lorentzian function to my experimental data. The LM algorithm should allow to specify limits, but the upper limit appears not to work as expected in my code. The parameter 'w', which is peak width at half maximuim always hits the upper limit if the limit is specified. I would expect the value to be in-between the upper and lower limit with

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Hi all, If we wanted to study the effect on the mean of the hourly data based on the hours within a day... and we wanted to do Anova analysis... We have two choices: Please see below: Why are these two approaches giving very different p-values? And which one shall I use? Thanks a lot! 1. treating the hours as double/floating numbers: anova(lm(hourlydata~as.double(hours_factors))) Df Sum

Hi, Here is the summary-output of the Coxph-model I used (the output is based on the best final model i.e. all significant explanatory variables and their interactions are included): coxph(formula = Y ~ LT + Food + Temp2 + LT:Food + LT:Temp2 + Food:Temp2 + LT:Food:Temp2) n= 555 coef exp(coef) se(coef) z

Sorry for the lack of reproducible data, but this seems to be a problem inherent to my dataset and I can't figure out where the issue is. I have several data frames set up as a time series with identical POSIXct date formats. If I keep the original data in data frame format and merge them using base merge- everything is perfect and everyone is happy. If I transform the data frames to zoo

Target: FreeBSD 6.2-STABLE on i386 autoconf says: configure:2122: checking build system type configure:2140: result: i386-unknown-freebsd6.2 [...] configure:2721: gcc -v >&5 Using built-in specs. Configured with: FreeBSD/i386 system compiler Thread model: posix gcc version 3.4.6 [FreeBSD] 20060305 [...] objdir != srcdir, for both llvm and gcc. Release build. llvm-gcc 4.2 from source.

Hi friends, I need to generate a list, that should contain the quantile value of one column in a data frame. while I am compiling this one i am facing one bug,, Really I cannot find out that where the problem has occurred? Could any one help me to come out from this bug?? Here is the code, data <- lapply(comb.data$zFE, function(x) aggregate(x,

Thanks, that was a really helpful suggestion. If you're curious- here are some of the high cost areas: ===-------------------------------------------------------------------------=== DWARF Emission ===-------------------------------------------------------------------------=== Total Execution Time: 2.0117 seconds (2.0185 wall clock) ---User Time---

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model =

Hello again, Jeremy and other Samba experts, I'm sorry to be such a pain in your neck(s), but I still need your help in looking for help trying to find out why SMB2/3.1.1 in Samba 4.6.5 performs so much worse than SMB/1.5 in Samba 3.6.15 in scenarios with a huge number of small files. As requested by Jeremy, I have done wireshark "pcapng" captures of the four scenarios as

Quick question about the usage of glht. I'm working with a data set from an experiment where the response is bounded at 0 whose variance increases with the mean, and is continuous. A Gamma error distribution with a log link seemed like the logical choice, and so I've modeled it as such. However, when I use glht to look for differences between groups, I get significant

Hello, I'd like to create a model with a factor-type response variable. This is an example: > mydata <- data.frame(factor_var = as.factor(c(rep('one', 100), rep('two', 100), rep('three', 100))), real_var = c(rnorm(150), rnorm(150) + 5)) > summary(mydata) factor_var real_var one :100 Min. :-2.742877 three:100 1st Qu.:-0.009493 two

Dear Sir, First of all Happy Holidays!,... I am writing to you because I am a bit confused about ARCH estimation. Is there a way to find what garch() exactly does, without the need of reading the source code (because I cannot understand it)? In Eviews (the results at the end) I am getting different results than in R (for those that have the program I do: Quick -> Estimage Equation ->

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