similar to: general question about dropping terms of glm model fits

hello list! I'm sorry, I just stumbled over this strange behaviour (at least I am not able to explain the behaviour, therefore I assume it to be a strange behaviour): attach(water) # I know, this is not recommended names(water[3:10]) [1] "temp" "pH" "DO" "BOD" "COD" "no3" "no2" "po4" for (i in

dear list! I KNOW this has been answered a million times before. But, as some might remember from their "freelance" times as a statistic consultant, sometimes you're blinded by the facts. I KNOW I have seen this problem solved, but due to the mental blockade i have been suffering from the last 3 hours, I can neither find the answer on the mailing list nor on google. I wrote a

Hi R forum, Each row of my data (below) show a new contact event between animals. In order to ultimately look at the patterns of intervals between contacts, I need to calculate a contact end time. The contact starts at the date and time shown in V4 and V5, and lasts for the duration shown IN SECONDS in V6: > data2<- read.csv(file=file.choose(), header=F, sep=" ") >

dear listers, I use gam (from mgcv) for evaluation of shape and strength of relationships between a response variable and several predictors. How can I interpret the 'F' values viven in the GAM summary? Is it appropriate to treat them in a similar manner as the T-statistics in a linear model, i.e. larger values mean that this variable has a stronger impact than a variable with smaller F?

Hello, I'm a beginner with R and it's the first time I'm using the R-help list... I hope I'm in the right place, if not: Sorry!! I need to do non linear regressions on a data set which columns are: "river.name" "Portata" "PTG.P" "PO4.P" "NT.N" "NH4.N" "NO3.N" "BOD5" "SiO2"

Hello I want to batch job the calculation of many GLM-models, extract some values and store them in a file. Almost everything in the script below works (read file, extract values and write them to file) except I fail in indexing the GLM with the modelstructure it should run. Running GLM's conventionally is no problem. Conventionally a GLM is calculated as:

Hello. I am wondering why I am getting NA for all in cors=sapply(pred,cor,y=resp). I suppose that each column in pred has NAs in them. Is there some way to fix this? Thanks > str(pred) 'data.frame':   200 obs. of  13 variables:  $ mnO2: num  9.8 8 11.4 4.8 9 13.1 10.3 10.6 3.4 9.9 ...  $ Cl  : num  60.8 57.8 40 77.4 55.4 ...  $ NO3 : num  6.24 1.29 5.33 2.3 10.42 ...  $ NH4 : num  578

Hi Second question in a day, i'm beginnning to feel incompetent... This time i'm having a weird problem, i'm importing the next data base: >car<-read.csv2("Historicos.csv") 'data.frame': 1818 obs. of 6 variables: $ Dpto : Factor w/ 11 levels "ANTIOQUIA","ATL?NTICO",..: 2 2 2 2 2 1 1 1 1 5 ... $ Rio : Factor w/ 43 levels

Dear R-users, I am a newbie to this site and a relative new-comer to S/R, so please tread lightly, for you tread... There have been several posting relating to problems with augPred() from the nlme library. Here is a "fix" for one of these problems which may lie at the root of others. In my case the problem with augPred() lay in gsummary(), which augPred() uses, causing it to fail.

Greetings, I am new to R and am having trouble with parsing a file with the following characteristics: * Individual results for a single sample are written to multiple lines. * First 16 columns are constant from sample to sample. * Remaining 10 need to be matched up (cross-tabbed?) o (the exact contents for the remaining 10 vary from sample to sample, as indicated in

I'm trying to run an SEM, but I keep getting the following error message. In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. I have 4 latent variables (plant, AMF, abiotic, and soilAgg) with 2 or 4 indicator variables for each latent variable.My model is specified as: >

Dear all, I'm trying to fit a model on ecological data in which I have measured a few biotic and abiotic factors over the course of a few days in several individuals. Specifically, I'm interested in modelling y ~ x1, with x2, x3, and 'factor' as independent variables. Because data suggests both slope and intercept (for y ~x1) might differ between individuals, I'd want to

Hi - I am trying to find and describe missing data in a time series. For instance, in the library openair, there is a data frame called "mydata": library(openair) head(mydata) date ws wd nox no2 o3 pm10 so2 co pm25 1 1998-01-01 00:00:00 0.60 280 285 39 1 29 4.7225 3.3725 NA 2 1998-01-01 01:00:00 2.16 230 NA NA NA 37 NA NA NA 3 1998-01-01 02:00:00

I've got a small problem. I have some observational data (environmental samples: abiotic explanatory variable and biological response) to which I've fitted both a multiple linear regression model and also a gam (mgcv) using smooths for each term. The gam clearly fits far better than the lm model based on AIC (difference in AIC ~ 8), in addition the adjusted R squared for the gam is

I have a data frame with 1 factor, one date, and 37 numeric values: str(waterchem) 'data.frame': 3525 obs. of 39 variables: site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1 1 1 1 ... $ sampdate : Date, format: "2007-12-12" "2008-03-15" ... $ CO3 : num 1 1 6.7 1 1 1 1 1 1 1 ... $ HCO3 : num 231 228 118 246

I tried the following command to produce boxplot of diagonals in pairs function: > pairs(t1.5,diag.panel=boxplot) Error in pairs.default(t1.5, diag.panel = boxplot) : The panel function made a new plot The graphics device draws two graphs, one with a rectangle at (1,1) in 7x7 layout and the other with boxplot(t1.5$wind) at (1,2). pairs function help file has a similar example with

Dear list, I am using R 2.1.1 on a Fedora 3 Linux, 32 bit PC. If I compute the aggregated mean and the standard deviation I get standard deviation values for factors where the mean was not computed. It seems to me that this is somehow related to the NA values. But I don't quite understand what is going wrong? Could it be related to the data import already? Some of the imported data got the

On Sun, 2009-07-26 at 13:17 +0200, Poersching wrote: > stvienna wiener schrieb: > > Hi all, > > > > I am plotting a financial time series, but I need a more detailed X-Axis. > > > > Example: > > x <- zoo(rnorm(1:6000), as.Date("1992-11-11")+c(1:6000)) > > plot(x) > > > > The X-Axis is labeled "1995", "2000"

Greetings, I would like to use a data.frame with strings to feed the expression() in the title of a plot. The way I did this is: molecules <-data.frame(name=c("o3","no","no2"),expression=c("quote(O[3])","quote(NO)","quote(NO[2])")) for (mol in c(5,7,9)) { plot(x, y, type="b",

Hello, Is tail call optimization performed if the ret instruction does not actually follow the tail call? Not according to the documentation, but in examples it seems to work. For example, consider the following definition of Ackerman's function in ML (it has two tail calls): let rec ack x y = if x <= 0 then y + 1 else if y <= 0 then ack (x - 1) 1 else ack (x - 1) (ack x (y -