similar to: writing lm summary to file?

I'd like to fit a GLM to each of a number of subsets of some data. The `family' argument to `lmList' (in lme4) has given me cause for optimism, but so far I've only been able to achieve linear model fits. For example > df <- data.frame(gp = gp.temp <- factor(rep(1:3, each = 100)), x = x.temp <- rnorm(300), y = rpois(300, exp((-1:1)[gp.temp] + x.temp))) Then a call to

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

How do we get 2-tailed p-values for the rlm summary? I'm using the following: > fit <- rlm(oatRT ~ oatoacData$erp, psi=psi.bisquare, maxit=100, na.action='na.omit') > fitsum <- summary(fit, cor=F) > print(fitsum) Call: rlm(formula = oatRT ~ oatoacData$erp, psi = psi.bisquare, maxit = 100, na.action = "na.omit") Residuals: Min 1Q Median

Dear All, I am working on a large matrix of dimension 20000x700 say 'mat'. I have calculated pearson correlation for the rows of the matrix and their p-values using rcorr function in library Hmisc. Now I wish to filter out those pairs who's PCC value is above 0.8 cut off and p-value is less than 0.05. >library(Hmisc) >mat_cor=rcorr(t(mat),type="pearson")

Hello all! I have a problem to extract values greater that for example 1820. I try this code: x[x[,1]>1820,]->x1 Please help me! Thank you! The data structure is: structure(c(2.576, 1.728, 3.434, 2.187, 1.928, 1.886, 1.2425, 1.23, 1.075, 1.1785, 1.186, 1.165, 1.732, 1.517, 1.4095, 1.074, 1.618, 1.677, 1.845, 1.594, 1.6655, 1.1605, 1.425, 1.099, 1.007, 1.1795, 1.3855, 1.4065, 1.138, 1.514,

Hi everybody, I have a huge longitude and latitude data. I have used raster package to get the since of its global distribution. But I wish to display the information using a few points on the world map. The data is of the form: 95.2156 0.8312 -65.3236 -3.3851 -65.2364 -3.2696 -65.2349 -3.2679 164.7025 -17.6404 148.8214 -4.8285 -67.6568 2.6477 -73.4833 -0.2350 40.9587 -16.8655 -61.6474 8.1179

Hi, I'm playing around with stepwise regression, using the step function, and believe I have found a bug (or at least, a strong case for improvement): > ex <- data.frame(y=rnorm(100),x=rnorm(100)) > l <- lm(y ~ x, data=ex) > step(l) [output is correct] > rm(ex) > step(l) Start: AIC=11.79 y ~ x Df Sum of Sq RSS AIC - x 1 0.120 108.221 9.900

I am having trouble understanding results I'm getting back from coxph doing a recurrent event analysis. I've included the model below and the summary. In some cases, with minor variations, the Robust variance and Wald tests are significant, but the individual covariates may or may not be significant. My main question is: If Wald and robust tests both take into account the

This command cdmoutcome<- glm(log(value)~factor(year) > +log(gdppcpppconst)+log(gdppcpppconstAII) > +log(co2eemisspc)+log(co2eemisspcAII) > +log(dist) > +fdiboth > +odapartnertohost > +corrupt > +log(infraindex) > +litrate > +africa >

I'm doing some chi-square tests, and I recall some arbitrary rule that says each band must have at least 5 events in order for the test to be meaningful. Is there some way to do the banding automagically in R ? For instance, in the following survdiff, I'm trying to see if ADL affects survival. But when ADL=3,5 and 6, the number observed is too little. Anyway for me to tell R how to group

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

Dear friends, I have written the following lines in R console wich already exist in pdf file systemfit: data( "GrunfeldGreene" ) library( "plm" ) GGPanel <- plm.data( GrunfeldGreene, c( "firm", "year" ) ) greeneSur <- systemfit( invest ~ value + capital, method = "SUR", + data = GGPanel ) greenSur I have obtained the following incomplete

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

Hi, I'm using RF to simulate a correlated variable with GaussRF set.seed=1 GaussRF(sim.kfinegrid, grid=F, model="exponential", param=c(0,0.5,0,0.2)) However when I simulate again using the same random seed I get different results. > set.seed=1 > summary(GaussRF(sim.kfinegrid, grid=F, model="exponential", param=c(0,0.5,0,0.2))) Min. 1st Qu. Median Mean 3rd

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728

Hello, I'm attempting to analyze a split-split plot model, currently using lmer. My design is similar to that used in Casella's Experimental Designs Ozone Example (example 5.7, p 197), but I have been unable to find any coding help for split-split plot models through searches of several textbooks and the R list serve. I have three treatments of interest (A,B,C), each with 2 treatment

Hi, I'm having trouble using the constrOptim function to generate the 9-component vector argmin of the function ELfsds: ELfsds <- function(pvechat){ LG=0 for(i in 1:9){ LG=LG+log(pvechat[i]) } return(-LG) } with accompanying gradient function: gradfunc <- function(thetavec){ g=1/(9*thetavec) return(g) } The constraints on the optimization problem are: 1 - components of

Hi , I know this question has been asked twice in the past but to my knowldege, it still hasn't been solved. I am doing a zero inflated binomial model using the VGAM package, I need to obtain p values for my Tvalues in the vglm output. code is as follows > mod2=vglm(dmat~Season+Diel+Tidal.phase+Tidal.cycle,zibinomial, data=mp1) > summary(mod2) Call: vglm(formula = dmat ~ Season +

Dear all, I'm new to frailty model. I have a question on the output from 'survival' pack. Below is the output. What does gamma1,2,3 refer to? How do I calculate joint hazard function or marginal hazard function using info below? Many thanks! Call: coxph(formula = surv ~ as.factor(tibia) + frailty(as.factor(bdcat)), data = try) n=877 (1 observation deleted due to missingness)

Hi every one, I am using the package 'drc' to model root elongation using dose response data. I don't know which model I should use. Though I don't know which model I should use, I tried the following codes given below. But it produced the error messages.Can any one tell me the code in 'drc' package to find out the EC (Effective Concentration) values and Confidence