similar to: Interpreting the Results of GLM

Hi, I've this dataframe: V1 V5 V6 1 MOD13Q1_2000049 0.1723 A1 2 MOD13Q1_2000049 0.1824 B1 3 MOD13Q1_2000049 0.1824 C1 4 MOD13Q1_2000049 0.1774 A2 5 MOD13Q1_2000049 0.1953 B2 6 MOD13Q1_2000049 0.1824 C2 7 MOD13Q1_2000065 0.1921 A1 8 MOD13Q1_2000065 0.1938 B1 9 MOD13Q1_2000065 0.2009 C1 10 MOD13Q1_2000065 0.2035 A2 11 MOD13Q1_2000065 0.2157 B2 12

Dear R-helpers, I'm stuck with a little problem that surely has an easy solution but I can't think of a way to solve it. I'd really appreciate any help you can offer me! I'll provide a small example. Given a dataframe data.txt that looks like this: ID freq Var Var_mean Ratio_mean Var_median Ratio_median Var_sum Ratio_min Var_max Ratio_max Var_min

Hi, R-users, data is a matrix like this AMR BS GE HR MO UK SP500 1974 -0.3505 -0.1154 -0.4246 -0.2107 -0.0758 0.2331 -0.2647 1975 0.7083 0.2472 0.3719 0.2227 0.0213 0.3569 0.3720 1976 0.7329 0.3665 0.2550 0.5815 0.1276 0.0781 0.2384 1977 -0.2034 -0.4271 -0.0490 -0.0938 0.0712 -0.2721 -0.0718 1978 0.1663 -0.0452 -0.0573 0.2751 0.1372 -0.1346

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

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm

Dear all, I have a data (bellow) and I want to make a correlation test with p-value structure(list(Name = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("CTJ", "PKR", "TTK"), class = "factor"), score = c(86.4371428571428, 89.7028571428572, 87.728, 89.99, 89.42, 85.6914285714286, 82.256,

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

Hi, My name is Jos?? Mar??a G??mez, and I am pretty new in R. Thus, I apologize deeply if my questions are extremmely na??ve.I have checked several available books and URL's, without finding any answer. I'm trying to fit Generalized Linear Mixed Models via PQL. Below I provide the structure of my data set. Year and Plot are random variables. Fate is the binomial dependent. I have severe

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 >

Dear R list, I've been attempting to interpret the results from a three-way ANOVA. I think I understand contrasts and the R defaults for these (treatment contrasts). My question is: what is the intercept in this test? As far as I can tell, its NOT the expected value of a point that belongs to the first level of all three explanatory factors (because there is only one point that satisfies these

Dear r-helpers, Following up on http://finzi.psych.upenn.edu/R/Rhelp02a/archive/ 81159.html where Douglas Bates gives a helpful application of lmer() to data(sleepstudy, package = 'lme4'), I need a bit more help in order to plot the correct confidence intervals of a designed experiment such as: > data(ratdrink, package = 'faraway') I follow the steps Douglas took in

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 was trying to update my wine package because synaptic said there was an update available and I got the following message W: Failed to fetch http://wine.budgetdedicated.com/apt/pool/main/w/wine/wine_1.1.18~winehq0~ubuntu~8.04-0ubuntu1_amd64.deb Could not connect to wine.budgetdedicated.com:80 (81.171.111.184). - connect (111 Connection refused) [IP: 81.171.111.184 80] I also can not connect

Dear all, I am using the ROCR library to compute the AUC and also the Hmisc library to compute the C-index of a predictor and a group variable. The results of AUC and C-index are similar and give a value of about 0.57. The Wilcoxon p-value is <0.001! Why the AUC is showing small value and the p-value is high significant? The AUC is based on Wilcoxon calculation? Many thanks, Lina

Running R version 2.6.1 under Gentoo Linux and using the fPortfolio package, I am having trouble specifying a sector constraint. One of the constraints to be imposed is that assets 1 and 2 together account for no more than 13.63% of the portfolio. My attempt at coding that constraint, "maxsumW[1:2Assets]=13.63" fails. The relevant section of my code file and the resulting error

I ran a negative binomial logit hurdle model and am now trying to plot the effects of a continuous predictor variable (the only variable in my model) on the count and zero component and the overall mean response. I'm confused because for some values, the predicted overall mean is higher than the mean of the non-zero counts (range of predicted overall means=2.2-11.0; range of non-zero count

I calculate an anova test in the following way: expdata<-read.table("/home/dorien/UA/meta-music/optimuse/optimuse1-build-desktop/results/results_processedCP", header=TRUE)

[code]>require("survival") > coxph(Surv(futime,fustat)~age + strata(rx),ovarian) Call: coxph(formula = Surv(futime, fustat) ~ age + strata(rx), data = ovarian) coef exp(coef) se(coef) z p age 0.137 1.15 0.0474 2.9 0.0038 Likelihood ratio test=12.7 on 1 df, p=0.000368 n= 26, number of events= 12 > coxph(Surv(futime,fustat)~age, ovarian, subset=rx==1)

I'm trying to use garchFit from fSeries, with Student or Skewed Student conditionnal distribution. Let's say that eps (vector) is my series of daily log-returns: data(EuStockMarkets) eps = diff(log(EuStockMarkets[,"CAC"])) library(fSeries) g = garchFit(series = eps, formula.var = ~garch(2,2), cond.dist = "dstd") s = g at fit$series All the coefficients are ok