similar to: time series questions?

## The following corrects what I believe to have been an error in my previous post: #### Two time series questions: I. FITTING TRANSFER FUNCTIONS WITH LAGS: Consider the following toy example: > dates <- paste(11:21, "/01/2005", sep="") > Dates <- as.Date(dates, "%d/%m/%Y") > set.seed(1) > DF <- data.frame(date=Dates, y=rnorm(11),

Dear R-helpers: It seems that I have a mental block. (Some say that it sits atop my shoulders.) For reasons too tedious to retell I have an R object: > input.line[7] [1] "-13.24, -11.24, -9.24, -7.24, -5.24, -3.24, -1.24, 0.76, 2.76, 4.76, 6.76, 8.76, 10.76, 12.76, 14.76, 16.76, 18.76, 20.76, 22.76, 24.76, 26.76, 28.76, 30.76, 32.76, 34.76, 36.76, 38.76, 40.76, 42.76, 44.76, "

Hi, I would consider the calculation of r-squared in the following to be a bug, but then, I've been wrong before. It seems that R looks to see if the model contains an intercept term, and if it does not, computes r-squared in a way I don't understand. To my mind, the following are two alternative parametrizations of the same model, and should yield the same r-squared. Any insight much

Hello, I am looking for more information about lazy loading introduced in R 2.0.0. Doing ?lazyLoad I got some and there is a 'see also' section that points to 'makeLazyLoading'... But I cannot reach this page. My problem is: I recompiled a library that uses a lot of functions from other libraries (of course I can give details if needed). I load it in my computer: library(svGUI),

Hi, I'm reading part of a table from postgres, so I'm getting a data frame. how can I extract the numerica values so I can operate on them. > res <- dbGetResult(mydata) > str(res) `data.frame': 5 obs. of 8 variables: $ cyx.1: num 0.149 -0.278 0.114 0.060 0.109 $ cyx.2: num 0.158 -0.070 0.063 0.149 0.150 $ cyx.3: num 0.052 -0.350 0.114 0.126 0.238

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

Hello R-Users,   I have one binary dependent variable and a set of independent variables (glm(formula,…,family=”binomial”) ) and I am using the function stepAIC (“MASS”) for choosing an optimal model. However I am not sure if stepAIC considers significance properties like Likelihood ratio test and Wald test (see example below).     > y <- rbinom(30,1,0.4) > x1 <- rnorm(30) > x2

Hi. I am currently trying to run some Spearman correlations, and have encountered two issues. 1) When using cor.test() with a variable that includes ties, I get the "Cannot compute exact p-values with ties" error. I have read that this function now uses an asymptotic formula that allows for ties, so do not understand why I am getting this error. (I am running version 2.4.0.) I

Hi, I have a dichotomous variable (Q1) whose answers are Yes or No. Also I have 2 categorical explanatory variables (V1 and V2) with two levels each. I used logistic regression to determine whether there is an effect of V1, V2 or an interaction between them. I used the R and SAS, just for the conference. It happens that there is disagreement about the effect of the explanatory variables

Hi, When I run the following Cox proportional hazards model on the Mayo clinic's PBC data set (given in the "survival" package), the regression coefficients do not agree with the results presented in Table 4.6.3 (p. 195) of Fleming & Harrington's book. library(survival) data(pbc) ans.cox <- coxph(Surv(time, status) ~ log(bili) + log(alb) + age + log(protime) +

I am working with a RCB 2x2x3 ANCOVA, and I have noticed a difference in the calculation of sum of squares in a Type III calculation. Anova output is a follows: > Anova(aov(MSOIL~Forest+Burn*Thin*Moisture+ROCK,data=env3l),type=3) Anova Table (Type III tests) Response: MSOIL Sum Sq Df F value Pr(>F) (Intercept) 22.3682 1 53.2141 3.499e-07 *** Forest

I want to do "moran's I test" in R language. I try to use "gearymoran" in Package "ade4","moran" in Package "spdep", and Moran.I in Package "ape". But I do not know how to do it because data format is different. My data: x y dbh 111.03 10.7 7 118.11 0.28 1.2 165.36 0.36 8.4

Hi there, Can anyone please help me because I am going to get crazy with the pbc data set. I just want to apply simple cox regression in the data set. I am a beginner in R but I don't think I am doing anything wrong. I have the book of Fleming and Harrington 1990. I perform cox regression by typing: out<- coxph(Surv(times/365,status)~log(bili)+log(proth)+edema+log(albumin)+age) out

The following patch adds ocfs support to blkid. --rusty # This is a BitKeeper generated patch for the following project: # Project Name: Ext2 filesystem utilities # This patch format is intended for GNU patch command version 2.5 or higher. # This patch includes the following deltas: # ChangeSet 1.1327 -> 1.1328 # lib/blkid/probe.c 1.15 -> 1.16 #

Hallo all, I have the following glm model: f1 <- as.formula(paste("factor(y.fondi)~", "flgsess + segmeta2 + udm + zona.geo + ultimo.prod.", "+flg.a2 + flg.d.na2 + flg.v2 + flg.cc2", " +(flg.a1 + flg.d.na1 + flg.v1 + flg.cc1)^2", " + flg.a2:flg.d.na2 + flg.a2:flg.v2 +

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Hi, my dependent variable is a proportion ("prob.bind"), and the independent variables are factors for group membership ("group") and a covariate ("capacity"). I am interested in the effects of group, capacity, and their interaction. Each subject is observed on all (4) levels of capacity (I use capacity as a covariate because the effect of this variable is normatively

Hi, a while ago I posted a question regarding the use of alternative models, including a quasibinomial mixed-effects model (see Results 1). I rerun the exact same model yesterday using R 2.7.2 and lme4_0.999375-26 (see Results 2) and today using R 2.7.2 and lme4_0.999375-27 (see Results 3). While the coefficient estimates are basically the same in all three regressions, the estimated standard

Hi:   I am using the following code to read a data set in txt in R and using the follwoing model. But it seems to give me an error ' expected variable name error pos 134022'. Any help is greatly appreciated.   Code: anal.data <- read.table("nausea.txt", header=T, sep="\t") list(names(anal.data)) attach(anal.data) n.samples <- dim(anal.data) [1] # number of data

Hi [macOSX 10.4.6; R-2.3.0] I have encountered a difference in behaviour between R-2.2.1 and R-2.3.0 when performing a linear model. Transcript follows for R-2.3.0 (R-2.2.1 worked as expected). How to make R-2.3.0 perform as R-2.2.1 did? > dput(x) c(29.13, 29.88, 30.09, 29.99, 29.74, 29.64, 29.65, 29.7, 30.04, 29.89, 29.96, 29.65, 28.76, 28.41, 28.38, 29.55, 29.76, 29.75, 29.84,