similar to: Linear mixed model using R

Dear all, I have a question on applying a function to the data according to factor levels. For example, for the data below, what is the best way to apply a function to "values" according to different levels of "samples" (1,2,3,4,5)? values ind sample 1 0.03325 1 1 2 0.03305 1 1 3 0.03185 1 1 4 0.03515 1 1 5 0.03375 1 1 6 0.01180

Working on modeling a wild animal population. Two data vectors: the herd count from year to year (estimated by a sampling procedure), and the number of animals killed by hunters. Task is to find the natural growth rate of the herd (A simplification, but preserves the essentials.) My question is whether the R procedure lm() is an appropriate tool to estimate the growth rate. year

Hi List, I have spent more than 30 minutes, but failed to read in this file using the read.table() function. I could not figure out how to fix the following error. Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 1 did not have 6 elements Any help would be be appreciated. Thanks, Pradip Muhuri ####### below is the reproducible example xd1 <-

Hi List I'm having difficulty understanding how plm should work with dynamic formulas. See the commands and output below on a standard data set. Notice that the first summary(plm(...)) call returns the same result as the second (it shouldn't if it actually uses the lagged variable requested). The third call results in error (trying to use diff'ed variable in regression) Other info:

I'm trying to do the following out of sample regression with autoregressive terms and additional x variables: y(t+1)=const+B(L)*y(t)+C(1)*x_1(t)...+C(K)*x_K(t) where: B(L) = lag polynom. for AR terms C(1..K) = are the coeffs. on K exogenous variables that have only 1 lag Question 1: ----------- Suppose I use arima to fit the model:

Dear all, I am having v.4.36 of Quantreg package and I noticed strange behaviour when weights were added. Could anyone please explain me what if the results are really strange or the behavioiur is normal. As an example I am using dataset Engel from the package and my own weights. x<-engel[1:50,1] y<-engel[1:50,2] w<-c(0.00123, 0.00050, 0.00126, 0.00183, 0.00036, 0.00100, 0.00122,

Hi, I need a help. I am new in R and I need to run a nested anova with fixed and random factors (Mixed Model). I have a design with three factors: Day, Area and Plot and the dependent variable is density. The factors Day and Area are fixed while Plot is random, factor Area is nested in factor Day, and factor Plot is nested in Area. I can do it using aov by: mod1<-aov(density~ day +

I am fitting a logistic model to binary data. The response variable is a factor (0 or 1) and all predictors are continuous variables. The main predictor is LT (I expect a logistic relation between LT and the probability of being mature) and the other are variables I expect to modify this relation. I want to test if all predictors contribute significantly for the fit or not I fit the full

Hi, I have been using arima.sim from the stats package recently, and I'm wondering why I get different results when using what seem to be the same parameters. For example, I've given examples of three different ways to run arima.sim with what I believe are the same parameters. It's my understanding from the R documentation that rnorm is the default function for rand.gen if not

Dear List, I have encountered an odd problem that I cannot understand. It stems from the calculation of true and false positives based on two input vectors x and y based on different thresholds of x, extracted using the quantile function. I am in certain cases getting different values of true positives for the same threshold value when the threshold was found under different quantiles (e.g. the

Greetings. I'm trying to recreate in R some regression models I've done in SAS, but I'm not getting the same results. My advisor suspects this may be due to differences in precision between R and SAS. Does anyone know where I can find specifications for R's type double? (It doesn't seem to be in the R Language Definition.) Thanks in advance for any help anyone can

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

Good morning gentlemen! How using a weighted model in nls2? Values with the nls are logical since values with nls2 are not. I believe that this discrepancy is due to I did not include the weights argument in nls2. Here's an example: MOISTURE <- c(28.41640, 28.47340, 29.05821, 28.52201, 30.92055, 31.07901, 31.35840, 31.69617, 32.07168, 31.87296, 31.35525, 32.66118, 33.23385,

Dear all, I'm quite new in R and especially with linear mixed effects models and I'm not completely sure to read the lmer table in the right way. for example: head(march.f) fam subjID Cond Code reg total first second log.total log.second cat 3 f 30 an fDan1 3 1.2304688 0.6679688 0.56250000 0.20739519 0.44628710 f

Full_Name: Tao Shi Version: 1.7.0 OS: Windows XP Professional Submission from: (NULL) (149.142.163.65) > x [,1] [,2] [1,] 149 151 [2,] 1 8 > c2x<-chisq.test(x, simulate.p.value=T, B=100000)$p.value > for(i in (1:20)){c2x<-c(c2x,chisq.test(x, simulate.p.value=T,B=100000)$p.value)} > c2tx<-chisq.test(t(x), simulate.p.value=T, B=100000)$p.value > for(i in

Dear all, I want to apply weights to my sample data set and I am struggling with the Efron Approximation with weights. I have got one sample data shown as below: customer week arrest fin age race weight 1 weight 2 weight 3 1 20 1 1 27 1 2 15 2 2 17 1 0 18 1 2 19 1 3 25 1 1 19 0 2 20 1 4 52 0 1 23 1 2 5 1 5 52 0 0 19 0 2 11 1 6 25 1 0 19 0 2 26 1 I applied four different weighted Efron

Dear list members, I fitted a glm model (See output below). My outcome is death, and weight (continuous), ClutchSize (3-level factor), EggVolume (continuous), Sex (obviously 2-level factor), and SiblingCompetence (2-level factor) are my covariates. I'd like to obtain the odds of death for a range of Weights, EggVolumes, and different combinations of ClutchSize. I've tried using the

Dear R users, I am using lmer combined with AIC model selection and averaging (in the MuMIn package) to try and assess how isotope values (which indicate diet) vary within a population of animals. I have multiple measures from individuals (variable 'Tattoo') and multiple individuals within social groups within 4 locations (A, B, C ,D) crucially I am interested if there are

Hello, Got Ruby 1.8.2, RAILS 1.0, Windows2kx and Ruby-OCI8.mswin (1.1.3) I try a migration test drive for one legacy DB from an original Ora win32 8.1.7 server to a better hardware running Win32 Oracle 9.2. The two DB have 99,999% identical datas. My rails app run on a independant serveur (W2003) using an Oracle Client 8.1.7. I''ve the same results using IIS/FastCgi or script/server

Hi, I have a matrix with few hundred by a few hundred and want to extract rows, e.g. (0,1,0), or (1.3, 2.4, 4). I made an example: > A = rep(rep(c(0,1),4),2) > B = rep(rep(c(0,1),each=4),2) > C = rep(rep(rep(c(0,1),2),each=2),2) > X = data.frame(A,B,C,rnorm(16)) A B C [1,] 0 0 0 0.72767870 [2,] 1 0 0 -0.09673298 [3,] 0 0 1 0.51192790 [4,] 1 0 1 -1.54350441 [5,] 0 1 0