similar to: Contrasts and MC

Hi you guys: I have been wondering if there is any way to change the labeling in plot.ts( ), for example , if I plot two sequences, i always got y labels as "series1", "series2", I tried to use ylab=c((expression(mu_1)),(expression(mu_2))), but it doesn't work. thanks in advance for any help best.

Hello, I have some parameters from Mclust function. The parameters are in the form *parametersDf * * mu_1 mu_2 var_mc1 var_mc2 c1 c2 * *2 1.357283 2.962736 0.466154 0.1320129 0.5258975 0.4741025 * *21 8.357283 9.962736 0.466154 0.1320129 0.5258975 0.4741025 * Each row in the above data frame

Hello, I've fitted a parametric survival model by > survreg(Surv(Week, Cens) ~ C(Treatment, srmod.contr), > data = poll.surv.wo3) where srmod.contr is the following matrix of contrasts: prep auto poll self home [1,] 1 1 1.0000000 0.0 0 [2,] -1 0 0.0000000 0.0 0 [3,] 0 -1 0.0000000 0.0 0 [4,] 0 0 -0.3333333 1.0 0 [5,] 0 0

Hi Folks, Can anyone give me the tip I've been groping for with the following question:? mu: kx2x2x2 array of reals corresponding to means of k RVs at the combinations of values (1,2)x(1,2)x(1,2) of dichotomous variables F1,F2,F3 mu prints out as k rows (one for each Xi) of 8 numbers M: N x (3+k) matrix of cases. The first 3 cols are values of

Peter Dalgaard writes (in response to a question about 2-way ANOVA with imbalance): > ... There are various > boneheaded ways in which people try to use to assign some kind of > SumSq to main effects in the presence of interaction, and they are all > wrong - although maybe not very wrong if the unbalance is slight. People keep saying this

Hi R-people, I am wanting to run Factorial ANOVA followed by Scheffe tests on some spatial subjective data. I'm comparing X-Y independent coordinates against x-y dependent coordinates. There are only four independent spatial coordinates that form a square. I am wondering whether I am doing the right thing, because there doesn't seem to be a simple way of doing this. I have attempted to

Hello, For a simple problem with 1 predictor (x) and 2 classes (0 and 1), the linear discriminant function should be something like 2(mu_0 - mu_1)/var x + x-independent-terms where var is the common variance.

Hello, Is there a built-in test in R for hypothesis testing with samples of known variance? For example, I've got a set of data, a mean to compare against, and a known variance, and I want to determine the p-value for which I can reject the null hypothesis (mu_1 = mu_0) and accept the alternative (mu_1 > mu_0). I've found that JMP and Minitab can both do this (in JMP, it's a

Hello, As a new user of R (less than a month) I have got my hands on several books and am pouting through the net looking for help in gaining understanding of this powerful tool. I am becoming more proficient with using basic functions to conduct basic statistics. I am now looking to learn how to write code. After a few small worthless functions I decided to try to create a function that was

R version 2.10.1 (2009-12-14) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with

To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood,

Dear all, I do not seem to grasp how contrasts are set for ordered factors. Perhaps someone can elighten me? When I work with ordered factors, I would often like to be able to reduce the used polynomial to a simpler one (where possible). Thus, I would like to explicetly code the polynomial but ideally, the intial model (thus, the full polynomial) would be identical to one with an ordered factor.

Here's a fairly minimal-case example in which the saved EPS does not match the screen. The error comes when using bquote(.(i)) instead of bquote(1), as demonstrated by the two minimally different cases below. Very strange. Any clues as to why? #---------------- begin ------------------- # Version A. X axis labels have subscripts as constants. EPS is correct. windows() layout( matrix( 1:2 ,

Hi, we are just about to evaluate R as a standard statistics package for our institute. We looked around the help - manual, the FAQ, etc. and did not find anything for the topics of random factors in ANOVA, repeated measures in ANOVA, or within subjects designs in R. Could anyone point us to any information concerning - univariate approach to repeated measures anova - multivariate

Does anyone know of some methods already programmed up ( ie freeware : ) ) in R for the post hoc methods in anova. Particularly Scheffe's method or Tukey's? Anna

Dear All: I apologize for asking such an elementary question, but I could not find an adequate response on line. I am hoping to receive some help with the interpretation of the Intercept coefficient in the gam model below. I1 through I3 are dummy coded "Item difficulty" parameters in a data set that includes 4 items. If the Intercept is the value of Y when all other terms are 0, am I

fit1<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit2<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.5,data=wbc) fit3<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit4<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)

Hi, I am analyzing a data set with greater than 1000 independent cases (collected in an unrestricted manner), where each case has 3 variables associated with it: one, a factor variable with 0/1 levels (called XX), another factor variable with 8 levels (X) and a third response variable with two levels (Y: 0/1). I am trying to see if X1 has an effect on the relationship between X2 and the

I am trying to write a loop to forecast realized volatility over successive days for the purpose of VaR prediction using the HAR-RV-CJ model which is as follows: log(RV_t+1) = ?_0 + ?_CD log(CV_t) + ?_CW log(CV_t-5) + ?_CM log(CV_t-22) + ?_JD log(J_t + 1) + ?_JW log(J_t-5 + 1) + ?_JM log(J_t-22 + 1) + e_t where RV is realized volatility, CV is continuous volatility and J is the jump which is

All, I have been reading Dr. Harrell's excellent "Regression Modeling Strategies" book and trying out the exercises. I understand that contrast( ) is used to obtain contrasts between two variables for given levels of other nuisance variables; is there a way to use contrast( ) to obtain, for example, Scheffe confidence intervals / hypothesis tests for many post hoc contrasts at