search for: alpha_i

Dear R users, I am trying to fit the following ANCOVA model in R2.4.0 Y_ij=mu+alpha_i+beta*(X_ij-X..)+epsilon_ij Particularly I am interested in obtaining estimates for mu, and the effects alpha_i I have this data (from the book Applied Linear Statistical Models by Neter et al (1996), page 1020) y<-c(38,43,24,39,38,32,36,38,31,45,27,21,33,34,28) x<-c(21,34,23,26,26,29,2...

I want to estimate the following fixed effect model: y_i,t = alpha_i + beta_1 x1_t + beta_2 x2_i,tx2_i,t = gamma_i + gamma_1 x1_t + gamma_2 Z1_i + gamma_3 Z2_i I can use ivreg from AER to do the iv regression. fm <- ivreg(y_i,t ~ x1_t + x2_i,t | x1_t + Z1_i + Z2_i, data = DataSet) But, I'm not sure how can I add the fixed effects. Tha...

...3 4 13 2 2303 3503 4 1 8 5 2 2304 3504 3 1 10 2 2 2305 3505 2 3 5 8 2 2306 3506 0 5 8 2 2 ... The model is channel0 ~ alpha_i + eps_{i, j} + channel1 + channel2 + channel3 where i is sample number, j is position, and: alpha_i: fixed effect for each samplenumber eps_{i, j}: random effect, here with correlation structure as AR(1) channel1, ..., channel3: fixed effect for each channel not d...

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes estimators? Any relevant work and codes in R (or S+) is highly appreciated. Kind regards, Ali [[alternativ...

Hallo, I have a question concerning SVM regression in R. I intend to use SVMs for feature selection (and knowledge discovery). For this purpose I will need to extract the weights that are associated with my features. I understand from a previous thread on SVM classification, that predictive models can be derived from SVs, coefficiants and rhos, but it is unclear for me how to transfer this

Dear R users, I'm running a SVR in package e1071 but I did not able to calculate the parameters w and b of the regression. I don't know how to do that and if it is possible to do it with this package. Someone have some idea. Any help would be much appreciated. Marlene [[alternative HTML version deleted]]

Dear list, I am currently writing up some of my R models in a more formal sense for a paper, and I am having trouble with the notation. Although this isn't really an 'R' question, it should help me to understand a bit better what I am actually doing when fitting my models! Using the analysis of co-variance example from MASS (fourth edition, p 142), what is the correct notation for the

...r to the lme function in Splus, does not use the cluster function option. This difference does not appear to be documented in the V&R `R Complements' file. I have data which is divided into 6 groups The lme model is of the form (simplified from the actual model a bit) y = a + b x_{ij} + \alpha_i + \beta_i x_{ij} + \epsilon_{ij} i = 1,...,6. where a and b are fixed effects and \alpha and \beta are random effects. I have a vector of the form clus = (1,1,1,2,5,6,4,4,4,3,...)' where the 1, 2 3 are all factors corresponding to different groups (or clusters in Splus terminology). In S...

...0.958 1 pb 0.985 1 bb 0.925 2 pp 0.971 2 pb 1.051 2 bb 0.952 3 pp 0.927 3 pb 0.891 3 bb 0.892 4 pp 0.971 4 pb 1.010 4 bb 0.955 The model used for ANOVA in the book is Y_{ij} = \mu + \alpha_i + B_i + [(\alpha B)_{ij}] + \epsilon_{ij} (I am not quite confident how to represent this model in R, see below) The ANOVA table from S&R looks like this: MSB series 3 0.021 0.007 10.23 ** MSA genotypes 2 0.010 0.005 6.97 * MSE error 6 0.004 0.001 In...

...9.90 tstnum5 0.47083 0.08461 5.56 tstnum6 0.97500 0.08461 11.52 The mean of (the columns of) the data matrix is 3.229167 3.695833 3.729167 4.066667 3.700000 4.204167 which is in exact agreement with the lmer() results when converted to the same parameterization (mu_i = mu + alpha_i, with alpha_1 = 0). (Notice the surprizing, depressing, and so far unexplained *drop* in the response over the second summer.) What I *don't* understand is the correlation structure of the estimates produced by lmer(), which is: Correlation of Fixed Effects: (Intr) tstnm2 tstnm3 tst...

Hi all, I have a random effect model that can be written as y_{ij} = \beta +\alpha_i+ \epslion_{ij} where \alpha_i ~ N(0,\sigma^2_effect) and \espilon_{ij} ~ N(0,\sigma^2_error) and i compute res<-lme(data~1,data=Data,random=~1 | veci) The estimation of \sigma_error is given by res$sigma but I don't understand how i can find the estimation of \sigma _effect with the...

I'd like to fit a multinomial log-linear model for 4 categories of the form log[(P(D=i | X)/P(D=0 | X)] = alpha_i + X beta_i ; i=1,2,3 but with beta_1 constrained to zero. Is there a way to impose such a constraint in the multinom function? Brad ------------------------------------------------------------------------- Brad McNeney email: mcneney at stat.sfu.ca Dept. of Statisti...

I know this has been dealt with before on this list, but the previous messages lacked detail, and I haven't figured it out yet. The model is: \x_{ij} = \mu + \alpha_i + \beta_j \alpha is a random effect (subjects), and \beta is a fixed effect (condition). I have a link function: p_{ij} = .5 + .5( 1 / (1 + exp{ -x_{ij} } ) ) Which is simply a logistic transformed to be between .5 and 1. The data y_{ij} ~ Binomial( p_{ij}, N_{ij} ) I've generated data u...

I feel bad even asking, but: Rgames> data(OrchardSprays) Rgames> model<-lm(decrease~.,data=OrchardSprays) Rgames> model Call: lm(formula = decrease ~ ., data = OrchardSprays) Coefficients: (Intercept) rowpos colpos treatmentB treatmentC 22.705 -2.784 -1.234 3.000 20.625 treatmentD treatmentE treatmentF treatmentG treatmentH

...ted is of any actual interest. This would go much further toward bringing the desciple to true enlightenment. Point 3 --- what hypothesis is being tested by SSA? Let factor A correspond to index i, and B to index j. Let the cell means be mu_ij. (In the overparameterized notation, mu_ij = mu + alpha_i + beta_j + gamma_ij.) The hypothesis being tested is H_0: mu_1.-bar = mu_2.-bar = ... = mu_a.-bar where factor A has a levels, and ``mu_i.-bar'' means the average (arithmetic mean) of mu_i1, mu_i2, ..., mu_ib. (Note --- factor B has b levels.) I.e. the hypothesis is that there is no d...

...'m also including a "fixed effect" in the econometrics sense of the term -- an individual-specific intercept. I also want to model the continuous variables flexibly -- I have no good priors on the proper specification for the function form. The model is the following: y_{it} = \alpha_i + \beta_1(T_{it}) + f(continuous.vars_{it}) + e_{it} To control for unobserved time-invariant heterogeneity, I want to de-mean the data as follows: y_{it}-\bar{y_i} = \beta_1(T_{it}-\bar{T_i}) + f(continuous.vars_{it}-\bar{continuous.vars_i}) + e_{it} - \bar{e}_i Fitting the demeaned model sho...

I don't have a copy of Dobson (1990) from which the glm.D93 example is taken in example("glm"), but I'm strongly suspecting that these are made-up data rather than real data; the means of the responses within each treatment are _identical_ (equal to 16 2/3), so two of the parameters are estimated as being zero (within machine tolerance). (At this moment I don't understand

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

....25 0.5 0.75 The column sums are all zero so the identification constraint is still the same, but they are not mutually orthogonal. The coefficients do have an easy interpretation, though: > fractions(ginverse(contr. sdif(4))) [,1] [,2] [,3] [,4] [l,] -1 1 0 0 [2,] 0 -1 1 0 [3,] 0 0 -1 1 Thus alpha_i = phi_(i+l) - phi_i, the "successive differences" of phi's, or equally of class means. Now (at last) returning to your case. In specifying the contrasts you want to have as the coefficients, you are specifying not C, but (C'C^{-l} C' (say M). So what you need to do is work ba...