search for: y_ij

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 =

...is no time dimension to the problem, it's possible a degenerate use of the Cox proportional hazards model (in the survival package of 1.7) will do what I want. Here's a little more detail on the function, though this is still quite terse. Pseudo Tex notation: We have cases in clusters. y_ij is the outcome (0 or 1) for the j'th case in the i'th cluster It has vector covariates X_ij. c_ij is a transform of y_ij and is in (0, 1) (it's actually the probability of being in cluster j). It may be specified a priori or to be estimated. Maximize the product over i of c_ij exp(s...

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this m...

Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 2. Model 2 Y_ij, Y_...

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

...scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels (e.g., happy, sad, and neutral). Maybe better denote the data as y_ij, effect size at the j-th level from the i-th source. I can code the levels with dummy variables into the X_i matrix, but apparently the data from the same source are correlated with each other. In this case, I would like to run a few tests one of which is, for example, whether there is any differen...

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...

...depend on the parametrisation of the model which by default in R is formed using the so-called "treatment" contrasts. To wander from R into statistics (sorry Bert) the problem arises because the "usual" parametrisation of the model is the "over-parametrised" form: Y_ij = mu + beta_i + E_ij (i = 1, ..., I, j = 1, ..., J_i) where Y_ij is the j-th observation corresponding to the i-th "treatment" or group. (Things get a bit more complicated in "multi-way" models; let's not go there.) The parameter "mu" is the "grand mean&q...

I am using the package nlme to fit a simple random effects (variance components model) with 3 parameters: overall mean (fixed effect), between subject variance (random) and within subject variance (random). I have 16 subjects with 1-4 obs per subject. I need a 3x3 variance-covariance matrix that includes all 3 parameters in order to compute the variance of a specific linear

This is really a statistical issue. What do you think the Intercept term represents? See ?contrasts. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Feb 15, 2018 at 5:27 PM, Marc Girondot via R-help < r-help at

...2 7150 Intra-subject trajectories of Y very close to linear. I'd like to check whether slope (and maybe also offset) of this line are (in part) predicted by X.baseline. Thus, I think the multilevel model specification should be as follows (i = subject, j=measurement): y_ij = \beta_i + b_i * TIME_ij + \epsilon_ij, with b_i = \zeta_i0 + \zeta_i1 * X.Baseline Is this correct? Now, I am completely unsure how to "translate" this into the syntax needed by lme. Is there any standard procedure on how to get from e.g. the Laird&Ware'82 matrix model notatio...

Dear R-er, I try to get the standard error of fitted parameters for factors with a glm, even the reference one: a <- runif(100) b <- sample(x=c("0", "1", "2"), size=100, replace = TRUE) df <- data.frame(A=a, B=b, stringsAsFactors = FALSE) g <- glm(a ~ b, data=df) summary(g)$coefficients # I don't get SE for the reference factor, here 0:

...), ylim=c(-10,5), # xlab="Day in ICU", # ylab="CRP (mg/dL)") # CRP98graph <- apply(CRP98, 1, lines, col="gray") par(mfrow=c(1,2)) plot(c(1:5), CRP7raw[1,], type = "n", xlim=c(1,5), ylim=c(-10,5) , xlab="t_i", ylab="y_ij", sub = "lambda = 0.7") CRP7graph <- apply(CRP7, 1, lines, col="gray") plot(c(1:5), CRP98raw[1,], type = "n", xlim=c(1,5), ylim=c(-10,5), xlab="Day in ICU", ylab="CRP (mg/dL",...

...ves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER). I wanted to fit the following model: for problem i, person j: logodds ( Y_ij ) = b_0j + b_1j TYPE_ij with b_0j = b_00 + b_01 POWER_j + u_0j and b_1j = b_10 + b_11 POWER_j I think it makes sense, but I'm not sure. Here are the observed cell means: conflict noconflict control 0.6896552 0.9568966 high 0.6935484 0....

Hi, everyone, Before everything, thanks. Lots of thanks ;)!!!! I don?t think you understood everything I need to do. I want to write t_i instead of "Day in ICU? [i subscript for t] and y_ij instead of "CRP (mg/dL)? [ij superscript for y]. The label of the axis? :( Can you help me on that task? Thanks!!!!! Best, Rosa Oliveira > On 31 Jul 2017, at 10:28, Martin Maechler <maechler at stat.math.ethz.ch> wrote: > >>>>>> PIKAL Petr <petr.pikal...

..., 1, lines, >> col="gray") >> > > >> > > par(mfrow=c(1,2)) >> > > >> > > plot(c(1:5), CRP7raw[1,], type = "n", xlim=c(1,5), >> ylim=c(-10,5) , > > xlab="t_i", > > ylab="y_ij", > > sub >> = "lambda = 0.7") >> > > >> > > CRP7graph <- apply(CRP7, 1, lines, col="gray") >> > > >> > > >> > > plot(c(1:5), CRP98raw[1,], type = "n", xlim=c(1,5),...

...ou try it? I basically suggested plot(c(1:5), type = "n", xlab=expression("t"[i]), ylab=expression("y"^ij)) mtext(expression(lambda^2)) which by my humble opinion is precisely what you wanted. I want to write t_i instead of "Day in ICU? [i subscript for t] and y_ij instead of "CRP (mg/dL)? [ij superscript for y]. If it is not you need to express yourself more clearly. My crystal ball is broken. Cheers Petr On 31 Jul 2017, at 13:44, PIKAL Petr <petr.pikal at precheza.cz<mailto:petr.pikal at precheza.cz>> wrote: Hi From: Rosa Oliveira [ma...

...and the survivor function for (in this case, right) censored results. Within (for example) an R environment, this is easy to do and gives the same solution as survreg even if it is a little heavy. But where there is an hierarchical situation, we need to consider the contributions at level 2. y_ij=X_ij.beta'+err2_i+err1_ij If all the units at level 1 for a given level 2 are censored, then the information we have for the level 2 is itself censored and we should presumably use the survivor function. Conversely if none of the units at level 1 are censored, then the information at level 2...

Hi everyone, I want to fit the following mixed effect model Y_ij = b_0i + b_1i * (t_ij*grp_ij == 1) + b_2i * (t_ij*grp_ij == 2) + v_0i + v_1i*t_ij + e_ij with a different covariance matrix of random effects for each group. (Y is the response t is time grp is the group indicator b 's are fixed effects v 's are random effects) I know that...

...r first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K. This is easily tested with a t-test (b-K=0). Now imagine that there are j groups. For each group j the model is: Y_ij= a_j + b_jX_ij + error. Both the intercepts (a) and the slopes (b) are allowed to vary between groups. The a priori (null) hypothesis of interest involved the between-group values of the slopes and is: b_j=Kj where Kj is specified a priori for each group j based on theoretical considerations but...