search for: cov2

I?m trying to set up proportional hazard model that is stratified with respect to covariate 1 and has an interaction between covariate 1 and another variable, covariate 2. Both variables are categorical. In the following, I try to illustrate the two problems that I?ve encountered, using the lung dataset. The first problem is the warning: To me, it seems that there are too many dummies

...ry to fix this in the future (Nabble issue?) As to the problems: handling strata by covariate interactions turns out to be a bit of a pain in the posteriorin the survival code. It would have worked, however, if you had done the following: fit <- coxph(Surv(time, status) ~ strata(cov1) * cov2, data=...) or ~ strata(cov1):cov2 or ~ strata(cov1):cov2 + cov2 But by using ~ strata(cov1) + cov1:cov2 you fooled the program into thinking that there was no strata by covariate interaction, and so it did not follow the special logic necessary for that case. Second issue: The...

I can use stepAIC on an lme object in 1.6.2, but I get the following error if I try to do the same in 1.7.0: Error in lme(fixed = resp ~ cov1 + cov2, data = a, random = structure(list( : unused argument(s) (formula ...) Does anybody know why? Here's an example: library(nlme) library(MASS) a <- data.frame( resp=rnorm(250), cov1=rnorm(250), cov2=rnorm(250), group=rep(letters[1:10],25) ) mod1 <- lme(resp~cov1...

the study design of the data I have to analyse is simple. There is 1 control group (CTRL) and 2 different treatment groups (TREAT_1 and TREAT_2). The data also includes 2 covariates COV1 and COV2. I have been asked to check if there is a linear or quadratic treatment effect in the data. I created a dummy data set to explain my situation: df1 <- data.frame( Observation = c(rep("CTRL",15), rep("TREAT_1",13), rep("TREAT_2", 12)), COV1 = c(rep("A1&q...

...t.seed(10) > ftime <- rexp(200) > fstatus <- sample(0:2,200,replace=TRUE) > gg <- factor(sample(1:3,200,replace=TRUE),1:3,c('a','b','c')) > cov <- matrix(runif(600),nrow=200) > dimnames(cov)[[2]] <- c('x1','x2','x3') > cov2=cbind(cov,gg) > print(z <- crr(ftime,fstatus,cov2)) convergence: TRUE coefficients: x1 x2 x3 gg 0.2624 0.6515 -0.8745 -0.1144 standard errors: [1] 0.3839 0.3964 0.4559 0.1452 two-sided p-values: x1 x2 x3 gg 0.490 0.100 0.055 0.430 > summary(z) Compe...

I have a fairly large model: > length(Y) [1] 3051 > dim(covariates) [1] 3051 211 All of these 211 covariates need to be nested hierarchically within a grouping "class", of which there are 8. I have an accessory vector, " cov2class" that specifies the mapping between covariates and the 8 classes. Now, I understand I can break all this information up into individual vectors (cov1, cov2, ..., cov211, class1, class2, ..., class8), and do something like this: model <- lmer(Y ~ 1 + cov1 + cov2 + ... + cov211 +...

...d argument supplied to the mahalanobis funtion). Any help or guidance would be greatly appreciated. Thank you! RL CODE fit<-lda(pop~v1 + v2 + v3 +...+vn, data=my.data) x1<-subset(my.data, pop==1) x2<-subset(my.data, pop==2) #Save Covariance Matices for each group cov1<-cov(x1) cov2<-cov(x2) #Determine number of rows in each matrix n1<-nrow(x1); n2<-nrow(x2); n.rows<-c(n1,n2) #store mean vectors from lda object mu1<-fit$means[1,] mu2<-fit$means[2,] #Calculate the common Covariance Matrix S<-(((n.rows[1]-1)*cov1)+((n.rows[2]-1)*cov2)/ (sum(n.rows[1:...

...question: How do I proper correct for multiple testing if I want to estimate in the presence of interaction the two average main effects. Can some one point me to some literature where I can learn these things? Here the example, 2x2 factorial with interaction, estimation of average main effects: cov2 <- matrix( c(1,-1,-1, 1, -1, 2, 1,-2, -1, 1, 2,-2, 1,-2,-2, 4) , nrow=4, ncol=4, byrow=TRUE) cm2 <- matrix(c(0, 1, 0, 1/2, 0, 0, 1, 1/2), nrow = 2, ncol=4, byrow=TRUE, dimnames = list(c("A", "B"), c("C.1", "C.2", "C.3", "C.4"))...

...col.grid="lightblue",angle=120, pch=20) em2mn<- function(y) { n<-length(y[,1]) p<-matrix(0,n,1) f1<-matrix(0,n,1) f2<-matrix(0,n,1) tau<-matrix(0,n,2) eps<-0.0001 mu01<-c(0,0) mu02<-c(0,0) sd01<-matrix(0,2,2) sd02<-matrix(0,2,2) cov1<-matrix(0,2,2) cov2<-matrix(0,2,2) # 1 inizializzare i valori alpha0= runif(1,0,1) for (j in 1:2) { mu01[j] <- runif(1,min=quantile(y[,j], probs =0.25), max=quantile(y[,j], probs =0.75)) mu02[j] <- runif(1,min=quantile(y[,j], probs =0.25), max=quantile(y[,j], probs =0.75)) } sd01<- var(y[1:round(n/2),])...

...col.grid="lightblue",angle=120, pch=20) em2mn<- function(y) { n<-length(y[,1]) p<-matrix(0,n,1) f1<-matrix(0,n,1) f2<-matrix(0,n,1) tau<-matrix(0,n,2) eps<-0.0001 mu01<-c(0,0) mu02<-c(0,0) sd01<-matrix(0,2,2) sd02<-matrix(0,2,2) cov1<-matrix(0,2,2) cov2<-matrix(0,2,2) # 1 inizializzare i valori alpha0= runif(1,0,1) for (j in 1:2) { mu01[j] <- runif(1,min=quantile(y[,j], probs =0.25), max=quantile(y[,j], probs =0.75)) mu02[j] <- runif(1,min=quantile(y[,j], probs =0.25), max=quantile(y[,j], probs =0.75)) } sd01<- var(y[1:round(n/2),])...

...rd errors: [1] 0.2881 0.3644 two-sided p-values: [1] 0.018 0.039 ...That I dont understand at all since it looks like if "treatment" covariate had also a significant effect of the first period of time !? This is absolutely not the case. So I m surely wrong with a part of this script... cov2 and tf are pretty obscure for me in the help file of the package. I would really appreciate advices regarding these 2 "terms". I was thinking that I might changed : cbind(ifelse(uft <= 1, 1, 0), ifelse(uft > 1, 1, 0) into: cbind(ifelse(uft <= 1, 0, 1),...

...f0e = f0 p0e = p0 if (nulltype == 0) { f0e = f00 p0e = p0theo } fdre = (p0e * f0e)/(p0e * f0e + f1e) EE[m] = sum(f1e * fdre)/sum(f1e) } EE = EE/EE[1] names(EE) = mul } Cov2.out = loccov2(X, X0, i0, f, fp0["cmest", ], N) Cov0.out = loccov2(X, matrix(1, length(x), 1), i0, f, fp0["thest", ], N) if (sw == 3) { if (nulltype == 0) Ilfdr = Cov0.out$Ilfdr else if (nulltype == 1) Ilfdr = ml.out$Ilfdr...

Hey, I am confused about how to generate the sample data from a mixture of Multivariate Gaussian ditribution. For example, there are 2 component Gaussian with prior probability of 0.4 and 0.6, the means and variances are u1=[1 1]', Cov1=[1 0;0 1] and u2=[-1 -1]', Cov2=[1 0;0 1] repectively. So how can I generate a sample of 500 data from the above mixture distribution? Thanks. Fred

Hi, what I would need are 2 vector pairs (x,y) and (x1,y1). x and x1 must have the same sd. y and y1 should also exhibit the same sd's but different ones as x and x1. Plotting x,y and x1,y1 should produce a plot with 2 vectors having a different slope. Plotting both vector pairs in one plot with fixed axes should reveal the different slope. many thanks syrvn -- View this message in

...rrors: [1] 0.2881 0.3644 two-sided p-values: [1] 0.018 0.039 ...That I dont understand at all since it looks like if "treatment" covariate had also a significant effect of the first period of time !? This is absolutely not the case. So I m surely wrong with a part of this script... cov2 and tf are pretty obscure for me in the help file of the package. I would really appreciate advices regarding these 2 "terms". I was thinking that I might changed : cbind(ifelse(uft <= 1, 1, 0), ifelse(uft > 1, 1, 0) ? ? ? ? ? ? ? ? ? into:??????? cbind(ifelse(uft <= 1, 0, 1)...

...,59638 ,23339,40697,47107,8536,57035,55090,44414,1321,12861,21108,32654,27068,38365,22255,31550,11789,45404,53969 ,13509,36350) Dist <- sqrt(outer(x,x, "-")^2 + outer(y,y, "-")^2) Dist <- Dist/max(Dist) library(spatial) Cov1 <- sphercov(Dist, 0.8, alpha=0, se=sqrt(2)) Cov2 <- sphercov(Dist, 0.6, alpha=0, se=sqrt(0.55)) library(geoR) Cov1b <- cov.spatial(Dist, cov.model= "spherical", cov.pars=c(2, 0.8)) Cov2b <- cov.spatial(Dist, cov.model= "spherical", cov.pars=c(0.55, 0.6)) library(mvtnorm) dmvnorm(rep(0, nrow(Cov)), sigma=Cov1) [1] 379...

...model proceeds - to a point... Maximum number of PQL iterations: 20 iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 iteration 6 Error: attempt to select less than one element Traceback() suggests that the model fits, but that lme can't calculate something? 2: extract.lme.cov2(ret$lme, mf, n.sr + 1) 1: gamm(present ~ s(week, bs = "cc") + s(week, bs = "cc", by = y1), random = list(garden = ~1), correlation = corAR1(form = ~1 | garden), family = binomial, data = count.data2) Question 2: Any hints on what might be causing this? Am I...

...wo-sided p-values: > [1] 0.018 0.039 > > ...That I dont understand at all since it looks like if "treatment" > covariate had also a significant effect of the first period of time !? > This is absolutely not the case. > So I m surely wrong with a part of this script... cov2 and tf are > pretty obscure for me in the help file of the package. I would really > appreciate advices regarding these 2 "terms". > > I was thinking that I might changed : cbind(ifelse(uft <= 1, 1, 0), ifelse(uft > 1, 1, 0) into: cbind(ifelse(...

Hi all I do not understand why I am getting the following error message. Can anybody help me with this? Thanks in advance. install.packages("cmprsk") library(cmprsk) result1 <-crr(ftime, fstatus, cov1, failcode=1, cencode=0 ) one.pout1 = predict(result1,cov1,X=cbind(1,one.z1,one.z2)) predict.crr(result1,cov1,X=cbind(1,one.z1,one.z2)) Error: could not find function

...F???????? TRUE? B 897?? B.1 RdRp:P323L RdRp P323L 323?? P L??????? FALSE? B 898?? B.1??? S:D614G??? S D614G 614?? D G??????? FALSE? B # B.1.1.529 and branches do not have any of the defining mutations of B.1.1; I have uploaded the code on GitHub: https://github.com/discoleo/R/blob/master/Stat/Infx/Cov2.Variants.R 1.) Does anyone have a better picture of what is going on? The sub-variants should have cumulative mutations. This should be the logic for the sub-lineages and I deduce it also by the data/post on the GitHub pango page: https://github.com/cov-lineages/pango-designation/issues/361 2....