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 +...

Homework questions are not answered on this list. Best, Uwe Ligges On 04.10.2024 10:32, Steven Yen wrote: > The following line calculates standard deviations of a column vector: > > se<-apply(dd,1,sd) > > How can I calculate the covariance matrix using apply? Thanks. > > ______________________________________________ > R-help at r-project.org mailing list -- To

...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:...

...;-3 > x<-rnorm(n); x [1] 0.92098449 0.80940115 > 0.60374785 > cov1<-outer(x-mean(x),x-mean(x))/(n-1); cov1 [,1] [,2] > [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 > 0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 > 0.0151896601 > cov2<-(x-mean(x))%*%t((x-mean(x)))/(n-1); cov2 [,1] > [,2] [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] > 0.0022410598 0.00049161983 -0.0027326797 [3,] -0.0124569805 > -0.00273267965 0.0151896601 > > On 10/4/2024 4:57 PM, Uwe Ligges wrote: >> Homework questions are...

...122345671) > n<-3 > x<-rnorm(n); x [1] 0.92098449 0.80940115 0.60374785 > cov1<-outer(x-mean(x),x-mean(x))/(n-1); cov1 [,1] [,2] [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 0.0151896601 > cov2<-(x-mean(x))%*%t((x-mean(x)))/(n-1); cov2 [,1] [,2] [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 0.0151896601 > On 10/4/2024 4:57 PM, Uwe Ligges wrote: > Homework questions are not answered on this li...

...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"))...

...n<-3 > x<-rnorm(n); x [1] 0.92098449 0.80940115 >0.60374785 > cov1<-outer(x-mean(x),x-mean(x))/(n-1); cov1 [,1] [,2] [,3] >[1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 >0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 >0.0151896601 > cov2<-(x-mean(x))%*%t((x-mean(x)))/(n-1); cov2 [,1] [,2] >[,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 >0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 >0.0151896601 > > >On 10/4/2024 4:57 PM, Uwe Ligges wrote: >> Homework questions are...

...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),])...

...t;-rnorm(n); x [1] 0.92098449 0.80940115 >> 0.60374785 > cov1<-outer(x-mean(x),x-mean(x))/(n-1); cov1 [,1] [,2] >> [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 >> 0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 >> 0.0151896601 > cov2<-(x-mean(x))%*%t((x-mean(x)))/(n-1); cov2 [,1] >> [,2] [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] >> 0.0022410598 0.00049161983 -0.0027326797 [3,] -0.0124569805 >> -0.00273267965 0.0151896601 > >> On 10/4/2024 4:57 PM, Uwe Ligges wrote: >>> Homewo...

...1] 0.92098449 0.80940115 >>> 0.60374785 > cov1<-outer(x-mean(x),x-mean(x))/(n-1); cov1 [,1] [,2] >>> [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 >>> 0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 >>> 0.0151896601 > cov2<-(x-mean(x))%*%t((x-mean(x)))/(n-1); cov2 [,1] >>> [,2] [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] >>> 0.0022410598 0.00049161983 -0.0027326797 [3,] -0.0124569805 >>> -0.00273267965 0.0151896601 > >>> On 10/4/2024 4:57 PM, Uwe Ligges wrote: &gt...

...;>>> 0.60374785 > cov1<-outer(x-mean(x),x-mean(x))/(n-1); cov1 [,1] [,2] >>>>> [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 >>>>> 0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 >>>>> 0.0151896601 > cov2<-(x-mean(x))%*%t((x-mean(x)))/(n-1); cov2 [,1] >>>>> [,2] [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] >>>>> 0.0022410598 0.00049161983 -0.0027326797 [3,] -0.0124569805 >>>>> -0.00273267965 0.0151896601 > >>>>> On 10/4/202...

...80940115 >>>> 0.60374785 > cov1<-outer(x-mean(x),x-mean(x))/(n-1); cov1 [,1] [,2] >>>> [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] 0.0022410598 >>>> 0.00049161983 -0.0027326797 [3,] -0.0124569805 -0.00273267965 >>>> 0.0151896601 > cov2<-(x-mean(x))%*%t((x-mean(x)))/(n-1); cov2 [,1] >>>> [,2] [,3] [1,] 0.0102159207 0.00224105983 -0.0124569805 [2,] >>>> 0.0022410598 0.00049161983 -0.0027326797 [3,] -0.0124569805 >>>> -0.00273267965 0.0151896601 > >>>> On 10/4/2024 4:57 PM, Uwe L...

...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...