similar to: dataframe - column value calculation in R

Dear Colleagues, I have a dataframe with variables: [1] "ID" "category" "a11" "a12" "a13" "a21" [7] "a22" "a23" "a31" "a32" "b11" "b12" [13] "b13" "b21"

Hi, I created the model below, which returns me the following warning message: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. ######### Model ######## mDPDF = data.frame(mj1,mj2,mj3,mj4,mj5,eL1,eL2,eL3,eL4,eL5,aC1,aC2,aC3,aC4,disR1,disR2,disR3,disR4,disR5,

I'm trying to build a SEM using the sem package. I'll attach my model specification below. When I turn debug=TRUE, it seems as if I'm getting to convergence because I get this message: Successive iterates within tolerance. Current iterate is probably solution. However, at the end of the process I get this message: Warning message: In sem.default(ram = ram, S = S, N = N,

Dear users, i'm using the sem package in R, because i need to improve a confermative factor analisys. I have so many questions in my survey, and i suppose, for example, that Question 1 (Q1) Q2 and Q3 explain the same thing (factor F1), Q4,Q5 and Q6 explain F2 and Q7 and Q8 explain F3... For check that what i supposed is true, i run this code to see if the values of loadings are big or not.

Someone can help me? I tried several things and always don't converge # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> Item54, lam54, NA F1 -> Item63, lam63, NA F1 -> Item65, lam55, NA F1 -> Item67, lam67, NA F1 ->

Hello list, I'm trying to figure out how exactly the specification of nested random effects works in the lmer function of lme4. To give a concrete example, consider the rat-liver dataset from the R book (rats.txt from: http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/ ). Crawley suggests to analyze this data in the following way: library(lme4) attach(rats) Treatment <-

Dear All, I'm not sure of the interpretation of interactions with contrasts. Can anyone help? I do an ANCOVA, dryweight is covariate, block and treatment are factors, c4 the response variable. model<-aov(log(c4+1)~dryweight+treatment+block+treatment:block) summary(model); Df Sum Sq Mean Sq F value Pr(>F) dryweight 1 3.947 3.947 6.6268 0.01076 *

This is a cryptographically signed message in MIME format. --------------ms010908060700000604050108 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Sorry. You're right about the univariate numeric operators. My bad. However, I was expecting !x to return a time series, just like the binary logical operators do. For example: > b <-

Hi All I have regression coefficients from an experiment and I want to plot them in lattice using panel curve but I have run into error messages. I want an 3 panel conditioned plot of 2 curves of Treatment 2 in each panel conditioned by Treatment1, the example curve expression is x+value*x^2 A rough toy example to give an idea of what I want is: Data: data = expand.grid(Treatment1 =

Hi, I have a dataset with a continuous response variable and, among other predictors, an ordinal variable. Here is what it could look like treatment <- factor(rep(c("AA", "AC", "AD","AE", "AB"), each = 10)) length <- c(75, 67, 70, 75, 65, 71, 67, 67, 76, 68, 57, 58, 60, 59, 62, 60, 60, 57, 59, 61, 58,

hpa, etal, pxelinux works great from a push of the reset button. machine boots normally. However, after i issue 'reboot' command, my box fails to find the tftp server during reboot. This looks to be happening before pxelinux has any chance to participate, so it cannot be to blame. Im seeking corroboration here before I go badgering the bios-provider here's the relevant

In S, NULL and list() are not the same. In R they are (I think). --------------------------------------------------- At least, is.list(NULL) #-> 'F' in S; 'TRUE' in R Yes: I had an instance where this broke correct S code: match(c("xlab","ylab"), names(list(...))) when '...' is empty, gives an error in R, but gives c(NA,NA) in S.

Hi all! I've got this error while running example(Glm) library("rms") > example(Glm) Glm> ## Dobson (1990) Page 93: Randomized Controlled Trial : Glm> counts <- c(18,17,15,20,10,20,25,13,12) Glm> outcome <- gl(3,1,9) Glm> treatment <- gl(3,3) Glm> f <- glm(counts ~ outcome + treatment, family=poisson()) Glm> f Call: glm(formula = counts ~

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

Hi all, I am trying to read multiple out variables for a sensitivity analysis. Currently using one output value as follows: Y<-(E1) However I need to run analysis against 12 values of Y. So E1-E12. My matrix will be: Inputs are Column=4, Rows = 40 i.e. 40 rows of 4 input variables in different combinations. These will be analysed against 40 rows of output variables for 12 columns.

Quick question, as I attempt to learn R. For post-hoc tests 1) Is there an easy function that will take, say the results of tukeyHSD and create a grouping table. e.g., if I have treatments 1, 2, and 3, with 1 and 2 being statistically the same and 3 being different from both Group Treatment A 1 A 2 B 3 2) I've been stumbling over the proper syntax for simple effects for a tukeyHSD

Dear R-help, I would like to compute the variance for the proportion of treatment effect by a surrogate in a survival model (Lin, Fleming, and De Gruttola 1997 in Statistics in Medicine). The paper mentioned that the covariance matrix matches that of the covariance matrix estimator for the marginal hazard modelling of multiple events data (Wei, Lin, and Weissfeld 1989 JASA), and is implemented

Hi, I installed LTSP and trying to boot diskless pc, but I got error PXE-E11 ARP timout. How did you get over it, I grab your mail from -- Maris Dembovskis Latvia, Valmiera, Gulbene Vidzeme - kur labas idejas dzimst ritdienai

Hi, I'm trying to get our 30 node cluster booting from remote server via pxelinux but have problems with the following mobo / switch combination. linksys srw2024 switch + asus a8n-vm csm mobo Maybe somebody saw similar problems elsewere and can recomend any solution. mobo bios is flashed to the newest available one. An extremly simplified test setup which shows our problem: PC1 ->

Hello, If I have two correlation matrices (e.g. one for each of two treatments) and then perform cor() on those two correlation matrices is this third correlation matrix interpreted as the correlation between the two treatments? In my sample below I would interpret that the treatments are 0.28 correlated. Is this correct? > var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,