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Dear Adrian and Ian (and r-helpers), I encountered a curious result in developing an example using the bicreg function in the BMA package: I noticed that pairs of models with equal R^2 and equal numbers of predictors had nevertheless different BIC values. Looking at the bicreg function, the definition of BIC appears to be the usual one, or close to it [bic <- n * log(1 - r2/100) + (size - 1) *

Dear R users, I'm currently using the Graphics package to display several hundred of matrix objects, using a layout and the image() function. It works well except for large matrices (> 1000*1000) or for a large number of matrices (there is a limitation around 400 if I remember well) To solve these issues, I move to the Matrix package which is much more efficient for large sparse matrix,

Hi, I have a long list of lists from which I want to efficiently extract and rbind elements. So I'm using the approach below: f <- function(i){ out <- replicate(5, list(matrix(rnorm(80), nc=20))) names(out) <- letters[1:5] out } set.seed(1) lst <- lapply(1:1.5e6, f) (t0 <- system.time(tmp <- do.call(rbind, lapply(lst, '[[', 'b')))) Is there

OS X R 4.3.3 Colleagues I have created objects using the Surv function in the survival package: > FIT.1 Call: survfit(formula = FORMULA1) n events median 0.95LCL 0.95UCL SUBDATA$ARM=1, SUBDATA[, EXP.STRAT]=0 18 13 345 156 NA SUBDATA$ARM=2, SUBDATA[, EXP.STRAT]=1 13 5 NA 186 NA SUBDATA$ARM=2, SUBDATA[, EXP.STRAT]=2 5

# I have a question about how factanal is calculating the regression factor # scores based on an oblique rotation (promax) of the factors. # # As is explained in the help file, regression factor scores are # obtained as # # hat f = Lambda' Sigma^-1 x # # However, according to Harman's "Modern Factor Analysis" (e.g. second # edition, pp. 351-352) the formula is # # hat f = Phi

I've just found the lavaan package, and I really appreciate it, as it seems to succeed with models that were failing in sem::sem. I need some clarification, however, in the output, and I was hoping the list could help me. I'll go with the standard example from the help documentation, as my problem is much larger but no more complicated than that. My question is, why is there one latent

On Fri, 20 Feb 2015 08:46:13 -0500, Andres wrote: > A "sip set debug on" will give you more info on why you are getting the > 404. It probably has to do something with your context/dialplan. on tleilax: tleilax*CLI> tleilax*CLI> sip set debug on SIP Debugging enabled tleilax*CLI> on doge: thufir at doge:~$ thufir at doge:~$ sudo sipsak -vv -s sip:devries at

Hi, In the following results I interpret exp(coef) as the factor that multiplies the base hazard rate if the corresponding variable is TRUE. For example, when the bucket is ks008 and fidelity <= 3, then the rate, compared to the base rate h_0(t), is h(t) = 0.200 h_0(t). My question is then, to what case does the base hazard rate correspond to? I would expect the reference to be the first

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare "log-linear model fits to

Hi, I'm trying to read a .mat file into R (2.11.1) with medium success so far. The file I have is a MATLAB 5.0 MAT-file exported from RiverSurveyor LIVE software (http://www.sontek.com/software.php). I have R.matlab and Rcompression installed and readMat() starts reading the file, as can be seen in verbose mode (hence medium success), but then gives the following error: Error in dim(matrix)

Hi Dennis, look at the help page for summary.survfit, the Value n.event. G?ran On 2024-05-15 22:41, Dennis Fisher wrote: > OS X > R 4.3.3 > > Colleagues > > I have created objects using the Surv function in the survival package: >> FIT.1 > Call: survfit(formula = FORMULA1) > > n events median 0.95LCL 0.95UCL >

Hi R-users,   I would like to search for the values of seq that match my rand values.  In excel I will use =VLOOKUP(G2,$E$2:$F$32,2).  For example, for rand=.262 it will give me approximately seq=120 and rand=0.964293344, seq=460 and etc.   E           F     G cdf         seq   rand 0.00E+00    0     0.262123478 1.56E-03    20    0.964293344 1.55E-02    40    0.494827113 5.30E-02    60   

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

On 2/20/15 2:29 PM, thufir wrote: > On Fri, 20 Feb 2015 08:46:13 -0500, Andres wrote: > > >> A "sip set debug on" will give you more info on why you are getting the >> 404. It probably has to do something with your context/dialplan. > > on tleilax: > > tleilax*CLI> > tleilax*CLI> sip set debug on > SIP Debugging enabled > tleilax*CLI>

Hi, I found a discrepancy between results in R and Stata for a factor analysis with a promax rotation. For Stata: . *rotate, factor(2) promax* (promax rotation) Rotated Factor Loadings Variable | 1 2 Uniqueness -------------+-------------------------------- pfq_amanag~y | -0.17802 0.64161 0.70698 pfq_bwalk_~ø | 0.72569 0.05570

Hello I am using the step function in order to do backward selection for a linear model of 52 variables with the following commands: object<-lm(vars[,1] ~ (vars[,2:(ncol(predictors)+1)]-1)) BackS<-step(object,direction="backward") but it isn't dropping any if the variables in the model, but there are lots of not significant variables as you can see here >

Hey, I am just starting to learn R now and I typed in this simple survival program: library(survival) t <- c(10,13,18,19,23,30,36,38,54,56,59,75,93,97,104,107,107,107) c <- c(1,0,0,1,0,1,1,0,0,0,1,1,1,1,0,1,0,0) data <- Surv(t,c) km <- survfit(data) summary(km) Call: survfit(formula = data) but everytime I run it I get this error: Error in

What's the difference between user "123" and "devries"? Based on the output here, they seem the same..? tleilax*CLI> tleilax*CLI> sip show users Username Secret Accountcode Def.Context ACL Forcerport 201 password 201 default No Yes 123

Dear all, I need an help because I don´t know how to perform the analysis in the right way, as I get different beheaviors using t-test and two ways ANOVA. In what follow I post the table, my goal and the strange results I got. I kindly ask you an help because I really don´t know how to solve this problem. So the table is this: number stimulus condition response 1