similar to: how to get residuals in factanal

Sir, I want to solve the equation Q(u)=mean, where Q(u) represents the quantile function. Here my Q(u)=(c*u^lamda1)/((1-u)^lamda2), which is the quantile function of Davies (Power-pareto) distribution. Hence I want to solve , *(c*u^lamda1)/((1-u)^lamda2)=28353.7....(Eq.1)* where lamda1=0.03399381, lamda2=0.1074444 and c=26104.50. When I used the package 'Davies' and solved Eq 1, I got the

On Fri, 25 Aug 2023 22:17:05 +0530 ASHLIN VARKEY <ashlinvarkey at gmail.com> wrote: > Sir, Please note that r-help is a mailing list, not a knight! ?? > I want to solve the equation Q(u)=mean, where Q(u) represents the > quantile function. Here my Q(u)=(c*u^lamda1)/((1-u)^lamda2), which is > the quantile function of Davies (Power-pareto) distribution. Hence I > want to

Hi all, I am a bit mystified by this error message that I get when I try to apply lmer() to a simple dataset with one between factor (age) and one within factor (item): "$ operator is invalid for atomic vectors" I'll just provide the code, because I don't see where the problem is: library(lme4) options(contrasts=c("contr.helmert","contr.poly")) data =

Ok I made Jarque-Bera test to the residuals (merv.reg$residual) library(tseries) jarque.bera.test(merv.reg$residual) X-squared = 1772.369, df = 2, p-value = < 2.2e-16 And I reject the null hypotesis (H0: merv.reg$residual are normally distributed) So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

Dear: Continuing the issue of 'ifelse'! I selecting the data whose 'x2'=1 for maximizing likelihood. I used two way to do this but the results are different. 1.Way one I use the data for x2=1 and run the program. It works for me. Tthe program is described as below: function (parameters,y1,x11) { p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3]

Hi all I'm using the Box-Ljung test (from within R) to test if a time-series in independently distributed. 2 questions: 1) p-value returned by Box-Ljung: IF I want to test if the time-series is independant at say 0.05 sig-level (it means that prob of erroneously accepting that the time-series is independent is 0.05 right?) --> then do I consider time-series as "independant"

I do not understand, from a PCA point of view, the option center=F of prcomp() According to the help page, the calculation in prcomp() "is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix" (as it's done by princomp()) . "This is generally the preferred method for numerical accuracy"

Hi. It seems that nearly every time I try to use factanal I get the following response: >faa2db1<-factanal(mretdb1,factors=2,method="mle",control=list(nstart=25)) Error in factanal(mretdb1, factors = 2, method = "mle", control = list(nstart = 25)) : unable to optimize from these starting value(s) > In the case cited above, mretdb1 is synthetic data created

How does one rotate the loadings from a principal component analysis? Help on function prcomp() from package mva mentions rotation: Arguments retx a logical value indicating whether the rotated variables should be returned. Values rotation the matrix of variable loadings (i.e., a matrix whose olumns contain the eigenvectors). The function princomp returns this in the element

i want to estimate parameters with maximum likelihood method with contraints (contant numbers). for example sum(Ai)=0 and sum(Bi)=0 i have done it without the constraints but i realised that i have to use the contraints. Without constraints(just a part-not complete): skellamreg_LL=function(parameters,z,design) { n=length(z); mu=parameters[1]; H=parameters[2]; Apar=parameters[3:10];

Dear R users, I want to generate two random variables (X1, X2) from uniform distribution (-0.5, 0.5) with a specified correlation coefficient r. Does anyone know how to do it in R? Many thanks! Menghui

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Hi. I tried running the following code. I don't understand the error. mydata <- read.table("C:/dataForR/radiology/wordFrequencies.csv", header=TRUE, sep=",") > dim(mydata) [1] 982 924 mydataN<-mydata[,-923] > dim(mydataN) [1] 982 923 cor(mydataN) factanal(mydataN, factors=3) Error in solve.default(cv) : Lapack routine dgesv: system is exactly singular

Dear R-users, I recently used factanal function in mva library to perform factor analysis. I notice that the function will give an error message that the log likelihood function is infinite whenever the observed covariance matrix is singular. This is because the function uses multivariate normal log-likelihood which involve log determinant of the observed covariance matrix. But I wonder if the

Dear all, I have a series of variables that looks roughly like the sample data below and I'm trying to conduct a factor analysis. I've omitted cases with missing values for the factor analysis, but now I'd like to use the scores on each component as new variables in the *original* data set for analysis. That is, I'd like to take the scores on each of the two factors and see how

I am performing a large (105 variable) factor analysis with factanal, specifying promax rotation. I kow that some loadings over 1.0 are not unsual with that rotation, but I have some as large as 1.2, which seems extreme. I am skirting the assumptions of the model by using responses on a 7-point rating scale as data; I may have to go back and compute polychoric correlations instead of product

Dear friends, I'm using R to produce the following Factor Analysis: > matriz.cor<-hetcor(matrix(as.factor(data), ncol=variables, byrow=T))$correlations > factanal(x=data, factors=2, covmat=matriz.cor, scores='regression') Then the screen output shows the following message: Error en factanal(x = data, factors = 2, covmat = matrix, : requested scores without

hi, Does factanal() force the user to use the formula interface if they wish to specify an na.action? v1 <- c(1,1,1,1,1,1,1,1,NA,1,3,3,3,3,3,4,5,6) v2 <- c(1,2,1,1,1,1,2,1,2,1,3,NA,3,3,3,4,6,5) v3 <- c(3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,5,4,6) v4 <- c(3,3,4,NA,3,1,1,2,1,1,1,1,2,NA,1,5,6,4) v5 <- c(1,1,1,1,1,3,3,3,3,3,1,1,1,1,1,6,4,5) v6 <- c(1,1,1,2,1,3,3,3,4,3,1,1,1,2,1,6,5,4) m1

I was wondering if there is a way of predicting factor scores of new data for factor analysis in R (similar to "predict" in S-plus). So far I have not been able to find it, nor found reference to it. Thanks, Chris Fonnesbeck -- ~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~* Christopher J. Fonnesbeck Ph.D. Student Georgia Cooperative Wildlife Unit University of Georgia Athens, GA 30602