search for: multinormality

is there any function under R that allows me to test the normality of my 92 sumples? -- View this message in context: http://www.nabble.com/multinormality-tp17717230p17717230.html Sent from the R help mailing list archive at Nabble.com.

Hello, I am currently writing a program whose goal is to fit usual distributions (estimating parameters and confidence intervals for a given distribution). After some research in R, R-help and google I have found most of what I was looking for (especially thanks to MASS - fitdistr() ), however there are still a few distributions I could not find R code for: Multinormal, Truncated normal,

Dear R community, I was trying to estimate density at point zero of a multivariate distribution (9 dimensions) and for this I was using a multinormal approximation and the function dmvnorm , gtools package. To have a sense of the error I tried to look the mismatch between a unidimensional version of my distribution and estimate density at point zero with function density, dmvnorm and dnorm. At

Good Night I made different test to check normality and multinormality in my dataset, but I don´t know which test is better. To verify univariate normality I checked: shapiro.test, cvm.test, ad.test, lillie.test, sf.test or jaque.bera.test and To verify multivariate normal distribution I use mardia, mvShapiro.Test, mvsf, mshapiro.test, mvnorm.e. I have a dataset wi...

Dear R list members, Since today seems to be the day for optimization questions, I have one that has been puzzling me: I've been doing some work on sem, my structural-equation modelling package. The models that the sem function in this package fits are essentially parametrizations of the multinormal distribution. The function uses optim and nlm sequentially to maximize a multinormal

I would like to estimate the sigma matrix of multinormal distribution through ML. But I don't know how to optimize the parameter sigma. Could any one help me? Thank you so much~ Yen Lee

Hello folks, many multivariate anayses (e.g., structural equation modeling) require multivariate normal distributions. Real data, however, most often significantly depart from the multinormal distribution. Some researchers (e.g., Yuan et al., 2000) have proposed a multivariate transformation of the variables. Can you tell me, if and how such a transformation can be handeled in R? Thanks in

Dear R-users (and developers), I am looking for an efficient framework to carry out parameter estimations based on MCMC (optionally with specified priors). My goal is as follow: * take ANY R-function returning a likelihood-value (this function may itself call external programmes or other code!) * run a sampler that covers the multidimensional parameter space (thus creating a posterior

Dear R list members, I have a question about the proper use of the typsize and fscale arguments to nlm. I use nlm in my sem package to fit general structural-equation models, which entails maximizing a multinormal likelihood with respect to parameters that represent regression coefficients and covariances of variables. The magnitudes of these parameters can be very different. The

...independent components, depending on the underlying assumptions. The result of the transformation is an object of the S4 class ics which is provided by this package. Besides generic functions to create and work with ics objects the package contains also some scatter matrices and two tests for multinormality. The 'ICSNP' package contains tools for nonparametric multivariate analysis, including the estimation of location and shape as well as some tests for location and independence. Shape matrices from this package can be used as one of the scatter matrices needed in the package ICS wherea...

Dear John, Dear Joseph, Thank you for your quick answers and the pointer to semnet. I try to clarify on my assumptions: - yes, I am willing to assume multivariate normality - no, I don't want to assume a single factor model - I assume there is an unknown number of factors, and I do not know which items belong to which factors but I still want to estimate single item reliabilities Is this

...independent components, depending on the underlying assumptions. The result of the transformation is an object of the S4 class ics which is provided by this package. Besides generic functions to create and work with ics objects the package contains also some scatter matrices and two tests for multinormality. The 'ICSNP' package contains tools for nonparametric multivariate analysis, including the estimation of location and shape as well as some tests for location and independence. Shape matrices from this package can be used as one of the scatter matrices needed in the package ICS wherea...

Dear R users, Jarrett Byrnes and I would like to announce version 2.0-0 of the sem package for fitting observed- and latent-variable structural equation models. This is a general reworking of the original sem package (which is still available on R-Forge as package sem1). Some highlights of sem 2.0-0 include: o More convenient and compact model specification, including the default automatic

Dear R users, Jarrett Byrnes and I would like to announce version 2.0-0 of the sem package for fitting observed- and latent-variable structural equation models. This is a general reworking of the original sem package (which is still available on R-Forge as package sem1). Some highlights of sem 2.0-0 include: o More convenient and compact model specification, including the default automatic

I am trying to run a confirmatory factor analysis using the SEM package. My data are ordinal. I have read http://socserv.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf. When I apply the hetcor function, I receive the following error: Error in checkmvArgs(lower = lower, upper = upper, mean = mean, corr = corr, : at least one element of 'lower' is larger than 'upper' Example:

...bctrans is used to calculate lambda for the variables. I use "yeo.johnson" since there are values=0 in the data. Doing this creates following output: 2> summary(bctrans(~v.obs+ snow+ pcpt+ Q.Enz+ qd+ HH6.1, data=sel.p1, family="yeo.johnson")) yeo.johnson Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) v.obs -49.9674 5.5747 -8.9632 -9.1426 snow -4.1130 0.3326 -12.3655 -15.3719 pcpt 0.6111 0.0811 7.5341 -4.7950 Q.Enz -0.8584 0.0904 -9.4967 -20.5601 qd -26.1100 2.3432 -1...

Version 0.1-0 of a new package `ffmanova' is now available on CRAN. Comments, suggestions, etc. are welcome. Please use the email address ffmanova (at) mevik.net. The package implements 50-50 MANOVA (Langsrud, 2002) with p-value adjustment based on rotation testing (Langsrud, 2005). The 50-50 MANOVA method is a modified variant of classical MANOVA made to handle several highly correlated

Version 0.1-0 of a new package `ffmanova' is now available on CRAN. Comments, suggestions, etc. are welcome. Please use the email address ffmanova (at) mevik.net. The package implements 50-50 MANOVA (Langsrud, 2002) with p-value adjustment based on rotation testing (Langsrud, 2005). The 50-50 MANOVA method is a modified variant of classical MANOVA made to handle several highly correlated

Sorry if this question has been asked previously, I searched but found little. There also doesn't seem to be a dedicated SEM list-serv so hopefully this will find its way to the appropriate audience. In discussing SEM with a colleague I mentioned that a model they were fitting in AMOS was equivalent to a linear regression and that the coefficients would be the same. This of course was the

I thought the point of adjusting the R^2 for degrees of freedom is to allow comparisons about goodness of fit between similar models with different numbers of data points. Someone has suggested to me off-list that this might not be the case. Is an ADJUSTED R^2 for a four-parameter, five-point model reliably comparable to the adjusted R^2 of a four-parameter, 100-point model? If such values