similar to: error about dimnames...

Hello, I've been unsuccessfully trying to reproduce a sem from Grace et al. (2010) published in Ecological Monographs: http://www.esajournals.org/doi/pdf/10.1890/09-0464.1 The model in question is presented in Figure 8, page 81. The errors that I've been getting are: 1. Using a correlation matrix: res.grace <- sem(grace.model, S = grace, N = 190) Warning message: In sem.default(ram

Hi R-colleagues. In the sem-package i have a problem to introduce hidden variables. As a simple example I take an ordinary factor analysis. The program: cmat=c(0.14855886, 0.05774635, 0.08003300, 0.04900990, 0.05774635, 0.18042029, 0.11213013, 0.03752475, 0.08003300, 0.11213013, 0.24646337, 0.03609901, 0.04900990, 0.03752475, 0.03609901, 0.31702970)

Hi everyone, I import data from access, and make an object (c) store the data, when I want to use it in function mnp, it gives the below error: Error in dimnames(x) <- dn : length of 'dimnames' [2] not equal to array extent > x<-odbcConnectAccess("db2.mdb") > c<-sqlFetch(x,"Sheet2") > odbcClose(x) > c choice clio punto polo 1

I am attempting to run a Poisson EWMA model using Patrick Brandt's source code. I get the following error when I run the code: Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent Dimnames(x) looks like this: [[1]] NULL [[2]] [1] "mip" "div" "nom" "unity" "mood"

Hello R Users, While I have attempted to dig into the R help files and I have not identified the answer to these questions, I apologize in advance if my questions were answered in the past. I also recognize that one of my questions unfortunately verges on statistical rather than code territory. I have two rather unrelated questions about using the sem and polycor packages for a relatively

Hello. I started using the sem package in R and after a lot of searching and trying things I am still having difficulty. I get the following error message when I use the sem() function: 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. I started with a

Hello R Community, I am using the Lavaan package in R 2.15.0 to analyze data collected from 1200 lakes across North America. My dataset includes 3 continuous independent variables (LOG_NTL, LOG_PTL, and LOG_SR_A_D) and 1 continuous dependent variable (BIOVOL) . I have successfully constructed structural equation models using the Lavaan package (example included below with code), but I have not

I've got a covariance matrix that I'd like to index using the dimnames: > vcov1 n0 x0 s n1 n2 n0 82.43824759 1.839505e-02 -4.975196e-01 2.882394e+03 -2.615986e-01 x0 0.01839505 6.134010e-03 -7.695922e-04 -6.373946e+01 6.086321e-03 s -0.49751964 -7.695922e-04 9.638943e-03 3.406594e+02 -3.173671e-02 n1 2882.39407745

I'm using rpart to fit a tree using a large dataset: 7000 observations, 4651 variables. All but one of the variables (age) are binary. When I run the code: fit1 <- rpart(lowergi ~ ., data=dset,method="class") I get the error: Error in dimnames(X) <-list(dn[1L]], unlist(collabs, use.names=FALSE)) : Length of `dimnames' [2] not equal to array extent I have no idea

Hi , I want to assign names to the rows in my matrix so that when I use the 'agnes' function from R cluster package , the dendogram that is produced represents the rows of the matrix. This way I would know what elements in the matrix are clustered together. But when I do the following, rownames(distF) <- paste("A",dim(distF)[1]) it gives the following error; Error in

Hi all, I am attempting to learn my way through the sem package by constructing a simple structural model for some of my data on bird diversity, abundance, and primary productivity. I have constructed a covariance matrix between these variables as per the following: >S_matrix = matrix(c( >+ 0.003083259, 0, 0, >+ 0.143870284, 89.7648490, 0, >+ 0.276950919,

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.

Could anyone help me with some little problem? When I plot the frontier I get the following message: *"Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent"*(see below for detail). How could I solve this. Thanks a lot. ##---------------------------- Portfolio construction & Optimisation------------------------ #Assets: LUTAX,

Hello all, I'm trying to use the user-created multinomRob to make some models for my data. While documentation for that function is fine and I think I'm using it correctly, I'm getting the following error when I try to use my lists of variables. data <- read.csv("data.csv", header=FALSE) subj1 <- data.frame(radius=data$V3[9:11], x1=data$V4[9:11], x2=data$V5[9:11],

Dear R-users, I am running a large number of simulations and estimating a structural equation model for each one using the SEM package. Each run of my program has around 8000 simulations. Most of the time the program completes all of them correctly but sometimes I get a segmentation fault in the sem routine and my program stops with the following error message: > *** caught

Hi - I am trying to fit sem model with intercepts. Here is what I have in my model. Exogeneous vars: x1 (continous), x2 (ordinal), x3 (ordinal), x4(continuous) Endogeneous vars: y1 (continuous), y2 (ordinal), y3 (ordinal) SEM model: x1 -> eta1; x2 -> eta1; x3 -> eta2; x4 -> eta2; eta1 -> y1, eta1 -> y2, eta2 -> y2, eta2 -> y3 However, in these arrow models, I

Hi, we are trying to do structural equation modelling on R. However, one of our predictor variables is categorical (smoker/nonsmoker). Now, if we want to run the sem() command (from the sem library), we need to specify a covariance matrix (cov). However, Pearson's correlation does not work on the dichotomous variable, so instead we produced a covariance matrix using the Spearman's (or

Hello, all, I'm a new R user (new to any programming language in general, really), so I apologize if this is easy/has already been answered (I've attempted searching online but did not understand the pages I found). My data is stored in text files with the headers LANE, RNA_NAME, SEQ, and SEQCNT. I've been using x = "filename.txt" y = aggregate(x$SEQCNT, list(x$RNA_NAME),

Hi everyone, I have the following problem: I want to perform a LDA with the function lda(). My data object mat.data is a matrix with dimensions > dim(mat.data) [1] 1228 44 and my grouping vector grp has length 1228: > length(grp) [1] 1228 Every time I call lda(), the following error message occurs: > lda(mat.data,grp) Error in lda.default(x, grouping, ...) : length of dimnames [2]

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. Send mail to mime@docserver.cac.washington.edu for more info. ---559023410-1251336619-945861747=:5357 Content-Type: TEXT/PLAIN; charset=US-ASCII While fiddling with apply I noticed that there appears to be an error in the dimnames, which actually