similar to: polycoric correlation

Dear R-users I have a probelm running stepAIC in R1.7.1 I wrote a program which used stepAIC as a part of it, and it worked fine while I was using the previous version of R1.7.0. However, I found the program did not work any more. Now, R produces a message which tells "Error in as.data.frame.default(data) : can't coerce function into a data.frame" every time I run the part of

Dear R users I have a data frame containing character and numeric variables, whose name is seishin. When I tried to assign NA to "" in the data frame, R, 2.00 showed an error message, such as > seishin[seishin==""]<-NA Error: NAs are not allowed in subscripted assignments This did not happen under R 1.9.0. More oddly, The following commands work just fine under R

Dear R-users I analysed the same data set by two different ways; analysis of covariance by using lm and anova functions and multiple comparison by using simtest function in the multcomp library. The output from the analysis of covariance is; > y<-lm(D~Cond+Q1,data=x) > anova(y) Analysis of Variance Table Response: D Df Sum Sq Mean Sq F value Pr(>F) Cond 2

Hello. Could someone help me? I can see Japanese language in R when I use RGui. I can use Japanese language in Emacs using Mule option. Now, Can I see Japanese language in R which is run with Emacs(ESS) ? I am using windows 98 second edition. Could someone help me ? Sincerely ----------------------- Hiroto Miyoshi ???? h_m_ at po.harenet.ne.jp

Dear R expert I encountered a bewildering situation, about which I am seeking help.? I wrote a following toy script which can recreate the situation. --- the script begins here --- ?? 1?? ? library(tidyverse) ?? 2?? ? library(rlist) ?? 3?? ? library(patchwork) ?? 4?? ? set.seed(42) ?? 5?? ? f <- function(x, y, z, x_axis) { ?? 6?? ??? rslt_with_I <- I(x^2 * 0.5) + I(x * y) ?? 7??

This is obviously not a complete answer, but if you look at the data closely: str(dta) 'data.frame': 40 obs. of 6 variables: $ x : num 0.915 0.937 0.286 0.83 0.642 ... $ y : num 0.3796 0.4358 0.0374 0.9735 0.4318 ... $ z : int 1 1 1 1 1 1 1 1 1 1 ... $ x_axis : int 1 2 3 4 5 6 7 8 9 10 ... $ rslt_without_I: num 0.7657 0.8474

I have been using the hetcor function in the polycor package. When I don't specify the use option everything runs smoothly. However, when I specify use either as "pairwise.complete.obs" or "complete.obs" I get this error Error in optim(rho, f, control = control, hessian = TRUE, method = "BFGS") : non-finite value supplied by optim Is this an error in

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A bit late, but you might like to look at http://www.qimr.edu.au/davidD/polyr.R Regarding the original posters queries: You can analyse polychoric correlations as if they were Pearson correlations using standard software (eg sem), and this usually doesn't do too badly, or go to AWLS (Browne) in LISREL etc, or ML analysis of the full multidimensional contingency table using programs such as

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

I am working on a project where we start with start with 2 long, equal-length vectors, massage them in various ways, and end up with a function mapping one interval to another. I'll call that function "f1." The last step in R is to generate f1 as the value of the approxfun function. I would like to put f1 into a package, but without having the package redo the creation of

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random

I am involved in a study where, as in most of life, men demonstrate themselves to be recalcitrant. So while we have many probands and most of their mothers we only have about 50% of the trios being complete. I have been running tdt and trio.types. It appears as if it is ignoring the duos. Sometimes a duo can be informative. For instance Father ..missing Mother 1/2 Proband 1/1 This duo shows that

About two years ago there was a thread about this which suggested that at that time nobody had these coefficients ready to go. (a) has anyone in the meanwhile programmed them? (b) I think I can see how to do the tetrachoric one with mvtnorm on similar lines to an example on the help page so will try that if nobody else already has (c) looking at the polychoric one makes me realise yet again

Hello, I found in the forum two threads about point biserial correlation. One of them (1) mentioned "a point-biserial correlation is just a Pearson correlation where one of the variables is dichotomous. Thus, the command is just the normal cor function". The other (2) mentioned "Professor Fox's package polycor" as a way to calculate point biserial correlation?

Dear community I'm pretty new to R and I'm trying to do a Point-baserial correlation for a nominal dichotomous variable with a interval scaled variable. It works fine, but the output just shows me the correlation and nothing else (p-Value would be important). I tried it with the following codes: - biseral.cor() - cor.biseral() - I also tried a polyserial() I've found on this

Hi the list. Is there a bug in mmlcr package ? The following code does not compile: mmlcrTest <- function(dataW){ dataL <- reshape(dataW,idvar="id",timevar="T",varying=list("T0","T1","T2"),direction="long",v.names="score") resultR <- mmlcr(outer= ~ 1 | id, components = list(list(formula =

Hi I am looking for a structural equation modeling package in R which can be used for categorical data. Is anyone aware of the existence of such a package? Would appreciate any help on this. Thank you Upasna -- --------------------------------------------------------------------- Upasna Sharma Research Scholar Shailesh J. Mehta School of Management, Indian Institute of Technology, Bombay

What test do I use to determine if there is a correlation between a discrete variable and a continuous variable? For example - I have water quality ratings for streams (excellent, good, fair, poor) and a corresponding nitrogen concentration for each rating. I want to know if the the ratings correlate with the concentration of nitrogen in the stream. Help?

Hello, I have a data set containing categorical and ordinal factors, as well as sampling weights (i.e., survey weights reflecting unequal probabilities of selection). I want to fit a structural equation model with sem(). I have run sem() on weighted covariance matrices using advice from John Fox (see <http://tolstoy.newcastle.edu.au/R/e5/help/08/12/8773.html> and