search for: biseri

Dear list, has anyone written a package/function in R for computing a point- biserial resp. biserial correlation? Thanks in advance Bernd

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 calcula...

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Dear R-listers, Can someone suggest a method for generating a finite Bernoulli sequence that is likely to have a given point-biserial correlation with an existing Bernoulli sequence? _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400 Charlottesville, VA 22904-4400 Parcels: Room 102 Gilmer Hall McCormick Road Charlottesville,...

...sons 181, 178, 182, 160, 187, 193, 184, 184, 175, 178, 184, 184, 174, 185, 175 height of female persons 164, 165, 160, 174, 167, 161, 164, 165, 169, 175, 165, 155, 172, 164, 172, 166, 160, 159, 158, 173 I want to determine if height is dependent on gender, and I need to find, as I understand, point biserial correlation. I had tried to use package "ltm", and obtained coefficient which is about 0.74. I also had tried to calculate it manually (with some MS Excel help for simple calculations), using equation given in Internet (e.g., in Wikipedia; this equation is also given in some copy-books)...

.../reliability of multiple choice tests. Doing things such as classical test analysis, factor analysis, item response theory. I've used psychometric (item.exam), MiscPsycho (alpha.Summary), and ltm (rcor.test). MiscPsycho reported the numbers most similar to what I get in SPSS: corrected point biserial correlations, cronbach's alpha. I didn't understand what the psychometric package meant by its "discrimination" and "item reliability" numbers output by the item.exam function. Perhaps the former is uncorrected point biserial correlations? They were higher values....

...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 question page: http://stackoverflow.com/questions/35880910/point-biserial-and-p-value Problem: It's not working (telling me y has fewer than 2 levels) and it would also be for a ordinal dichotomous variable, but I only have a nominal dichotomous variable. In the forum if found following Syntax that I've tried out: https://stat.ethz.ch/pipermail/r-help/2003-A...

...estimation of the Rasch model 2) alpha() , coefficient alpha 3) alpha.Summary(), gives what alpha would be if item i were excluded 3) scoreCon(), create score conversion tables, 4) class.acc(), classification accuracy statistics 5) simRasch(), simulate Rasch item responses 6) classical(), point-biserial and p-values Future versions will include Master's partial credit model, DIF statistics, procedures for linking scales (e.g., Stocking-Lord), lattice graphics for item characteristic curves, and bayesian scoring methods (EAP). Below is a sample session: set.seed(1) # for replication only...

...estimation of the Rasch model 2) alpha() , coefficient alpha 3) alpha.Summary(), gives what alpha would be if item i were excluded 3) scoreCon(), create score conversion tables, 4) class.acc(), classification accuracy statistics 5) simRasch(), simulate Rasch item responses 6) classical(), point-biserial and p-values Future versions will include Master's partial credit model, DIF statistics, procedures for linking scales (e.g., Stocking-Lord), lattice graphics for item characteristic curves, and bayesian scoring methods (EAP). Below is a sample session: set.seed(1) # for replication only...

...without the correlation between variables. I thought I could use for the continous (numerical) and ordered factor a spearman correlation that is using the ranks. But I thought also that I have to use a contingency table for the dichotomous factors. I read also that it is possible to use a point-biserial correlation to calculate the correlation between dichotomous and continuous variables. Now I am confused what I should use to calculate the correlation using all my variables and how I could do that in R. Is it possible with cor(), rcorr(), cormat() or other R-functions using one of the availabl...

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

My data set has a mixed level of measurement: Nominal scale - location (city) Ordinal scale - temperature (low, medium, high) Interval scale - age & value Just curious if there is an R package available that will handle the mixed "Level of Measurement". Looking to do graphical presentation of the correlation and also a qualitative analysis of the correlation.  Thanks again for

Dear R-users Could anyone tell me which library contains a function to compute polycoric correlations? I wonder the same question was asked a while ago, but I could not locate the mail in the R-help archives. Sorry for bothering you. Sincerely ------------------------ Hiroto Miyoshi ???? h_m_ at po.harenet.ne.jp

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

I am looking to fit one or more latent categorical variables to data that is a mixture of categorical and continuous variables. Factor analysis would work for continuous data, latent class analysis for categorical data. I understand that in a package such as MPlus I could perform a single analysis of both data types. Are there similar routines available in R? Stuart -----Original Message-----

...is wrong because the modification should be done with kendall too, or * The variable PVAL has to be assigned in the kendall block. I hope this is clear so far. Please send me some comments, because I'm not sure if my observation is ok. And currently I try to figure out the significance in the biserial case which of course makes heavy use of the tied case. Cheers, Dirk

Hello, I am running polychoric correlations on a dataset composed of 12 ordinal and binary variables (N =384), using the polycor package. One of the association (between 2 dichotomous variables) is very high using the 2-step estimate (0.933 when polychoric run only between the two variables; but 0.801 when polychoric run on the 12 variables). The same correlation run with ML estimate returns a

...Content-Type: text/plain; charset="us-ascii" Dear Upasna, I apologize for responding so late, but I was out of town when you posted your query to r-help. The sem() function in the sem package can handle dichotomous and ordered categorical observed variables via tetrachoric, polychoric, biserial, and polyserial correlations computed by the polycor package. See in particular the hetcor() function in that package, which can compute "heterogeneous" correlation matrices. Standard errors for parameter estimates in the SEM can then be computed by bootstrapping; see ?boot.sem in the s...