iRepro package - installation and usage guidelines
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1. In the case that the data require transforming such as adding a constant and applying natural logarithm which would be appropriate in this example the transformation needs to be applied only to c limits For more information on the package or its functions please consult the iRepro user manual References 1 Heatherton TF Kozlowski LT Frecker RC Fagerstrom KO The Fagerstr m test for nicotine dependence a revision of the Fagerstr m Tolerance Questionnaire Br J Addict 1991 86 1119272. iRepro package installation and usage guidelines In this document we describe how to install the R package iRepro files iRepro_1 0 tar gz and iRepro_1 0 zip If you are able to install the package but the package does not load the issues should be resolved by updating to the latest version of R or at least to R version 3 0 0 or higher Any updates to the package will be posted to the R online package repository http cran r project org 1 Installing the iRepro package on Windows 1 1 Installation from iRepro_1 0 tar gz file To install the package from iRepro_1 0 tar gz file you will need the Rtools available from r project org bin windows Rtools Download the file iRepro_1 0 tar gz InR change working directory to the directory where iRepro_1 0 tar gz is saved by using command setwd in R Console or by clicking File Change dir To install the package type install packages iRepro_1 0 tar gz repos NULL type source in R console Note If installation did not complete correctly the issues should be resolved by updating to the latest version of R Otherwise installation from iRepro_1 0 zip file should work correctly 1 2 Installation from iRepro_1 0 zip file Download the file iRepro_1 0 zip In R click Packages Install package s from local zip files and select the downloaded file 2 Installing the iRepro package on OS X The procedure is the same as in L1 except that the Rtools is not needed 3 Using
3. the iRepro package to estimate ICC from grouped data The iRepro package calculates ICC from any kind of interval censored data not necessarily grouped In this section we work through one example of grouped data For each predefined category in questionnaire we first need to specify category label and cut off points e g for the question How many cigarettes day do you smoke from the Fagerstr m Test for Nicotine Dependence Category Label Lower cut off point Upper cut off point 10 or less 0 0 10 5 11 20 1 10 5 20 5 21 30 2 20 5 30 5 31 and more 3 30 5 40 In iRepro s main function intervalICC category labels correspond to the argument classes while cut off points correspond to c limits In R we would type classes lt 0 3 c limits lt matrix c 0 10 5 10 5 20 5 20 5 30 5 30 5 40 byrow TRUE nrow 4 Now we specify questionnaire data If we have N 10 respondents we need to specify two vectors of length N consisting of category labels e g gi lt c 0 0 3 2 1 0 1 2 1 0 q2 lt c 0 1 2 3 1 0 0 2 1 0 In our example this would mean that the first respondent answered that he smoked 10 or less cigarettes in both questionnaires The second respondent selected the category 10 or less in the first questionnaire and the category 11 20 in the second one and so on Finally calling intervalICC ri q1 r2 q2 predefined classes TRUE classes classes c limits c limits gives us ICC of 0 87
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