Dear Bob, The reasons you mentioned are supposedly good features in R -- not giving lots of output you do not necessarily need. I guess the question is why do you want R to produce what you get from SPSS? SPSS is hardly a gold standard in statistical software. But I agree that it is quite difficult for users of SPSS to unlearn SPSS (or SAS) while using R.
Best Marwan ---------------------------------------------- Marwan Khawaja http://staff.aub.edu.lb/~mk36 ---------------------------------------------- > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Bob Green > Sent: Wednesday, January 04, 2006 3:37 AM > To: r-help@stat.math.ethz.ch > Subject: Re: [R] A comment about R: > > > >Hello, > > > >Unlike most posts on the R mailing list I feel qualified to > comment on > >this one. For about 3 months I have been trying to learn > use R, after > >having used various versions of SPSS for about 10 years. > > > I think it is far too simplistic to ascribe non-use of R to > laziness. This may well be the case for some, however, I > have read 5-6 books on R, waded through on-line resources, > read the documentation and asked multiple questions via > e-mails - and still find even some of the basics very difficult. > > There are several reasons for this: > > 1. For some tasks R is extremely user-unfriendly. Some > comparative examples: > > (a) In running a chi-square analysis in SPSS the following > syntax is included > > /STATISTIC=CHISQ > /CELLS= COUNT EXPECTED ROW COLUMN TOTAL RESID . > > this produces expected and observed counts, row & column > percentages, residuals, chi-square & Fisher's exact test + > other output. > > In R, it is a herculean task to produce similar output . It > certainly, can't be produced in 2 lines as far as I can tell. > > (b) in SPSS if I want to compare multiple variables by a > single dependent variable this is readily performed > > CROSSTABS > /TABLES=baserdis baserenh basersoc baseradd socbest > disbest entbest addbest worsdis worsphy by group > > I used the chi-square example again, but the same applies for > a t-test. I started looking into how to do something similar > in R, with the t-test command but gave up. R does force the > user to take a more considered approach to analysis. > > (c) To obtain a correlation matrix in R with the correlation > & p-value is no simple task - > > In SPSS this is obtained via: > > GET > FILE='D:\a study\data\dat\key data\master data.sav'. > NONPAR CORR > /VARIABLES= goodnum badnum good5 bad5 avfreq avdayamt > /PRINT=KENDALL TWOTAIL > /MISSING=PAIRWISE . > > In R something like this is required - > > > by(mydat, mydat$group, function(x) { > + nm <- names(x) > + rho <- matrix(, 6, 2) > + rho.nm <- matrix(, 6, 2) > + k <- 1 > + for(i in 2:4) { > + for(j in (i + 1):5) { > + x.i <- x[, i] > + x.j <- x[, j] > + ct <- cor.test(x.i, x.j, method=c("kendall") , alternative > + =c("two-sided")) rho[k, 1] <- ct$estimate rho[k, 2] <- > + round(ct$p-value, 3) rho.nm[k, ] <- c(nm[i], nm[j]) k <- k > + 1 } } rho > + <- cbind(as.data.frame(rho.nm), as.data.frame(rho)) > + names(rho) <- c("freq.i", "freq.j", "cor", "p-value") rho > + }) > > 2) It is not always clear what the output produced by R, is. > The Mann-Whitney U-test is a good example. In R, it seems a > standardised value is obtained. I was advised that it is easy > enough to check this as R is open-source, but at least for > me, I don't believe I would understand this code anyway. It > is confusing when comparative programs such as R and SPSS > produce dis-similar results. For the user it is important to > be able to fairly easily reconcile such differences, to > engender confidence in results. > > 3) I find the help files in R quite difficult to understand. > For example, see help(t.test). It is almost assumed by the > examples that you know what to do. Personally, I would find > some form of simple decision tree easier -e.g. If you want to > perform a t-test with the dependent variable in one column > and the dependent use in another use t.test(AVFREQ~GROUP) . > If you want to perform a t-test with the dependent variable > in separate columns (each column representing a different > group) use - t.test(AVFREQ1, AVFREQ2) . > > 4) My initial approach to using R, was to run commands I had > used commonly in SPSS and compare the results. I have only > got as far as basic ANOVA. > This has been time-consuming and at times it has been > difficult to obtain advice. Some people on the R list have > been extremely generous with their time and knowledge, and I > have much appreciated this assistance. At other times I see > responses met with something like arrogance. With the > sophistication of R, there is also an elitism. This is a > barrier to R being more widely accepted and used. > > 5) differences in terminology - this is just part of the > learning process, but I still found it took quite some time > to work out simple commands and what different analyses were called. > > 6) system administrators may be wary of freeware. > > No doubt for the sophisticated user, my comments may seem > trite and easily resolved, however I believe my comments have > some relevance as to why R is not more readily used or accepted. > > > Bob Green > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html