Re: [R] MCA in R
Dear Kimmo, -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of K. Elo Sent: June-13-08 1:43 AM To: r-help@r-project.org Subject: Re: [R] MCA in R Dear John, thanks for Your quick reply. John Fox wrote: Dear Kimmo, MCA is a rather old name (introduced, I think, in the 1960s by Songuist and Morgan in the OSIRIS package) for a linear model consisting entirely of factors and with only additive effects -- i.e., an ANOVA model will no interactions. It is true, that MCA is an old name, but the technique itself is still robust, I think. The problem I am facing is that I have a research project where I try to find out which factors affect measured knowledge of a specific issue. As predictors I have formal education, interest, gender and consumption of different medias (TV, newspapers etc.). Now, these are correlated predictors and running e.g. a simple anova (anova(lm(...)) as You suggested) won't - if I have understood correctly - consider the problem of correlated predictors. MCA would do this. That's because anova() calculates sequential (type-I) sums of squares; if you use the Anova() function in the car package, for example, you'll get so-called type-II sums of squares -- for each factor after the others. You could also more tediously do these tests directly using the anova() function, by contrasting alternative models: the full model and the model deleting each factor in turn. A colleague of mine has run anova and MCA in SPSS and the results differ significantly. Yes, see above. Because I am more familiar with R, I just hoped that this marvelous statistical package could handle MCA, too :) Typically, the results of an MCA are reported using adjusted means. You could compute these manually, or via the effects package. Well, I am interested in the eta and beta values, too. Aren't the eta values just the square-roots of the R^2's from the individual one-way ANOVAs? I don't remember how the betas are defined, but do recall that they are a peculiar attempt to define standardized partial regression coefficients for factors that combine all of the levels. I have tried to use the effects package but my attempts with all.effects resulted in errors. I have to figure out what's going wrong here :) If you tell me what you did, ideally including an example that I can reproduce, I can probably tell you what's wrong. Regards, John Kind regards, Kimmo Elo -- University of Turku, Finland Dep. of political science __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] MCA in R
Although John Fox naturally mentions his Anova function, I would like to point out that drop1() (and MASS::dropterm) also does the tests of Type-II ANOVA of which John says 'more tediously do these tests directly'. It seems a lot easier to teach newcomers about drop1() than to introduce the SAS terminology and then say (to quote ?Anova) 'the definitions used here do not correspond precisely to those employed by SAS' (I would welcome a description of the precise differences on the Anova help page.) On Fri, 13 Jun 2008, John Fox wrote: Dear Kimmo, -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of K. Elo Sent: June-13-08 1:43 AM To: r-help@r-project.org Subject: Re: [R] MCA in R Dear John, thanks for Your quick reply. John Fox wrote: Dear Kimmo, MCA is a rather old name (introduced, I think, in the 1960s by Songuist and Morgan in the OSIRIS package) for a linear model consisting entirely of factors and with only additive effects -- i.e., an ANOVA model will no interactions. It is true, that MCA is an old name, but the technique itself is still robust, I think. The problem I am facing is that I have a research project where I try to find out which factors affect measured knowledge of a specific issue. As predictors I have formal education, interest, gender and consumption of different medias (TV, newspapers etc.). Now, these are correlated predictors and running e.g. a simple anova (anova(lm(...)) as You suggested) won't - if I have understood correctly - consider the problem of correlated predictors. MCA would do this. That's because anova() calculates sequential (type-I) sums of squares; if you use the Anova() function in the car package, for example, you'll get so-called type-II sums of squares -- for each factor after the others. You could also more tediously do these tests directly using the anova() function, by contrasting alternative models: the full model and the model deleting each factor in turn. A colleague of mine has run anova and MCA in SPSS and the results differ significantly. Yes, see above. Because I am more familiar with R, I just hoped that this marvelous statistical package could handle MCA, too :) Typically, the results of an MCA are reported using adjusted means. You could compute these manually, or via the effects package. Well, I am interested in the eta and beta values, too. Aren't the eta values just the square-roots of the R^2's from the individual one-way ANOVAs? I don't remember how the betas are defined, but do recall that they are a peculiar attempt to define standardized partial regression coefficients for factors that combine all of the levels. I have tried to use the effects package but my attempts with all.effects resulted in errors. I have to figure out what's going wrong here :) If you tell me what you did, ideally including an example that I can reproduce, I can probably tell you what's wrong. Regards, John Kind regards, Kimmo Elo -- University of Turku, Finland Dep. of political science __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] MCA in R
Dear Brian, -Original Message- From: Prof Brian Ripley [mailto:[EMAIL PROTECTED] Sent: June-13-08 8:13 AM To: John Fox Cc: 'K. Elo'; r-help@r-project.org Subject: Re: [R] MCA in R Although John Fox naturally mentions his Anova function, I would like to point out that drop1() (and MASS::dropterm) also does the tests of Type-II ANOVA of which John says 'more tediously do these tests directly'. It's true that for an additive model (such as Kimmo's), drop1() and Anova() produce the same sums of squares, but for a model in which some terms are marginal to others, drop1() produces tests only for the high-order terms. One could specify scope = ~ . to drop1(), but that produces so-called type-III tests. Perhaps there's some convenient way around this of which I'm unaware. It seems a lot easier to teach newcomers about drop1() than to introduce the SAS terminology and then say (to quote ?Anova) 'the definitions used here do not correspond precisely to those employed by SAS' (I would welcome a description of the precise differences on the Anova help page.) As I recall, the differences are for type-III tests, where in Anova() these are dependent upon contrast coding. Regards, John On Fri, 13 Jun 2008, John Fox wrote: Dear Kimmo, -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of K. Elo Sent: June-13-08 1:43 AM To: r-help@r-project.org Subject: Re: [R] MCA in R Dear John, thanks for Your quick reply. John Fox wrote: Dear Kimmo, MCA is a rather old name (introduced, I think, in the 1960s by Songuist and Morgan in the OSIRIS package) for a linear model consisting entirely of factors and with only additive effects -- i.e., an ANOVA model will no interactions. It is true, that MCA is an old name, but the technique itself is still robust, I think. The problem I am facing is that I have a research project where I try to find out which factors affect measured knowledge of a specific issue. As predictors I have formal education, interest, gender and consumption of different medias (TV, newspapers etc.). Now, these are correlated predictors and running e.g. a simple anova (anova(lm(...)) as You suggested) won't - if I have understood correctly - consider the problem of correlated predictors. MCA would do this. That's because anova() calculates sequential (type-I) sums of squares; if you use the Anova() function in the car package, for example, you'll get so-called type-II sums of squares -- for each factor after the others. You could also more tediously do these tests directly using the anova() function, by contrasting alternative models: the full model and the model deleting each factor in turn. A colleague of mine has run anova and MCA in SPSS and the results differ significantly. Yes, see above. Because I am more familiar with R, I just hoped that this marvelous statistical package could handle MCA, too :) Typically, the results of an MCA are reported using adjusted means. You could compute these manually, or via the effects package. Well, I am interested in the eta and beta values, too. Aren't the eta values just the square-roots of the R^2's from the individual one-way ANOVAs? I don't remember how the betas are defined, but do recall that they are a peculiar attempt to define standardized partial regression coefficients for factors that combine all of the levels. I have tried to use the effects package but my attempts with all.effects resulted in errors. I have to figure out what's going wrong here :) If you tell me what you did, ideally including an example that I can reproduce, I can probably tell you what's wrong. Regards, John Kind regards, Kimmo Elo -- University of Turku, Finland Dep. of political science __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting- guide.html and provide commented, minimal, self-contained, reproducible code. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
Re: [R] MCA in R
Dear John, thanks for Your quick reply. John Fox wrote: Dear Kimmo, MCA is a rather old name (introduced, I think, in the 1960s by Songuist and Morgan in the OSIRIS package) for a linear model consisting entirely of factors and with only additive effects -- i.e., an ANOVA model will no interactions. It is true, that MCA is an old name, but the technique itself is still robust, I think. The problem I am facing is that I have a research project where I try to find out which factors affect measured knowledge of a specific issue. As predictors I have formal education, interest, gender and consumption of different medias (TV, newspapers etc.). Now, these are correlated predictors and running e.g. a simple anova (anova(lm(...)) as You suggested) won't - if I have understood correctly - consider the problem of correlated predictors. MCA would do this. A colleague of mine has run anova and MCA in SPSS and the results differ significantly. Because I am more familiar with R, I just hoped that this marvelous statistical package could handle MCA, too :) Typically, the results of an MCA are reported using adjusted means. You could compute these manually, or via the effects package. Well, I am interested in the eta and beta values, too. I have tried to use the effects package but my attempts with all.effects resulted in errors. I have to figure out what's going wrong here :) Kind regards, Kimmo Elo -- University of Turku, Finland Dep. of political science __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] MCA in R
Dear Kimmo, MCA is a rather old name (introduced, I think, in the 1960s by Songuist and Morgan in the OSIRIS package) for a linear model consisting entirely of factors and with only additive effects -- i.e., an ANOVA model will no interactions. You can fit such a model with lm() -- e.g., lm(y ~ f1 + f2 + etc.). Typically, the results of an MCA are reported using adjusted means. You could compute these manually, or via the effects package. I hope this helps, John -- John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of K. Elo Sent: June-11-08 1:07 AM To: r-help@r-project.org Subject: [R] MCA in R Hi! Is there any possibilities to do multiple classification analysis (MCA) in R? (MCA examines the relationships between several categorical independent variables and a single dependent variable, and determines the effects of each predictor before and after adjustment for its inter-correlations with other predictors in the analysis). Kind regrads, Kimmo Elo --- University of Turku, Finland Dep. of political science __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
