Re: [R] Data manipulation for random intercept GLMM
Giovanni Petris GPetris at uark.edu writes: ## Example: Ratings of prime minister (Agresti, Table 12.1, p.494) rating - matrix(c(794, 86, 150, 570), 2, 2) dimnames(rating) - list(First = c(approve, disapprove), + Second = c(approve, disapprove)) rating Second Firstapprove disapprove approve794150 disapprove 86570 [snip] approval - factor(c(Approve, Disapprove), +levels = c(Disapprove, Approve)) survey - factor(c(First, Second)) tmp - data.frame(approval = unlist(expand.grid(approval, approval)), + survey = rep(survey, each = 4)) rat.df - cbind(tmp[rep(1:8, rep(rating, 2)), ], + id = factor(rep(1:sum(rating), 2))) row.names(rat.df) - NULL That does the job, since now I can call lmer: m1 - lmer(approval ~ survey + (1 | id), family = binomial, data = rat.df, +method = Laplace) [snip] Consider also that the next simplest example is the following, in which there are three items on a questionnaire and gender is included in the model: ### Example: Support for legalizing abortion (Agresti, Table 10.13, p.441) legalize - matrix(c(342, 440, 26, 25, 6, 14, 21, 18, 11, 14, + 32, 47, 19, 22, 356, 457), nr = 2) dimnames(legalize) - list(Gender = c(Male, Female), +Three = c(111, 112, 211, 212, 121, +122, 221, 222)) legalize Three Gender 111 112 211 212 121 122 221 222 Male 342 26 6 21 11 32 19 356 Female 440 25 14 18 14 47 22 457 (Here '111' means (Yes, Yes, Yes) on the three items, etc.) How can I tranform elegantly this table into a dataframe that I can feed to lmer? I would like to know the answers to this as well. I thought I was going to be able to get away with as.data.frame.table() [which is handy and underappreciated], but it doesn't expand counts. I hope you get an elegant solution: in the meantime, I guess my advice is to try to write a reasonably general (if not necessarily absolutely elegant) function that you can supply your students with. If they have an expand.table() function, they won't care how complex it is internally ... Ben Bolker __ 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] Data manipulation for random intercept GLMM
Is this what you are trying to do? tt - matrix( 5:10, ncol=2 ) df - as.data.frame.table(tt) df2 - df[ rep( 1:nrow(df), df$Freq ), ] And is that elegant enough? -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare [EMAIL PROTECTED] (801) 408-8111 -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Ben Bolker Sent: Friday, May 02, 2008 7:36 AM To: [EMAIL PROTECTED] Subject: Re: [R] Data manipulation for random intercept GLMM Giovanni Petris GPetris at uark.edu writes: ## Example: Ratings of prime minister (Agresti, Table 12.1, p.494) rating - matrix(c(794, 86, 150, 570), 2, 2) dimnames(rating) - list(First = c(approve, disapprove), + Second = c(approve, disapprove)) rating Second Firstapprove disapprove approve794150 disapprove 86570 [snip] approval - factor(c(Approve, Disapprove), +levels = c(Disapprove, Approve)) survey - factor(c(First, Second)) tmp - data.frame(approval = unlist(expand.grid(approval, approval)), + survey = rep(survey, each = 4)) rat.df - cbind(tmp[rep(1:8, rep(rating, 2)), ], + id = factor(rep(1:sum(rating), 2))) row.names(rat.df) - NULL That does the job, since now I can call lmer: m1 - lmer(approval ~ survey + (1 | id), family = binomial, data = rat.df, +method = Laplace) [snip] Consider also that the next simplest example is the following, in which there are three items on a questionnaire and gender is included in the model: ### Example: Support for legalizing abortion (Agresti, Table 10.13, p.441) legalize - matrix(c(342, 440, 26, 25, 6, 14, 21, 18, 11, 14, + 32, 47, 19, 22, 356, 457), nr = 2) dimnames(legalize) - list(Gender = c(Male, Female), +Three = c(111, 112, 211, 212, 121, +122, 221, 222)) legalize Three Gender 111 112 211 212 121 122 221 222 Male 342 26 6 21 11 32 19 356 Female 440 25 14 18 14 47 22 457 (Here '111' means (Yes, Yes, Yes) on the three items, etc.) How can I tranform elegantly this table into a dataframe that I can feed to lmer? I would like to know the answers to this as well. I thought I was going to be able to get away with as.data.frame.table() [which is handy and underappreciated], but it doesn't expand counts. I hope you get an elegant solution: in the meantime, I guess my advice is to try to write a reasonably general (if not necessarily absolutely elegant) function that you can supply your students with. If they have an expand.table() function, they won't care how complex it is internally ... Ben Bolker __ 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.
[R] Data manipulation for random intercept GLMM
Hello, I am working on some examples of GLMM for my students but I am afraid that my way of preparing a dataframe to pass to lmer will make them think that R is a very difficult and un-natural language. Here is for example a simple data set about approval ratings on two different surveys for a random sample of 1600 individuals. ## Example: Ratings of prime minister (Agresti, Table 12.1, p.494) rating - matrix(c(794, 86, 150, 570), 2, 2) dimnames(rating) - list(First = c(approve, disapprove), + Second = c(approve, disapprove)) rating Second Firstapprove disapprove approve794150 disapprove 86570 It seems to me that, in order to fit a model using lmer, I cannot use the table directly, but I need a dataframe with 1600 x 2 rows and columns response (Approve/Disapprove), survey (First/Second), and subject id. So I proceeded to create such a dataframe: approval - factor(c(Approve, Disapprove), +levels = c(Disapprove, Approve)) survey - factor(c(First, Second)) tmp - data.frame(approval = unlist(expand.grid(approval, approval)), + survey = rep(survey, each = 4)) rat.df - cbind(tmp[rep(1:8, rep(rating, 2)), ], + id = factor(rep(1:sum(rating), 2))) row.names(rat.df) - NULL That does the job, since now I can call lmer: m1 - lmer(approval ~ survey + (1 | id), family = binomial, data = rat.df, +method = Laplace) The issue I have is that creating the 'rat.df' dataframe above will likely make all of my students look for a different software. So my question is the following. Is there a more elegant way to create the dataframe needed by lmer from the tabular form in which one is more likely to find these kind of data? Consider also that the next simplest example is the following, in which there are three items on a questionnaire and gender is included in the model: ### Example: Support for legalizing abortion (Agresti, Table 10.13, p.441) legalize - matrix(c(342, 440, 26, 25, 6, 14, 21, 18, 11, 14, + 32, 47, 19, 22, 356, 457), nr = 2) dimnames(legalize) - list(Gender = c(Male, Female), +Three = c(111, 112, 211, 212, 121, +122, 221, 222)) legalize Three Gender 111 112 211 212 121 122 221 222 Male 342 26 6 21 11 32 19 356 Female 440 25 14 18 14 47 22 457 (Here '111' means (Yes, Yes, Yes) on the three items, etc.) How can I tranform elegantly this table into a dataframe that I can feed to lmer? Thank you in advance for your replies! Giovanni Petris -- Giovanni Petris [EMAIL PROTECTED] Department of Mathematical Sciences University of Arkansas - Fayetteville, AR 72701 Ph: (479) 575-6324, 575-8630 (fax) http://definetti.uark.edu/~gpetris/ __ 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.
