Re: [R] metaprogramming with lm
The special name . may be used on the right side of the ~ operator, to stand for all the variables in a data.frame other than the response. --John Chambers, Statistical Models in S, p. 101 So, if the y and Xi (in your case) were the only variables in mydata, then lm(y ~ . , data = mydata) would be of use. Erik June Kim wrote: Hello, Say I want to make a multiple regression model with the following expression: lm(y~x1 + x2 + x3 + ... + x_n,data=mydata) It gets boring to type in the whole independent variables, in this case x_i. Is there any simple way to do the metaprogramming for this? (There are different cases where the names of the independent variables might sometimes have apparent patterns or not) __ 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] metaprogramming with lm
Two possible ways around this are 1. If the x's are *all* the other variables in your data frame you can use a dot: fm - lm(y ~ ., data = myData) 2. Here is another idea as.formula(paste(y~, paste(x,1:10, sep=, collapse=+))) y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 (You bore easily!) Bill Venables http://www.cmis.csiro.au/bill.venables/ -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of June Kim Sent: Thursday, 13 November 2008 10:27 AM To: r-help@r-project.org Subject: [R] metaprogramming with lm Hello, Say I want to make a multiple regression model with the following expression: lm(y~x1 + x2 + x3 + ... + x_n,data=mydata) It gets boring to type in the whole independent variables, in this case x_i. Is there any simple way to do the metaprogramming for this? (There are different cases where the names of the independent variables might sometimes have apparent patterns or not) __ 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] metaprogramming with lm
You can construct the formula on the fly. Say you have a data frame with columns: y, x1,...x10: dat - data.frame(matrix(rnorm(1100), ncol=11, dimnames=list(NULL,c(y, paste(x, 1:10, sep=) Then you could construct the formula using: form - formula(paste(y ~ , paste(names(dat)[which(names(dat) != y)], collapse=+))) fit - lm(form, data=dat) HTH, Simon. On Thu, 2008-11-13 at 09:27 +0900, June Kim wrote: Hello, Say I want to make a multiple regression model with the following expression: lm(y~x1 + x2 + x3 + ... + x_n,data=mydata) It gets boring to type in the whole independent variables, in this case x_i. Is there any simple way to do the metaprogramming for this? (There are different cases where the names of the independent variables might sometimes have apparent patterns or not) __ 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. -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320 Goddard Building (8) T: +61 7 3365 2506 http://www.uq.edu.au/~uqsblomb email: S.Blomberg1_at_uq.edu.au Policies: 1. I will NOT analyse your data for you. 2. Your deadline is your problem. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. __ 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] metaprogramming with lm
Yet again my baroque programming style shows itself. The . notation is great, although solution 2. is perhaps more versatile, allowing you to pick and choose your predictors more easily. On Thu, 2008-11-13 at 11:56 +1100, [EMAIL PROTECTED] wrote: Two possible ways around this are 1. If the x's are *all* the other variables in your data frame you can use a dot: fm - lm(y ~ ., data = myData) 2. Here is another idea as.formula(paste(y~, paste(x,1:10, sep=, collapse=+))) y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 (You bore easily!) Bill Venables http://www.cmis.csiro.au/bill.venables/ -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of June Kim Sent: Thursday, 13 November 2008 10:27 AM To: r-help@r-project.org Subject: [R] metaprogramming with lm Hello, Say I want to make a multiple regression model with the following expression: lm(y~x1 + x2 + x3 + ... + x_n,data=mydata) It gets boring to type in the whole independent variables, in this case x_i. Is there any simple way to do the metaprogramming for this? (There are different cases where the names of the independent variables might sometimes have apparent patterns or not) __ 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. -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320 Goddard Building (8) T: +61 7 3365 2506 http://www.uq.edu.au/~uqsblomb email: S.Blomberg1_at_uq.edu.au Policies: 1. I will NOT analyse your data for you. 2. Your deadline is your problem. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. __ 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.
