Hi, in the limma guide, there are several different examples which are well described. However, there are two different functions for doing the fit - lm.series, and lmFit. I'm not clear as to why one would use either one or the other. Its not stated as to why one uses lmFit in the Swirl example, but uses lm.series in the ApolA1 example. From their respective help menus, I cant tell the difference except that lmFit seems to call the least squares regression by default, while as lm.series calls the lm.fit function for the regression. Are there some general guidelines as to which fit function to use in particular experimental contexts? Any help would be much appreciated thanks Simon.

Simon, At 05:48 PM 6/02/2004, Simon Melov wrote: >Hi, >in the limma guide, there are several different examples which are well >described. However, there are two different functions for doing the fit - >lm.series, and lmFit. > >I'm not clear as to why one would use either one or the other. Its not >stated as to why one uses lmFit in the Swirl example, but uses lm.series >in the ApolA1 example. From their respective help menus, I cant tell the >difference except that lmFit seems to call the least squares regression by >default, while as lm.series calls the lm.fit function for the regression. There are actually four linear model functions in limma, as explained in the help page on "LinearModels". I have extracted a part of this help page and appended it to the end of this email. lmFit() is a wrapper function, if you want to use that term, which calls the lower-level functions lm.series, gls.series or rlm.series as appropriate. There is therefore no difference whatever between lmFit and lm.series in your case except in the user interface: lmFit simply calls lm.series. I agree that it is potentially a bit confusing that both lm.series and lmFit are used in the User's Guide. Ideally only lmFit would be there, but I haven't had time to update all the examples yet. This will be done is due course. The ApoAI example is still correct although it uses the older function call. You say that can't tell the difference between the functions from their help pages. Now I know that documentation which is clear to one person is not necessarily clear to another, but on my reading of the help pages the relationship between the functions seems to be well-described. The help page for lmFit says "A linear model is fitted for each gene by calling one of 'lm.series', 'gls.series' or 'rlm.series'." The reader is then referred to the help page on 'LinearModels' which gives "an overview of linear model functions in limma". Then one can read the extract quoted below. Gordon >Are there some general guidelines as to which fit function to use in >particular experimental contexts? > >Any help would be much appreciated > >thanks > >Simon. Quote from the help page "5.LinearModels": There are four functions in the package which fit linear models: 'lmFit' This is a high level function which accepts objects and provides an entry point to the following three functions. 'lm.series' Straightforward least squares fitting of a linear model for each gene. 'rlm.series' An alternative to 'lm.series' using robust regression as implemented by the 'rlm' function in the MASS package. 'gls.series' Generalized least squares taking into account correlations between duplicate spots (i.e., replicate spots on the same array). The functions 'duplicateCorrelation' or 'dupcor.series' are used to estimate the inter-duplicate correlation before using 'gls.series'. Each of these functions accepts essentially the same argument list and produces a fitted model object of the same form. The first function 'lmFit' formally produces an object of class 'MArrayLM'. The other three functions are lower level functions which produce similar output but in unclassed lists.