# Create An `amData` Object That Pairs Datasets With Associated Metadata Creates an object of class `amData`, which is typically a data frame of covariate data or model fitting data, with mandatory metadata. It is worth noting that some models include the data as part of the fitted model object, e.g. `lm` and `glm`, which access embedded data with `model.frame`. For large datasets that are embedded in the model, it may be worth documenting metadata a placeholder object such as the character string `"embedded data"` rather than a redundant data object. An S4 class to store data with descriptive metadata. class ```r amData(data, ...) ``` ## Arguments - `data`: A dataset, typically data frame but may be in any structure. - ``...``: Named metadata elements, either supplied individually and coerced to list or supplied as a named list. ## Returns An object of class `amData` suitable for inclusion in an `amModelLib` object. ## Slots - **`data`**: A single object containing data relevant to a model. There are no restrictions on how the data are used in the model; for example, they may be covariate data (for use on the right side of the equation) or observed data (for use on the left side of the equation). - **`metadata`**: A named list of length 1 character vectors that form name:value pairs, e.g the source, the collection method, etc. When embedded in an `amModelLib`, data metadata may be retrieved or set with `dataMeta`. ## Examples ```r # create dataset from lm helpfile ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <- c(ctl, trt) # create a dataset that is of class data.frame plant.data <- data.frame(weight, group) # create an amData data object dat1 <- amData(data = plant.data, comment='Dataset from lm helpfile.', taxa = 'plants') # the class of dat1 is amData class(dat1) # the summary function will invoke the summary method for the dataset's original class summary(dat1) # use the amModelLib function to create a new amModelLib called mymodels that # includes dat1; data must be supplied in a named list mymodels <- amModelLib( data=list(dat1 = dat1), description = "An example amModelLib called mymodels." ) # use the lsData function to list the amData objects in an amModelLib lsData(mymodels) # the dataMeta function can be used to retrieve an amData object's metadata dataMeta(amml = mymodels, 'dat1') # the dataMeta function can alse be used to set metadata dataMeta(mymodels, 'dat1') <- list( url = "https://stat.ethz.ch/R-manual/R-devel/library/stats/html/lm.html" ) dataMeta(amml = mymodels, 'dat1') # use the getAMData function to extract the dataset back to its orginal form getAMData(amml = mymodels, 'dat1', as.list = FALSE) # the retrieved datset is in its original class class(getAMData(amml = mymodels, 'dat1', as.list = FALSE)) # use the amModelLib function to create an empty amModelLib mymodels2 <- amModelLib(description = "An example amModelLib called mymodels2.") # use the insertAMModelLib function to insert the amData object to an # existing amModelLib mymodels2 <- insertAMModelLib(data = list(dat1 = dat1)) # use rmData to remove an amData object from an amModelLib rmData('dat1', amml = mymodels2) ``` ## See Also Other amModelLib: `AMModels`, `amModelLib`, `amModel`, `getters`, `grepAMModelLib`, `insertAMModelLib`, `lsModels`, `rmModel`

amData function