# Construct a dataset from a data.frame or a matrix. The function creates a `march.Dataset-class` from a **dataframe** or a **matrix**, where each row (resp. column) represents an independent data series when **MARGIN** is 2 (resp. 1). ```r march.dataset.loadFromDataFrame( dataframe, MARGIN = 2, weights = NA, missingDataRep = NA, covariates = NULL ) ``` ## Arguments - `dataframe`: A `data.frame` containing the dataset. - `MARGIN`: The dimension of the matrix/data.frame that contains the sequences and of the covariates (resp 1 for the column, 2 for the rows). - `weights`: If specified, contains the weight of each sequence. - `missingDataRep`: If specified, the symbol representing a missing data. - `covariates`: If specified, a three dimensional array of integers, representing the covariates. The data for the i-th covariates should be in [, , i]. If the data are column-wise (respectively row-wise), each table of covariates should be column-wise (respectively row-wise). If we only have one covariate, we can simply pass a two-dimensional array. The covariates should be coded as integers from 1 to the number of possible outputs. ## Returns A `march.Dataset-class` object containing the data contructed from the matrix or data.frame. ## Examples ```r # Create a march dataset from the sleep_df dataframe included in the march package. sleep <- march.dataset.loadFromDataFrame(sleep_df, MARGIN = 2, weights = NA, missingDataRep = NA) # Each row of sleep_df contains the data for one subject, so MARGIN was set to 2. # Most of the subjects have been observed during 7 consecutive years, # but some subjects have been observed for only 5 or 6 years. # To load only the first 5 observations of each subject: sleep.5 <- march.dataset.loadFromDataFrame(sleep_df[,1:5], MARGIN = 2 , weights = NA, missingDataRep = NA) # The sleep data are not weighted. # To add a weighting variable taking value 1.5 for the 500 first subjects # and value 0.5 for the 500 next: weighting <- rep(1.5,1000) weighting[501:1000] <- rep(0.5,500) sleep.w <- march.dataset.loadFromDataFrame(sleep_df, MARGIN = 2, weights = weighting, missingDataRep = NA) # We add two covariates to the sleep data. The first is the sex of the subject # (1 for male, 2 for female), and the second is the age range (1 for younger # than 40, 2 for older than 40). We suppose that the first 250 sequences # represent men older than 40, the next 250 sequences men younger than 40, # the next 250 women younger than 40 and the last 250 women older than 40. # We build the two tables of covariates and bind them together to obtain a # three dimensional array that can be handled by MARCH. covariates.sex<-rbind(matrix(1,500,7),matrix(1,500,7)) covariates.age<-rbind(matrix(1,250,7), matrix(2,250,7), matrix(1,250,7), matrix(2,250,7)) covariates<-array(0,c(1000,7,2)) covariates[ , ,1]<-covariates.sex covariates[ , ,2]<-covariates.age # We build a MARCH dataset object containing these covariates. sleep.covariates<-march.dataset.loadFromDataFrame(sleep_df,covariates=covariates) ``` ## Author(s) Ogier Maitre

march.dataset.loadFromDataFrame function