# Function to produce a table ranks the models being analyzed. Prepare a table that ranks previously fitted models according to a series of statistics ```r rankmod( tabstatmod = tabstatmod, all.refstat = TRUE, refstat = refstat, want.all.outputs = FALSE ) ``` ## Arguments - `tabstatmod`: a data object having the statistics values for each model. The first column must have the name of the model, and the other columns are the statistics. - `all.refstat`: A logic option to specify the statistics to be used for ranking the models. By default is to `TRUE`, implying that all the statistics provided in the object (i.e., columns) are going to be used for developing the ranking. If the option `all.refstat` is set to `FALSE`, then the option `refstat` must be specified. - `refstat`: A vector with the names of the columns (i.e., statistics) to be used. By the default is the name of all the columns of the object `tabstatmod` but not including the first column (it has the name of the model). - `want.all.outputs`: A logic option to save a full set of result elements in the output, thus the output is class `list`. By default is set to `FALSE`. ## Returns The main result is a table having assigned scores for each of the statistics, as well as the sum of the scores. The column `ranking` has between the best ranking (number `1`) to the lowest ranking (number equal to the quantity of models being analyzed). The actual output is a list with two elements: (1) a dataframe sorted by the number of the model, and (2) a dataframe sorted by the ranking of the model. ## Details The function assigns scores to models based on an array of statistics already computed. For instance, the function can use the three prediction capabilities statistics RMSD, AD, and AAD, that are computed by the function `valesta()`. Nonetheless, any other statistics can be provided to the function. The only requirement is that the lower the value of the statistics (in absolute value), the better. The current version of the function is based upon the approach proposed by Salas (2002). ## Examples ```r ##Creates a fake dataframe set.seed(1234);y<-rnorm(30, 30,9);x<-rnorm(30, 450,133);z<-rbeta(30, .1,2) db <- as.data.frame(cbind(y, x,z)) ## Fitting some models mod1 <- lm(y~x, data=db) mod2 <- lm(y~x+I(x^2), data=db) mod3 <- lm(y~z+I(x^2), data=db) ## Preparing the format of the input-data for the function df.m1<-df.m2<-df.m3<-db df.m1$model<-"mod1";df.m1$y.aju=fitted(mod1) df.m2$model<-"mod2";df.m2$y.aju=fitted(mod2) df.m3$model<-"mod3";df.m3$y.aju=fitted(mod3) df<-rbind(df.m1,df.m2,df.m3) head(df) ##Assign validation class df<-assigncl(data=df,variable="y") table(df$model) table(df$y.class) head(df) ##Computes prediction capabilities statistics df.torank<-valestamod(data=df,y.obs = "y", y.pred="y.aju", want.by.valcl = TRUE,val.class = "y.class") df.torank ##Example 1: getting the main output, sorted by the ranking rankmod(tabstatmod = df.torank) ##Example 2: only consider a portion of the availables statistics rankmod(tabstatmod = df.torank, all.refstat=FALSE, refstat=c("rmsd","ad","aad")) ``` ## References * Salas C. 2002. Ajuste y validación de ecuaciones de volumen para un relicto del bosque de roble-laurel-lingue. Bosque 23(2):81–92. tools:::Rd_expr_doi("10.4067/S0717-92002002000200009") . ## Author(s) Christian Salas-Eljatib and Marcos Marivil.

rankmod function