# Function that predicts the responses from the covariates for a new sample ```r multivariatePredictGlm(Xnew, family, beta, offset = NULL) ``` ## Arguments - `Xnew`: a data frame containing the values of the covariates for the new sample. - `family`: a vector of character specifying the distributions of the responses. - `beta`: the matrix of coefficients estimated from the calibration sample. - `offset`: used for the poisson dependent variables. A vector or a matrix of size: number of observations * number of Poisson dependent variables is expected. ## Returns a matrix of predicted values. ## Examples ```r ## Not run: library(SCGLR) # load sample data data(genus) # get variable names from dataset n <- names(genus) ny <- n[grep("^gen",n)] # Y <- names that begins with "gen" nx <- n[-grep("^gen",n)] # X <- remaining names # remove "geology" and "surface" from nx as surface is # offset and we want to use geology as additional covariate nx <-nx[!nx%in%c("geology","surface")] # build multivariate formula # we also add "lat*lon" as computed covariate form <- multivariateFormula(ny,c(nx,"I(lat*lon)"),c("geology")) # split genus dataset sub <- sample(1:nrow(genus),100,replace=FALSE) sub_fit <- (1:nrow(genus))[-sub] # define family fam <- rep("poisson",length(ny)) # fit the model genus.scglr <- scglr(formula=form, data=genus, family=fam, K=4, offset=genus$surface, subset=sub_fit) # xnew, the design matrix associated to sub-sample used for prediction # rhs parameters is introduced to take into account that the covariate # of the formula is composed of two differents sets xnew <- model.matrix(form, data=genus[sub,], rhs=1:2)[,-1] # prediction based on the scglr approch pred.scglr <- multivariatePredictGlm(xnew,family=fam, beta=genus.scglr$beta,offset=genus$surface[sub]) cor.scglr <-diag(cor(pred.scglr,genus[sub,ny])) plot(cor.scglr, col="red",ylim=c(-1,1)) # prediction based on classical poisson glm X <- model.matrix(form, data=genus)[,-1] Y <- genus[,ny] genus.glm <- multivariateGlm.fit(Y[sub_fit,,drop=FALSE],X[sub_fit,,drop=FALSE], family=fam, offset=matrix(genus$surface[sub_fit], length(sub_fit),length(ny)),size=NULL) coefs <- sapply(genus.glm,coef) # rhs parameters is introduced to take into account that the covariate # part of the formula is composed of two differents sets pred.glm <- multivariatePredictGlm(xnew,family=fam,beta=coefs, offset=genus$surface[sub]) cor.glm <- diag(cor(pred.glm,genus[sub,ny])) points(cor.glm, col="blue") ## End(Not run) ```

multivariatePredictGlm function