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working with one-variable models in, for example, `fDMA`. It produces a `matrix` corresponding to the set of models consisting of models with a constant and just one extra variable, and a model with a constant only.\n\n\n\n\n```r\nonevar(x)\n```\n\n## Arguments\n\n- `x`: `matrix` of independent variables, see `mods.incl` in `fDMA`\n\n## Returns\n\n`matrix`, inclusion of a variable is indicated by 1, omitting by 0\n\n## Examples\n\n```r\nwti <- crudeoil[-1,1]\ndrivers <- (lag(crudeoil[,-1],k=1))[-1,]\nld.wti <- (diff(log(wti)))[-1,]\nld.drivers <- (diff(log(drivers)))[-1,]\n\nmds <- diag(1,ncol(ld.drivers),ncol(ld.drivers))\nmds <- cbind(rep(1,ncol(ld.drivers)),mds)\nmds <- rbind(rep(0,ncol(mds)),mds)\nmds[1,1] <- 1\n\nm1 <- fDMA(y=ld.wti,x=ld.drivers,alpha=0.99,lambda=0.90,initvar=10,mods.incl=mds)\n\n# Equivalently: \n\nm2 <- fDMA(y=ld.wti,x=ld.drivers,alpha=0.99,lambda=0.90,initvar=10,mods.incl=onevar(ld.drivers))\n```"}]}]}]}]}]}],["$","div",null,{"className":"lg:w-1/3 lg:border-l px-1 sm:px-8 pt-8 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