R/QuickGAMPredict.R

In gfalbery/ggregplot:

QuickGAMPredict <-

  function(Data,
           Model,
           Covariates, #Response = "y",
           OutputCovariate,
           HoldFactors = c(" " = " "),
           Output = "Link", Family = "Gaussian",
           # LineAlpha = 0.05,
           # AddPoints = F, TestDF = NULL, PointAlpha = 1, PointColour = NA,
           # AddP = F, AddEstimate = F, LimitClip = T, TextColour = NA,
           # SmoothFillAlpha = 0.1, SmoothColour = AlberColours[[1]],
           # ReturnPlot = F,
           ...){

    OutputList <- list()

    i <- 1

    if(any(class(Model) %in% c("bam", "gam"))){

      PredList <- MakePredictDF(Data %>% dplyr::select(Covariates),
                                HoldNumeric = Covariates %>% setdiff(OutputCovariate),
                                HoldFactor = HoldFactors)

      PredDF <- PredList %>% expand.grid()

      PredValues <- predict.gam(Model,
                                newdata = PredDF,
                                se.fit = T)

      PredDF[,c("Fit", "SE")] <- PredValues %>% bind_cols %>% as.data.frame()

      PredDF %<>% mutate(Value = Fit,
                         Lower = Fit - SE,
                         Upper = Fit + SE)

      if(Output == "Logistic"){

        PredDF %<>% mutate_at(c("Value", "Lower", "Upper"), logistic)

      }

      PredDF$x <- PredDF[,OutputCovariate]

    }

    PredDF %>% return

  }