## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## -----------------------------------------------------------------------------
library(modelObj)
object1 <- buildModelObj(model=~x1, solver.method='lm')

## -----------------------------------------------------------------------------
mylm <- function(X,Y){
  obj <- list()
  obj$lm <- lm.fit(x=X, y=Y)
  obj$var <- "does something neat"
  class(obj) = "mylm"
  return(obj)
}
predict.mylm <- function(obj,data=NULL){
  if( is(data,"NULL") ) {
    obj <- exp(obj$lm$fitted.values)
  } else {
    obj <- data %*% obj$lm$coefficients
    obj <- exp(obj)
  }
  return(obj)
}

## -----------------------------------------------------------------------------
object2 <- buildModelObj(model = ~x1, 
                         solver.method = mylm, 
                         solver.args = list('X' = "x", 'Y' = "y"),
                         predict.method = predict.mylm, 
                         predict.args = list('obj' = "object", 
                                             'data' = "newdata"))

## -----------------------------------------------------------------------------
summary(pressure)

## -----------------------------------------------------------------------------
exampleFun <- function(modelObj, data, Y){

    fitObj <- fit(object = modelObj, data = data, response = Y)

    ##Test that coef() is an available method
    cfs <- try(coef(fitObj), silent=TRUE)
    if(class(cfs) == 'try-error'){
      warning("Provided regression method does not have a coef method.\n")
      cfs <- NULL
    } 

    fitted <- predict(fitObj)^2

    return(list("fittedSq"=fitted, "coef"=cfs))
}

## -----------------------------------------------------------------------------
ylog <- log(pressure$pressure)
objlm <- buildModelObj(model = ~temperature,
                       solver.method = "lm",
                       predict.method = "predict.lm",
                       predict.args = list("type"="response"))
fitObjlm <- exampleFun(objlm, pressure, ylog)
print(fitObjlm$coef)

## -----------------------------------------------------------------------------
objnls <- buildModelObj(model = ~exp(a + b*temperature),
                        solver.method = "nls",
                        solver.args = list('start'=list(a=1, b=0.1)),
                        predict.method = "predict",
                        predict.args = list("type" = "response"))
fitObjnls <- exampleFun(objnls, pressure, pressure$pressure)
print(fitObjnls$coef)

## -----------------------------------------------------------------------------
objectnew <- buildModelObj(model = ~temperature, 
                           solver.method = mylm, 
                           solver.args = list('X' = "x", 'Y' = "y"),
                           predict.method = predict.mylm, 
                           predict.args = list('obj'="object", 
                                               'data'="newdata"))
fitObjnew <- exampleFun(objectnew, pressure, ylog)
print(fitObjnew$coef)