## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## -----------------------------------------------------------------------------
library(manymodelr)
set.seed(520)
# Create a simple dataset with a binary target
# Here normal is a fictional target where we assume that it meets 
# some criterion means 
data("yields", package = "manymodelr")

## -----------------------------------------------------------------------------
set.seed(520)
train_set<-createDataPartition(yields$normal,p=0.6,list=FALSE)
valid_set<-yields[-train_set,]
train_set<-yields[train_set,]
ctrl<-trainControl(method="cv",number=5)
m<-multi_model_1(train_set,"normal",".",c("knn","rpart"), 
                 "Accuracy",ctrl,new_data =valid_set)


## -----------------------------------------------------------------------------

m$metric


## -----------------------------------------------------------------------------

head(m$predictions)


## -----------------------------------------------------------------------------
# fit a linear model and get predictions
lin_model <- multi_model_2(mtcars[1:16,],mtcars[17:32,],"mpg","wt","lm")

lin_model[c("predicted", "mpg")]


## -----------------------------------------------------------------------------

multi_lin <- multi_model_2(mtcars[1:16, ], mtcars[17:32,],"mpg", "wt + disp + drat","lm")

multi_lin[,c("predicted", "mpg")]


## -----------------------------------------------------------------------------
lm_model <- fit_model(mtcars,"mpg","wt","lm")
lm_model


## -----------------------------------------------------------------------------

models<-fit_models(df=yields,yname=c("height", "weight"),xname="yield",
                   modeltype="glm") 


## -----------------------------------------------------------------------------


res_residuals <- lapply(models[[1]], add_model_residuals,yields)
res_predictions <- lapply(models[[1]], add_model_predictions, yields, yields)
# Get height predictions for the model height ~ yield 
head(res_predictions[[1]])


## -----------------------------------------------------------------------------
m_models<-fit_models(df=yields,yname=c("height","weight"),
           xname=".",modeltype=c("lm","glm"), drop_non_numeric = TRUE)
m_models[[1]]


## -----------------------------------------------------------------------------
report_model(m_models[[2]][[1]])

## -----------------------------------------------------------------------------

extract_model_info(lm_model, "r2")


## -----------------------------------------------------------------------------

extract_model_info(lm_model, "adj_r2")


## -----------------------------------------------------------------------------

extract_model_info(lm_model, "p_value")


## -----------------------------------------------------------------------------

extract_model_info(lm_model,c("p_value","response","call","predictors"))