Fit and predict in one function
multi_model_2(old_data, new_data, yname, xname, modeltype, ...)
| old_data | The data set to which predicted values will be added. |
|---|---|
| new_data | The data set to use for predicting. |
| yname | The outcome variable |
| xname | The predictor variable(s) |
| modeltype | A character specifying the model type e.g lm for linear model |
| ... | Other arguments to specific model types. |
# fit a linear model and get predictions multi_model_2(iris[1:50,],iris[50:99,],"Sepal.Length","Petal.Length","lm") #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species predicted #> 1 5.1 3.5 1.4 0.2 setosa 4.972378 #> 2 4.9 3.0 1.4 0.2 setosa 6.761943 #> 3 4.7 3.2 1.3 0.2 setosa 6.653485 #> 4 4.6 3.1 1.5 0.2 setosa 6.870402 #> 5 5.0 3.6 1.4 0.2 setosa 6.382339 #> 6 5.4 3.9 1.7 0.4 setosa 6.707714 #> 7 4.6 3.4 1.4 0.3 setosa 6.653485 #> 8 5.0 3.4 1.5 0.2 setosa 6.761943 #> 9 4.4 2.9 1.4 0.2 setosa 6.002734 #> 10 4.9 3.1 1.5 0.1 setosa 6.707714 #> 11 5.4 3.7 1.5 0.2 setosa 6.328109 #> 12 4.8 3.4 1.6 0.2 setosa 6.111192 #> 13 4.8 3.0 1.4 0.1 setosa 6.490797 #> 14 4.3 3.0 1.1 0.1 setosa 6.382339 #> 15 5.8 4.0 1.2 0.2 setosa 6.761943 #> 16 5.7 4.4 1.5 0.4 setosa 6.165422 #> 17 5.4 3.9 1.3 0.4 setosa 6.599256 #> 18 5.1 3.5 1.4 0.3 setosa 6.653485 #> 19 5.7 3.8 1.7 0.3 setosa 6.436568 #> 20 5.1 3.8 1.5 0.3 setosa 6.653485 #> 21 5.4 3.4 1.7 0.2 setosa 6.328109 #> 22 5.1 3.7 1.5 0.4 setosa 6.816173 #> 23 4.6 3.6 1.0 0.2 setosa 6.382339 #> 24 5.1 3.3 1.7 0.5 setosa 6.870402 #> 25 4.8 3.4 1.9 0.2 setosa 6.761943 #> 26 5.0 3.0 1.6 0.2 setosa 6.545026 #> 27 5.0 3.4 1.6 0.4 setosa 6.599256 #> 28 5.2 3.5 1.5 0.2 setosa 6.816173 #> 29 5.2 3.4 1.4 0.2 setosa 6.924631 #> 30 4.7 3.2 1.6 0.2 setosa 6.653485 #> 31 4.8 3.1 1.6 0.2 setosa 6.111192 #> 32 5.4 3.4 1.5 0.4 setosa 6.273880 #> 33 5.2 4.1 1.5 0.1 setosa 6.219651 #> 34 5.5 4.2 1.4 0.2 setosa 6.328109 #> 35 4.9 3.1 1.5 0.2 setosa 6.978860 #> 36 5.0 3.2 1.2 0.2 setosa 6.653485 #> 37 5.5 3.5 1.3 0.2 setosa 6.653485 #> 38 4.9 3.6 1.4 0.1 setosa 6.761943 #> 39 4.4 3.0 1.3 0.2 setosa 6.599256 #> 40 5.1 3.4 1.5 0.2 setosa 6.436568 #> 41 5.0 3.5 1.3 0.3 setosa 6.382339 #> 42 4.5 2.3 1.3 0.3 setosa 6.599256 #> 43 4.4 3.2 1.3 0.2 setosa 6.707714 #> 44 5.0 3.5 1.6 0.6 setosa 6.382339 #> 45 5.1 3.8 1.9 0.4 setosa 6.002734 #> 46 4.8 3.0 1.4 0.3 setosa 6.490797 #> 47 5.1 3.8 1.6 0.2 setosa 6.490797 #> 48 4.6 3.2 1.4 0.2 setosa 6.490797 #> 49 5.3 3.7 1.5 0.2 setosa 6.545026 #> 50 5.0 3.3 1.4 0.2 setosa 5.840046 # multilinear multi_model_2(iris[1:50,],iris[50:99,],"Sepal.Length", "Petal.Length + Sepal.Width","lm") #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species predicted #> 1 5.1 3.5 1.4 0.2 setosa 4.902999 #> 2 4.9 3.0 1.4 0.2 setosa 5.771541 #> 3 4.7 3.2 1.3 0.2 setosa 5.714857 #> 4 4.6 3.1 1.5 0.2 setosa 5.761483 #> 5 5.0 3.6 1.4 0.2 setosa 4.972473 #> 6 5.4 3.9 1.7 0.4 setosa 5.476232 #> 7 4.6 3.4 1.4 0.3 setosa 5.447890 #> 8 5.0 3.4 1.5 0.2 setosa 5.838282 #> 9 4.4 2.9 1.4 0.2 setosa 4.840821 #> 10 4.9 3.1 1.5 0.1 setosa 5.542974 #> 11 5.4 3.7 1.5 0.2 setosa 5.211097 #> 12 4.8 3.4 1.6 0.2 setosa 4.630538 #> 13 4.8 3.0 1.4 0.1 setosa 5.496348 #> 14 4.3 3.0 1.1 0.1 setosa 4.905731 #> 15 5.8 4.0 1.2 0.2 setosa 5.571316 #> 16 5.7 4.4 1.5 0.4 setosa 5.259555 #> 17 5.4 3.9 1.3 0.4 setosa 5.619773 #> 18 5.1 3.5 1.4 0.3 setosa 5.581374 #> 19 5.7 3.8 1.7 0.3 setosa 5.267781 #> 20 5.1 3.8 1.5 0.3 setosa 5.047441 #> 21 5.4 3.4 1.7 0.2 setosa 5.077614 #> 22 5.1 3.7 1.5 0.4 setosa 5.799883 #> 23 4.6 3.6 1.0 0.2 setosa 5.306181 #> 24 5.1 3.3 1.7 0.5 setosa 5.361033 #> 25 4.8 3.4 1.9 0.2 setosa 5.504574 #> 26 5.0 3.0 1.6 0.2 setosa 5.457948 #> 27 5.0 3.4 1.6 0.4 setosa 5.553032 #> 28 5.2 3.5 1.5 0.2 setosa 5.532916 #> 29 5.2 3.4 1.4 0.2 setosa 5.723083 #> 30 4.7 3.2 1.6 0.2 setosa 5.514632 #> 31 4.8 3.1 1.6 0.2 setosa 5.030988 #> 32 5.4 3.4 1.5 0.4 setosa 4.982530 #> 33 5.2 4.1 1.5 0.1 setosa 4.954189 #> 34 5.5 4.2 1.4 0.2 setosa 5.211097 #> 35 4.9 3.1 1.5 0.2 setosa 5.551200 #> 36 5.0 3.2 1.2 0.2 setosa 5.581374 #> 37 5.5 3.5 1.3 0.2 setosa 5.848340 #> 38 4.9 3.6 1.4 0.1 setosa 5.704799 #> 39 4.4 3.0 1.3 0.2 setosa 5.085840 #> 40 5.1 3.4 1.5 0.2 setosa 5.468006 #> 41 5.0 3.5 1.3 0.3 setosa 5.105956 #> 42 4.5 2.3 1.3 0.3 setosa 5.286065 #> 43 4.4 3.2 1.3 0.2 setosa 5.609716 #> 44 5.0 3.5 1.6 0.6 setosa 5.172698 #> 45 5.1 3.8 1.9 0.4 setosa 4.774079 #> 46 4.8 3.0 1.4 0.3 setosa 5.296123 #> 47 5.1 3.8 1.6 0.2 setosa 5.496348 #> 48 4.6 3.2 1.4 0.2 setosa 5.429606 #> 49 5.3 3.7 1.5 0.2 setosa 5.457948 #> 50 5.0 3.3 1.4 0.2 setosa 4.822537 # glm multi_model_2(iris[1:50,],iris[50:99,],"Sepal.Length","Petal.Length","glm") #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species predicted #> 1 5.1 3.5 1.4 0.2 setosa 4.972378 #> 2 4.9 3.0 1.4 0.2 setosa 6.761943 #> 3 4.7 3.2 1.3 0.2 setosa 6.653485 #> 4 4.6 3.1 1.5 0.2 setosa 6.870402 #> 5 5.0 3.6 1.4 0.2 setosa 6.382339 #> 6 5.4 3.9 1.7 0.4 setosa 6.707714 #> 7 4.6 3.4 1.4 0.3 setosa 6.653485 #> 8 5.0 3.4 1.5 0.2 setosa 6.761943 #> 9 4.4 2.9 1.4 0.2 setosa 6.002734 #> 10 4.9 3.1 1.5 0.1 setosa 6.707714 #> 11 5.4 3.7 1.5 0.2 setosa 6.328109 #> 12 4.8 3.4 1.6 0.2 setosa 6.111192 #> 13 4.8 3.0 1.4 0.1 setosa 6.490797 #> 14 4.3 3.0 1.1 0.1 setosa 6.382339 #> 15 5.8 4.0 1.2 0.2 setosa 6.761943 #> 16 5.7 4.4 1.5 0.4 setosa 6.165422 #> 17 5.4 3.9 1.3 0.4 setosa 6.599256 #> 18 5.1 3.5 1.4 0.3 setosa 6.653485 #> 19 5.7 3.8 1.7 0.3 setosa 6.436568 #> 20 5.1 3.8 1.5 0.3 setosa 6.653485 #> 21 5.4 3.4 1.7 0.2 setosa 6.328109 #> 22 5.1 3.7 1.5 0.4 setosa 6.816173 #> 23 4.6 3.6 1.0 0.2 setosa 6.382339 #> 24 5.1 3.3 1.7 0.5 setosa 6.870402 #> 25 4.8 3.4 1.9 0.2 setosa 6.761943 #> 26 5.0 3.0 1.6 0.2 setosa 6.545026 #> 27 5.0 3.4 1.6 0.4 setosa 6.599256 #> 28 5.2 3.5 1.5 0.2 setosa 6.816173 #> 29 5.2 3.4 1.4 0.2 setosa 6.924631 #> 30 4.7 3.2 1.6 0.2 setosa 6.653485 #> 31 4.8 3.1 1.6 0.2 setosa 6.111192 #> 32 5.4 3.4 1.5 0.4 setosa 6.273880 #> 33 5.2 4.1 1.5 0.1 setosa 6.219651 #> 34 5.5 4.2 1.4 0.2 setosa 6.328109 #> 35 4.9 3.1 1.5 0.2 setosa 6.978860 #> 36 5.0 3.2 1.2 0.2 setosa 6.653485 #> 37 5.5 3.5 1.3 0.2 setosa 6.653485 #> 38 4.9 3.6 1.4 0.1 setosa 6.761943 #> 39 4.4 3.0 1.3 0.2 setosa 6.599256 #> 40 5.1 3.4 1.5 0.2 setosa 6.436568 #> 41 5.0 3.5 1.3 0.3 setosa 6.382339 #> 42 4.5 2.3 1.3 0.3 setosa 6.599256 #> 43 4.4 3.2 1.3 0.2 setosa 6.707714 #> 44 5.0 3.5 1.6 0.6 setosa 6.382339 #> 45 5.1 3.8 1.9 0.4 setosa 6.002734 #> 46 4.8 3.0 1.4 0.3 setosa 6.490797 #> 47 5.1 3.8 1.6 0.2 setosa 6.490797 #> 48 4.6 3.2 1.4 0.2 setosa 6.490797 #> 49 5.3 3.7 1.5 0.2 setosa 6.545026 #> 50 5.0 3.3 1.4 0.2 setosa 5.840046