focusing on selected variables in the model, and eliminating impacts from other variables.

1 2 |

`model` |
an output of lm or glm |

`focus_var_coeff` |
NULL or a character vector, choose coeff vars you want to focus. The unselected vars will have coeff values as 0. Default is NULL, which means to choosing nothing. |

`focus_var_raw` |
NULL or a character vector, choose raw vars you want to focus. The unselected vars will have coeff values as 0. Default is NULL, which means to choosing nothing. |

`intercept_include` |
a boolean, whether to include the intercept (default is TRUE). |

`data` |
optional, a new dataset to evaluate the categorical variables. If NULL, then use the data used in model itself. |

In a model `y ~ a + b`

. Sometimes you want to fix value of `a`

and see the variations of `b`

in `y`

.
The most straightforward way to code this, as we did in this function, is to make `a`

's coefficients as 0, and then use the predict().

a new model with only focused vars having coeff unchanged, and all other vars having coeff as 0.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
focus_var_raw = 'carat'
model = lm(price~ cut + carat + I(carat^2) + I(carat^3) +
I(carat * depth) + depth,ggplot2::diamonds)
# all coeffs except carat's will be 0
focusing_var_coeff(model, focus_var_coeff = 'carat')
# all coeffs except cut.L's will be 0
focusing_var_coeff(model, focus_var_coeff = 'cut.L')
# all coeffs without raw vars cut or carat will be 0
focusing_var_coeff(model, focus_var_raw = c('cut','carat'))
# if you didn't specify anything, then all vars' coeff will become 0 except intercept
focusing_var_coeff(model)
# if cannot find the focus_var_coeff or focus_var_raw in the model
tryCatch(focusing_var_coeff(model, focus_var_coeff = 'caratdsd'),
error = function(err) warning(err))
tryCatch(focusing_var_coeff(model, focus_var_raw = '3213'),
error = function(err) warning(err))
``` |

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