Manipulate Formulas and Evaluate Marginal Effects

Provides tools to manipulate formulas, such as getting x, y or contrasts from the model/formula, and functions to evaluate and check the marginal effects of a linear model.

AuthorFan Yang
Date of publication2016-07-06 09:50:47
MaintainerFan Yang <>

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check_names_delete: check whether list names (tobechecked) is within specified...

check_single_numeric: check whether an object is a single numeric number

check_vec_meaningful: check x is a meaningful vector

deleting_wrongeffect: check monotonicity of marginal impacts and re-estimate the...

effect: evaluate the marginal effects of the selected raw variable on...

Enter_to_Continue: Enter_to_Continue: wait your response to continue

expand_grid: a wrap up of 'expand.grid()' that takes list

focusing_var_coeff: focusing on selected variables in the model, and eliminating...

get_contrast: get contrast of categorical variables in a model

get_data_from_lm: get raw data from lm or glm

get_model_pair: get a list of model vars with their corresponding coeff vars...

get_model_with_coeff: get a list of model variables with their corresponding coeff...

get_model_with_raw: get a list of model vars with their corresponding raw vars.

get_valid_rows: identify missing rows for model/formula.

get_x: get x (left hand of var) from model or formula

get_x_all: a unique combinations of model vars, coeff vars and raw vars

get_x_hidden: the underlying function of 'get_x'

get_y: get y (right hand of var)

is_decrease: check whether the vector is decreasing

is_increase: check whether the vector is increasing

Mode: get mode number from a numeric vector

paste_formula: paste a formula as string

sanity_check: check x with various characters

stepwise2: same as 'step()' in R, but able to check marginal effects.

stripGlmLR: make the lm or glm thin

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