This package has two parts:
library(knitr) opts_chunk$set( fig.width=5, fig.height=3 ) library(linear.tools) # source("/Users/yangguodaxia/Dropbox/Tech/R/linear_tools_origin.R")
Variables in R's linear formula/model can have different forms:
data = ggplot2::diamonds diamond_lm = lm(log(price)~ I(carat^ 2) + cut + carat + table + carat:table, data)
At the first sight, the linear model above contains 5 variables:
In linear.tools we call them model variables and can access them using function
Note that in the original formula, there are redundant spaces 'I(carat^ 2)'; in
get_x(.,'model') we deleted them.
Sometimes you want to get the underlying raw variables used in the formula, which are
In linear.tools we call them raw variables and can access them using function
get_x(.,'model') will show the linkage between model variables and raw variables: it will return a list with names as model variables and elements as their corresponding raw variables.
get_model_pair(diamond_lm, data, 'raw')
Sometimes you want the the coefficient names of the model
You may also want to see how 'model' variables are linked with 'coeff' variables:
get_x(.,'coeff') will return a list with names as model variables and elements as their corresponding coeff variables.
get_model_pair(diamond_lm, data, 'coeff')
get_x_all() function will return a data.frame showing all the model variables and their corresponding raw & coefficient variables.
get_x_all(model = diamond_lm)
Contrasts are how categorical variables show up in coefficients.
When R evaluate categorical variables in the linear model, R will transform them into sets of 'contrasts' using certain contrast encoding schedule. See UCLA idre for details.
For example, for categorical variable 'cut' in the above model, we can get its contrasts through function
# get_contrast will return a list with each element as the contrasts of a categorical variable in the model get_contrast(diamond_lm)
You can also return the contrast method.
get_contrast(diamond_lm, return_method = T)
y ~ a + I(a^2) + b, We define 'Marginal Effect' of
y as: fixing
b, how the change of
a will affect value of
y. Note that the marginal effect here is not just the coefficients for
I(a^2), neither the sum.
We provide a easy tool to show the marginal effect and check its monotonicity. The example below will evaluate how the
carat of the diamond will affect its
price in a particular model.
# more carats, higher price. diamond_lm3 = lm(price~ carat + I(carat^2) + I(carat^3) , ggplot2::diamonds) # a GLM test1 = effect(model = diamond_lm3, focus_var_raw = c('carat'), focus_value =list(carat = seq(0.5,1,0.1))) test1$Monoton_Increase
You can see that the model did a good job to model monotonic increasing relations between
carat ranges from 0.5 to 1 (
PS: A more interesting case is that, if you interact
carat with the categorical variable
cut, you can examine the marginal effects
carat under different categories of
test_interaction = effect(model = lm(price~ carat*cut + I(carat^2)*cut, ggplot2::diamonds), focus_var_raw = c('carat','cut'), focus_value =list(carat = seq(0.5,1,0.1)) )
However, in the model
diamond_lm3 when we let the
carat ranges from 0.5 to 6, the model failed to get the monotonic increasing relations: in the model below, when carat is larger than 3 approximately, the higher the carat, the lower the price!
test2 = effect(model = diamond_lm3, focus_var_raw = c('carat'), focus_value =list(carat = seq(0.5,6,0.1))) test2$Monoton_Increase
When a model has a wrong marginal effect, we can use function
deleting_wrongeffect to delete a model variable that potentially causes the wrong marginal impacts and then re-estimate the model. This function can keep doing this until the correct marginal impacts are found.
The example below will
caratin the most right, and then recheck the marginal effect.
model_correct_effect = deleting_wrongeffect(model = diamond_lm3, focus_var_raw = 'carat', focus_value = list(carat=seq(0.5,6,0.1)), data = ggplot2::diamonds, PRINT = T,STOP =F, PLOT = T, Reverse = F) model_correct_effect
Stepwise regression is popular in variable selection, but it failed to consider the correctness of marginal effects.
stepwise2 enables checking the marginal effects during each step of iteration in stepwise regression; so in each step we will skip those models with wrong marginal effects, and only only choose models among those that have correct marginal effect.
The example below is to use stepwise regression to find the model with highest BIC and with the correct marginal effect.
scope = list(lower = price ~ 1, upper = price ~ carat + I(carat^2) + I(carat^3) + I(carat * depth) + depth) ### specify the correct marginal effect here test_suit = list( carat = list( # the list name must be the raw var focus_var_raw = "carat", # must specify the focus_var_raw (see deleting_wrongeffect() ) as the raw var focus_value = list(carat=seq(0.5,6,0.1)) ) ) model_correct_effect = stepwise2(model = diamond_lm3, scope = scope, trace = T, data = ggplot2::diamonds, STOP = F, test_suit = test_suit) # the returned model model_correct_effect
The model using
stepwise2 got correct marginal effect:
test_model_correct_effect = effect(model = model_correct_effect, focus_var_raw = c('carat'), focus_value =list(carat = seq(0.5,6,0.1)))
whereas the model using traditional algorithm
step got wrong marginal effect:
model_wrong_effect = step(diamond_lm3, scope = scope, trace = F, data = ggplot2::diamonds) model_wrong_effect test_wrong_effect = effect(model_wrong_effect, focus_var_raw = c('carat'), focus_value =list(carat = seq(0.5,6,0.1)))
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.