Kernel: R (R-Project)

BreakDown library in R

To help understanding which factor is behind specific predictors ...

In [1]:

require(breakDown)
head(HR_data, 3)

Loading required package: breakDown

satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	left	sales	salary
0.38	0.53	2	157	3	1	sales	low
0.80	0.86	5	262	6	1	sales	medium
0.11	0.88	7	272	4	1	sales	medium

Logistig regression model for the "left" data

In [2]:

model <- glm(left~., data = HR_data, family = "binomial")

In [3]:

explain_1 <- broken(model, HR_data[11,])
explain_1

                           contribution
(Intercept)                      -1.601
satisfaction_level = 0.45         0.673
number_project = 2                0.568
salary = low                      0.388
average_montly_hours = 135       -0.295
Work_accident = 0                 0.221
time_spend_company = 3           -0.133
last_evaluation = 0.54           -0.129
promotion_last_5years = 0         0.030
sales = sales                     0.014
final_prognosis                  -0.262
baseline:  0 

Let's visualize it

In [4]:

library(ggplot2)
plot(explain_1) + ggtitle("breakDown plot for linear predictors")

... and with intercept at origin

In [5]:

explain_2 <- broken(model, HR_data[11,], baseline = "intercept")
explain_2

                           contribution
satisfaction_level = 0.45         0.673
number_project = 2                0.568
salary = low                      0.388
average_montly_hours = 135       -0.295
Work_accident = 0                 0.221
time_spend_company = 3           -0.133
last_evaluation = 0.54           -0.129
promotion_last_5years = 0         0.030
sales = sales                     0.014
final_prognosis                   1.339
baseline:  -1.601457 

In [6]:

plot(explain_2, trans = function(x) exp(x)/(1+exp(x))) + ggtitle("Predicted probability of leaving the company") + scale_y_continuous(limits = c(0,1), name = "probability", expand = c(0,0))

Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.

In [0]: