Extra information about the package

2019-09-24

This sections provides some additional information and features that xspliner provides.

Monotonic splines approximation

For qualitative variables only

In some cases you may want to transform variables with monotonic function. xspliner provides an option for monotonic spline approximation. You just need to specify monotonic parameter for the local, or global xs transition. It actually can have 4 values:

• “not” the default one. Monotonicity is not required
• “up” approximation is increasing function
• “down” approximation is decreasing function
• “auto” compare increasing and decreasing approximation and chooses better one (basing on $$R^2$$ statistic)

Let’s see below example:

library(randomForest)
library(pdp)
library(xspliner)
data(boston)
set.seed(123)
boston_rf <- randomForest(cmedv ~ lstat + ptratio + age, data = boston)
model_xs <- xspline(
cmedv ~
xs(lstat, transition = list(k = 6), effect = list(type = "pdp", grid.resolution = 100)) +
xs(ptratio, transition = list(k = 5), effect = list(type = "pdp", grid.resolution = 100)) +
age,
model = boston_rf,
xs_opts = list(transition = list(monotonic = "auto"))
)

plot(model_xs, "ptratio", plot_deriv = TRUE) plot(model_xs, "lstat", plot_deriv = TRUE) Choose if approximation is better

For qualitative variables only

When the response function has linear form, approximating it with splines may make the result worse. xspline function offers automatic check if the spline approximation is better than linear one, and use it in the final model.

You may find two parameters responsible for that:

• alter - The sub-parameter of transition. We already know how this parameter works for “always” and “never” values. There is also the third option, “auto”. In this case xspline automatically chooses whether variable should be transformed with splines
• compare_stat - function of lm class object. It defines statistic that should be used in decision between spline model and linear one. The function should have the attribute higher. When the attribute has "better" value then the model with higher statistic value is chosen.

You can see the feature in above example:

set.seed(123)
boston_rf <- randomForest(cmedv ~ lstat + ptratio + age, data = boston)
model_pdp_auto <- xspline(
cmedv ~
xs(lstat, transition = list(k = 6), effect = list(type = "pdp", grid.resolution = 60)) +
xs(ptratio, transition = list(k = 4), effect = list(type = "pdp", grid.resolution = 40)) +
age,
model = boston_rf,
xs_opts = list(transition = list(alter = "auto"))
)

# aic statistic is used by default

summary(model_pdp_auto)
##
## Call:
## stats::glm(formula = cmedv ~ xs(lstat) + ptratio + age, family = family,
##     data = data)
##
## Deviance Residuals:
##      Min        1Q    Median        3Q       Max
## -12.7619   -3.2031   -0.6366    2.8787   26.9953
##
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  4.638819   2.952287   1.571    0.117
## xs(lstat)    1.248040   0.048545  25.709  < 2e-16 ***
## ptratio     -0.858557   0.113027  -7.596 1.51e-13 ***
## age          0.054359   0.009827   5.532 5.11e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 24.87722)
##
##     Null deviance: 42578  on 505  degrees of freedom
## Residual deviance: 12488  on 502  degrees of freedom
## AIC: 3068.2
##
## Number of Fisher Scoring iterations: 2

Linear approximation was better for ptratio response function.

Transformed response

In some cases you may want to transform model response with you own function. Let’s check the example below with random forest model:

set.seed(123)
x <- rnorm(100, 10)
z <- rnorm(100, 10)
y <- x * z * rnorm(100, 1, 0.1)
data <- data.frame(x, z, y)
model_rf <- randomForest(log(y) ~ x + z, data = data)

In this case log transformation for y, removes interaction of x and z. In xspliner same transformation is used by default:

model_xs <- xspline(model_rf)
summary(model_xs)
##
## Call:
## stats::glm(formula = log(y) ~ xs(x) + xs(z), family = family,
##     data = data)
##
## Deviance Residuals:
##       Min         1Q     Median         3Q        Max
## -0.187175  -0.057420  -0.002821   0.058715   0.213533
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -6.3310     0.8078  -7.838 5.93e-12 ***
## xs(x)         1.1833     0.1400   8.449 2.96e-13 ***
## xs(z)         1.1946     0.1062  11.248  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.008017078)
##
##     Null deviance: 2.36576  on 99  degrees of freedom
## Residual deviance: 0.77766  on 97  degrees of freedom
## AIC: -193.88
##
## Number of Fisher Scoring iterations: 2
plot_model_comparison(model_xs, model = model_rf, data = data) Multiplicative form

When interactions between predictors occurs black box models in fact deal much better that linear models. xspliner offers using formulas with variables interactions.

You can do it in two possible forms.

Lets start with creating data and building black box:

x <- rnorm(100)
z <- rnorm(100)
y <- x + x * z + z + rnorm(100, 0, 0.1)
data <- data.frame(x, y, z)
model_rf <- randomForest(y ~ x + z, data = data)

The first option is specifying formula with * sign, as in standard linear models.

model_xs <- xspline(y ~ x * z, model = model_rf)
summary(model_xs)
##
## Call:
## stats::glm(formula = y ~ x * z, family = family, data = data)
##
## Deviance Residuals:
##      Min        1Q    Median        3Q       Max
## -0.25207  -0.07359  -0.00393   0.05399   0.35369
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003076   0.011178   0.275    0.784
## x           0.979334   0.011650  84.062   <2e-16 ***
## z           1.007693   0.010496  96.007   <2e-16 ***
## x:z         0.991345   0.011292  87.792   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.01193796)
##
##     Null deviance: 295.424  on 99  degrees of freedom
## Residual deviance:   1.146  on 96  degrees of freedom
## AIC: -153.1
##
## Number of Fisher Scoring iterations: 2
plot_model_comparison(model_xs, model = model_rf, data = data) The second one is adding form parameter equal to “multiplicative” in case of passing just the model or dot formula.

model_xs <- xspline(model_rf, form = "multiplicative")
summary(model_xs)
##
## Call:
## stats::glm(formula = y ~ xs(x) * xs(z), family = family, data = data)
##
## Deviance Residuals:
##      Min        1Q    Median        3Q       Max
## -0.62089  -0.27456   0.00258   0.16788   1.42959
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06527    0.03838  -1.701   0.0923 .
## xs(x)        1.11395    0.04946  22.521   <2e-16 ***
## xs(z)        0.95928    0.03859  24.856   <2e-16 ***
## xs(x):xs(z)  1.09751    0.05076  21.620   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1419257)
##
##     Null deviance: 295.424  on 99  degrees of freedom
## Residual deviance:  13.625  on 96  degrees of freedom
## AIC: 94.46
##
## Number of Fisher Scoring iterations: 2
plot_model_comparison(model_xs, model = model_rf, data = data) model_xs <- xspline(y ~ ., model = model_rf, form = "multiplicative")
summary(model_xs)
##
## Call:
## stats::glm(formula = y ~ xs(x) * xs(z), family = family, data = data)
##
## Deviance Residuals:
##      Min        1Q    Median        3Q       Max
## -0.62089  -0.27456   0.00258   0.16788   1.42959
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06527    0.03838  -1.701   0.0923 .
## xs(x)        1.11395    0.04946  22.521   <2e-16 ***
## xs(z)        0.95928    0.03859  24.856   <2e-16 ***
## xs(x):xs(z)  1.09751    0.05076  21.620   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1419257)
##
##     Null deviance: 295.424  on 99  degrees of freedom
## Residual deviance:  13.625  on 96  degrees of freedom
## AIC: 94.46
##
## Number of Fisher Scoring iterations: 2
plot_model_comparison(model_xs, model = model_rf, data = data) Subset formula

Every example we saw before used to use the same variables in black box and xspliner model. In fact this is not obligatory. How can it be used? For example to build a simpler model based on truncated amount of predictors. Let’s see below example:

library(randomForest)
library(xspliner)
data(airquality)
air <- na.omit(airquality)
model_rf <- randomForest(Ozone ~ ., data = air)
varImpPlot(model_rf) As we can see Wind and Temp variables are of the highest importance. Let’s build xspliner basing on just the Two variables.

model_xs <- xspline(Ozone ~ xs(Wind) + xs(Temp), model = model_rf)
summary(model_xs)
##
## Call:
## stats::glm(formula = Ozone ~ xs(Wind) + xs(Temp), family = family,
##     data = data)
##
## Deviance Residuals:
##     Min       1Q   Median       3Q      Max
## -45.078   -9.027   -1.908    8.083   81.449
##
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -89.4457     8.2639 -10.824  < 2e-16 ***
## xs(Wind)      1.5576     0.2095   7.436 2.60e-11 ***
## xs(Temp)      1.5583     0.1855   8.399 1.95e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 321.0854)
##
##     Null deviance: 121802  on 110  degrees of freedom
## Residual deviance:  34677  on 108  degrees of freedom
## AIC: 960.62
##
## Number of Fisher Scoring iterations: 2
plot_model_comparison(model_xs, model = model_rf, data = air) Or model including variables interaction:

model_xs <- xspline(Ozone ~ xs(Wind) * xs(Temp), model = model_rf)
summary(model_xs)
##
## Call:
## stats::glm(formula = Ozone ~ xs(Wind) * xs(Temp), family = family,
##     data = data)
##
## Deviance Residuals:
##     Min       1Q   Median       3Q      Max
## -38.940  -10.049   -0.665    7.755   66.926
##
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -198.99004   36.90872  -5.391 4.21e-07 ***
## xs(Wind)             4.18800    0.88861   4.713 7.39e-06 ***
## xs(Temp)             3.80619    0.76085   5.003 2.23e-06 ***
## xs(Wind):xs(Temp)   -0.05251    0.01728  -3.040  0.00298 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 298.3263)
##
##     Null deviance: 121802  on 110  degrees of freedom
## Residual deviance:  31921  on 107  degrees of freedom
## AIC: 953.43
##
## Number of Fisher Scoring iterations: 2
plot_model_comparison(model_xs, model = model_rf, data = air) 