GHP stands for General Hierarchical Partitioning. ghp is an
implementation of the technique of hierarchical partitioning first
mentioned by Chevan and Sutherland (1991). This method fits all possible
models for a set of covariates and then extracts a goodness of fit
(e.g. (R^2) for linear models) to obtain independent and joint
contributions of the independent variables on the selected figure.
This package is an extension of the hier.part R package, developed by
C. Walsh and R. Mac Nally in 2003. While hier.part is fast and simple
at what it does, it is limited in the range of possible models as well
as goodness of fit figures. Specifically, the motivation of this package
is the ability to do deviance partitioning.
You can install ghp from github with:
# install.packages("devtools")
devtools::install_github("Stan125/ghp")Just call the ghp function with the name of the dependent variable (arg:
dep) and a data.frame with all relevant variables to obtain the
independent and joint effects of the explanatory covariates.
india <- ghp::india
results_lm <- ghp(depname = "stunting", india, method = "lm", gof = "r.squared")
results_lm
#> $results
#> # A tibble: 7 x 7
#> var param indep_effects joint_effects total_effects indep_perc
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 cage mu 0.0323 0.0123 0.0446 0.269
#> 2 csex mu 0.000746 -0.000167 0.000579 0.00623
#> 3 brea… mu 0.0295 0.0166 0.0461 0.246
#> 4 ctwin mu 0.0000974 -0.0000199 0.0000775 0.000814
#> 5 mage mu 0.0322 0.00878 0.0410 0.269
#> 6 mbmi mu 0.0196 0.00872 0.0283 0.164
#> 7 mrel… mu 0.00538 0.000626 0.00600 0.0449
#> # … with 1 more variable: joint_perc <dbl>
#>
#> $npar
#> [1] 1
#>
#> $method
#> [1] "lm"
#>
#> $gof
#> [1] "r.squared"
#>
#> $joint_results
#> # A tibble: 8 x 15
#> var cage csex breastfeeding ctwin mage mbmi mreligion
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cage 0 1.74e-5 0.00404 3.57e-6 0.00140 9.85e-4 2.53e-4
#> 2 csex 0.00180 0. 0.00237 -1.12e-6 0.00128 1.18e-3 7.06e-5
#> 3 brea… 0.00343 -3.08e-5 0 2.86e-6 0.00139 1.77e-3 1.41e-4
#> 4 ctwin 0.00177 -2.21e-5 0.00238 0. 0.00126 1.22e-3 8.88e-5
#> 5 mage 0.00191 1.23e-6 0.00251 3.11e-6 0 2.30e-3 -3.25e-5
#> 6 mbmi 0.00150 -8.99e-5 0.00289 -2.49e-5 0.00231 0. 1.05e-4
#> 7 mrel… 0.00193 -4.27e-5 0.00243 -3.44e-6 0.00113 1.26e-3 0.
#> 8 SUM 0.0123 -1.67e-4 0.0166 -1.99e-5 0.00878 8.72e-3 6.26e-4
#> # … with 7 more variables: cage_perc <dbl>, csex_perc <dbl>,
#> # breastfeeding_perc <dbl>, ctwin_perc <dbl>, mage_perc <dbl>,
#> # mbmi_perc <dbl>, mreligion_perc <dbl>
#>
#> attr(,"class")
#> [1] "part"The first dataframe captures the actual mean influence of the variable
on the goodness-of-fit. Also, joint effects are calculated. The second
dataframe shows the percentage influence. We can see that cage has the
highest influence with (~43%).
It is now possible to do deviance partitiong of gamlss models. Gamlss
models can model multiple parameters of a distribution. ghp can handle
up to two modeled parameters, so you can find out what influence
covariates have on the second modeled parameter (e.g. the variance).
results_gamlss <- ghp("stunting", india, method = "gamlss",
gof = "deviance", npar = 2)
results_gamlss
#> $results
#> # A tibble: 14 x 7
#> var param indep_effects joint_effects total_effects indep_perc
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 cage mu -13.3 -4.35 -17.6 0.269
#> 2 csex mu -0.309 0.0854 -0.224 0.00628
#> 3 brea… mu -12.1 -6.14 -18.2 0.245
#> 4 ctwin mu -0.0396 0.00969 -0.0299 0.000804
#> 5 mage mu -13.3 -2.87 -16.2 0.270
#> 6 mbmi mu -8.03 -3.05 -11.1 0.163
#> 7 mrel… mu -2.23 -0.0941 -2.32 0.0453
#> 8 cage sigma -9.96 -2.14 -12.1 0.280
#> 9 csex sigma -0.577 0.0372 -0.540 0.0162
#> 10 brea… sigma -8.73 -1.01 -9.74 0.246
#> 11 ctwin sigma -11.7 2.47 -9.23 0.329
#> 12 mage sigma -0.360 0.290 -0.0696 0.0101
#> 13 mbmi sigma -0.100 0.0936 -0.00681 0.00282
#> 14 mrel… sigma -4.13 1.19 -2.94 0.116
#> # … with 1 more variable: joint_perc <dbl>
#>
#> $npar
#> [1] 2
#>
#> $method
#> [1] "gamlss"
#>
#> $gof
#> [1] "deviance"
#>
#> $joint_results
#> $joint_results$res_mu
#> # A tibble: 8 x 15
#> var cage csex breastfeeding ctwin mage mbmi mreligion
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cage 0 -0.00400 -1.53 -1.10e-3 -0.433 -0.302 -0.0747
#> 2 csex -0.637 0 -0.873 7.02e-4 -0.420 -0.409 -0.00566
#> 3 brea… -1.27 0.0158 0 -8.18e-4 -0.429 -0.628 -0.0279
#> 4 ctwin -0.624 0.0115 -0.879 0. -0.413 -0.427 -0.0132
#> 5 mage -0.644 0.00257 -0.896 -9.37e-4 0 -0.849 0.0430
#> 6 mbmi -0.486 0.0395 -1.07 1.02e-2 -0.823 0 -0.0156
#> 7 mrel… -0.682 0.0200 -0.891 1.60e-3 -0.354 -0.438 0
#> 8 SUM -4.35 0.0854 -6.14 9.69e-3 -2.87 -3.05 -0.0941
#> # … with 7 more variables: cage_perc <dbl>, csex_perc <dbl>,
#> # breastfeeding_perc <dbl>, ctwin_perc <dbl>, mage_perc <dbl>,
#> # mbmi_perc <dbl>, mreligion_perc <dbl>
#>
#> $joint_results$res_sigma
#> # A tibble: 8 x 15
#> var cage csex breastfeeding ctwin mage mbmi mreligion
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cage 0 0.00701 -0.588 0.497 0.0424 0.0123 0.162
#> 2 csex -0.304 0 -0.145 0.352 0.0388 0.0128 0.178
#> 3 brea… -0.750 0.00498 0 0.521 0.0316 0.0163 0.308
#> 4 ctwin -0.161 0.00491 0.0243 0 0.0669 0.0284 0.169
#> 5 mage -0.305 0.00264 -0.154 0.378 0 0.00911 0.202
#> 6 mbmi -0.307 0.00478 -0.141 0.367 0.0372 0 0.171
#> 7 mrel… -0.314 0.0129 -0.00579 0.351 0.0732 0.0147 0
#> 8 SUM -2.14 0.0372 -1.01 2.47 0.290 0.0936 1.19
#> # … with 7 more variables: cage_perc <dbl>, csex_perc <dbl>,
#> # breastfeeding_perc <dbl>, ctwin_perc <dbl>, mage_perc <dbl>,
#> # mbmi_perc <dbl>, mreligion_perc <dbl>
#>
#>
#> attr(,"class")
#> [1] "part"Since 0.3.0 you can specify variable groups. The partitioning is now not
happening with specific variables, but by testing all group
combinations. In the given ghp::india dataset, which captures the
nutrition of children in india we can now divide all covariates into to
groups: those that give information about the child, and those that give
information about the mother. Let’s try that out:
# Specifying the groups should happen in a data.frame
groupings <- data.frame(varnames = colnames(india),
groups = c("0", "child", "child", "mother",
"child", "mother", "mother", "mother"))
results_groups <- ghp(depname = "stunting", india, method = "lm", gof = "r.squared",
group_df = groupings)
results_groups
#> $results
#> # A tibble: 2 x 7
#> var param indep_effects joint_effects total_effects indep_perc
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 child mu 0.0332 0.0116 0.0447 0.277
#> 2 moth… mu 0.0866 0.0116 0.0981 0.723
#> # … with 1 more variable: joint_perc <dbl>
#>
#> $npar
#> [1] 1
#>
#> $method
#> [1] "lm"
#>
#> $gof
#> [1] "r.squared"
#>
#> $joint_results
#> # A tibble: 3 x 5
#> var child mother child_perc mother_perc
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 child 0 0.0116 0 1
#> 2 mother 0.0116 0 1 0
#> 3 SUM 0.0116 0.0116 1 1
#>
#> attr(,"class")
#> [1] "part"We can now see that both groups have almost the same amount of influence on the (R^2).
To get a bar plot of the percentage independent effects, use
plot_ghp():
plot_ghp(results_lm)
plot_ghp(results_gamlss) +
ggplot2::scale_fill_grey()
Since 0.4.0, ghp is almost as fast as its counterpart hier.part,
because the core partitioning was written with C++. A quick comparison:
hp <- function()
hier.part::hier.part(india$stunting, dplyr::select(india,-stunting),
gof = "Rsqu", barplot = FALSE)
ghp <- function()
ghp::ghp("stunting", india, method = "lm", gof = "r.squared")
microbenchmark::microbenchmark(hp, ghp)
#> Unit: nanoseconds
#> expr min lq mean median uq max neval
#> hp 29 30 56.78 31 32 2585 100
#> ghp 29 30 39.26 31 32 801 100This README.Rmd was run on:
date()
#> [1] "Wed Feb 6 10:41:55 2019"