-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'dev' # Conflicts: # README.md
- Loading branch information
Showing
2 changed files
with
142 additions
and
3 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,141 @@ | ||
|
|
||
| #### General | ||
|
|
||
| [](https://travis-ci.org/pat-s/oddsratio) [](https://ci.appveyor.com/project/pat-s/oddsratio) [](http://www.repostatus.org/#active) [](https://codecov.io/gh/pat-s/oddsratio) [](https://cran.r-project.org/) [](/commits/master) | ||
|
|
||
| #### CRAN | ||
|
|
||
| [](http://cran.r-project.org/package=oddsratio) [](http://www.r-pkg.org/pkg/oddsratio)  | ||
|
|
||
| #### Github | ||
|
|
||
| [](commits/master) | ||
|
|
||
| oddsratio | ||
| ========= | ||
|
|
||
| Functions for calculation and plotting of odds ratios of Generalized Additive (Mixed) Models and Generalized Linear (Mixed) Models with a binomial response variable (i.e. logistic regression models). | ||
|
|
||
| Installation | ||
| ------------ | ||
|
|
||
| Install from CRAN: | ||
|
|
||
| ``` r | ||
| install.packages("oddsratio") | ||
| ``` | ||
|
|
||
| Get the development version from Github: | ||
|
|
||
| ``` r | ||
| devtools::install_github("pat-s/oddsratio", build_vignettes = TRUE) | ||
| ``` | ||
|
|
||
| Examples | ||
| -------- | ||
|
|
||
| ### GLM | ||
|
|
||
| Odds ratio calculation of predictors `gre` & `gpa` of a fitted model `fit.glm` with increment steps of 380 and 5, respectively. | ||
| For factor variables (here: `rank` with 4 levels), automatically all odds ratios corresponding to the base level (here: `rank1`) are returned including their respective confident intervals. The default level is 95%. However, other levels can be specified with the param `CI`. Data source: <http://www.ats.ucla.edu/stat/r/dae/logit.htm> | ||
|
|
||
| ``` r | ||
| library(oddsratio) | ||
| dat <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") | ||
| dat$rank <- factor(dat$rank) | ||
| fit.glm <- glm(admit ~ gre + gpa + rank, data = dat, family = "binomial") | ||
|
|
||
| calc.oddsratio.glm(data = dat, model = fit.glm, | ||
| incr = list(gre = 380, gpa = 5, CI = 0.95)) | ||
| ``` | ||
|
|
||
| ## predictor oddsratio CI.low (2.5 %) CI.high (97.5 %) increment | ||
| ## 1 gre 2.364 1.054 5.396 380 | ||
| ## 2 gpa 55.712 2.229 1511.282 5 | ||
| ## 3 rank2 0.509 0.272 0.945 Indicator variable | ||
| ## 4 rank3 0.262 0.132 0.512 Indicator variable | ||
| ## 5 rank4 0.212 0.091 0.471 Indicator variable | ||
|
|
||
| ### GAM | ||
|
|
||
| For GAMs, the calculation of odds ratio is different. Due to its non-linear definition, odds ratios do only apply to specific value changes and are not constant throughout the whole value range of the predictor as for GLMs. Hence, odds ratios of GAMs can only be computed for one predictor at a time by holding all other predictors at a fixed value while changing the value of the specific predictor. Confident intervals are currently fixed to the 95% level for GAMs. Data source: `?mgcv::predict.gam()` | ||
|
|
||
| Here, the usage of `calc.oddsratio.gam()` is shown by calculating odds ratios of pred `x2` for a 20% steps across the whole value range of the predictor. | ||
|
|
||
| ``` r | ||
| suppressPackageStartupMessages(library(mgcv)) | ||
| set.seed(1234) | ||
| n <- 200 | ||
| sig <- 2 | ||
| dat <- gamSim(1, n = n,scale = sig, verbose = FALSE) | ||
| dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50), rep("D", 50))) | ||
| fit.gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4, data = dat) | ||
|
|
||
| calc.oddsratio.gam(data = dat, model = fit.gam, pred = "x2", | ||
| percentage = 20, slice = TRUE) | ||
| ``` | ||
|
|
||
| ## predictor value1 value2 perc1 perc2 oddsratio CI.low (2.5%) | ||
| ## 1 x2 0.001 0.200 0 20 2510.77 1091.68 | ||
| ## 2 x2 0.200 0.400 20 40 0.03 0.03 | ||
| ## 3 x2 0.400 0.599 40 60 0.58 0.56 | ||
| ## 4 x2 0.599 0.799 60 80 0.06 0.06 | ||
| ## 5 x2 0.799 0.998 80 100 0.41 0.75 | ||
| ## CI.high (97.5%) | ||
| ## 1 5774.53 | ||
| ## 2 0.03 | ||
| ## 3 0.60 | ||
| ## 4 0.06 | ||
| ## 5 0.22 | ||
|
|
||
| If you want to compute a single odds ratio for specific values, simply set param `slice = FALSE`: | ||
|
|
||
| ``` r | ||
| calc.oddsratio.gam(data = dat, model = fit.gam, | ||
| pred = "x2", values = c(0.099, 0.198)) | ||
| ``` | ||
|
|
||
| ## predictor value1 value2 oddsratio CI.low (2.5%) CI.high (97.5%) | ||
| ## 1 x2 0.099 0.198 23.32353 23.30424 23.34283 | ||
|
|
||
| From version 0.3.0 on (Github version 0.2.0.9000) you can now do the following things: | ||
|
|
||
| - Plot smoothing functions of GAM(M)s | ||
|
|
||
| ``` r | ||
| pl.smooth.gam(fit.gam, pred = "x2", title = "Predictor 'x2'") | ||
| ``` | ||
|
|
||
|  | ||
|
|
||
| - Insert the calculated odds ratios into the smoothing function | ||
|
|
||
| ``` r | ||
| plot.object <- pl.smooth.gam(fit.gam, pred = "x2", title = "Predictor 'x2'") | ||
| or.object <- calc.oddsratio.gam(data = dat, model = fit.gam, | ||
| pred = "x2", values = c(0.099, 0.198)) | ||
|
|
||
| plot <- add.oddsratio.into.plot(plot.object, or.object, or.yloc = 3, | ||
| values.xloc = 0.04, line.size = 0.5, | ||
| line.type = "dotdash", values.yloc = 0.5, | ||
| arrow.col = "red") | ||
| plot | ||
| ``` | ||
|
|
||
|  | ||
|
|
||
| If you want to insert multiple odds ratios, just go on! | ||
|
|
||
| ``` r | ||
| or.object2 <- calc.oddsratio.gam(data = dat, model = fit.gam, pred = "x2", | ||
| values = c(0.4, 0.6)) | ||
|
|
||
| add.oddsratio.into.plot(plot, or.object2, or.yloc = 2.1, values.yloc = 2, | ||
| line.col = "green4", text.col = "black", | ||
| rect.col = "green4", rect.alpha = 0.2, | ||
| line.alpha = 1, line.type = "dashed", | ||
| arrow.xloc.r = 0.01, arrow.xloc.l = -0.01, | ||
| arrow.length = 0.01, rect = T) | ||
| ``` | ||
|
|
||
|  |