ciTools is an R package that makes working with model uncertainty as easy as possible. It gives the user easy access to confidence or prediction intervals for the fitted values of (log-) linear models, generalized linear models, and (log-) linear mixed models. Additionally, we provide functions to determine probabilities and quantiles of the conditional response distribution given each of these models.
I no longer have time to actively maintain ciTools. There are some known bugs and limitations.
Uncertainty intervals (confidence or prediction intervals) for fitted values are easy enough if one is just working with linear models, but when models become more complex, e.g. generalized linear models or linear mixed models, the facilities for quantifying uncertainty for predictions are either more cumbersome to work with or nonexistent. We hope that ciTools provides uniform, model invariant access to these statistics.
This package has three main features that make it useful to R users:
add_ci(data, fit, ...) - get confidence intervals and append to dataadd_pi(data, fit, ...) - get prediction intervals and append to dataadd_probs(data, fit, q, ...) - get conditional response probabilities, Pr(Response|Covariates < q)add_quantile(data, fit, p, ...) - get conditional response quantiles at level pInstall from CRAN:
install.packages("ciTools")
library(ciTools)
Install ciTools from Github with devtools:
library(devtools)
devtools::install_github("jthaman/ciTools")
library(ciTools)
If you are behind a corporate firewall, you may have to instead run:
library(httr)
library(devtools)
httr::set_config(config(ssl_verifypeer = FALSE))
devtools::install_github("jthaman/ciTools")
httr::set_config(config(ssl_verifypeer = TRUE))
library(ciTools)
Here is a short example featuring a linear mixed model, but the idea is the same for any other model.
## Example with a linear mixed model
library(ciTools)
library(lme4)
library(dplyr)
dat <- sleepstudy ## data included with lme4
## Fit a random intercept model
fit <- lmer(Reaction ~ Days + (1 | Subject), data = dat)
## New data for predictions
## A random sample of 20 observations from sleepstudy
new_dat <- sleepstudy[sample(NROW(dat), 20),]
## Append (conditional) 50% CIs and PIs to the new data:
new_dat <- add_ci(new_dat, fit, alpha = 0.5) %>%
add_pi(fit, alpha = 0.5)
## Append 50% CIs and PIs, but ignore the random effects:
add_ci(new_dat, fit, alpha = 0.5, includeRanef = FALSE,
names = c("lcb_uncond", "ucb_uncond"), ## custom interval names
yhatName = "pred_uncond") %>%
add_pi(fit, alpha = 0.5, includeRanef = FALSE,
names = c("lpb_uncond", "upb_uncond"))
## Determine the probability that a new Reaction time will be less
## than 300 (given the model and new data):
add_probs(new_dat, fit, 300)
## Or greater than:
add_probs(new_dat, fit, 300, comparison = ">")
## Determine the 0.9-quantile of the predictive distribution, given the
## model and new data:
add_quantile(new_dat, fit, 0.9)
The end result is a data frame that is amenable to plotting with ggplot2. See the documentation for more examples with other types of statistical regression models.
Model Confidence Intervals Prediction Intervals Response Probabilities Response Quantiles Linear [X] [X] [X] [X] Log-Linear [X] [X] [X] [X] GLM [X] [X] [X] [X] Neg. Binomial [X] [X] [X] [X] Linear Mixed [X] [X] [X] [X] Log-Linear Mixed [TODO] [X] [X] [X] Survival (AFT model) [X] [X] [X] [X] GLMM [X] [X] [X] [X][X] = Implemented
We still have work to do. Submit an Issue if you run into a bug, or a PR if you think you can help us out (see the TODO file).
John Haman and Matt Avery
ciTools (C) 2017 Institute for Defense Analyses
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