add_ci.negbin {ciTools}  R Documentation 
Confidence Intervals for Negative Binomial Linear Model Predictions
Description
This function is one of the methods for add_ci
, and is
called automatically when add_ci
is used on a fit
of
class negbin
.
Usage
## S3 method for class 'negbin'
add_ci(
df,
fit,
alpha = 0.05,
names = NULL,
yhatName = "pred",
response = TRUE,
type = "parametric",
nSims = 2000,
...
)
Arguments
df 
A data frame of new data. 
fit 
An object of class 
alpha 
A real number between 0 and 1. Controls the confidence level of the interval estimates. 
names 

yhatName 
A string. Name of the vector of predictions made for each observation in df 
response 
A logical. The default is 
type 
A string. Must be 
nSims 
An integer. Number of simulations to perform if the bootstrap method is used. 
... 
Additional arguments. 
Details
The default link function is loglink. Confidence Intervals are
determined by making an interval on the scale of the linear
predictor, then applying the inverse link function from the model
fit to transform the linear level confidence intervals to the
response level. Alternatively, bootstrap confidence intervals may
be formed. The bootstrap intervals are formed by first resampling
cases from the data frame used to calculate fit
, then bias
corrected and accelerated intervals are calculated. See
boot::boot.ci
for more details.
Value
A dataframe, df
, with predicted values, upper and lower
confidence bounds attached.
See Also
add_pi.negbin
for prediction intervals for
negbin
objects, add_probs.negbin
for conditional
probabilities of negbin
objects, and
add_quantile.negbin
for response quantiles of
negbin
objects.
Examples
x1 < rnorm(300, mean = 1)
y < MASS::rnegbin(n = 300, mu = exp(5 + 0.5 * x1), theta = 2)
df < data.frame(x1 = x1, y = y)
fit < MASS::glm.nb(y ~ x1, data = df)
df < df[sample(100),]
add_ci(df, fit, names = c("lcb", "ucb"))