Description Usage Arguments Details Value See Also Examples
This is a generic function to append confidence intervals for
predictions of a model fit to a data frame. A confidence interval
is generated for the fitted value of each observation in
df
. These confidence intervals are then appended to
df
and returned to the user as a data frame. The fit
may
be a linear, log-linear, linear mixed, generalized linear model,
generalized linear mixed, or accelerated failure time model.
1 |
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 character vector of length one. Name of the
vector of the predictions made for each observation in |
... |
Additional arguments. |
For more specific information about the arguments that are applicable in each method, consult:
add_ci.lm
for linear model confidence intervals
add_ci.glm
for generalized linear model confidence intervals
add_ci.lmerMod
for linear mixed model confidence intervals
add_ci.glmerMod
for generalized linear mixed model confidence intervals
add_ci.survreg
for accelerated failure time confidence intervals
Note that add_ci
calculates confidence intervals for
fitted values, not model coefficients. For confidence
intervals of model coefficients, see confint
.
A dataframe, df
, with predicted values, upper and lower
confidence bounds attached.
add_pi
for prediction intervals,
add_probs
for response level probabilities, and
add_quantile
for quantiles of the conditional
response distribution.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # Fit a linear model
fit <- lm(dist ~ speed, data = cars)
# Make a confidence interval for each observation in cars, and
# append to the data frame
add_ci(cars, fit)
# Make new data
new_data <- cars[sample(NROW(cars), 10), ]
add_ci(new_data, fit)
# Fit a Poisson model
fit2 <- glm(dist ~ speed, family = "poisson", data = cars)
# Append CIs
add_ci(cars, fit2)
# Fit a linear mixed model using lme4
fit3 <- lme4::lmer(Reaction ~ Days + (1|Subject), data = lme4::sleepstudy)
# Append CIs
# Generally, you should use more than 100 bootstrap replicates
add_ci(lme4::sleepstudy, fit3, nSims = 100)
# Fit a logistic model
fit4 <- glm(I(dist > 20) ~ speed, family = "binomial", data = cars)
# Append CIs
add_ci(cbind(cars, I(cars$dist > 20)), fit4)
|
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