It is entirely for the purpose of plotting fits and intervals on a scatterplot (or histogram).
It is a generic function to predict models for loon smooth layer (a wrap of the function predict).
However, the output is unified.
Usage
l_predict(model, ...)
# Default S3 method
l_predict(model, ...)
# S3 method for class 'lm'
l_predict(
model,
newdata = NULL,
interval = c("none", "confidence", "prediction"),
level = 0.95,
...
)
# S3 method for class 'nls'
l_predict(
model,
newdata = NULL,
interval = c("none", "confidence", "prediction"),
level = 0.95,
...
)
# S3 method for class 'glm'
l_predict(
model,
newdata = NULL,
interval = c("none", "confidence"),
level = 0.95,
...
)
# S3 method for class 'loess'
l_predict(
model,
newdata = NULL,
interval = c("none", "confidence", "prediction"),
level = 0.95,
...
)Arguments
- model
a model object for which prediction is desired
- ...
arguments passed in
predict- newdata
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.
- interval
type of interval, could be "none", "confidence" or "prediction" (not for
glm)- level
confidence level
Value
A data frame is returned with x (if newdata is given) and y.
If the interval is not none,
two more columns, lower (lower interval) and upper (upper interval) will be returned.
Examples
y <- rnorm(10)
x <- rnorm(10)
model1 <- lm(y ~ x)
# formal output
pre <- l_predict(model1, newdata = data.frame(x = sort(x)),
interval = "conf")
head(pre)
#> x y lower upper
#> 1 -1.47615111 -0.193835067 -1.7666154 1.3789453
#> 2 -1.26047622 -0.159577084 -1.5549521 1.2357979
#> 3 -0.82054972 -0.089698785 -1.1650372 0.9856396
#> 4 -0.22545194 0.004827053 -0.8269545 0.8366086
#> 5 -0.11465603 0.022425969 -0.8035391 0.8483910
#> 6 -0.08441358 0.027229706 -0.7996783 0.8541377
if(interactive()) {
p <- with(cars, l_plot(speed, dist))
# Example taken from
# https://stackoverflow.com/questions/23852505/how-to-get-confidence-interval-for-smooth-spline
#
l_predict.smooth.spline <- function(model, interval = c("confidence", "none"),
level = 0.95, ...) {
# confidence interval of `smooth.spline`
interval <- match.arg(interval)
res <- (model$yin - model$y)/(1 - model$lev) # jackknife residuals
sigma <- sqrt(var(res)) # estimate sd
std <- stats::qnorm(level / 2 + 0.5)
upper <- model$y + std * sigma * sqrt(model$lev) # upper 95% conf. band
lower <- model$y - std * sigma * sqrt(model$lev) # lower 95% conf. band
data.frame(y = model$yin, lower = lower, upper = upper)
}
l <- l_layer_smooth(p, method = "smooth.spline", interval = "confidence")
}