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Interactive visual checks for additive quantile fits

cqcheckI.Rd

Given an additive quantile model, fitted using qgam, cqcheck2DI provides some interactive 2D plots that allow to check what proportion of responses, y, falls below the fitted quantile. This is an interactive version of the cqcheck function.

Usage

cqcheckI(
  obj,
  v,
  X = NULL,
  y = NULL,
  run = TRUE,
  width = "100%",
  height = "680px"
)

Arguments

obj

the output of a qgam call.

v

if a 1D plot is required, v should be either a single character or a numeric vector. In the first case v should be the names of one of the variables in the dataframe X. In the second case, the length of v should be equal to the number of rows of X. If a 2D plot is required, v should be either a vector of two characters or a matrix with two columns.

X

a dataframe containing the data used to obtain the conditional quantiles. By default it is NULL, in which case predictions are made using the model matrix in obj$model.

y

vector of responses. Its i-th entry corresponds to the i-th row of X. By default it is NULL, in which case it is internally set to obj$y.

run

if TRUE (default) the function produces an interactive plot, otherwise it returns the corresponding shiny app.

width

the width of the main plot. Default is "100%".

height

width the width of the main plot. Default is "680px".

Value

Simply produces an interactive plot.

Details

This is an interactive version of the cqcheck, see ?cqcheck for details. The main interactive feature is that one can select an area by brushing, and then double-click to zoom in. In the 1D case the vertical part of the selected area is not use: we zoom only along the x axis. Double-clicking without brushing zooms out.

Author

Matteo Fasiolo <matteo.fasiolo@gmail.com>.

Examples

if (FALSE) { # \dontrun{ 
#######
# Example 1: Bivariate additive model y~1+x+x^2+z+x*z/2+e, e~N(0, 1)
#######
library(qgam)
set.seed(15560)
n <- 1000
x <- rnorm(n, 0, 1); z <- rnorm(n)
X <- cbind(1, x, x^2, z, x*z)
beta <- c(0, 1, 1, 1, 0.5)
y <- drop(X %*% beta) + rnorm(n) 
dataf <- data.frame(cbind(y, x, z))
names(dataf) <- c("y", "x", "z")

#### Fit a constant model for median
qu <- 0.5
fit <- qgam(y~1, qu = qu, data = dataf)

# Look at what happens along x: clearly there is non linear pattern here
cqcheckI(obj = fit, v = c("x"), X = dataf, y = y) 

#### Add a smooth for x
fit <- qgam(y~s(x), qu = qu, data = dataf)
cqcheckI(obj = fit, v = c("x"), X = dataf, y = y) # Better!

# Lets look across across x and z. As we move along z (x2 in the plot) 
# the colour changes from green to red
cqcheckI(obj = fit, v = c("x", "z"), X = dataf, y = y)

# The effect look pretty linear
cqcheckI(obj = fit, v = c("z"), X = dataf, y = y)

#### Lets add a linear effect for z 
fit <- qgam(y~s(x)+z, qu = qu, data = dataf)

# Looks better!
cqcheckI(obj = fit, v = c("z"))

# Lets look across x and y again: green prevails on the top-left to bottom-right
# diagonal, while the other diagonal is mainly red.
cqcheckI(obj = fit, v = c("x", "z"))

### Maybe adding an interaction would help?
fit <- qgam(y~s(x)+z+I(x*z), qu = qu, data = dataf)

# It does! The real model is: y ~ 1 + x + x^2 + z + x*z/2 + e, e ~ N(0, 1)
cqcheckI(obj = fit, v = c("x", "z"))
} # }