# Interactive visual checks for additive quantile fits 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. ```r 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". ## Returns 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. ## Examples ```r ## Not run: ####### # 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")) ## End(Not run) ``` ## Author(s) Matteo Fasiolo .

cqcheckI function