Plot Residual Diagnostics for an maxlogL Object — plot.maxlogL • EstimationTools

Provides plots of Cox-Snell, martingale Randomized quantile residuals.

Usage

# S3 method for maxlogL
plot(
  x,
  type = c("rqres", "cox-snell", "martingale", "right-censored-deviance"),
  parameter = NULL,
  which.plots = NULL,
  caption = NULL,
  xvar = NULL,
  ...
)

Arguments

x: a maxlogL object.
type: a character with the type of residuals to be plotted. The default value is type = "rqres", which is used to compute the normalized randomized quantile residuals.
parameter: which parameter fitted values are required for type = "rqres". The default is the first one defined in the pdf,e.g, in dnorm, the default parameter is mean.
which.plots: a subset of numbers to specify the plots. See details for further information.
caption: title of the plots. If caption = NULL, the default values are used.
xvar: an explanatory variable to plot the residuals against.
...: further parameters for the plot method.

Value

Returns specified plots related to the residuals of the fitted maxlogL model.

Details

If type = "rqres", the available subset is 1:4, referring to:

1. Quantile residuals vs. fitted values (Tukey-Ascomb plot)
2. Quantile residuals vs. index
3. Density plot of quantile residuals
4. Normal Q-Q plot of the quantile residuals.

Author

Jaime Mosquera Gutiérrez, jmosquerag@unal.edu.co

Examples

library(EstimationTools)

#----------------------------------------------------------------------------
# Example 1: Quantile residuals for a normal model
n <- 1000
x <- runif(n = n, -5, 6)
y <- rnorm(n = n, mean = -2 + 3 * x, sd = exp(1 + 0.3* x))
norm_data <- data.frame(y = y, x = x)

# It does not matter the order of distribution parameters
formulas <- list(sd.fo = ~ x, mean.fo = ~ x)
support <- list(interval = c(-Inf, Inf), type = 'continuous')

norm_mod <- maxlogLreg(formulas, y_dist = y ~ dnorm, support = support,
                       data = norm_data,
                       link = list(over = "sd", fun = "log_link"))

# Quantile residuals diagnostic plot
plot(norm_mod, type = "rqres")

plot(norm_mod, type = "rqres", parameter = "sd")


# Exclude Q-Q plot
plot(norm_mod, type = "rqres", which.plots = 1:3)


#----------------------------------------------------------------------------
# Example 2: Cox-Snell residuals for an exponential model
data(ALL_colosimo_table_4_1)
formulas <- list(scale.fo = ~ lwbc)
support <- list(interval = c(0, Inf), type = 'continuous')

ALL_exp_model <- maxlogLreg(
  formulas,
  fixed = list(shape = 1),
  y_dist = Surv(times, status) ~ dweibull,
  data = ALL_colosimo_table_4_1,
  support = support,
  link = list(over = "scale", fun = "log_link")
)

summary(ALL_exp_model)
#> _______________________________________________________________
#> Optimization routine: nlminb 
#> Standard Error calculation: Hessian from optim 
#> _______________________________________________________________
#>        AIC      BIC
#>   171.7541 173.4205
#> _______________________________________________________________
#> Fixed effects for log(scale) 
#> ---------------------------------------------------------------
#>             Estimate Std. Error Z value  Pr(>|z|)    
#> (Intercept)  8.47750    1.71122  4.9541 7.268e-07 ***
#> lwbc        -1.10930    0.41357 -2.6822  0.007313 ** 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> _______________________________________________________________
#> Note: p-values valid under asymptotic normality of estimators 
#> ---
plot(ALL_exp_model, type = "cox-snell")


plot(ALL_exp_model, type = "right-censored-deviance")


plot(ALL_exp_model, type = "martingale", xvar = NULL)

plot(ALL_exp_model, type = "martingale", xvar = "lwbc")



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