residuals.gnm: Residuals for Generalized Nonlinear Models

In glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models

Residuals for Generalized Nonlinear Models

Description

Computes residuals for a fitted generalized nonlinear model.

Usage

## S3 method for class 'gnm'
residuals(
  object,
  type = c("quantile", "deviance", "pearson"),
  standardized = FALSE,
  plot.it = FALSE,
  identify,
  dispersion = NULL,
  ...
)

Arguments

object	a object of the class gnm.
type	an (optional) character string giving the type of residuals which should be returned. The available options are: (1) "quantile", (2) "deviance", and (3) "pearson". As default, type is set to "quantile".
standardized	an (optional) logical switch indicating if the residuals should be standardized by dividing by the square root of (1-h), where h is a measure of leverage. As default, standardized is set to FALSE.
plot.it	an (optional) logical switch indicating if a plot of the residuals versus the fitted values is required. As default, plot.it is set to FALSE.
identify	an (optional) integer value indicating the number of individuals to identify on the plot of residuals. This is only appropriate when plot.it=TRUE.
dispersion	an (optional) value indicating the dispersion parameter estimate that must be used to calculate residuals.
...	further arguments passed to or from other methods

Value

A vector with the observed residuals type type.

References

Atkinson A.C. (1985) Plots, Transformations and Regression. Oxford University Press, Oxford.

Davison A.C., Gigli A. (1989) Deviance Residuals and Normal Scores Plots. Biometrika 76, 211-221.

Dunn P.K., Smyth G.K. (1996) Randomized Quantile Residuals. Journal of Computational and Graphical Statistics 5, 236-244.

Pierce D.A., Schafer D.W. (1986) Residuals in Generalized Linear Models. Journal of the American Statistical Association 81, 977-986.

Examples

###### Example 1: The effects of fertilizers on coastal Bermuda grass
data(Grass)
fit1 <- gnm(Yield ~ b0 + b1/(Nitrogen + a1) + b2/(Phosphorus + a2) + b3/(Potassium + a3),
            family=gaussian(inverse), start=c(b0=0.1,b1=13,b2=1,b3=1,a1=45,a2=15,a3=30), data=Grass)
residuals(fit1, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
                col.axis="blue", col.main="black", family="mono", cex=0.8)

###### Example 2: Assay of an Insecticide with a Synergist
data(Melanopus)
fit2 <- gnm(Killed/Exposed ~ b0 + b1*log(Insecticide-a1) + b2*Synergist/(a2 + Synergist),
            family=binomial(logit), weights=Exposed, start=c(b0=-3,b1=1.2,a1=1.7,b2=1.7,a2=2),
		   data=Melanopus)
residuals(fit2, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
                col.axis="blue", col.main="black", family="mono", cex=0.8)

###### Example 3: Developmental rate of Drosophila melanogaster
data(Drosophila)
fit3 <- gnm(Duration ~ b0 + b1*Temp + b2/(Temp-a), family=Gamma(log),
            start=c(b0=3,b1=-0.25,b2=-210,a=55), weights=Size, data=Drosophila)
residuals(fit3, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
                col.axis="blue", col.main="black", family="mono", cex=0.8)

###### Example 4: Radioimmunological Assay of Cortisol
data(Cortisol)
fit4 <- gnm(Y ~ b0 + (b1-b0)/(1 + exp(b2+ b3*lDose))^b4, family=Gamma(identity),
            start=c(b0=130,b1=2800,b2=3,b3=3,b4=0.5), data=Cortisol)
residuals(fit4, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
                col.axis="blue", col.main="black", family="mono", cex=0.8)

###### Example 5: Age and Eye Lens Weight of Rabbits in Australia
data(rabbits)
fit5 <- gnm(wlens ~ b1 - b2/(age + b3), family=Gamma(log),
            start=c(b1=5.5,b2=130,b3=35), data=rabbits)
residuals(fit5, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
                col.axis="blue", col.main="black", family="mono", cex=0.8)

###### Example 6: Calls to a technical support help line
data(calls)
fit6 <- gnm(calls ~ SSlogis(week, Asym, xmid, scal), family=poisson(identity), data=calls)
residuals(fit6, type="quantile", plot.it=TRUE, col="red", pch=20, col.lab="blue",
                col.axis="blue", col.main="black", family="mono", cex=0.8)

glmtoolbox documentation built on Sept. 11, 2024, 7:32 p.m.