localInfluence.gnm | R Documentation |
Computes some measures and, optionally, display graphs of them to perform influence analysis based on the approaches described by Cook (1986).
## S3 method for class 'gnm'
localInfluence(
object,
type = c("total", "local"),
perturbation = c("case-weight", "response"),
coefs,
plot.it = FALSE,
identify,
...
)
object |
an object of class gnm. |
type |
an (optional) character string indicating the type of approach to study the
local influence. The options are: the absolute value of the elements of the eigenvector which corresponds to the maximum absolute eigenvalue ("local"); and the absolute value of the elements of the main diagonal ("total"). As default, |
perturbation |
an (optional) character string indicating the perturbation scheme
to apply. The options are: case weight perturbation of observations ("case-weight") and perturbation of response ("response"). As default, |
coefs |
an (optional) character string which (partially) match with the names of some of the parameters in the 'linear' predictor. |
plot.it |
an (optional) logical indicating if the plot of the measures of local
influence is required or just the data matrix in which that plot is based. As default,
|
identify |
an (optional) integer indicating the number of observations to identify
on the plot of the measures of local influence. This is only appropriate if
|
... |
further arguments passed to or from other methods. If |
A matrix as many rows as observations in the sample and one column with the values of the measures of local influence.
Cook D. (1986) Assessment of Local Influence. Journal of the Royal Statistical Society: Series B (Methodological) 48, 133-155.
Thomas W., Cook D. (1989) Assessing Influence on Regression Coefficients in Generalized Linear Models. Biometrika 76, 741-749.
###### 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)
localInfluence(fit1, type="local", perturbation="case-weight", plot.it=TRUE, col="red",
lty=1, lwd=1, col.lab="blue", col.axis="blue", col.main="black", family="mono")
###### 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)
### Local Influence just for the parameter "b1"
localInfluence(fit2, type="local", perturbation="case-weight", plot.it=TRUE, coefs="b1", col="red",
lty=1, lwd=1, col.lab="blue", col.axis="blue", col.main="black", family="mono")
###### 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)
localInfluence(fit3, type="total", perturbation="case-weight", plot.it=TRUE, col="red",
lty=1, lwd=1, col.lab="blue", col.axis="blue", col.main="black", family="mono")
###### 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)
localInfluence(fit4, type="total", perturbation="case-weight", plot.it=TRUE, col="red",
lty=1, lwd=1, col.lab="blue", col.axis="blue", col.main="black", family="mono")
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