fungible (version 2.4.4)

fungibleL: Generate Fungible Logistic Regression Weights

Description

Generate fungible weights for Logistic Regression Models.

Usage

fungibleL(
  X,
  y,
  Nsets = 1000,
  method = "LLM",
  RsqDelta = NULL,
  rLaLb = NULL,
  s = 0.3,
  Print = TRUE
)

Value

model

A glm model object.

call

The function call to glm().

ftable

A data frame with the mle estimates and the minimum and maximum fungible coefficients.

lnLML

The maximum likelihood log likelihood value.

lnLf

The decremented, fungible log likelihood value.

pseudoRsq

The pseudo R-squared.

fungibleRsq

The fungible pseudo R-squared.

fungiblea

The Nsets by Nvar + 1 matrix of fungible (alternate) coefficients.

rLaLb

The correlation between the logits.

maxPosCoefChange

The maximum positive change in a single coefficient holding all other coefficients constant.

maxNegCoefChange

The maximum negative change in a single coefficient holding all other coefficients constant.

Arguments

X

An n by nvar matrix of predictor scores without the leading column of ones.

y

An n by 1 vector of dichotomous criterion scores.

Nsets

The desired number of fungible coefficient vectors.

method

Character: "LLM" = Log-Likelihood method. "EM" = Ellipsoid Method. Default: method = "LLM".

RsqDelta

The desired decrement in the pseudo-R-squared - used when method = "LLM".

rLaLb

The desired correlation between the logits - used when method = "EM".

s

Scale factor for random deviates. s controls the range of random start values for the optimization routine. Recommended 0 <= s < 1. Default: s = 0.3.

Print

Boolean (TRUE/FALSE) for printing output summary.

Author

Jeff Jones and Niels Waller

Details

fungibleL provides two methods for evaluating parameter sensitivity in logistic regression models by computing fungible logistic regression weights. For for additional information on the underlying theory of these methods see Jones and Waller (in press).

References

Jones, J. A. & Waller, N. G. (in press). Fungible weights in logistic regression. Psychological Methods.

Examples

# Example: Low Birth Weight Data from Hosmer Jr, D. W. & Lemeshow, S.(2000).         
# low : low birth rate (0 >= 2500 grams, 1 < 2500 grams)
# race: 1 = white, 2 = black, 3 = other
# ftv : number of physician visits during the first trimester

library(MASS)
attach(birthwt)

race <- factor(race, labels = c("white", "black", "other"))
predictors <- cbind(lwt, model.matrix(~ race)[, -1])

# compute mle estimates
BWght.out <- glm(low ~ lwt + race, family = "binomial")

# compute fungible coefficients
fungible.LLM <- fungibleL(X = predictors, y = low, method = "LLM", 
                          Nsets = 10, RsqDelta = .005, s = .3)

# Compare with Table 2.3 (page 38) Hosmer Jr, D. W. & Lemeshow, S.(2000). 
# Applied logistic regression.  New York, Wiley.  

print(summary(BWght.out))
print(fungible.LLM$call)
print(fungible.LLM$ftable)
cat("\nMLE log likelihod       = ", fungible.LLM$lnLML,
    "\nfungible log likelihood = ", fungible.LLM$lnLf)
cat("\nPseudo Rsq              = ", round(fungible.LLM$pseudoRsq, 3))
cat("\nfungible Pseudo Rsq     = ", round(fungible.LLM$fungibleRsq, 3))


fungible.EM <- fungibleL(X = predictors, y = low, method = "EM" , 
                         Nsets = 10, rLaLb = 0.99)

print(fungible.EM$call)
print(fungible.EM$ftable)

cat("\nrLaLb = ", round(fungible.EM$rLaLb, 3))