fungibleL: Generate Fungible Logistic Regression Weights

In fungible: Psychometric Functions from the Waller Lab

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
)

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.

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).

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.

Author(s)

Jeff Jones and Niels Waller

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))

fungible documentation built on March 31, 2023, 5:47 p.m.