fungibleL | R Documentation |

Generate fungible weights for Logistic Regression Models.

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

`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. |

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).

`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. |

Jeff Jones and Niels Waller

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

```
# 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))
```

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