blm {blm} | R Documentation |

## Fit a binomial linear regression model

### Description

A direct probability model for regression with a binary outcome from observational data.

### Usage

```
blm(formula, data, na.action = na.omit, weights = NULL,
strata = NULL, par.init = NULL, warn=FALSE,...)
```

### Arguments

`formula` |
formula for linear model for binary outcome, |

`data` |
data.frame containing the variables of |

`na.action` |
function specifying how missing data should be handled, na.action |

`weights` |
Vector of weights equal to the number of observations. For population-based case-control study, weights are the inverse sampling fractions for controls. |

`strata` |
vector indicating the stratification for weighted regression with stratified observational data |

`par.init` |
vector (optional) of initial parameters |

`warn` |
logical indicator whether to include warnings during algorithm fitting. Default of |

`...` |
Additional arguments passed to |

### Details

The `blm`

model coefficients are the solutions to the maximum of a pseudo log-likelihood using a constrained optimization algorithm with an adaptive barrier method, `constrOptim`

(Lange, 2010). Variance estimates are based on Taylor linearization (Shah, 2002). When `weights`

are not NULL, it is assumed that the study is a case-control design.

### Value

Returns an object of class `blm`

.

### Author(s)

S. Kovalchik s.a.kovalchik@gmail.com

### References

Kovalchik S, Varadhan R (2013). Fitting Additive Binomial Regression Models with the R Package blm. *Journal of Statistical Software*, 54(1), 1-18. URL: https://www.jstatsoft.org/v54/i01/.

Lange, K. (2010) *Numerical Analysis for Statisticians*, Springer.

Shah, BV. (2002) Calculus of Taylor deviations. Joint Statistical Meetings.

### See Also

### Examples

```
data(ccdata)
fit <- blm(y~female+packyear, weights = ccdata$w,strata=ccdata$strata,
data=ccdata)
summary(fit)
data(aarp)
# ABSOLUTE RISK OF BLADDER CANCER BY 70 YEARS
# FOR DIFFERENT GENDER AND RISK GROUP
fit <- blm(bladder70~female * smoke_status,
data = aarp,
weight=aarp$w)
logLik(fit)
# INTERCEPT IS BASELINE RISK
# ALL OTHER COEFFICIENTS ARE RISK DIFFERENCES FROM BASELINE
summary(fit)
# RISK DIFFERENCE CONFIDENCE INTERVALS (PER 1,000 PERSONS)
confint(fit)*1000
```

*blm*version 2022.0.0.1 Index]