Description Usage Arguments Value Author(s) References Examples

View source: R/DAMisc_functions.R

Beck et. al. (1998) identified that binary time-series cross-section data are discrete-time duration data and time dependence can be modeled in a logistic regression by including a flexible function (e.g., cubic spline) of time since the last event as a covariate. This function creates the variable identifying time since last event.

1 |

`data` |
A data frame. |

`event` |
Character string giving the name of the dichotomous variable identifying the event (where an event is coded 1 and the absence of an event is coded 0). |

`tvar` |
Character string giving the name of the time variable. |

`csunit` |
Character string giving the name of the cross-sectional unit. |

`pad.ts` |
Logical indicating whether the time-series should be filled in, when panels are unbalanced. |

The original data frame with one additional variable. The `spell`

variable identifies the number of observed periods since the last event.

Dave Armstrong (UW-Milwaukee, Department of Political Science)

Alvarez, M., J.A. Cheibub, F. Limongi and A. Przeworski. 1996. Classifying political regimes. Studies in Comparative International Development 31 (Summer): 1-37.

Beck, N.. J. Katz and R. Tucker. 1998. Beyond Ordinary Logit: Taking Time Seriously in Binary-Time-Series-Cross-Section Models. American Journal of Political Science 42(4): 1260-1288.

1 2 3 4 5 6 7 8 9 10 11 | ```
library(splines)
## Data from Alvarez et. al. (1996)
data(aclp)
newdat <- btscs(aclp, "democ", "year", "country")
# Estimate Model with and without spell
full.mod <- glm(democ ~ log(gdpw) + popg + bs(spell, df=4), data=newdat, family=binomial)
restricted.mod <- glm(democ ~ log(gdpw) + popg, data=newdat, family=binomial)
# Incremental F-test of time dependence
anova(restricted.mod, full.mod, test='Chisq')
``` |

DAMisc documentation built on May 31, 2017, 2:26 a.m.

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