Generates two conditional effects plots for two interacted continuous covariates in linear models.

1 2 3 |

`obj` |
A model object of class |

`varnames` |
A two-element character vector where each element is the name of a variable involved in a two-way interaction. |

`varcov` |
A variance-covariance matrix with which to calculate the conditional standard errors. If |

`rug` |
Logical indicating whether a rug plot should be included. |

`ticksize` |
A scalar indicating the size of ticks in the rug plot (if included) positive values put the rug inside the plotting region and negative values put it outside the plotting region. |

`hist` |
Logical indicating whether a histogram of the x-variable should be included in the plotting region. |

`hist.col` |
Argument to be passed to |

`nclass` |
vector of two integers indicating the number of bins in the two histograms, which will be passed to |

`scale.hist` |
A scalar in the range (0,1] indicating how much vertical space in the plotting region the histogram should take up. |

`border` |
Argument passed to |

`name.stem` |
A character string giving filename to which the appropriate extension will be appended |

`xlab` |
Optional vector of length two giving the x-labels for the two plots that are generated. The first element of the vector corresponds to the figure plotting the conditional effect of the first variable in |

`ylab` |
Optional vector of length two giving the y-labels for the two plots that are generated. The first element of the vector corresponds to the figure plotting the conditional effect of the first variable in |

`plot.type` |
One of ‘pdf’, ‘png’, ‘eps’ or ‘screen’, where the one of the first three will produce two graphs starting with |

This function produces graphs along the lines suggested by Brambor, Clark and Golder (2006) and Berry, Golder and Milton (2012), that show the conditional effect of one variable in an interaction given the values of the conditioning variable. This is an alternative to the methods proposed by John Fox in his `effects`

package, upon which this function depends heavily.

Specifically, if the model is

*y = b[0] + b[1]x1 + b[2]x2 + b[3]x1*x2 + ... + e[i],*

this function plots calculates the conditional effect of *X1* given *X2*

*dy/dX1 = b[1] + b[3]X2*

and the variances of the conditional effects

*V(b[1] + b[3]X[2]) = V(b[1] + (X[2]^2)V(b[3]) + 2(1)(X[2])V(b[1],b[3]))*

for different values of *X2* and then switches the places of *X1* and *X2*, calculating the conditional effect of *X2* given a range of values of *X1*. 95% confidence bounds are then calculated and plotted for each conditional effects along with a horizontal reference line at 0.

`graphs` |
Either a single graph is printed on the screen (using |

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

Brambor, T., W.R. Clark and M. Golder. (2006) Understanding Interaction Models: Improving Empirical Analyses. Political Analysis 14, 63-82.

Berry, W., M. Golder and D. Milton. (2012) Improving Tests of Theories Positing Interactions. Journal of Politics.

1 2 3 | ```
data(InteractionEx)
mod <- lm(y ~ x1*x2 + z, data=InteractionEx)
DAintfun2(mod, c("x1", "x2"), hist=TRUE, scale.hist=.3)
``` |

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