origmodel {dyad}R Documentation

Original Gottman-Murray Marriage Model

Description

This is an R adaptation of the original FORTRAN marriage model described in ``The Mathematics of Marriage". The code uses a two-phased method of parameter estimation that has been superceded by simultaneous parameter estimation procedures implemented in bilinmodel, ojivemodel and combimodel. These functions extend and generalize the influence functions.

Usage

origmodel(observations, inf = "bilin")

Arguments

observations A data frame with two columns, the first for the wife (person 1) and the second for the husband (person 2) scores for each unit of time. It is expected that these are in the range -24 to 24, and that the data frame has 150 observations.
inf The influence is one of ``bilin" for bilinear influence, ``ojive" for ojive influence, or ``rd" for repair and damping.

Details

This model is designed to work with input from John Gottman's conflict discussion protocol. In this protocol, a topic of disagreement is identified and couples are instructed to discuss this topic for 15 minutes. They are videotaped during this discussion. The discussion is then coded according to the Specific Affect Coding System (SPAFF), version 4.0. These codes are discretized so that each second is described by a single code, the one that occurs for the longest time within the second. The codes are then weighted, producing a range of values ranging from -4 (very negative) to 4 (very positive) where neutral has a value of .1 (just slightly positive). This produces a sequence of 900 data points for both the husband and the wife.

After coding, the sequence is aggregated by 6-second intervals to produce 150 data points ranging from -24 to 24 for both the husband and the wife. This program expects a data frame consisting of these two sequences (wife and husband, in that order).

This model uses a two phase algorithm to fit the data. First, the weighted SPAFF 6-second sums are converted so that any value <= .6 is converted to zero. This means that any neutral window (which may have a weight of up to .6) is converted to a weight of zero. Then the remaining values are rounded to the nearest integer value.

Then the uninfluenced parameters are estimated using the fraction of the 150 windows that have value zero. These parameters are factored out, leaving the residuals that correspond to the influence, and these are fit to an influence function. When the influence selected is a repair/damping model, only those residuals in the upper right and lower left quadrants are used to fit the bilinear influence function and then the repair/damping terms. Therefore, slopes calculated with the bilinear influence function alone and in the context of a repair/damping model are different.

This code was translated from the original FORTRAN version with minimal changes to the program logic. Graphing functions are not yet implemented. There is little error checking.

Value

origmodel returns a list consisting of the following parameters.

r1 Wife emotional inertia
a Wife initial state
natw Wife uninfluenced steady state
wmin Minimum wife score
wmax Maximum wife score
r2 Husband emotional inertia
b Husband initial state
nath Husband uninfluenced steady state
wmin Minimum husband score
wmax Maximum husband score
hsetpoints Husband setpoints
wsetpoints Wife setpoints
inf Influence function specified in the function call (``bilin", ``ojive" or ``rd")
rw Wife influence on husband for positive affect (slope)
lw Wife influence on husband for negative affect (slope)
rh Husband influence on wife for positive affect (slope)
lh Husband influence on wife for negative affect (slope)
ah Wife influence on husband for positive affect below threshold
bh Wife influence on husband for positive affect above threshold
ch Threshold of wife influence on husband for positive affect
dh Wife influence on husband for negative affect below threshold
eh Wife influence on husband for negative affect above threshold
fh Threshold of wife influence on husband for negative affect
aw Husband influence on wife for positive affect below threshold
bw Husband influence on wife for positive affect above threshold
cw Threshold of husband influence on wife for positive affect
dw Husband influence on wife for negative affect below threshold
ew Husband influence on wife for negative affect above threshold
fw Threshold of husband influence on wife for negative affect
hsetpoints Husband setpoints, indexed by quadrant
wsetpoints Wife setpoints, indexed by quadrant
kdw Husband damping of wife's influence(w score trigger)
cdh Husband damping of wife's influence (h effectiveness)
krw Husband repair of wife's influence (w score trigger)
crh Husband repair of wife's influence (h effectiveness)
kdh Wife damping of husband's influence (h score trigger)
cdw Wife damping of husband's influence (w effectiveness)
krh Wife repair of husband's influence (h score trigger)
crw Wife repair of husband's influence (w effectiveness)
setpts Strongest stable setpoint in each quadrant
allsetpts Stable and unstable steady states in each quadrant


Note that quadrants are numbered counterclockwise from upper left on a plot where husband score is on the x-axis and wife influence is on the y-axis: Quadrant 1 is H<0, W>0, Quadrant 2 is H<0, W>0, Quadrant 3 is H>0, W>0, and Quadrant 4 is H>0, W<0.

Author(s)

Tara Madhyastha (translated from the original FORTRAN version developed and written by Catherine C. Swanson)

References

For description of expected input, the marriage model and influence functions implemented by this function, see Gottman, J. M., Murray, J. D., Swanson, C., Tyson, R., & Swanson, K. R. (2003). The Mathematics of Marriage: Dynamic Nonlinear Models. The MIT Press.

See Also

noinfmodel, linmodel, bilinmodel,ojivemodel,combimodel

Examples

require(dyad)
data(couple)
## fit a bilinear model
fit <- origmodel(couple, inf="bilin")

[Package dyad version 1.0 Index]