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Partition variance in one variable due to another (EXPERIMENTAL)

Source: R/utility.R
partition_variance.Rd

Calculate the proportion of variance for a response variable that is attributed to another set of predictor variables, calculated across lags from from 0 (simultaneous effects) to a user-specified maximum lag.

Usage

partition_variance(object, which_response, n_times = 10)

Arguments

object

Output from dsem

which_response

string matching colnames from tsdata identifying response variable

n_times

Number of lags over which to calculate total effects

Value

A list with two elements:

total_variance

A matrix of the total variance for each variable (column) and each time from 1 to n_times

proportion_variance_explained

A matrix of the proportion of variance explained for variable which_response by each model variable (column) and each time from 1 to n_times

Note that in a model with lagged effects, the total_variance and variance_explained will vary for each time (row), and the analyst might want to either choose a time for which the value has stabilized.

Details

This function calculates the variance for each variable and lag, and then recalculates it when setting exogenous variance to zero for all variables except which_pred. It then calculates the ratio of the diagonal of these two. This represents the proportion of variance in the full model that is attributable to one or more variables.

This function is under development and may still change or be removed.

Examples

# Simulate linear model
x = rnorm(100)
y = 1 + 1 * x + rnorm(100)
data = data.frame(x=x, y=y)

# Fit as DSEM
fit = dsem( sem = "x -> y, 0, beta",
            tsdata = ts(data),
            control = dsem_control(quiet=TRUE) )

# Apply
partition_variance( fit,
                    which_response = "y",
                    n_times = 10 )
#> $total_variance
#>             x       y
#> t_1  1.068235 2.28624
#> t_2  1.068235 2.28624
#> t_3  1.068235 2.28624
#> t_4  1.068235 2.28624
#> t_5  1.068235 2.28624
#> t_6  1.068235 2.28624
#> t_7  1.068235 2.28624
#> t_8  1.068235 2.28624
#> t_9  1.068235 2.28624
#> t_10 1.068235 2.28624
#> 
#> $proportion_variance_explained
#>              x         y
#> t_1  0.5263344 0.4736656
#> t_2  0.5263344 0.4736656
#> t_3  0.5263344 0.4736656
#> t_4  0.5263344 0.4736656
#> t_5  0.5263344 0.4736656
#> t_6  0.5263344 0.4736656
#> t_7  0.5263344 0.4736656
#> t_8  0.5263344 0.4736656
#> t_9  0.5263344 0.4736656
#> t_10 0.5263344 0.4736656
#>