conflate combines methods for any S3 standard generic allowing arguments
of various methods to be specified in a single function that it returns.
Arguments
- generic_spec
[
call] Generic function as applied to arguments shared between all methods to be combined.
Value
Function with additional class conflate with arguments being
conflate_func(generic_spec_args, ..., evaluate = TRUE)... specifies additional arguments to be supplied in the form
object_class.arg. The evaluate argument is useful for debugging purposes.
Combining Methods
The generic_spec argument is the generic function as applied to
shared arguments between methods that are to be combined. Once this is
decided, all additional arguments passed to the generated function need to
be of the form object_class.arg that are arguments unique to each method
as dispatched for each object class.
See also
Other function assemblers:
convoke()
Examples
# first construct two models
glm.model <- glm(Sepal.Length ~ Sepal.Width, data = iris)
lm.model <- lm(Sepal.Length ~ Sepal.Width, data = iris)
# create summary with extra object arguments using `conflate`
(conflated_summary <- conflate(summary(x)))
#> conflate function for summary
#> args: x, object.args
purrr::map(
list(glm.model, lm.model),
~ conflated_summary(.,
lm.correlation = TRUE, glm.correlation = TRUE,
glm.symbolic.cor = TRUE
)
)
#> [[1]]
#>
#> Call:
#> glm(formula = Sepal.Length ~ Sepal.Width, data = iris)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.5561 -0.6333 -0.1120 0.5579 2.2226
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 6.5262 0.4789 13.63 <2e-16 ***
#> Sepal.Width -0.2234 0.1551 -1.44 0.152
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for gaussian family taken to be 0.6807844)
#>
#> Null deviance: 102.17 on 149 degrees of freedom
#> Residual deviance: 100.76 on 148 degrees of freedom
#> AIC: 371.99
#>
#> Number of Fisher Scoring iterations: 2
#>
#> Correlation of Coefficients:
#>
#> (Intercept) 1
#> Sepal.Width B 1
#> attr(,"legend")
#> [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1
#>
#>
#> [[2]]
#>
#> Call:
#> lm(formula = Sepal.Length ~ Sepal.Width, data = iris)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1.5561 -0.6333 -0.1120 0.5579 2.2226
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 6.5262 0.4789 13.63 <2e-16 ***
#> Sepal.Width -0.2234 0.1551 -1.44 0.152
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.8251 on 148 degrees of freedom
#> Multiple R-squared: 0.01382, Adjusted R-squared: 0.007159
#> F-statistic: 2.074 on 1 and 148 DF, p-value: 0.1519
#>
#> Correlation of Coefficients:
#> (Intercept)
#> Sepal.Width -0.99
#>
#>
# alternatively, you could supply `correlation` as a default in conflate itself
(conflated_summary <- conflate(summary(x, correlation = TRUE)))
#> conflate function for summary
#> args: x, correlation = TRUE, object.args
purrr::map(
list(glm.model, lm.model),
~ conflated_summary(., lm.symbolic.cor = TRUE)
)
#> [[1]]
#>
#> Call:
#> glm(formula = Sepal.Length ~ Sepal.Width, data = iris)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.5561 -0.6333 -0.1120 0.5579 2.2226
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 6.5262 0.4789 13.63 <2e-16 ***
#> Sepal.Width -0.2234 0.1551 -1.44 0.152
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for gaussian family taken to be 0.6807844)
#>
#> Null deviance: 102.17 on 149 degrees of freedom
#> Residual deviance: 100.76 on 148 degrees of freedom
#> AIC: 371.99
#>
#> Number of Fisher Scoring iterations: 2
#>
#> Correlation of Coefficients:
#> (Intercept)
#> Sepal.Width -0.99
#>
#>
#> [[2]]
#>
#> Call:
#> lm(formula = Sepal.Length ~ Sepal.Width, data = iris)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1.5561 -0.6333 -0.1120 0.5579 2.2226
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 6.5262 0.4789 13.63 <2e-16 ***
#> Sepal.Width -0.2234 0.1551 -1.44 0.152
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.8251 on 148 degrees of freedom
#> Multiple R-squared: 0.01382, Adjusted R-squared: 0.007159
#> F-statistic: 2.074 on 1 and 148 DF, p-value: 0.1519
#>
#> Correlation of Coefficients:
#>
#> (Intercept) 1
#> Sepal.Width B 1
#> attr(,"legend")
#> [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1
#>
#>