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idem-lab / conmat

Create Contact Matrices from Population Data
https://idem-lab.github.io/conmat/dev/
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consider autoplot method #89

Closed njtierney closed 1 year ago

njtierney commented 2 years ago

to avoid plot_setting_matrix perhaps?

njtierney commented 2 years ago

The interface would look something like this:

polymod_contact_data <- get_polymod_setting_data()
polymod_survey_data <- get_polymod_population()

setting_models <- fit_setting_contacts(
 contact_data_list = polymod_contact_data,
 population = polymod_survey_data
)

fairfield_age_pop <- abs_age_lga("Fairfield (C)")

synthetic_settings_5y_fairfield <- predict_setting_contacts(
 population = fairfield_age_pop,
 contact_model = setting_models,
 age_breaks = c(seq(0, 85, by = 5), Inf)
)

# instead of this
plot_matrix(synthetic_settings_5y_fairfield$home)

# you write
autoplot(synthetic_settings_5y_fairfield$home)

# And under the hood, the class of `synthetic_settings_5y_fairfield$home`
# is something like `predicted_setting_contacts`
njtierney commented 2 years ago

A bit more detail on implementing autoplot

fit <- lm(mpg ~ cyl, data = mtcars)

summary(mtcars)
#>       mpg             cyl             disp             hp       
#>  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
#>  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
#>  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
#>  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
#>  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
#>  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
#>       drat             wt             qsec             vs        
#>  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
#>  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
#>  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
#>  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
#>  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
#>  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
#>        am              gear            carb      
#>  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
#>  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
#>  Median :0.0000   Median :4.000   Median :2.000  
#>  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
#>  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
#>  Max.   :1.0000   Max.   :5.000   Max.   :8.000
summary(fit)
#> 
#> Call:
#> lm(formula = mpg ~ cyl, data = mtcars)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -4.9814 -2.1185  0.2217  1.0717  7.5186 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  37.8846     2.0738   18.27  < 2e-16 ***
#> cyl          -2.8758     0.3224   -8.92 6.11e-10 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 3.206 on 30 degrees of freedom
#> Multiple R-squared:  0.7262, Adjusted R-squared:  0.7171 
#> F-statistic: 79.56 on 1 and 30 DF,  p-value: 6.113e-10

summary.data.frame(mtcars)
#>       mpg             cyl             disp             hp       
#>  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
#>  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
#>  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
#>  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
#>  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
#>  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
#>       drat             wt             qsec             vs        
#>  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
#>  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
#>  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
#>  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
#>  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
#>  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
#>        am              gear            carb      
#>  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
#>  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
#>  Median :0.0000   Median :4.000   Median :2.000  
#>  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
#>  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
#>  Max.   :1.0000   Max.   :5.000   Max.   :8.000
summary.lm(fit)
#> 
#> Call:
#> lm(formula = mpg ~ cyl, data = mtcars)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -4.9814 -2.1185  0.2217  1.0717  7.5186 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  37.8846     2.0738   18.27  < 2e-16 ***
#> cyl          -2.8758     0.3224   -8.92 6.11e-10 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 3.206 on 30 degrees of freedom
#> Multiple R-squared:  0.7262, Adjusted R-squared:  0.7171 
#> F-statistic: 79.56 on 1 and 30 DF,  p-value: 6.113e-10

summary.my_class <- function(x, ...){
  # my own code here
  paste0("This is your object, named ", class(x))
}

# so this part would happen just before the "return" part of the function
# to assign the class
# importantly, you want to retain the original class, but put your new class
# first
class(fit) <- c("my_class", class(fit))

class(fit)
#> [1] "my_class" "lm"

summary(fit)
#> [1] "This is your object, named my_class" "This is your object, named lm"

plot(fit)


library(ggplot2)


autoplot(fit)
#> Error in `autoplot()`:
#> ! Objects of type my_class/lm not supported by autoplot.

autoplot.my_class <- function(object, ...){

  plot_df <- tibble::tibble(
    predictions = predict(object),
    residuals = residuals(object),
  ) |> 
    tibble::rownames_to_column()

  ggplot(plot_df,
         aes(x = predictions,
             y = residuals)) + 
    geom_point()
}

autoplot(fit)

Created on 2022-09-06 by the reprex package (v2.0.1)

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