Model term contributions to predicted response
prediction_contributions_data.RdThe helper function for preparing the data to split the predicted response from a regression model into contributions (predictor coefficient * predictor value) by the terms in the model. The output of this function can be passed to the `prediction_contributions_plot` function to visualise the results.
Arguments
- data
A user-defined data-frame containing values for compositional variables along with any additional variables that the user wishes to predict for. If left blank, a selection of observations (2 from each level of richness) from the original data used to fit the model would be selected.
- model
A Diversity Interactions model object fit by using the
DI()function from theDImodelspackage.- coefficients
If a regression model is not available (or can't be fit in R), the regression coefficients from a model fit in some other language can be used to calculate predictions. However, the user would have to ensure there's an appropriate one-to-one positional mapping between the data columns and the coefficient values. Further, they would also have to provide a variance-covariance matrix of the coefficients in the `vcov` parameter if they want the associated CI for the prediction or it would not be possible to calculate confidence/prediction intervals using this method.
- coeff_cols
If `coefficients` are specified and a one-to-one positional mapping between the data-columns and coefficient vector is not present. A character string or numeric index can be specified here to reorder the data columns and match the corresponding coefficient value to the respective data column. See the "Use model coefficients for prediction" section in examples.
- vcov
If regression coefficients are specified, then the variance-covariance matrix of the coefficients can be specified here to calculate the associated confidence interval around each prediction. Failure to do so would result in no confidence intervals being returned. Ensure `coefficients` and `vcov` have the same positional mapping with the data.
- add_var
A list specifying values for additional predictor variables in the model independent of the compositional predictor variables. This could be useful for comparing the predictions across different values for a non-compositional variable. If specified as a list, it will be expanded to show a plot for each unique combination of values specified, while if specified as a data-frame, one plot would be generated for each row in the data and they will be arranged in a grid according to the value specified in `nrow` and `ncol`.
- groups
A list specifying groupings to arrange coefficients into. The coefficients within a group will be added together and shown as a single component on the respective bars in the plot. This could be useful for grouping multiple similar terms into a single term for better visibility.
- conf.level
The confidence level for calculating confidence or prediction intervals.
- interval
Type of interval to calculate:
- "none"
No interval to be calculated.
- "confidence" (default)
Calculate a confidence interval.
- "prediction"
Calculate a prediction interval.
- bar_labs
The labels to be shown for each bar in the plot. The user has three options: - By default, the row-names in the data would be used as labels for the bars. - A character string or numeric index indicating an ID column in data. - A character vector of same length as the number of rows in the data, which manually specifies the names for each bar. If none of the three options are available, the function would assign a unique ID for each bar.
Value
A data-frame with the following columns. Any additional columns which weren't used when fitting the model would also be present.
- .Community
An identifier column to discern each observation in the data. These are the labels which will be displayed for the bars in the plot.
- .add_str_ID
An identifier column for grouping the cartesian product of all additional columns specified in `add_var` parameter (if `add_var` is specified).
- .Pred
The predicted repsonse for each observation.
- .Lower
The lower limit of the prediction interval for each observation.
- .Upper
The lower limit of the prediction interval for each observation.
- .Contributions
An identifier describing the name of the coefficient contributing to the response.
- .Value
The contributed value of the respective coefficient/group to the total prediction.
Examples
library(DImodels)
library(dplyr)
## Load data
data(sim2)
## Fit model
mod <- glm(response ~ 0 + (p1 + p2 + p3 + p4)^2, data = sim2)
prediction_contributions_data(data = sim2[c(1,5,9,11), ],
model = mod)
#> ✔ Finished data preparation.
#> # A tibble: 40 × 9
#> .Community community block response .Pred .Lower .Upper .Contributions .Value
#> <fct> <int> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 1 1 1 20.2 18.4 17.6 19.3 p1 7.49
#> 2 1 1 1 20.2 18.4 17.6 19.3 p2 1.02
#> 3 1 1 1 20.2 18.4 17.6 19.3 p3 0.894
#> 4 1 1 1 20.2 18.4 17.6 19.3 p4 0.853
#> 5 1 1 1 20.2 18.4 17.6 19.3 p1:p2 2.37
#> 6 1 1 1 20.2 18.4 17.6 19.3 p1:p3 2.63
#> 7 1 1 1 20.2 18.4 17.6 19.3 p1:p4 2.29
#> 8 1 1 1 20.2 18.4 17.6 19.3 p2:p3 0.267
#> 9 1 1 1 20.2 18.4 17.6 19.3 p2:p4 0.332
#> 10 1 1 1 20.2 18.4 17.6 19.3 p3:p4 0.278
#> # ℹ 30 more rows
## Specific coefficients can also be grouped together
## Either by their indices in the model coefficient vector
prediction_contributions_data(data = sim2[c(1,5,9,11), ],
model = mod,
groups = list("Interactions" = 5:10))
#> ✔ Finished data preparation.
#> # A tibble: 20 × 9
#> .Community community block response .Pred .Lower .Upper .Contributions .Value
#> <fct> <int> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 1 1 1 20.2 18.4 17.6 19.3 p1 7.49
#> 2 1 1 1 20.2 18.4 17.6 19.3 p2 1.02
#> 3 1 1 1 20.2 18.4 17.6 19.3 p3 0.894
#> 4 1 1 1 20.2 18.4 17.6 19.3 p4 0.853
#> 5 1 1 1 20.2 18.4 17.6 19.3 Interactions 8.17
#> 6 5 2 1 17.2 17.5 16.7 18.4 p1 1.07
#> 7 5 2 1 17.2 17.5 16.7 18.4 p2 7.16
#> 8 5 2 1 17.2 17.5 16.7 18.4 p3 0.894
#> 9 5 2 1 17.2 17.5 16.7 18.4 p4 0.853
#> 10 5 2 1 17.2 17.5 16.7 18.4 Interactions 7.55
#> 11 9 3 1 17.9 16.6 15.8 17.5 p1 1.07
#> 12 9 3 1 17.9 16.6 15.8 17.5 p2 1.02
#> 13 9 3 1 17.9 16.6 15.8 17.5 p3 6.26
#> 14 9 3 1 17.9 16.6 15.8 17.5 p4 0.853
#> 15 9 3 1 17.9 16.6 15.8 17.5 Interactions 7.44
#> 16 11 3 3 17.6 16.6 15.8 17.5 p1 1.07
#> 17 11 3 3 17.6 16.6 15.8 17.5 p2 1.02
#> 18 11 3 3 17.6 16.6 15.8 17.5 p3 6.26
#> 19 11 3 3 17.6 16.6 15.8 17.5 p4 0.853
#> 20 11 3 3 17.6 16.6 15.8 17.5 Interactions 7.44
## Or by specifying the coefficient names as character strings
prediction_contributions_data(data = sim2[c(1,5,9,11), ],
model = mod,
groups = list("p1_Ints" = c("p1:p2",
"p1:p3",
"p1:p4")))
#> ✔ Finished data preparation.
#> # A tibble: 32 × 9
#> .Community community block response .Pred .Lower .Upper .Contributions .Value
#> <fct> <int> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 1 1 1 20.2 18.4 17.6 19.3 p1 7.49
#> 2 1 1 1 20.2 18.4 17.6 19.3 p2 1.02
#> 3 1 1 1 20.2 18.4 17.6 19.3 p3 0.894
#> 4 1 1 1 20.2 18.4 17.6 19.3 p4 0.853
#> 5 1 1 1 20.2 18.4 17.6 19.3 p2:p3 0.267
#> 6 1 1 1 20.2 18.4 17.6 19.3 p2:p4 0.332
#> 7 1 1 1 20.2 18.4 17.6 19.3 p3:p4 0.278
#> 8 1 1 1 20.2 18.4 17.6 19.3 p1_Ints 7.29
#> 9 5 2 1 17.2 17.5 16.7 18.4 p1 1.07
#> 10 5 2 1 17.2 17.5 16.7 18.4 p2 7.16
#> # ℹ 22 more rows
## Additional variables can also be added to the data by either specifying
## them directly in the `data` or by using the `add_var` argument
## Refit model
sim2$block <- as.numeric(sim2$block)
new_mod <- update(mod, ~. + block, data = sim2)
## This model has block so we can either specify block in the data
subset_data <- sim2[c(1,5,9,11), 2:6]
subset_data
#> block p1 p2 p3 p4
#> 1 1 0.7 0.1 0.1 0.1
#> 5 1 0.1 0.7 0.1 0.1
#> 9 1 0.1 0.1 0.7 0.1
#> 11 3 0.1 0.1 0.7 0.1
head(prediction_contributions_data(data = subset_data,
model = new_mod))
#> ✔ Finished data preparation.
#> # A tibble: 6 × 6
#> .Community .Pred .Lower .Upper .Contributions .Value
#> <fct> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 1 19.1 18.2 20.1 p1 8.29
#> 2 1 19.1 18.2 20.1 p2 1.14
#> 3 1 19.1 18.2 20.1 p3 1.01
#> 4 1 19.1 18.2 20.1 p4 0.967
#> 5 1 19.1 18.2 20.1 block -0.456
#> 6 1 19.1 18.2 20.1 p1:p2 2.37
## Or we could add the variable using `add_var`
subset_data <- sim2[c(1,5,9,11), 3:6]
subset_data
#> p1 p2 p3 p4
#> 1 0.7 0.1 0.1 0.1
#> 5 0.1 0.7 0.1 0.1
#> 9 0.1 0.1 0.7 0.1
#> 11 0.1 0.1 0.7 0.1
head(prediction_contributions_data(data = subset_data,
model = new_mod,
add_var = list(block = c(1, 2))))
#> ✔ Finished data preparation.
#> # A tibble: 6 × 7
#> .Community .add_str_ID .Pred .Lower .Upper .Contributions .Value
#> <fct> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 1 block: 1 19.1 18.2 20.1 p1 8.29
#> 2 1 block: 1 19.1 18.2 20.1 p2 1.14
#> 3 1 block: 1 19.1 18.2 20.1 p3 1.01
#> 4 1 block: 1 19.1 18.2 20.1 p4 0.967
#> 5 1 block: 1 19.1 18.2 20.1 block -0.456
#> 6 1 block: 1 19.1 18.2 20.1 p1:p2 2.37
## The benefit of specifying the variable this way is we have an ID
## columns now called `.add_str_ID` which would be used to create a
## separate plot for each value of the additional variable
## Model coefficients can also be used, but then user would have
## to specify the data with all columns corresponding to each coefficient
coef_data <- sim2 %>%
mutate(`p1:p2` = p1*p2, `p1:p3` = p1*p2, `p1:p4` = p1*p4,
`p2:p3` = p2*p3, `p2:p4` = p2*p4, `p3:p4` = p3*p4) %>%
select(p1, p2, p3, p4,
`p1:p2`, `p1:p3`, `p1:p4`,
`p2:p3`, `p2:p4`, `p3:p4`) %>%
slice(1,5,9,11)
print(coef_data)
#> p1 p2 p3 p4 p1:p2 p1:p3 p1:p4 p2:p3 p2:p4 p3:p4
#> 1 0.7 0.1 0.1 0.1 0.07 0.07 0.07 0.01 0.01 0.01
#> 5 0.1 0.7 0.1 0.1 0.07 0.07 0.01 0.07 0.07 0.01
#> 9 0.1 0.1 0.7 0.1 0.01 0.01 0.01 0.07 0.01 0.07
#> 11 0.1 0.1 0.7 0.1 0.01 0.01 0.01 0.07 0.01 0.07
print(mod$coefficients)
#> p1 p2 p3 p4 p1:p2 p1:p3 p1:p4 p2:p3
#> 10.699426 10.228917 8.939289 8.532857 33.894874 37.552444 32.720996 26.739691
#> p2:p4 p3:p4
#> 33.188799 27.771368
prediction_contributions_data(data = coef_data,
coefficients = mod$coefficients,
interval = "none")
#> ✔ Finished data preparation.
#> # A tibble: 40 × 4
#> .Community .Pred .Contributions .Value
#> <fct> <dbl> <chr> <dbl>
#> 1 1 18.4 p1 7.49
#> 2 1 18.4 p2 1.02
#> 3 1 18.4 p3 0.894
#> 4 1 18.4 p4 0.853
#> 5 1 18.4 p1:p2 2.37
#> 6 1 18.4 p1:p3 2.63
#> 7 1 18.4 p1:p4 2.29
#> 8 1 18.4 p2:p3 0.267
#> 9 1 18.4 p2:p4 0.332
#> 10 1 18.4 p3:p4 0.278
#> # ℹ 30 more rows
## To get uncertainity using coefficients vcov matrix would have to specified
prediction_contributions_data(data = coef_data,
coefficients = mod$coefficients,
vcov = vcov(mod))
#> ✔ Finished data preparation.
#> # A tibble: 40 × 6
#> .Community .Pred .Lower .Upper .Contributions .Value
#> <fct> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 1 18.4 17.6 19.3 p1 7.49
#> 2 1 18.4 17.6 19.3 p2 1.02
#> 3 1 18.4 17.6 19.3 p3 0.894
#> 4 1 18.4 17.6 19.3 p4 0.853
#> 5 1 18.4 17.6 19.3 p1:p2 2.37
#> 6 1 18.4 17.6 19.3 p1:p3 2.63
#> 7 1 18.4 17.6 19.3 p1:p4 2.29
#> 8 1 18.4 17.6 19.3 p2:p3 0.267
#> 9 1 18.4 17.6 19.3 p2:p4 0.332
#> 10 1 18.4 17.6 19.3 p3:p4 0.278
#> # ℹ 30 more rows
## Specifying `bar_labs`
## Our data has four rows so we'd need four labels in bar_labs
prediction_contributions_data(data = coef_data,
coefficients = mod$coefficients,
vcov = vcov(mod),
bar_labs = c("p1 Domm", "p2 Domm",
"p3 Domm", "p4 Domm"))
#> ✔ Finished data preparation.
#> # A tibble: 40 × 6
#> .Community .Pred .Lower .Upper .Contributions .Value
#> <fct> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 p1 Domm 18.4 17.6 19.3 p1 7.49
#> 2 p1 Domm 18.4 17.6 19.3 p2 1.02
#> 3 p1 Domm 18.4 17.6 19.3 p3 0.894
#> 4 p1 Domm 18.4 17.6 19.3 p4 0.853
#> 5 p1 Domm 18.4 17.6 19.3 p1:p2 2.37
#> 6 p1 Domm 18.4 17.6 19.3 p1:p3 2.63
#> 7 p1 Domm 18.4 17.6 19.3 p1:p4 2.29
#> 8 p1 Domm 18.4 17.6 19.3 p2:p3 0.267
#> 9 p1 Domm 18.4 17.6 19.3 p2:p4 0.332
#> 10 p1 Domm 18.4 17.6 19.3 p3:p4 0.278
#> # ℹ 30 more rows