# Confidence and prediction intervals with the mixpoissonreg package

#### Alexandre B. Simas

#### 2021-03-08

# Standard errors for the means

## Standard errors for the means using the `predict` method

The `predict` method for `mixpoissonreg` objects allows one to obtain standard errors for the means in the scale of the linear predictors. To this end one must set the argument `type` to “link” to indicate that the predicted values will be given in the scale of the linear predictors and `se.fit` to `TRUE`. The `predict` method will return a list with two components: `fit` containing the fitted values and `se.fit` containing the standard errors.

For example:

    library(mixpoissonreg)

    fit <- mixpoissonreg(daysabs ~ gender + math + prog,
                            data = Attendance, em_controls = list(maxit=1))

    pred_fit <- predict(fit, type = "link", se.fit = TRUE)

    head(pred_fit$fit)
    #>        1        2        3        4        5        6
    #> 1.679089 1.903565 2.158279 2.183221 2.270517 1.840271

    head(pred_fit$se.fit)
    #>         1         2         3         4         5         6
    #> 0.1181008 0.1074558 0.1151626 0.1203237 0.1424633 0.1231964

We are using the option `em_controls = list(maxit=1)` to reduce computational cost.

Since no data was passed, the predicted values were obtained with respect to the model’s data.

To pass a new data, one must pass a `data.frame` with the values of the covariates of interest to the `newdata` argument. For instance:

    predict(fit, newdata = data.frame(gender = c("male", "female"), math = c(34, 85),
                                      prog = factor(c("General", "Academic"),
                                      levels = c("General", "Academic", "Vocational"))),
            type = "link", se.fit = TRUE)
    #> $fit
    #>        1        2
    #> 2.284493 1.752975
    #>
    #> $se.fit
    #>         1         2
    #> 0.1787343 0.1461214

## Standard errors for the means using the `augment` method

One can also obtain the standard errors in a tidy manner by using the augment method with the argument `type.predict` to “link” and the argument `se_fit` to `TRUE`. This will return a `tibble::tibble` object with several columns, including `.fitted` containing the fitted values and `.se.fit` containing the standard errors.

The following example shows how to use the `augment` method to obtain the fitted values in the scale of the linear predictor along with the corresponding standard errors:

    library(dplyr)

    augment(fit, type.predict = "link", se_fit = TRUE) %>% dplyr::select(.fitted, .se.fit)
    #> # A tibble: 314 x 2
    #>    .fitted .se.fit
    #>      <dbl>   <dbl>
    #>  1   1.68    0.118
    #>  2   1.90    0.107
    #>  3   2.16    0.115
    #>  4   2.18    0.120
    #>  5   2.27    0.142
    #>  6   1.84    0.123
    #>  7   1.89    0.113
    #>  8   2.05    0.137
    #>  9   1.75    0.107
    #> 10   0.938   0.129
    #> # … with 304 more rows

# Confidence intervals for the means

## Confidence intervals for the means using the `predict` method

The `predict` method for `mixpoissonreg` objects does not provide standard errors for the fitted values at the response scale, however it provides confidence intervals for the means at the response scale. To this end it is enough to set the `interval` argument to “confidence” since the default type of prediction is already “response”. It is also possible to change the level of confidence by setting the `level` argument, which defaults to `0.95`.

For example:

    fit_math <- mixpoissonreg(daysabs ~ math,
                            data = Attendance,
                            em_controls = list(maxit=1))

    head(predict(fit_math, interval = "confidence"))
    #>        fit      lwr       upr
    #> 1 4.930072 4.245108  5.725558
    #> 2 7.152910 6.080109  8.415001
    #> 3 7.689724 6.387717  9.257118
    #> 4 8.014373 6.560871  9.789885
    #> 5 9.262442 7.166667 11.971092
    #> 6 4.538743 3.817703  5.395965

    head(predict(fit_math, interval = "confidence", level = 0.99))
    #>        fit      lwr       upr
    #> 1 4.930072 4.050190  6.001105
    #> 2 7.152910 5.777451  8.855831
    #> 3 7.689724 6.026016  9.812760
    #> 4 8.014373 6.161026 10.425241
    #> 5 9.262442 6.611655 12.975998
    #> 6 4.538743 3.615709  5.697414

One can, for instance, use this matrix to create a plot of the fitted values against the `math` variable along with their confidence intervals using R’s base graphics:

    data("Attendance", package = "mixpoissonreg")

    new_math <- seq(0, 100, by=0.25)
    conf_fit <- predict(fit_math, newdata = data.frame(math = new_math), interval = "confidence")

    graphics::plot(Attendance$math, Attendance$daysabs, xlab = "Math score", ylab = "Days abscent")
    curve(exp(fit_math$coefficients$mean[1] + fit_math$coefficients$mean[2]*x), add = TRUE)

    graphics::lines(new_math, conf_fit[, 2], col = grDevices::rgb(0.7, 0.7, 0.7))
    graphics::lines(new_math, conf_fit[, 3], col = grDevices::rgb(0.7, 0.7, 0.7))
    graphics::polygon(c(new_math, rev(new_math)), c(conf_fit[, 2], rev(conf_fit[, 3])),
                      col = grDevices::rgb(0.7, 0.7, 0.7, 0.7), border = NA)

Observe that the confidence intervals tend to be narrow. They are confidence intervals for the means, not for the response variables. The intervals that play the role of “confidence intervals” for response variables are the prediction intervals, which are much wider and provide estimate of intervals for which future observations will fall with the prescribed probability.

## Confidence intervals for the means using the `augment` method

The `augment` method produces confidence intervals for the mean parameters in the response scale by default. The confidence level can be changed by setting the `level` argument to the desired level.

For example, let us consider the `fit_math` object and get the confidence intervals with 99% level along with the response variable `daysabs` and the covariate `math`:

    augment(fit_math, level = 0.99) %>% select(math, daysabs, .fittedlwrconf, .fitteduprconf)
    #> # A tibble: 314 x 4
    #>     math daysabs .fittedlwrconf .fitteduprconf
    #>    <dbl>   <dbl>          <dbl>          <dbl>
    #>  1    63       4           4.25           5.73
    #>  2    27       4           6.08           8.42
    #>  3    20       2           6.39           9.26
    #>  4    16       3           6.56           9.79
    #>  5     2       3           7.17          12.0
    #>  6    71      13           3.82           5.40
    #>  7    63      11           4.25           5.73
    #>  8     3       7           7.12          11.8
    #>  9    51      10           4.90           6.35
    #> 10    49       9           5.01           6.48
    #> # … with 304 more rows

Let us now use the `augment` method to produce a plot of the data, the fitted curve and the confidence intervals with confidence level of 99%:

    library(ggplot2)

    fit_data <- augment(fit_math) %>% dplyr::select(math, daysabs) %>%
      dplyr::rename("Math Score" = math, "Days Abscent" = daysabs)

    new_math <- seq(0, 100, by=0.25)

    fit_int <- augment(fit_math, newdata = data.frame(math = new_math), level = 0.99) %>%
      dplyr::rename("Math Score" = math) %>% mutate("Days Abscent" = 0)

    ggplot(fit_data, aes(x = `Math Score`, y = `Days Abscent`)) + geom_point() +
      geom_function(fun = function(x){exp(fit_math$coefficients$mean[1] +
                                            fit_math$coefficients$mean[2]*x)}, colour = "blue") +
      geom_ribbon(data = fit_int, aes(ymin = .fittedlwrconf, ymax = .fitteduprconf),
                  fill = "grey70", alpha = 0.7)

# Prediction intervals for the response variables

## Prediction intervals for the response variables using the `predict` method

Let us now use the `predict` method to obtain prediction intervals for the reponse variables. As we did not obtain an approximate distribution for the response variable, we obtain the prediction intervals by simulation, which is computationally intensive.

To obtain prediction intervals, one must set the `interval` argument to “prediction” since the default type of prediction is already “response”. It is also possible to change the significance level by entering the `level` argument, which defaults to `0.95`, to change the number of mean and prediction parameters generated by setting the `nsim_pred` argument to the desired value, the default is 100, and to change the number of response variables y generated for each pair of mean and precision parameters by setting the `nsim_pred_y` to the desired value, the default is 100.

For example, we will consider `nsim_pred = 50` and `nsim_pred_y = 50` to avoid long computations in our example:

    fit_math <- mixpoissonreg(daysabs ~ math,
                            data = Attendance,
                            em_controls = list(maxit=1))

    head(predict(fit_math, interval = "prediction", nsim_pred = 50, nsim_pred_y = 50))
    #>        fit lwr upr
    #> 1 4.930072   0  20
    #> 2 7.152910   0  33
    #> 3 7.689724   0  33
    #> 4 8.014373   0  35
    #> 5 9.262442   0  40
    #> 6 4.538743   0  20

    head(predict(fit_math, interval = "prediction", level = 0.99, nsim_pred = 50, nsim_pred_y = 50))
    #>        fit lwr upr
    #> 1 4.930072   0  29
    #> 2 7.152910   0  45
    #> 3 7.689724   0  44
    #> 4 8.014373   0  53
    #> 5 9.262442   0  58
    #> 6 4.538743   0  27

Notice that the intervals are now much wider.

We will now use the above matrix to create a plot of the fitted values against the `math` variable along with their prediction intervals using R’s base graphics:

    new_math <- seq(0, 100, by=0.25)
    pred_fit <- predict(fit_math, newdata = data.frame(math = new_math), interval = "prediction",
                        nsim_pred = 50, nsim_pred_y = 50)

    plot(Attendance$math, Attendance$daysabs, xlab = "Math score", ylab = "Days abscent")
    curve(exp(fit_math$coefficients$mean[1] + fit_math$coefficients$mean[2]*x), add = TRUE)

    lines(new_math, pred_fit[, 2], col = grDevices::rgb(0.7, 0.7, 0.7))
    lines(new_math, pred_fit[, 3], col = grDevices::rgb(0.7, 0.7, 0.7))
    polygon(c(new_math, rev(new_math)), c(pred_fit[, 2], rev(pred_fit[, 3])),
                      col = grDevices::rgb(0.7, 0.7, 0.7, 0.7), border = NA)

Observe that unlike the confidence intervals, the prediction intervals now cover the majority of the observed response variables. These intervals should be used for future observations as they are intervals such that the future response variables will fall with the prescribed probability.

## Prediction intervals for the response variables using the `augment` method

To obtain prediction intervals using the `augment` method, one must set the `pred_int` argument to `TRUE`. The significance level can be changed by setting the `level` argument to the desired level. To change the number of mean and prediction parameter generated, one must set the `nsim_pred` argument to the desired value, the default is 100, and to change the number of response variables y generated for each pair of mean and precision parameters, one must set the `nsim_pred_y` to the desired value, the default is 100.

For example, let us consider the `fit_math` object and obtain prediction intervals with 99% level along with the response variable `daysabs` and the covariate `math`. We will also set `nsim_pred` and `nsim_pred_y` to 10 to reduce computational cost.

    augment(fit_math, pred_int = TRUE, level = 0.99,
            nsim_pred = 10, nsim_pred_y = 10) %>% select(math, daysabs, .fittedlwrpred, .fitteduprpred)
    #> # A tibble: 314 x 4
    #>     math daysabs .fittedlwrpred .fitteduprpred
    #>    <dbl>   <dbl>          <dbl>          <dbl>
    #>  1    63       4              0             24
    #>  2    27       4              0             42
    #>  3    20       2              0             41
    #>  4    16       3              0             49
    #>  5     2       3              0             68
    #>  6    71      13              0             38
    #>  7    63      11              0             19
    #>  8     3       7              0             52
    #>  9    51      10              0             42
    #> 10    49       9              0             36
    #> # … with 304 more rows

Let us now use the `augment` method to produce a plot of the data, the fitted curve and the prediction intervals with the default significance level:

    fit_data <- augment(fit_math) %>%
      dplyr::select(math, daysabs) %>%
      dplyr::rename("Math Score" = math, "Days Abscent" = daysabs)

    new_math <- seq(0, 100, by=0.25)

    fit_pred <- augment(fit_math, newdata = data.frame(math = new_math),
                       pred_int = TRUE, nsim_pred = 10, nsim_pred_y = 10) %>%
      dplyr::rename("Math Score" = math) %>% mutate("Days Abscent" = 0)

    ggplot(fit_data, aes(x = `Math Score`, y = `Days Abscent`)) + geom_point() +
      geom_function(fun = function(x){exp(fit_math$coefficients$mean[1] +
                                            fit_math$coefficients$mean[2]*x)}, colour = "blue") +
      geom_ribbon(data = fit_pred, aes(ymin = .fittedlwrpred, ymax = .fitteduprpred),
                  fill = "grey70", alpha = 0.7)