Baseline estimator for mortality. If a random effect is present the negative binomial distribution is used. Otherwise a quisipoisson is fitted to the data. We use simmulations to generate n_sim responses for each row in the dataset to create a baseline.
Value
Residualplots for all ncor_i and some evaluationmetrixs for each of them as well as a plot containing credible intervals using the simulation
Details
For more details see the help vignette:
vignette("nowcast", package="attrib")
Examples
if (FALSE) { # \dontrun{
data <- attrib::data_fake_nowcasting_county_aggregated
n_sim <- 100
fixef <- "sin(2 * pi * (week) / 53) + cos(2 * pi * (week ) / 53) + year"
ranef <- "(1|location_code)"
response <- "n_death"
data_train <- data[cut_doe< "2019-06-30"]
data_predict <- data
offset <- "log(pop)"
baseline_est(data_train, data_predict, n_sim = 1000, fixef, ranef, response, offset)
} # }
data <- data.table::as.data.table(data_fake_nowcasting_county_aggregated)
data <- data[location_code == "county_nor03"]
n_sim <- 100
fixef <- "sin(2 * pi * (week) / 53) + cos(2 * pi * (week ) / 53) + year"
ranef <- NULL
response <- "n_death"
data_train <- data[cut_doe< "2019-06-30"]
data_predict <- data
offset <- "log(pop)"
baseline_est(data_train, data_predict, n_sim = 1000, fixef, ranef, response, offset)
#> $simulations
#> Key: <cut_doe, location_code, n_death, n0_0, p0_0, n0_1, p0_1, n0_2, p0_2, n0_3, p0_3, n0_4, p0_4, n0_5, p0_5, week, year, pop, id_row, type>
#> cut_doe location_code n_death n0_0 p0_0 n0_1 p0_1 n0_2
#> <Date> <fctr> <int> <num> <num> <num> <num> <num>
#> 1: 2018-01-01 county_nor03 13 0 0 4 0.3076923 9
#> 2: 2018-01-01 county_nor03 13 0 0 4 0.3076923 9
#> 3: 2018-01-01 county_nor03 13 0 0 4 0.3076923 9
#> 4: 2018-01-01 county_nor03 13 0 0 4 0.3076923 9
#> 5: 2018-01-01 county_nor03 13 0 0 4 0.3076923 9
#> ---
#> 103996: 2019-12-23 county_nor03 0 0 NA NA NA NA
#> 103997: 2019-12-23 county_nor03 0 0 NA NA NA NA
#> 103998: 2019-12-23 county_nor03 0 0 NA NA NA NA
#> 103999: 2019-12-23 county_nor03 0 0 NA NA NA NA
#> 104000: 2019-12-23 county_nor03 0 0 NA NA NA NA
#> p0_2 n0_3 p0_3 n0_4 p0_4 n0_5 p0_5 week year pop id_row
#> <num> <num> <num> <num> <num> <num> <num> <int> <num> <num> <int>
#> 1: 0.6923077 13 1 13 1 13 1 1 2018 673469 1
#> 2: 0.6923077 13 1 13 1 13 1 1 2018 673469 1
#> 3: 0.6923077 13 1 13 1 13 1 1 2018 673469 1
#> 4: 0.6923077 13 1 13 1 13 1 1 2018 673469 1
#> 5: 0.6923077 13 1 13 1 13 1 1 2018 673469 1
#> ---
#> 103996: NA NA NA NA NA NA NA 52 2019 681071 104
#> 103997: NA NA NA NA NA NA NA 52 2019 681071 104
#> 103998: NA NA NA NA NA NA NA 52 2019 681071 104
#> 103999: NA NA NA NA NA NA NA 52 2019 681071 104
#> 104000: NA NA NA NA NA NA NA 52 2019 681071 104
#> sim_id sim_value type
#> <num> <int> <char>
#> 1: 1 27 quasi_poisson
#> 2: 2 10 quasi_poisson
#> 3: 3 20 quasi_poisson
#> 4: 4 13 quasi_poisson
#> 5: 5 16 quasi_poisson
#> ---
#> 103996: 996 21 quasi_poisson
#> 103997: 997 18 quasi_poisson
#> 103998: 998 14 quasi_poisson
#> 103999: 999 19 quasi_poisson
#> 104000: 1000 10 quasi_poisson
#>
#> $aggregated
#> Key: <cut_doe, location_code, n_death, n0_0, p0_0, n0_1, p0_1, n0_2, p0_2, n0_3, p0_3, n0_4, p0_4, n0_5, p0_5, week, year, pop, id_row, type>
#> cut_doe location_code n_death n0_0 p0_0 n0_1 p0_1 n0_2
#> <Date> <fctr> <int> <num> <num> <num> <num> <num>
#> 1: 2018-01-01 county_nor03 13 0 0 4 0.3076923 9
#> 2: 2018-01-08 county_nor03 15 0 0 2 0.1333333 10
#> 3: 2018-01-15 county_nor03 6 0 0 1 0.1666667 5
#> 4: 2018-01-22 county_nor03 14 0 0 4 0.2857143 9
#> 5: 2018-01-29 county_nor03 15 0 0 3 0.2000000 11
#> ---
#> 100: 2019-11-25 county_nor03 13 0 0 0 0.0000000 10
#> 101: 2019-12-02 county_nor03 9 0 0 5 0.5555556 8
#> 102: 2019-12-09 county_nor03 12 0 0 2 0.1666667 12
#> 103: 2019-12-16 county_nor03 1 0 0 1 1.0000000 NA
#> 104: 2019-12-23 county_nor03 0 0 NA NA NA NA
#> p0_2 n0_3 p0_3 n0_4 p0_4 n0_5 p0_5 week year pop
#> <num> <num> <num> <num> <num> <num> <num> <int> <num> <num>
#> 1: 0.6923077 13 1.0000000 13 1 13 1 1 2018 673469
#> 2: 0.6666667 15 1.0000000 15 1 15 1 2 2018 673469
#> 3: 0.8333333 6 1.0000000 6 1 6 1 3 2018 673469
#> 4: 0.6428571 14 1.0000000 14 1 14 1 4 2018 673469
#> 5: 0.7333333 14 0.9333333 15 1 15 1 5 2018 673469
#> ---
#> 100: 0.7692308 13 1.0000000 13 1 NA NA 48 2019 681071
#> 101: 0.8888889 9 1.0000000 NA NA NA NA 49 2019 681071
#> 102: 1.0000000 NA NA NA NA NA NA 50 2019 681071
#> 103: NA NA NA NA NA NA NA 51 2019 681071
#> 104: NA NA NA NA NA NA NA 52 2019 681071
#> id_row type median.sim_value q025.sim_value q975.sim_value
#> <int> <char> <num> <num> <num>
#> 1: 1 quasi_poisson 14 7 22.000
#> 2: 2 quasi_poisson 14 7 21.025
#> 3: 3 quasi_poisson 13 7 21.000
#> 4: 4 quasi_poisson 13 7 22.000
#> 5: 5 quasi_poisson 13 7 21.000
#> ---
#> 100: 100 quasi_poisson 15 8 24.000
#> 101: 101 quasi_poisson 15 8 23.000
#> 102: 102 quasi_poisson 15 7 22.000
#> 103: 103 quasi_poisson 15 8 24.000
#> 104: 104 quasi_poisson 15 8 23.000
#>
#> $fit
#>
#> Call: stats::glm(formula = stats::as.formula(formula), family = "quasipoisson",
#> data = data_train)
#>
#> Coefficients:
#> (Intercept) sin(2 * pi * (week)/53) cos(2 * pi * (week)/53)
#> -88.02798 -0.06323 0.08444
#> year log(pop)
#> 0.04489 NA
#>
#> Degrees of Freedom: 77 Total (i.e. Null); 74 Residual
#> Null Deviance: 78.14
#> Residual Deviance: 73.16 AIC: NA
#>