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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.

Usage

baseline_est(
  data_train,
  data_predict,
  n_sim = 1000,
  fixef,
  ranef,
  response,
  offset
)

Arguments

data_train

Data to train on.

data_predict

Data to predict on

n_sim

number of simulations to preform. Default setting is n_sim = 1000

fixef

Fixed effekts

ranef

Random effekts, default is NULL

response

Response

offset

Offset, can be NULL

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) {
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
#>            cut_doe location_code n_death n0_0 p0_0 n0_1      p0_1 n0_2
#>      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 sim_id
#>      1: 0.6923077   13    1   13    1   13    1    1 2018 673469      1      1
#>      2: 0.6923077   13    1   13    1   13    1    1 2018 673469      1      2
#>      3: 0.6923077   13    1   13    1   13    1    1 2018 673469      1      3
#>      4: 0.6923077   13    1   13    1   13    1    1 2018 673469      1      4
#>      5: 0.6923077   13    1   13    1   13    1    1 2018 673469      1      5
#>     ---                                                                       
#> 103996:        NA   NA   NA   NA   NA   NA   NA   52 2019 681071    104    996
#> 103997:        NA   NA   NA   NA   NA   NA   NA   52 2019 681071    104    997
#> 103998:        NA   NA   NA   NA   NA   NA   NA   52 2019 681071    104    998
#> 103999:        NA   NA   NA   NA   NA   NA   NA   52 2019 681071    104    999
#> 104000:        NA   NA   NA   NA   NA   NA   NA   52 2019 681071    104   1000
#>         sim_value          type
#>      1:        14 quasi_poisson
#>      2:        11 quasi_poisson
#>      3:         9 quasi_poisson
#>      4:        13 quasi_poisson
#>      5:        10 quasi_poisson
#>     ---                        
#> 103996:        16 quasi_poisson
#> 103997:        11 quasi_poisson
#> 103998:         9 quasi_poisson
#> 103999:        15 quasi_poisson
#> 104000:        18 quasi_poisson
#> 
#> $aggregated
#>         cut_doe location_code n_death n0_0 p0_0 n0_1      p0_1 n0_2      p0_2
#>   1: 2018-01-01  county_nor03      13    0    0    4 0.3076923    9 0.6923077
#>   2: 2018-01-08  county_nor03      15    0    0    2 0.1333333   10 0.6666667
#>   3: 2018-01-15  county_nor03       6    0    0    1 0.1666667    5 0.8333333
#>   4: 2018-01-22  county_nor03      14    0    0    4 0.2857143    9 0.6428571
#>   5: 2018-01-29  county_nor03      15    0    0    3 0.2000000   11 0.7333333
#>  ---                                                                         
#> 100: 2019-11-25  county_nor03      13    0    0    0 0.0000000   10 0.7692308
#> 101: 2019-12-02  county_nor03       9    0    0    5 0.5555556    8 0.8888889
#> 102: 2019-12-09  county_nor03      12    0    0    2 0.1666667   12 1.0000000
#> 103: 2019-12-16  county_nor03       1    0    0    1 1.0000000   NA        NA
#> 104: 2019-12-23  county_nor03       0    0   NA   NA        NA   NA        NA
#>      n0_3      p0_3 n0_4 p0_4 n0_5 p0_5 week year    pop id_row          type
#>   1:   13 1.0000000   13    1   13    1    1 2018 673469      1 quasi_poisson
#>   2:   15 1.0000000   15    1   15    1    2 2018 673469      2 quasi_poisson
#>   3:    6 1.0000000    6    1    6    1    3 2018 673469      3 quasi_poisson
#>   4:   14 1.0000000   14    1   14    1    4 2018 673469      4 quasi_poisson
#>   5:   14 0.9333333   15    1   15    1    5 2018 673469      5 quasi_poisson
#>  ---                                                                         
#> 100:   13 1.0000000   13    1   NA   NA   48 2019 681071    100 quasi_poisson
#> 101:    9 1.0000000   NA   NA   NA   NA   49 2019 681071    101 quasi_poisson
#> 102:   NA        NA   NA   NA   NA   NA   50 2019 681071    102 quasi_poisson
#> 103:   NA        NA   NA   NA   NA   NA   51 2019 681071    103 quasi_poisson
#> 104:   NA        NA   NA   NA   NA   NA   52 2019 681071    104 quasi_poisson
#>      median.sim_value q025.sim_value q975.sim_value
#>   1:               14          7.000         22.000
#>   2:               14          7.000         22.000
#>   3:               13          7.000         21.000
#>   4:               13          7.000         21.000
#>   5:               13          7.000         21.000
#>  ---                                               
#> 100:               15          8.000         24.025
#> 101:               15          8.000         23.000
#> 102:               15          7.000         23.000
#> 103:               15          7.975         24.000
#> 104:               14          8.000         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
#>