tests/testthat/_snaps/snapshots.md
In cmu-delphi/epipredict: Basic epidemiology forecasting methods
flatline_forecaster snapshots
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"list")), forecast_date = structure(c(18992, 18992, 18992, 18992,
18992, 18992), class = "Date"), target_date = structure(c(18999,
18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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18992, 18992), class = "Date"), target_date = structure(c(18999,
18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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cdc_baseline_forecaster snapshots
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0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.202265200637946,
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0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75,
0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0, 0, 0, 0, 0, 0.0290260214229144, 0.0653218111708617,
0.0966336637233373, 0.124670861123061, 0.149775978614687,
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0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6,
0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.975, 0.99)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution",
"vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992), class = "Date"), target_date = structure(c(18997, 19002,
19007, 19012, 19017, 18997, 19002, 19007, 19012, 19017, 18997,
19002, 19007, 19012, 19017, 18997, 19002, 19007, 19012, 19017,
18997, 19002, 19007, 19012, 19017, 18997, 19002, 19007, 19012,
19017), class = "Date")), row.names = c(NA, -30L), class = c("tbl_df",
"tbl", "data.frame"))
arx_forecaster snapshots
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
), .pred = c(0.353013358779435, 0.648525432444877, 0.667670289394328,
1.1418673907239, 0.830448695683587, 0.329799431948649), .pred_distn = structure(list(
structure(list(values = c(0.171022956902288, 0.244945899624723,
0.308032696431071, 0.353013358779435, 0.397994021127798,
0.461080817934147, 0.535003760656582), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.46653503056773, 0.540457973290166, 0.603544770096514,
0.648525432444877, 0.693506094793241, 0.756592891599589,
0.830515834322024), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.485679887517181,
0.559602830239616, 0.622689627045964, 0.667670289394328,
0.712650951742692, 0.77573774854904, 0.849660691271475),
quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9,
0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd",
"vctrs_vctr")), structure(list(values = c(0.959876988846753,
1.03379993156919, 1.09688672837554, 1.1418673907239, 1.18684805307226,
1.24993484987861, 1.32385779260105), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.64845829380644, 0.722381236528875, 0.785468033335223,
0.830448695683587, 0.875429358031951, 0.938516154838299,
1.01243909756073), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.147809030071502,
0.221731972793937, 0.284818769600285, 0.329799431948649,
0.374780094297013, 0.437866891103361, 0.511789833825796),
quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9,
0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd",
"vctrs_vctr"))), class = c("distribution", "vctrs_vctr",
"list")), forecast_date = structure(c(18992, 18992, 18992, 18992,
18992, 18992), class = "Date"), target_date = structure(c(18999,
18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645,
0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
structure(list(values = c(0.0961118191398634, 0.118312393281548,
0.13840396557592, 0.149303403634373, 0.160202841692825, 0.180294413987198,
0.202494988128882), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.0865730800114383,
0.108773654153123, 0.128865226447495, 0.139764664505948,
0.1506641025644, 0.170755674858773, 0.192956249000457), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.279994736572136, 0.30219531071382, 0.322286883008193,
0.333186321066645, 0.344085759125097, 0.36417733141947,
0.386377905561154), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.417153993342634,
0.439354567484319, 0.459446139778691, 0.470345577837144,
0.481245015895596, 0.501336588189969, 0.523537162331653),
quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9,
0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd",
"vctrs_vctr")), structure(list(values = c(0.672794520917498,
0.694995095059183, 0.715086667353556, 0.725986105412008,
0.73688554347046, 0.756977115764833, 0.779177689906517),
quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9,
0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd",
"vctrs_vctr")), structure(list(values = c(0.159495080779498,
0.181695654921182, 0.201787227215555, 0.212686665274007,
0.223586103332459, 0.243677675626832, 0.265878249768516),
quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9,
0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd",
"vctrs_vctr"))), class = c("distribution", "vctrs_vctr",
"list")), forecast_date = structure(c(18992, 18992, 18992, 18992,
18992, 18992), class = "Date"), target_date = structure(c(18993,
18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598,
0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
structure(list(values = c(0.136509784083987, 0.202348949370703,
0.263837900408968, 0.303244704017742, 0.342651507626517,
0.404140458664782, 0.469979623951498), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.364597933377326, 0.430437098664042, 0.491926049702307,
0.531332853311081, 0.570739656919856, 0.632228607958121,
0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224,
0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754,
0.689723699333019, 0.755562864619735), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.821955329282475, 0.887794494569191, 0.949283445607456,
0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9,
0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd",
"vctrs_vctr")), structure(list(values = c(0.628067077067884,
0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414,
0.895697751648679, 0.961536916935395), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.140160537291566, 0.205999702578282, 0.267488653616547,
0.306895457225321, 0.346302260834096, 0.407791211872361,
0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr"))), class = c("distribution",
"vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997,
18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998,
18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598,
0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
structure(list(values = c(0.136509784083987, 0.202348949370703,
0.263837900408968, 0.303244704017742, 0.342651507626517,
0.404140458664782, 0.469979623951498), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.364597933377326, 0.430437098664042, 0.491926049702307,
0.531332853311081, 0.570739656919856, 0.632228607958121,
0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224,
0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754,
0.689723699333019, 0.755562864619735), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.821955329282475, 0.887794494569191, 0.949283445607456,
0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9,
0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd",
"vctrs_vctr")), structure(list(values = c(0.628067077067884,
0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414,
0.895697751648679, 0.961536916935395), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.140160537291566, 0.205999702578282, 0.267488653616547,
0.306895457225321, 0.346302260834096, 0.407791211872361,
0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr"))), class = c("distribution",
"vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997,
18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998,
18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
arx_forecaster output format snapshots
Code
out1
Message
== A basic forecaster of type ARX Forecaster ===================================
This forecaster was fit on 1999-01-01.
Training data was an <epi_df> with:
* Geography: state,
* Time type: day,
* Using data up-to-date as of: 2023-03-10.
* With the last data available on 2021-12-31
-- Predictions -----------------------------------------------------------------
A total of 56 predictions are available for
* 56 unique geographic regions,
* At forecast date: 2021-12-31,
* For target date: 2022-01-07,
Code
out2
Message
== A basic forecaster of type ARX Forecaster ===================================
This forecaster was fit on 1999-01-01.
Training data was an <epi_df> with:
* Geography: state,
* Time type: day,
* Using data up-to-date as of: 2023-03-10.
* With the last data available on 2021-12-31
-- Predictions -----------------------------------------------------------------
A total of 56 predictions are available for
* 56 unique geographic regions,
* At forecast date: 2022-01-03,
* For target date: 2022-01-10,
* Lags adjusted per column: case_rate=3, death_rate=3
Code
out3
Message
== A basic forecaster of type ARX Forecaster ===================================
This forecaster was fit on 1999-01-01.
Training data was an <epi_df> with:
* Geography: state,
* Time type: day,
* Using data up-to-date as of: 2023-03-10.
* With the last data available on 2021-12-31
-- Predictions -----------------------------------------------------------------
A total of 56 predictions are available for
* 56 unique geographic regions,
* At forecast date: 2022-01-03,
* For target date: 2022-01-10,
* Aheads adjusted for death_rate=3
arx_classifier snapshots
structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co",
"ct", "dc", "de", "fl", "ga", "hi", "ia", "id", "il", "in", "ks",
"ky", "la", "ma", "me", "mi", "mn", "mo", "ms", "mt", "nc", "nd",
"ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", "pa", "pr",
"ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", "wi", "wv",
"wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999,
18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999,
18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999,
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18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999,
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18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA,
-51L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co",
"ct", "dc", "de", "fl", "ga", "hi", "ia", "id", "il", "in", "ks",
"ky", "la", "ma", "me", "mi", "mn", "mo", "ms", "mt", "nc", "nd",
"ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", "pa", "pr",
"ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", "wi", "wv",
"wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("(-Inf,0.25]",
"(0.25, Inf]"), class = "factor"), forecast_date = structure(c(18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994), class = "Date"), target_date = structure(c(19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001), class = "Date")), row.names = c(NA,
-51L), class = c("tbl_df", "tbl", "data.frame"))
cmu-delphi/epipredict documentation built on March 5, 2025, 12:17 p.m.
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flatline_forecaster snapshots
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844,
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18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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-6L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA,
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structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
cdc_baseline_forecaster snapshots
structure(list(geo_value = c("ca", "ca", "ca", "ca", "ca", "fl",
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4L, 5L, 1L, 2L, 3L, 4L, 5L), .pred_distn = structure(list(structure(list(
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arx_forecaster snapshots
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
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0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.140160537291566, 0.205999702578282, 0.267488653616547,
0.306895457225321, 0.346302260834096, 0.407791211872361,
0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr"))), class = c("distribution",
"vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997,
18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998,
18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598,
0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
structure(list(values = c(0.136509784083987, 0.202348949370703,
0.263837900408968, 0.303244704017742, 0.342651507626517,
0.404140458664782, 0.469979623951498), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.364597933377326, 0.430437098664042, 0.491926049702307,
0.531332853311081, 0.570739656919856, 0.632228607958121,
0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224,
0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754,
0.689723699333019, 0.755562864619735), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.821955329282475, 0.887794494569191, 0.949283445607456,
0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9,
0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd",
"vctrs_vctr")), structure(list(values = c(0.628067077067884,
0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414,
0.895697751648679, 0.961536916935395), quantile_levels = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles",
"dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
values = c(0.140160537291566, 0.205999702578282, 0.267488653616547,
0.306895457225321, 0.346302260834096, 0.407791211872361,
0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25,
0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default",
"vctrs_rcrd", "vctrs_vctr"))), class = c("distribution",
"vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997,
18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998,
18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
arx_forecaster output format snapshots
Code
out1
Message
== A basic forecaster of type ARX Forecaster ===================================
This forecaster was fit on 1999-01-01.
Training data was an <epi_df> with:
* Geography: state,
* Time type: day,
* Using data up-to-date as of: 2023-03-10.
* With the last data available on 2021-12-31
-- Predictions -----------------------------------------------------------------
A total of 56 predictions are available for
* 56 unique geographic regions,
* At forecast date: 2021-12-31,
* For target date: 2022-01-07,
Code
out2
Message
== A basic forecaster of type ARX Forecaster ===================================
This forecaster was fit on 1999-01-01.
Training data was an <epi_df> with:
* Geography: state,
* Time type: day,
* Using data up-to-date as of: 2023-03-10.
* With the last data available on 2021-12-31
-- Predictions -----------------------------------------------------------------
A total of 56 predictions are available for
* 56 unique geographic regions,
* At forecast date: 2022-01-03,
* For target date: 2022-01-10,
* Lags adjusted per column: case_rate=3, death_rate=3
Code
out3
Message
== A basic forecaster of type ARX Forecaster ===================================
This forecaster was fit on 1999-01-01.
Training data was an <epi_df> with:
* Geography: state,
* Time type: day,
* Using data up-to-date as of: 2023-03-10.
* With the last data available on 2021-12-31
-- Predictions -----------------------------------------------------------------
A total of 56 predictions are available for
* 56 unique geographic regions,
* At forecast date: 2022-01-03,
* For target date: 2022-01-10,
* Aheads adjusted for death_rate=3
arx_classifier snapshots
structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co",
"ct", "dc", "de", "fl", "ga", "hi", "ia", "id", "il", "in", "ks",
"ky", "la", "ma", "me", "mi", "mn", "mo", "ms", "mt", "nc", "nd",
"ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", "pa", "pr",
"ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", "wi", "wv",
"wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("(-Inf,0.25]",
"(0.25, Inf]"), class = "factor"), forecast_date = structure(c(18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992, 18992,
18992, 18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18999,
18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999,
18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999,
18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999,
18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999,
18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999, 18999,
18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA,
-51L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(geo_value = c("ak", "al", "ar", "az", "ca", "co",
"ct", "dc", "de", "fl", "ga", "hi", "ia", "id", "il", "in", "ks",
"ky", "la", "ma", "me", "mi", "mn", "mo", "ms", "mt", "nc", "nd",
"ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or", "pa", "pr",
"ri", "sc", "sd", "tn", "tx", "ut", "va", "vt", "wa", "wi", "wv",
"wy"), .pred_class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("(-Inf,0.25]",
"(0.25, Inf]"), class = "factor"), forecast_date = structure(c(18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994, 18994,
18994, 18994, 18994, 18994, 18994), class = "Date"), target_date = structure(c(19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001, 19001,
19001, 19001, 19001, 19001, 19001), class = "Date")), row.names = c(NA,
-51L), class = c("tbl_df", "tbl", "data.frame"))
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