The goal of daps is to …
You can install the development version of daps from
GitHub with:
# install.packages("remotes")
remotes::install_github("NikKrieger/daps")Create a daps-class object and add a metastate model as well as
individual variable models.
library(ordinal)
library(nnet)
library(tidyverse)
#> ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
#> ✓ tibble 2.1.3 ✓ dplyr 0.8.3
#> ✓ tidyr 1.0.2 ✓ stringr 1.4.0
#> ✓ readr 1.3.1 ✓ forcats 0.4.0
#> ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x dplyr::slice() masks ordinal::slice()
library(daps)
#>
#> Attaching package: 'daps'
#> The following objects are masked from 'package:stats':
#>
#> lag, simulatetopological sorting: first do all the static variables and then temporal check for intra-timeslice dependencies.
# (1) get model (models have binary flag that indicate intra-timeslice dependencies)
# if max of flags is 1, go into subroutine in order to generate dag and topological sort
daps1 <-
daps() %>%
add_models(
diabetes(t) := glm(~sex + lag(dbp, 2) + lag(sbp) + lag(dbp) + lag(sbp, 2) + race + age, family = binomial),
age(t) := lag(age) + 1,
sbp(t) := list(
lm(~ lag(sbp) + lag(dbp) + sex + race + age),
lm(~ sex + race + age),
lag(sbp),
rnorm(1, 130, 15)
),
dbp(t) := lm(~lag(sbp) + lag(dbp) + sex + race + age),
dbp(t) := lm(~sex + race + age),
dbp(t) := lm(~sbp),
# Find a way to determine the intratimeslice variables each model is using.
# During simulation, when you come across one that has at least one intraslice model, do the others first.
diabetes(t) := glm(~sex + race + age + lag(sbp) + lag(dbp), family = binomial),
diabetes(t) := glm(~sex + race + age, family = binomial),
chd_risk(t) := list(
clm(~ lag(sbp) + lag(dbp) + sex + race + age),
multinom(~ lag(sbp) + slide_mean(dbp) + sex + race + age)
),
glucose(t) := lm(~sbp + dbp + diabetes + age + sex + race),
glucose(t) := lm(~sbp + dbp + age + sex + race),
glucose(t) := lm(~diabetes + age + sex + race),
glucose(t) := lm(~age + sex + race),
glucose(t) := lag(glucose)
# sex := categorical(c("M", "F"), M = .495),
# sex := factor(rbinom(1, 1, .505), 0:1, c("Male", "Female")),
#
#
# race := categorical(M = .495, F = .505),
# factor(
# 1:3 %*% rmultinom(1, 1, c(.7, .25, .05)),
# 1:3,
# c("White", "Black", "Other")
# )
) %>%
add_metastate_model(
metastate = c("state1", "state2", "state3"),
nodes =
list(
c("age", "sbp", "dbp", "chd_risk", "diabetes"),
c("age", "sbp", "dbp", "chd_risk", "diabetes"),
c("age", "sbp", "dbp", "chd_risk", "diabetes", "glucose")
),
transitions =
list(
~case_when(
lag(sbp) >= 135 | lag(dbp) >= 80 ~ "state3",
lag(chd_risk) == "elevated" | sex == "male" & lag(age) > 35 ~ "state2",
TRUE ~ "state1"
),
~case_when(
lag(sbp) >= 135 | lag(dbp) >= 80 ~ "state3",
TRUE ~ "state2"
),
~case_when(
TRUE ~ "state3"
)
)
)
daps1
#> $model_table
#> # A tibble: 18 x 4
#> model_id var dynamic model_quo
#> * <int> <chr> <lgl> <list>
#> 1 1 diabetes TRUE <dps_ftt_>
#> 2 2 age TRUE <dps_nnf_>
#> 3 3 sbp TRUE <dps_ftt_>
#> 4 4 sbp TRUE <dps_ftt_>
#> 5 5 sbp TRUE <dps_nnf_>
#> 6 6 sbp TRUE <dps_nnf_>
#> 7 7 dbp TRUE <dps_ftt_>
#> 8 8 dbp TRUE <dps_ftt_>
#> 9 9 dbp TRUE <dps_ftt_>
#> 10 10 diabetes TRUE <dps_ftt_>
#> 11 11 diabetes TRUE <dps_ftt_>
#> 12 12 chd_risk TRUE <dps_ftt_>
#> 13 13 chd_risk TRUE <dps_ftt_>
#> 14 14 glucose TRUE <dps_ftt_>
#> 15 15 glucose TRUE <dps_ftt_>
#> 16 16 glucose TRUE <dps_ftt_>
#> 17 17 glucose TRUE <dps_ftt_>
#> 18 18 glucose TRUE <dps_nnf_>
#>
#> $metastate_model
#> # A tibble: 3 x 3
#> metastate nodes transitions
#> * <chr> <list> <list>
#> 1 state1 <chr [5]> <quosure>
#> 2 state2 <chr [5]> <quosure>
#> 3 state3 <chr [6]> <quosure>
#>
#> attr(,"class")
#> [1] "daps"teststatic <-
tribble(
~id, ~sex, ~race,
1, "male", "black",
2, "female", "white",
3, "male", "black",
4, "male", "white",
6, "female", "black"
)
testtemporal <-
tribble(
~id, ~t, ~sbp, ~dbp, ~age, ~diabetes, ~glucose, ~chd_risk,
1, 1, 120, 65, 35, FALSE, NA, "low",
1, 2, 119, 66, 36, FALSE, NA, "normal",
1, 3, 118, 68, 50, FALSE, NA, "normal",
1, 4, 150, 100, 51, TRUE, NA, "elevated",
2, 1, 139, 81, 64, FALSE, NA, "normal",
2, 2, 140, 111, 66, FALSE, 90, "elevated",
2, 3, 137, 85, 66, TRUE, 100, "elevated",
2, 4, 155, 90, 66, TRUE, 99, "elevated",
3, 1, 100, 40, 34, TRUE, NA, "low",
3, 2, 114, 45, 34, FALSE, NA, "low",
3, 3, 100, 50, 34, FALSE, NA, "low",
3, 4, 103, 56, 34, FALSE, NA, "low",
4, 1, 115, 110, 85, FALSE, 125, "elevated",
4, 2, 140, 125, 86, TRUE, NA, "elevated",
4, 3, NA, NA, 87, TRUE, 100, "elevated",
4, 4, NA, NA, 88, TRUE, NA, "elevated",
6, 1, 114, 111, 86, FALSE, 99, "normal"
) %>%
mutate_at("chd_risk", ordered, levels = c("low", "normal", "elevated"))daps_trained <- daps1 %>% train(teststatic, testtemporal)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: (1) Hessian is numerically singular: parameters are not uniquely determined
#> In addition: Absolute convergence criterion was met, but relative criterion was not met
#> # weights: 21 (12 variable)
#> initial value 12.084735
#> iter 10 value 1.962247
#> iter 20 value 0.075340
#> iter 30 value 0.000969
#> final value 0.000040
#> converged
daps_trained
#> $model_table
#> # A tibble: 18 x 4
#> model_id var dynamic model_quo
#> * <int> <chr> <lgl> <list>
#> 1 1 diabetes TRUE <dps_ftt_>
#> 2 2 age TRUE <dps_nnf_>
#> 3 3 sbp TRUE <dps_ftt_>
#> 4 4 sbp TRUE <dps_ftt_>
#> 5 5 sbp TRUE <dps_nnf_>
#> 6 6 sbp TRUE <dps_nnf_>
#> 7 7 dbp TRUE <dps_ftt_>
#> 8 8 dbp TRUE <dps_ftt_>
#> 9 9 dbp TRUE <dps_ftt_>
#> 10 10 diabetes TRUE <dps_ftt_>
#> 11 11 diabetes TRUE <dps_ftt_>
#> 12 12 chd_risk TRUE <dps_ftt_>
#> 13 13 chd_risk TRUE <dps_ftt_>
#> 14 14 glucose TRUE <dps_ftt_>
#> 15 15 glucose TRUE <dps_ftt_>
#> 16 16 glucose TRUE <dps_ftt_>
#> 17 17 glucose TRUE <dps_ftt_>
#> 18 18 glucose TRUE <dps_nnf_>
#>
#> $metastate_model
#> # A tibble: 3 x 3
#> metastate nodes transitions
#> * <chr> <list> <list>
#> 1 state1 <chr [5]> <quosure>
#> 2 state2 <chr [5]> <quosure>
#> 3 state3 <chr [6]> <quosure>
#>
#> $trained_models
#> # A tibble: 18 x 3
#> model_id var model
#> <int> <chr> <list>
#> 1 1 diabetes <glm>
#> 2 2 age <dps_nnf_>
#> 3 3 sbp <lm>
#> 4 4 sbp <lm>
#> 5 5 sbp <dps_nnf_>
#> 6 6 sbp <dps_nnf_>
#> 7 7 dbp <lm>
#> 8 8 dbp <lm>
#> 9 9 dbp <lm>
#> 10 10 diabetes <glm>
#> 11 11 diabetes <glm>
#> 12 12 chd_risk <clm>
#> 13 13 chd_risk <multinom>
#> 14 14 glucose <lm>
#> 15 15 glucose <lm>
#> 16 16 glucose <lm>
#> 17 17 glucose <lm>
#> 18 18 glucose <dps_nnf_>
#>
#> $pred_setup
#> $pred_setup$models
#> # A tibble: 18 x 6
#> var check_nonimputa… check_imputable imputable_vars imputable_lags
#> <chr> <list> <list> <list> <list>
#> 1 diab… <quosure> <quosure> <chr [4]> <dbl [4]>
#> 2 age <quosure> <quosure> <chr [1]> <int [1]>
#> 3 sbp <quosure> <quosure> <chr [2]> <int [2]>
#> 4 sbp <quosure> <quosure> <chr [0]> <int [0]>
#> 5 sbp <quosure> <quosure> <chr [1]> <int [1]>
#> 6 sbp <quosure> <quosure> <chr [0]> <int [0]>
#> 7 dbp <quosure> <quosure> <chr [2]> <int [2]>
#> 8 dbp <quosure> <quosure> <chr [0]> <int [0]>
#> 9 dbp <quosure> <quosure> <chr [0]> <int [0]>
#> 10 diab… <quosure> <quosure> <chr [2]> <int [2]>
#> 11 diab… <quosure> <quosure> <chr [0]> <int [0]>
#> 12 chd_… <quosure> <quosure> <chr [2]> <int [2]>
#> 13 chd_… <quosure> <quosure> <chr [1]> <int [1]>
#> 14 gluc… <quosure> <quosure> <chr [0]> <int [0]>
#> 15 gluc… <quosure> <quosure> <chr [0]> <int [0]>
#> 16 gluc… <quosure> <quosure> <chr [0]> <int [0]>
#> 17 gluc… <quosure> <quosure> <chr [0]> <int [0]>
#> 18 gluc… <quosure> <quosure> <chr [1]> <int [1]>
#> # … with 1 more variable: pred_expr <list>
#>
#> $pred_setup$mm
#> # A tibble: 3 x 3
#> metastate nodes transitions
#> <chr> <list> <list>
#> 1 state1 <chr [5]> <quosure>
#> 2 state2 <chr [5]> <quosure>
#> 3 state3 <chr [6]> <quosure>
#>
#> $pred_setup$sim_row_env
#> <environment: 0xa0d0a40>
#>
#>
#> attr(,"class")
#> [1] "daps"daps_trained %>%
simulate(
static = teststatic,
longitudinal = testtemporal,
h = NULL,
from = "last",
to = 10,
impute = TRUE,
seed = 20200123
)
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> # A tibble: 33 x 11
#> id sex race t metastate sbp dbp age diabetes glucose chd_risk
#> <int> <chr> <chr> <int> <chr> <dbl> <dbl> <dbl> <lgl> <dbl> <ord>
#> 1 1 male black 5 state3 134. 95.9 52 TRUE -391. elevated
#> 2 1 male black 6 state3 137. 90.2 53 FALSE 523. elevated
#> 3 1 male black 7 state3 139. 95.1 54 TRUE -307. elevated
#> 4 1 male black 8 state3 140. 96.8 55 FALSE 381. elevated
#> 5 1 male black 9 state3 142. 99.0 56 FALSE 343. elevated
#> 6 1 male black 10 state3 143. 101. 57 FALSE 303. elevated
#> 7 2 fema… white 5 state3 145. 107. 67 FALSE 264. elevated
#> 8 2 fema… white 6 state3 146. 98.8 68 FALSE 512. elevated
#> 9 2 fema… white 7 state3 149. 103. 69 FALSE 411. elevated
#> 10 2 fema… white 8 state3 150. 105. 70 FALSE 376. elevated
#> # … with 23 more rows
daps_trained %>%
simulate(
static = teststatic,
longitudinal = testtemporal,
h = 1:3,
from = "last",
to = 3,
impute = FALSE,
seed = 20200123
)
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> # A tibble: 33 x 12
#> id sex race t h metastate sbp dbp age diabetes glucose
#> <int> <chr> <chr> <int> <int> <chr> <dbl> <dbl> <dbl> <lgl> <dbl>
#> 1 1 male black 4 1 state3 134. 95.9 52 TRUE -391.
#> 2 1 male black 4 2 state3 137. 90.2 53 FALSE 523.
#> 3 1 male black 4 3 state3 139. 95.1 54 TRUE -307.
#> 4 1 male black 3 1 state2 136. 85.3 51 TRUE NA
#> 5 1 male black 3 2 state3 136. 92.2 52 TRUE -270.
#> 6 1 male black 3 3 state3 137. 92.1 53 FALSE 470.
#> 7 2 fema… white 4 1 state3 145. 107. 67 FALSE 264.
#> 8 2 fema… white 4 2 state3 146. 98.8 68 FALSE 512.
#> 9 2 fema… white 4 3 state3 149. 103. 69 FALSE 411.
#> 10 2 fema… white 3 1 state3 146. 98.3 67 TRUE -199.
#> # … with 23 more rows, and 1 more variable: chd_risk <ord>
daps_trained %>%
simulate(
static = teststatic,
longitudinal = testtemporal,
h = NULL,
from = 5,
to = 7,
impute = "locf"
)
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type
#> == : prediction from a rank-deficient fit may be misleading
#> Warning in predict.lm(structure(list(coefficients = c(`(Intercept)` =
#> 1464.99999999999, : prediction from a rank-deficient fit may be misleading
#> # A tibble: 15 x 11
#> id sex race t metastate sbp dbp age diabetes glucose chd_risk
#> <int> <chr> <chr> <int> <chr> <dbl> <dbl> <dbl> <lgl> <dbl> <ord>
#> 1 1 male black 5 state3 134. 95.9 52 TRUE -391. elevated
#> 2 1 male black 6 state3 137. 90.2 53 FALSE 523. elevated
#> 3 1 male black 7 state3 139. 95.1 54 TRUE -307. elevated
#> 4 2 fema… white 5 state3 145. 107. 67 FALSE 264. elevated
#> 5 2 fema… white 6 state3 146. 98.8 68 FALSE 512. elevated
#> 6 2 fema… white 7 state3 149. 103. 69 FALSE 411. elevated
#> 7 3 male black 5 state1 109. 50.1 35 TRUE NA normal
#> 8 3 male black 6 state1 111. 56.9 36 TRUE NA normal
#> 9 3 male black 7 state2 112. 58.1 37 TRUE NA normal
#> 10 4 male white 5 state3 143. 140. 89 FALSE -480. elevated
#> 11 4 male white 6 state3 144. 140. 90 FALSE -451. elevated
#> 12 4 male white 7 state3 145. 142. 91 FALSE -491. elevated
#> 13 6 fema… black 5 state3 235. 130. 87 TRUE -188. elevated
#> 14 6 fema… black 6 state3 231. 193. 88 TRUE -2024. elevated
#> 15 6 fema… black 7 state3 228. 175. 89 FALSE -816. elevated
# Add in a count of patients/observations with incomplete predictions
# impute options:
# - default: FALSE
# - locf
# - simulate at the preceding rows with missingness (cut it off if you have to back more than lookback_steps steps)
# daps() %>%
# add_models() %>%
# add_metastate_model() %>%
# train() %>%
# simulate()