Perform binless Visual Predictive Check (VPC)

Use this function in place of traditional binning methods to derive VPC. For continuous VPC, this is obtained using additive quantile regression (quantreg::rqss()) and LOESS for pcVPC. While for categorical VPC, this is obtained using a generalized additive model (gam(family = "binomial")).

binless(o, ...)

# S3 method for tidyvpcobj
binless(
  o,
  optimize = TRUE,
  optimization.interval = c(0, 7),
  loess.ypc = NULL,
  lambda = NULL,
  span = NULL,
  sp = NULL,
  ...
)

Arguments

o: A tidyvpcobj.
...: Other arguments to include will be ignored.
optimize: Logical indicating whether smoothing parameters should be optimized using AIC.
optimization.interval: Numeric vector of length 2 specifying the min/max range of smoothing parameter for optimization. Only applicable if optimize = TRUE.
loess.ypc: (Deprecated) Argument is ignored. For a LOESS pcVPC using the `binless` method, usage of predcorrect will automatically perform LOESS prediction correction.
lambda: Numeric vector of length 3 specifying lambda values for each quantile. If stratified, specify a data.frame with given strata represented the column name, and value specified as a numeric vector of length 3. See below examples. Only applicable to continuous VPC with optimize = FALSE.
span: Numeric between 0,1 specifying smoothing parameter for LOESS prediction correction. Only applicable for continuous VPC with optimize = FALSE and usage of predcorrect.
sp: List of smoothing parameters applied to mgcv::gam(). Elements of list must be in the same order as unique values of DV. If one or more stratification variables present, the order of sp should be specified as unique combination of strata + DV, in ascending order. See below examples. Only applicable for categorical VPC, if optimize = FALSE.

Value

For continuous VPC, updates tidyvpcobj with additive quantile regression fits for observed and simulated data for quantiles specified in the qpred argument of vpcstats(). If the optimize = TRUE argument is specified, the resulting tidyvpcobj will contain optimized lambda values according to AIC. For prediction corrected VPC (pcVPC), specifying loess.ypc = TRUE will return optimized span value for LOESS smoothing. For categorical VPC, updates tidyvpcobj with fits obtained by gam(family="binomial") for observed and simulated data for each category of DV (in each stratum if stratify defined). If optimize = TRUE argument is specified, the resulting tidyvpcobj wil contain optimized sp values according to AIC.

Examples

# \donttest{

require(magrittr)
#> Loading required package: magrittr
require(data.table)
#> Loading required package: data.table

obs_data <- obs_data[MDV == 0]
sim_data <- sim_data[MDV == 0]


 vpc <- observed(obs_data, y = DV, x = TIME) %>%
      simulated(sim_data, y = DV) %>%
      binless() %>%
      vpcstats()
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct

 # Binless example with LOESS prediction correction

 obs_data$PRED <- sim_data[REP == 1, PRED]

 vpc <- observed(obs_data, y = DV, x = TIME) %>%
      simulated(sim_data, y = DV) %>%
      binless(optimize = TRUE) %>%
      predcorrect(pred = PRED) %>% 
      vpcstats()
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct

# Binless example with user specified lambda values stratified on
# "GENDER" with 2 levels ("M", "F"), 10%, 50%, 90% quantiles.

 lambda_strat <- data.table(
 GENDER_M = c(3,5,2),
 GENDER_F = c(1,3,4)
 )

 vpc <- observed(obs_data, y = DV, x = TIME) %>%
      simulated(sim_data, y = DV) %>%
      stratify(~ GENDER) %>%
      binless(optimize = FALSE, lambda = lambda_strat) %>%
      vpcstats(qpred = c(0.1, 0.5, 0.9))
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct

 # Binless example for categorical DV with optimized smoothing
 vpc <- observed(obs_cat_data, x = agemonths, yobs = zlencat) %>%
       simulated(sim_cat_data, ysim = DV) %>%
       stratify(~ Country_ID_code) %>%
       binless() %>%
       vpcstats(vpc.type = "cat", quantile.type = 6)

 # Binless example for categorical DV with user specified sp values
 user_sp <- list(
 Country1_prob0 = 100,
 Country1_prob1 = 3,
 Country1_prob2 = 4,
 Country2_prob0 = 90,
 Country2_prob1 = 3,
 Country2_prob2 = 4,
 Country3_prob0 = 55,
 Country3_prob1 = 3,
 Country3_prob2 = 200)

 vpc <- observed(obs_cat_data, x = agemonths, yobs = zlencat) %>%
        simulated(sim_cat_data, ysim = DV) %>%
        stratify(~ Country_ID_code) %>%
        binless(optimize = FALSE, sp = user_sp) %>%
        vpcstats(vpc.type = "categorical", conf.level = 0.9, quantile.type = 6)
# }

Perform binless Visual Predictive Check (VPC)

Arguments

Value

See also

Examples