Nothing
## ---- results='asis', echo=FALSE----------------------------------------------
cat(gsub("\\n ", "", packageDescription("saeSim", fields = "Description")))
## ---- echo=FALSE--------------------------------------------------------------
set.seed(1)
## -----------------------------------------------------------------------------
library(saeSim)
setup <- sim_base() %>%
sim_gen_x() %>%
sim_gen_e() %>%
sim_gen_v() %>%
sim_resp_eq(y = 100 + 2 * x + v + e) %>%
sim_simName("Doku")
setup
## ----eval=FALSE---------------------------------------------------------------
# dataList <- sim(setup, R = 10)
## ----eval=FALSE---------------------------------------------------------------
# simData <- sim_base() %>%
# sim_gen_x() %>%
# sim_gen_e() %>%
# as.data.frame
# simData
## ---- eval=FALSE--------------------------------------------------------------
# setup <- sim_base() %>%
# sim_gen_x() %>%
# sim_gen_e() %>%
# sim_resp_eq(y = 100 + 2 * x + e)
#
# setup1 <- setup %>% sim_sample(sample_fraction(0.05))
# setup2 <- setup %>% sim_sample(sample_number(5))
## -----------------------------------------------------------------------------
setup <- sim_base_lmm()
## ----eval = FALSE-------------------------------------------------------------
# plot(setup)
# autoplot(setup)
# autoplot(setup, "e")
# autoplot(setup %>% sim_gen_vc())
## -----------------------------------------------------------------------------
base_id(2, 3) %>%
sim_gen(gen_generic(rnorm, mean = 5, sd = 10, name = "x", groupVars = "idD"))
## -----------------------------------------------------------------------------
library(saeSim)
setup <- sim_base() %>%
sim_gen_x() %>% # Variable 'x'
sim_gen_e() %>% # Variable 'e'
sim_gen_v() %>% # Variable 'v' as a random-effect
sim_gen(gen_v_sar(name = "vSp")) %>% # random-effect following a SAR(1)
sim_resp_eq(y = 100 + x + v + vSp + e) # Computing 'y'
setup
## -----------------------------------------------------------------------------
contSetup <- setup %>%
sim_gen_cont(
gen_v_sar(sd = 40, name = "vSp"), # defining the model
nCont = 0.05, # 5 per cent outliers
type = "area", # whole areas are outliers, i.e. all obs within
areaVar = "idD", # var name to identify domain
fixed = TRUE # if in each iteration the same area is an outlier
)
## -----------------------------------------------------------------------------
base_id(3, 4) %>%
sim_gen_x() %>%
sim_gen_e() %>%
sim_gen_ec(mean = 0, sd = 150, name = "eCont", nCont = c(1, 2, 3)) %>%
as.data.frame
## -----------------------------------------------------------------------------
base_id(2, 3) %>%
sim_gen_x() %>%
sim_gen_e() %>%
sim_gen_ec() %>%
sim_resp_eq(y = 100 + x + e) %>%
# the mean in each domain:
sim_comp_pop(comp_var(popMean = mean(y)), by = "idD")
## -----------------------------------------------------------------------------
comp_linearPredictor <- function(dat) {
dat$linearPredictor <- lm(y ~ x, dat) %>% predict
dat
}
sim_base_lm() %>%
sim_comp_pop(comp_linearPredictor)
## -----------------------------------------------------------------------------
sim_base_lm() %>%
sim_comp_pop(function(dat) lm(y ~ x, dat)) %>%
sim(R = 1)
comp_linearModelAsAttr <- function(dat) {
attr(dat, "linearModel") <- lm(y ~ x, dat)
dat
}
dat <- sim_base_lm() %>%
sim_comp_pop(comp_linearModelAsAttr) %>%
as.data.frame
attr(dat, "linearModel")
## -----------------------------------------------------------------------------
sim_base_lm() %>%
sim_sample() %>%
sim_comp_sample(comp_linearPredictor)
## -----------------------------------------------------------------------------
sim_base_lm() %>%
sim_sample() %>%
sim_agg() %>%
sim_comp_agg(comp_linearPredictor)
## -----------------------------------------------------------------------------
base_id(3, 4) %>%
sim_gen_x() %>%
sim_sample(sample_number(1L))
base_id(3, 4) %>%
sim_gen_x() %>%
sim_sample(sample_number(1L, groupVars = "idD"))
## -----------------------------------------------------------------------------
# simple random sampling:
sim_base_lm() %>% sim_sample(sample_number(size = 10L))
sim_base_lm() %>% sim_sample(sample_fraction(size = 0.05))
# srs in each domain/cluster
sim_base_lm() %>% sim_sample(sample_number(size = 10L, groupVars = "idD"))
sim_base_lm() %>% sim_sample(sample_fraction(size = 0.05, groupVars = "idD"))
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