context("Model Predictions")
test_that("model_prediction works", {
source("examples_fcn_doc/examples_model_prediction.R")
expect_equal(length(unique(df_2$ID)),32)
expect_null(df_3$DV)
expect_null(df_4$a_i)
expect_equal(length(unique(df_5$Group)),2)
expect_equal(length(unique(df_5$a_i)),2)
expect_equal(length(unique(df_5$ID)),6)
expect_equal(length(unique(df_6$Group)),2)
expect_true(all(is.na(df_6$PRED)))
expect_true(all(c("WT","AGE") %in% names(df_7)))
expect_equal(length(unique(df_8$WT)),2)
expect_equal(length(unique(df_8$AGE)),2)
expect_equal(length(unique(df_9$WT)),2)
expect_equal(length(unique(df_9$AGE)),2)
expect_equal(length(unique(df_9$ID)),6)
expect_equal(length(unique(df_10$WT)),6)
expect_equal(length(unique(df_11$AGE)),6)
expect_equal(length(unique(df_12$AMT)),3)
expect_equal(length(unique(df_13$AMT)),2)
expect_equal(length(unique(df_15$AMT[df_15$ID==1])),3)
df_16 <- model_prediction(design=design_3,DV=TRUE,dosing=dosing_4,filename="test.csv")
expect_true("test.csv" %in% list.files())
unlink("test.csv")
dosing_2 <- list(list(AMT=1000,RATE=NA,Time=0.5),list(AMT=3000,RATE=NA,Time=0.5),list(AMT=6000,RATE=NA,Time=0.5))
expect_error(model_prediction(design=design_3,DV=T,dosing=dosing_2))
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db.2 <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=1),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
plot_model_prediction(poped.db.2,PI=T,DV=T)#,groupsize_sim = 500)
df_20 <- model_prediction(poped.db.2,PI=TRUE)
expect_true(all(c("PI_l","PI_u") %in% names(df_20)))
sfg.3 <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU=a[2])
return(parameters)
}
poped.db.3 <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg.3,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=1),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=c(DOSE=70,TAU=200))
plot_model_prediction(poped.db.3,PI=T,DV=T)#,groupsize_sim = 500)
})
test_that("plot_model_prediction works", {
source("examples_fcn_doc/examples_plot_model_prediction.R")
expect_output(str(plot_model_prediction(poped.db)))
})
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