2 FOCE Analysis

library(knitr)
library(PKPDmisc)
library(tidyverse)
library(decorator)

pallete <- Pallete$new("material_design")
mcp <- pallete$colors() 

sdtab1 <- read_nonmem("../modeling/sdtab001")
patab1 <- read_nonmem("../modeling/patab001")
sdtab2 <- read_nonmem("../modeling/sdtab002")
patab2 <- read_nonmem("../modeling/patab002")

2.0.1 Output previews

kable(head(sdtab1))

ID	TIME	IPRED	DV	CWRES	PRED
1	0	0.00	0.00	0.000	0.00
1	2	24.88	24.96	0.039	22.94
1	4	21.79	24.87	0.750	19.17
1	8	16.70	16.14	0.073	13.39
1	16	9.81	11.03	0.912	6.53
1	24	5.77	6.33	0.783	3.18

kable(head(patab1))

ID	CL	V	TVCL	TVV	nCL	nV
1	2.42	36.4	3.42	38.1	-0.347	-0.047
2	2.99	43.2	3.42	38.1	-0.135	0.126
3	4.20	47.1	3.42	38.1	0.204	0.211
4	3.50	30.8	3.42	38.1	0.024	-0.213
5	5.55	45.9	3.42	38.1	0.484	0.185
6	2.82	38.0	3.42	38.1	-0.195	-0.003

2.0.2 Visual summaries

2.0.2.1 Parameter values

patab1 %>% 
    select(ID:TVV) %>%
    gather(iparam, value, CL, V) %>%
    mutate(TVP = ifelse(iparam == "CL", TVCL, TVV)) %>%
    ggplot(aes(x = value, group = iparam)) +
    geom_density() +  
    geom_vline(aes(xintercept = TVP), color = mcp$blue, size = 1.2) + 
    facet_wrap(~iparam, scales = "free") + theme_bw() +
    base_theme() +
    labs(x = "Individual parameter value", subtitle="blue lines represent population typical value")

2.0.2.2 Individual diagnostic plots

sdtab1 %>%
    filter(TIME >0) %>%
    ggplot(aes(x = TIME, y = CWRES)) + geom_point() +
    geom_smooth(se = FALSE, color = mcp$blue) +
    theme_bw() +
    base_theme() +
    geom_smooth(aes(y = abs(CWRES)), se = F, color = mcp$red) + 
    geom_smooth(aes(y = -abs(CWRES)), se = F, color = mcp$red) 
#> `geom_smooth()` using method = 'loess'
#> `geom_smooth()` using method = 'loess'
#> `geom_smooth()` using method = 'loess'

sdtab1 %>%
    ggplot(aes(x = IPRED, y = DV)) + 
    geom_abline(color = mcp$blue, size = 1.1) +
    geom_point() +
    theme_bw() +
    base_theme() +
    labs(x = "Individual Predicted Concentration (mg/L)", 
         y = "Observed Concentration (mg/L)")

plot_list <- sdtab1 %>%
    mutate(PNUM = ids_per_plot(ID)) %>%
    split(.$PNUM) %>%
    map(~
    ggplot(., aes(x = TIME, y = IPRED, group = ID)) +
    geom_line(size = 1.1) +
    geom_line(aes(y = PRED), size = 1.1, color = mcp$blue) + 
    geom_point(aes(y = DV), size = 2.5) + facet_wrap(~ID) +
    theme_bw() +
    base_theme() +
    labs(y = "Population, Individual Predicted and Observed Concentrations (mg/L)", 
         x = "Time (hours)",
         subtitle = "blue line = population predicted"
         ) +
    scale_y_log10(breaks = c(1, 5, 10, 20, ceiling(max(.$DV)/10)*10))
    )

print_plots(plot_list)

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session_details <- devtools::session_info()
session_details$platform
#>  setting  value                       
#>  version  R version 3.3.2 (2016-10-31)
#>  system   x86_64, mingw32             
#>  ui       RTerm                       
#>  language (EN)                        
#>  collate  English_United States.1252  
#>  tz       America/New_York            
#>  date     2016-12-13
knitr::kable(session_details$packages)

package	*	version	date	source
backports		1.0.4	2016-10-24	CRAN (R 3.3.2)
bookdown		0.2	2016-11-12	CRAN (R 3.3.2)
devtools		1.12.0	2016-06-24	CRAN (R 3.3.2)
digest		0.6.10	2016-08-02	CRAN (R 3.3.2)
evaluate		0.10	2016-10-11	CRAN (R 3.3.2)
htmltools		0.3.5	2016-03-21	CRAN (R 3.3.2)
httpuv		1.3.3	2015-08-04	CRAN (R 3.3.2)
knitr		1.15	2016-11-09	CRAN (R 3.3.2)
magrittr		1.5	2014-11-22	CRAN (R 3.3.2)
memoise		1.0.0	2016-01-29	CRAN (R 3.3.2)
mime		0.5	2016-07-07	CRAN (R 3.3.2)
miniUI		0.1.1	2016-01-15	CRAN (R 3.3.2)
R6		2.2.0	2016-10-05	CRAN (R 3.3.2)
Rcpp		0.12.8	2016-11-17	CRAN (R 3.3.2)
rmarkdown		1.2	2016-11-21	CRAN (R 3.3.2)
rprojroot		1.1	2016-10-29	CRAN (R 3.3.2)
shiny		0.14.2	2016-11-01	CRAN (R 3.3.2)
stringi		1.1.2	2016-10-01	CRAN (R 3.3.2)
stringr		1.1.0	2016-08-19	CRAN (R 3.3.2)
withr		1.0.2	2016-06-20	CRAN (R 3.3.2)
xtable		1.8-2	2016-02-05	CRAN (R 3.3.2)
yaml		2.1.13	2014-06-12	CRAN (R 3.3.2)