Description Usage Arguments See Also Examples
Creates a metaplot for class 'data.frame'. Implements a rule to decided whether to make a density plot, a boxplot, a scatter plot, or a scatterplot matrix, given the supplied column names.
1 2 3 4 5 6 7 | ## S3 method for class 'data.frame'
metaplot(x, ..., univariate = metOption("univariate",
"densplot"), mixedvariate = metOption("mixedvariate", "boxplot"),
bivariate = metOption("bivariate", "scatter"),
multivariate = metOption("multivariate", "corsplom"),
categorical = metOption("categorical", "categorical"),
verbose = metOption("verbose", FALSE))
|
x |
object |
... |
passed arguments |
univariate |
function for univariate arguments |
mixedvariate |
function for bivariate combinations of numeric and categoral arguments |
bivariate |
function for arguments that resolve to two numerics (see rules) |
multivariate |
function for more than two numeric arguments |
categorical |
function for categorical arguments |
verbose |
generate messages describing process; passed to called functions if explicitly supplied |
Other methods: axislabel.data.frame
,
boxplot.data.frame
,
categorical.data.frame
,
corsplom.data.frame
,
densplot.data.frame
,
pack.data.frame
,
plot.metaplot_gtable
,
print.metaplot_gtable
,
scatter.data.frame
,
unpack.data.frame
Other univariate plots: dens_panel
,
densplot.data.frame
,
densplot_data_frame
,
densplot
,
panel.meta_densityplot
Other bivariate plots: iso_prepanel
,
scatter.data.frame
,
scatter_data_frame
, scatter
Other multivariate plots: corsplom.data.frame
,
corsplom_data_frame
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ## Not run:
library(magrittr)
library(dplyr)
library(csv)
library(nlme)
x <- Theoph
# mixed effects model
m1 <- nlme(
conc ~ SSfol(Dose, Time, lKe, lKa, lCl),
data = x,
fixed = lKe + lKa + lCl ~ 1,
random = lKe + lKa + lCl ~ 1
)
# some numeric and categorical properties
names(x) <- tolower(names(x))
x %<>% mutate(arm = ifelse(as.numeric(as.character(subject)) %% 2 == 0, 1, 2))
x %<>% mutate(site = ifelse(as.numeric(as.character(subject)) < 6, 1, 2))
x %<>% mutate(cohort = ifelse(as.numeric(as.character(subject)) %in% c(1:2,6:8), 1,2))
x %<>% mutate(pred = predict(m1,level = 0) %>% signif(4))
x %<>% mutate(ipred = predict(m1) %>% signif(4))
x %<>% mutate(res = residuals(m1) %>% signif(4))
x %<>% mutate(sres = residuals(m1, type = 'pearson') %>% signif(4))
r <- ranef(m1) %>% signif(4)
r$subject <- rownames(r)
x %<>% left_join(r)
# metadata
attr(x$subject,'label') <- 'subject identifier'
attr(x$wt,'label') <- 'subject weight'
attr(x$dose,'label') <- 'theophylline dose'
attr(x$time,'label') <- 'time since dose administration'
attr(x$conc,'label') <- 'theophylline concentration'
attr(x$arm,'label') <- 'trial arm'
attr(x$site,'label') <- 'investigational site'
attr(x$cohort,'label') <- 'recruitment cohort'
attr(x$pred,'label') <- 'population-predicted concentration'
attr(x$ipred,'label') <- 'individual-predicted concentration'
attr(x$res,'label') <- 'residuals'
attr(x$sres,'label') <- 'standardized residuals'
attr(x$lKe,'label') <- 'natural log of elimination rate constant'
attr(x$lKa,'label') <- 'natural log of absorption rate constant'
attr(x$lCl,'label') <- 'natural log of clearance'
attr(x$subject,'guide') <- '....'
attr(x$wt,'guide') <- 'kg'
attr(x$dose,'guide') <- 'mg/kg'
attr(x$time,'guide') <- 'h'
attr(x$conc,'guide') <- 'mg/L'
attr(x$arm,'guide') <- '//1/Arm A//2/Arm B//'
attr(x$site,'guide') <- '//1/Site 1//2/Site 2//'
attr(x$cohort,'guide') <- '//1/Cohort 1//2/Cohort 2//'
attr(x$pred,'guide') <- 'mg/L'
attr(x$ipred,'guide') <- 'mg/L'
attr(x$lKe,'reference') <- 0
attr(x$lKa,'reference') <- 0
attr(x$lCl,'reference') <- 0
attr(x$res,'reference') <- 0
attr(x$sres,'reference') <- '//-1.96//1.96//'
attr(x$subject,'symbol') <- 'ID_i'
attr(x$wt,'symbol') <- 'W_i'
attr(x$dose,'symbol') <- 'A_i'
attr(x$time,'symbol') <- 't_i,j'
attr(x$conc,'symbol') <- 'C_i,j'
attr(x$arm,'symbol') <- 'Arm_i'
attr(x$site,'symbol') <- 'Site_i'
attr(x$cohort,'symbol') <- 'Cohort_i'
attr(x$pred,'symbol') <- 'C_pred_p'
attr(x$ipred,'symbol') <- 'C_pred_i'
attr(x$res,'symbol') <- '\\epsilon'
attr(x$sres,'symbol') <- '\\epsilon_st'
attr(x$lKe,'symbol') <- 'ln(K_e.)'
attr(x$lKa,'symbol') <- 'ln(K_a.)'
attr(x$lCl,'symbol') <- 'ln(Cl_c./F)'
x %>% unpack %>% as.csv('theoph.csv')
## End(Not run)
|
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