metaplot.data.frame: Create Metaplot for Data Frame.

Description Usage Arguments See Also Examples

View source: R/metaplot.R

Description

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.

Usage

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))

Arguments

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

See Also

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

Examples

 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)

metaplot documentation built on Sept. 30, 2018, 5:05 p.m.