Last data update: 2014.03.03
R: Empirical Distribution Function and Quantiles
BIFIE.ecdf R Documentation
Empirical Distribution Function and Quantiles
Description
Computes an empirical distribution function (and quantiles).
If only some quantiles should
be calculated, then an appropriate vector of breaks (which are quantiles)
must be specified.
Statistical inference is not conducted for this method.
Usage
BIFIE.ecdf( BIFIEobj, vars , breaks=NULL, quanttype=1, group=NULL , group_values=NULL )
## S3 method for class 'BIFIE.ecdf'
summary(object,digits=4,...)
Arguments
BIFIEobj
Object of class BIFIEdata
vars
Vector of variables for which statistics should be computed.
breaks
Optional vector of breaks. Otherwise, it will be automatically defined.
quanttype
Type of calculation for quantiles. In case of quanttype=1,
a linear interpolation is used while for quanttype=2 it is not.
group
Optional grouping variable
group_values
Optional vector of grouping values. This can be omitted and grouping
values will be determined automatically.
object
Object of class BIFIE.ecdf
digits
Number of digits for rounding output
...
Further arguments to be passed
Value
A list with following entries
ecdf
Data frame with probabilities and the empirical
distribution function (See Examples).
output
More extensive output
...
More values
See Also
Hmisc::wtd.ecdf,
Hmisc::wtd.quantile
Examples
#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################
data(data.timss1)
data(data.timssrep)
# create BIFIE.dat object
bifieobj <- BIFIE.data( data.list=data.timss1 , wgt= data.timss1[[1]]$TOTWGT ,
wgtrep=data.timssrep[, -1 ] )
# ecdf
vars <- c( "ASMMAT" , "books")
group <- "female" ; group_values <- 0:1
# quantile type 1
res1 <- BIFIE.ecdf( bifieobj , vars = vars , group=group )
summary(res1)
res2 <- BIFIE.ecdf( bifieobj , vars = vars , group=group , quanttype=2)
# plot distribution function
ecdf1 <- res1$ecdf
plot( ecdf1$ASMMAT_female0 , ecdf1$yval , type="l")
plot( res2$ecdf$ASMMAT_female0 , ecdf1$yval , type="l" , lty=2)
plot( ecdf1$books_female0 , ecdf1$yval , type="l" , col="blue")
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BIFIEsurvey)
|-----------------------------------------------------------------
| BIFIEsurvey 1.9.4-0 (2016-06-01)
| http://www.bifie.at
|-----------------------------------------------------------------
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/BIFIEsurvey/BIFIE.ecdf.Rd_%03d_medium.png", width=480, height=480)
> ### Name: BIFIE.ecdf
> ### Title: Empirical Distribution Function and Quantiles
> ### Aliases: BIFIE.ecdf summary.BIFIE.ecdf
> ### Keywords: Empirical distribution function Quantiles summary
>
> ### ** Examples
>
> #############################################################################
> # EXAMPLE 1: Imputed TIMSS dataset
> #############################################################################
>
> data(data.timss1)
> data(data.timssrep)
>
> # create BIFIE.dat object
> bifieobj <- BIFIE.data( data.list=data.timss1 , wgt= data.timss1[[1]]$TOTWGT ,
+ wgtrep=data.timssrep[, -1 ] )
+++ Generate BIFIE.data object
|*****|
|-----|
>
> # ecdf
> vars <- c( "ASMMAT" , "books")
> group <- "female" ; group_values <- 0:1
> # quantile type 1
> res1 <- BIFIE.ecdf( bifieobj , vars = vars , group=group )
|*****|
> summary(res1)
------------------------------------------------------------
BIFIEsurvey 1.9.4-0 (2016-06-01)
Function 'BIFIE.ecdf'
Call:
BIFIE.ecdf(BIFIEobj = bifieobj, vars = vars, group = group)
Date of Analysis: 2016-07-04 14:24:51
Time difference of 0.01668668 secs
Computation time: 0.01668668
Multiply imputed dataset
Number of persons = 4668
Number of imputed datasets = 5
Number of Jackknife zones per dataset = 0
Fay factor = 1
Empirical Distribution Function
yval ASMMAT_female0 ASMMAT_female1 books_female0 books_female1
1 0.00 289.4060 289.2616 1.0000 1.0000
2 0.01 360.4102 350.5463 1.0000 1.0000
3 0.02 378.2956 370.2925 1.0000 1.0000
4 0.03 390.7284 382.2644 1.0000 1.0000
5 0.04 398.2713 391.3413 1.0000 1.0000
6 0.05 404.7193 399.0253 1.0000 1.0000
7 0.06 410.9382 404.2058 1.0000 1.0000
8 0.07 416.3854 409.1407 1.0000 2.0000
9 0.08 420.9947 413.9343 1.0000 2.0000
10 0.09 425.1791 418.1969 1.0000 2.0000
11 0.10 429.8868 422.6586 1.0000 2.0000
12 0.11 433.8429 425.8044 1.0000 2.0000
13 0.12 437.6019 428.9276 1.0000 2.0000
14 0.13 440.4902 432.0660 1.0824 2.0000
15 0.14 443.2934 434.9671 2.0000 2.0000
16 0.15 446.2621 437.8459 2.0000 2.0000
17 0.16 449.0259 441.0204 2.0000 2.0000
18 0.17 452.1626 443.5597 2.0000 2.0000
19 0.18 454.5535 446.1485 2.0000 2.0000
20 0.19 457.0869 448.4907 2.0000 2.0000
21 0.20 458.9986 450.8571 2.0000 2.0000
22 0.21 460.9323 453.0013 2.0000 2.0000
23 0.22 462.7966 455.4279 2.0000 2.0000
24 0.23 464.8803 458.1117 2.0000 2.0000
25 0.24 466.9136 460.2872 2.0000 2.0000
26 0.25 469.2726 462.6059 2.0000 2.0000
27 0.26 471.2657 464.5962 2.0000 2.0000
28 0.27 473.5467 466.6757 2.0000 2.0000
29 0.28 475.4000 468.6870 2.0000 2.0000
30 0.29 477.8102 470.4226 2.0000 2.0000
31 0.30 480.0956 472.2576 2.0000 2.0000
32 0.31 482.2891 474.2782 2.0000 2.0000
33 0.32 484.0860 475.9431 2.0000 2.0000
34 0.33 485.9087 477.6245 2.0000 2.4603
35 0.34 487.6431 479.6084 2.0000 3.0000
36 0.35 489.5604 481.2537 2.0000 3.0000
37 0.36 491.2799 483.2301 2.0000 3.0000
38 0.37 493.2986 484.8385 2.0000 3.0000
39 0.38 495.0179 486.7814 2.0000 3.0000
40 0.39 496.6917 488.4322 2.0000 3.0000
41 0.40 498.2431 490.2857 3.0000 3.0000
42 0.41 500.2859 492.0067 3.0000 3.0000
43 0.42 501.9409 493.5864 3.0000 3.0000
44 0.43 503.5877 495.2050 3.0000 3.0000
45 0.44 505.3102 497.0009 3.0000 3.0000
46 0.45 507.0328 498.5194 3.0000 3.0000
47 0.46 508.6513 499.8466 3.0000 3.0000
48 0.47 510.1753 501.5958 3.0000 3.0000
49 0.48 511.8156 503.2402 3.0000 3.0000
50 0.49 513.4574 504.9194 3.0000 3.0000
51 0.50 514.8680 506.4329 3.0000 3.0000
52 0.51 516.4589 507.9330 3.0000 3.0000
53 0.52 518.0709 509.7049 3.0000 3.0000
54 0.53 519.4852 511.5210 3.0000 3.0000
55 0.54 520.8204 512.9131 3.0000 3.0000
56 0.55 522.5602 514.5176 3.0000 3.0000
57 0.56 524.3329 516.1179 3.0000 3.0000
58 0.57 525.9898 517.9863 3.0000 3.0000
59 0.58 527.8937 519.5515 3.0000 3.0000
60 0.59 529.2754 521.1383 3.0000 3.0000
61 0.60 530.7957 522.9874 3.0000 3.0000
62 0.61 532.3495 524.4240 3.0000 3.0000
63 0.62 534.0085 526.0524 3.0000 3.0000
64 0.63 535.8988 527.7227 3.0000 3.0000
65 0.64 537.9137 529.3551 3.0000 3.0000
66 0.65 539.5138 531.0225 3.0000 3.0000
67 0.66 541.2964 532.4859 3.0000 3.0000
68 0.67 542.9439 534.1731 3.0000 3.0000
69 0.68 544.7335 535.5444 3.0000 3.0000
70 0.69 546.2788 537.1540 3.0000 3.0000
71 0.70 548.0536 538.7727 3.0000 3.0000
72 0.71 549.7821 540.3485 3.0000 3.0000
73 0.72 551.8408 541.8885 3.0000 4.0000
74 0.73 553.7181 543.8178 3.0000 4.0000
75 0.74 555.4481 545.5449 4.0000 4.0000
76 0.75 557.4563 547.2582 4.0000 4.0000
77 0.76 559.2689 549.2598 4.0000 4.0000
78 0.77 561.3302 550.8590 4.0000 4.0000
79 0.78 563.8103 552.8609 4.0000 4.0000
80 0.79 566.0606 554.5632 4.0000 4.0000
81 0.80 567.8897 556.6981 4.0000 4.0000
82 0.81 569.7978 559.0376 4.0000 4.0000
83 0.82 571.6767 560.6622 4.0000 4.0000
84 0.83 574.1766 562.9315 4.0000 4.0000
85 0.84 576.3203 565.0438 4.0000 4.0000
86 0.85 578.6383 567.4220 4.0000 4.0000
87 0.86 580.9051 569.8376 4.0000 4.0000
88 0.87 583.1823 572.2332 5.0000 4.0000
89 0.88 585.9448 574.8882 5.0000 4.0000
90 0.89 589.1086 578.0211 5.0000 5.0000
91 0.90 591.7039 580.5636 5.0000 5.0000
92 0.91 595.0740 583.8702 5.0000 5.0000
93 0.92 598.3910 586.9706 5.0000 5.0000
94 0.93 602.9135 589.8593 5.0000 5.0000
95 0.94 607.5578 593.8227 5.0000 5.0000
96 0.95 613.1091 597.9249 5.0000 5.0000
97 0.96 618.9041 603.1582 5.0000 5.0000
98 0.97 627.0231 609.0026 5.0000 5.0000
99 0.98 637.2005 619.0757 5.0000 5.0000
100 0.99 651.9489 629.8061 5.0000 5.0000
101 1.00 720.2110 739.7378 5.0000 5.0000
> res2 <- BIFIE.ecdf( bifieobj , vars = vars , group=group , quanttype=2)
|*****|
> # plot distribution function
> ecdf1 <- res1$ecdf
> plot( ecdf1$ASMMAT_female0 , ecdf1$yval , type="l")
> plot( res2$ecdf$ASMMAT_female0 , ecdf1$yval , type="l" , lty=2)
> plot( ecdf1$books_female0 , ecdf1$yval , type="l" , col="blue")
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.
