old/lucid_poster_abstract.r
In kwstat/lucid: Printing Floating Point Numbers in a Human-Friendly Format
# lucid: An R package for pretty printing floating point numbers
Abstract
Real numbers can be efficiently stored in computer memory with the floating
point format, but printed floating point numbers can be difficult for people
to interpret, especially when the exponents are not all the same. The R
package "lucid" is useful for formatting vectors and tables of floating-point
numbers in a human-friendly format. Examples are given to illustrate the
benefits with variance components from mixed models.
Keywords: R, floating-point, tables
Abstract Number 319969 has been submitted.
Draft manuscript emailed 5.11.16
---
Try to connect to the theme. Think about a prop like a big diamond or magnifying glass. Have 50 ? copies of the poster printed on 8.5x11 paper or paper strips linking to vignette.
Presentations may be given on any topic of statistical interest; however, authors are encouraged to submit papers on the theme set by 2016 ASA President Jessica Utts, "The Extraordinary Power of Statistics." Additionally, abstracts with a primary focus on statistical applications are encouraged.
JSM registraiton: 123013
password: jsm2016
A six-foot table, an 8 x 4 foot display board, and push pins are provided.
# ----------------------------------------------------------------------------
The sequence of steps used by lucid() is:
1. Zap small numbers to zero.
2. Round using 3 significant digits.
3. Drop trailing zeros.
4. Align numbers at the decimal point.
5. Format as text.
One recommendation for improving the display of tables of numbers
is to round numbers to 2 [@wainer1997improving]
or 3 [@feinberg2011extracting] digits for the following reasons:
1. We cannot comprehend more than three digits very easily.
2. We seldom care about accuracy of more than three digits.
3. We can rarely justify more than three digits of accuracy statistically.
# ----------------------------------------------------------------------------
df1 <- data.frame(effect=c(113.5, -13.5, 4.5, 24.5, 6.927792e-14, -1.75, 16.5))
rownames(df1) <- c("(Intercept)","A","B","C","C1","C2","D")
print(df1)
lucid(df1)
round(df1,4)
signif(df1,4)
R> print(df1)
effect
(Intercept) 1.135000e+02
A -1.350000e+01
B 4.500000e+00
C 2.450000e+01
C1 6.927792e-14
C2 -1.750000e+00
D 1.650000e+01
R> lucid(df1)
effect
(Intercept) 114
A -13.5
B 4.5
C 24.5
C1 0
C2 -1.75
D 16.5
# ----------------------------------------------------------------------------
# Compare lucid to round, signif
d4 <- cbind(default=format(antibiotic$penicillin),
signif=format(signif(antibiotic$penicillin)),
round=format(round(antibiotic$penicillin,3)),
lucid=format(lucid(antibiotic$penicillin)))
as.data.frame(d4)
default signif round lucid
870.000 870.000 870.000 870
1.000 1.000 1.000 1
0.001 0.001 0.001 0.001
0.005 0.005 0.005 0.005
100.000 100.000 100.000 100
850.000 850.000 850.000 850
800.000 800.000 800.000 800
3.000 3.000 3.000 3
850.000 850.000 850.000 850
1.000 1.000 1.000 1
10.000 10.000 10.000 10
0.007 0.007 0.007 0.007
0.030 0.030 0.030 0.03
1.000 1.000 1.000 1
0.001 0.001 0.001 0.001
0.005 0.005 0.005 0.005
# ----------------------------------------------------------------------------
print(antibiotic)
lucid(antibiotic)
bacteria penicillin streptomycin neomycin stain
Aerobacter aerogenes 870.000 1.00 1.600 neg
Brucella abortus 1.000 2.00 0.020 neg
Escherichia coli 100.000 0.40 0.100 neg
Klebsiella pneumoniae 850.000 1.20 1.000 neg
Mycobacterium tuberculosis 800.000 5.00 2.000 neg
Proteus vulgaris 3.000 0.10 0.100 neg
Pseudomonas aeruginosa 850.000 2.00 0.400 neg
Salmonella typhosa 1.000 0.40 0.008 neg
Salmonella schottmuelleri 10.000 0.80 0.090 neg
Bacillis anthracis 0.001 0.01 0.007 pos
Diplococcus pneumoniae 0.005 11.00 10.000 pos
Staphylococcus albus 0.007 0.10 0.001 pos
Staphylococcus aureus 0.030 0.03 0.001 pos
Streptococcus fecalis 1.000 1.00 0.100 pos
Streptococcus hemolyticus 0.001 14.00 10.000 pos
Streptococcus viridans 0.005 10.00 40.000 pos
bacteria penicillin streptomycin neomycin stain
Aerobacter aerogenes 870 1 1.6 neg
Brucella abortus 1 2 0.02 neg
Escherichia coli 100 0.4 0.1 neg
Klebsiella pneumoniae 850 1.2 1 neg
Mycobacterium tuberculosis 800 5 2 neg
Proteus vulgaris 3 0.1 0.1 neg
Pseudomonas aeruginosa 850 2 0.4 neg
Salmonella typhosa 1 0.4 0.008 neg
Salmonella schottmuelleri 10 0.8 0.09 neg
Bacillis anthracis 0.001 0.01 0.007 pos
Diplococcus pneumoniae 0.005 11 10 pos
Staphylococcus albus 0.007 0.1 0.001 pos
Staphylococcus aureus 0.03 0.03 0.001 pos
Streptococcus fecalis 1 1 0.1 pos
Streptococcus hemolyticus 0.001 14 10 pos
Streptococcus viridans 0.005 10 40 pos
# ----------------------------------------------------------------------------
# Orange tree
require(dplyr)
require(broom)
require(lattice)
require(lucid)
xyplot(circumference ~ age, data=Orange, group=Tree,
auto.key=list(columns=5), type=c('p','r'), main="Orange")
Orange %>% group_by(Tree) %>% do(tidy(lm(circumference ~ age, data=.)))
Orange %>% group_by(Tree) %>% do(tidy(lm(circumference ~ age, data=.))) %>% lucid
Tree term estimate std.error statistic p.value
<ord> <chr> <dbl> <dbl> <dbl> <dbl>
3 (Intercept) 19.20353638 5.863410215 3.275148 2.207255e-02
3 age 0.08111158 0.005628105 14.411881 2.901046e-05
1 (Intercept) 24.43784664 6.543311039 3.734783 1.350409e-02
1 age 0.08147716 0.006280721 12.972581 4.851902e-05
5 (Intercept) 8.75834459 8.176436207 1.071169 3.330518e-01
5 age 0.11102891 0.007848307 14.146861 3.177093e-05
2 (Intercept) 19.96090337 9.352361105 2.134317 8.593318e-02
2 age 0.12506176 0.008977041 13.931291 3.425041e-05
4 (Intercept) 14.63762022 11.233762751 1.303002 2.493507e-01
4 age 0.13517222 0.010782940 12.535748 5.733090e-05
Tree term estimate std.error statistic p.value
<ord> <chr> <chr> <chr> <chr> <chr>
3 (Intercept) 19.2 5.86 3.28 0.0221
3 age 0.0811 0.00563 14.4 0.000029
1 (Intercept) 24.4 6.54 3.73 0.0135
1 age 0.0815 0.00628 13 0.0000485
5 (Intercept) 8.76 8.18 1.07 0.333
5 age 0.111 0.00785 14.1 0.0000318
2 (Intercept) 20 9.35 2.13 0.0859
2 age 0.125 0.00898 13.9 0.0000343
4 (Intercept) 14.6 11.2 1.3 0.249
4 age 0.135 0.0108 12.5 0.0000573
# ----------------------------------------------------------------------------
df1 <- data.frame(effect=c(-13.5, 4.5, 24.5, 6.927792e-14, -1.75,
16.5, 113.5000))
rownames(df1) <- c("A","B","C","C1","C2","D","(Intercept)")
print(df1)
lucid(df1)
effect
(Intercept) 1.135000e+02
A -1.350000e+01
B 4.500000e+00
C 2.450000e+01
C1 6.927792e-14
C2 -1.750000e+00
D 1.650000e+01
effect
(Intercept) 114
A -13.5
B 4.5
C 24.5
C1 0
C2 -1.75
D 16.5
# ----------------------------------------------------------------------------
d1 <- structure(list(grp = c("new.gen", "one", "one.1", "one.2", "one.3",
"one.4", "one.5", "one.6", "one.7", "one.8", "one.9", "one.10",
"one.11", "one.12", "one.13", "Residual"), var1 = c("(Intercept)",
"r1:c3", "r1:c2", "r1:c1", "c8", "c6", "c4", "c3", "c2", "c1",
"r10", "r8", "r4", "r2", "r1", NA), var2 = c(NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_),
vcov = c(2869.44692139271, 5531.57239635089, 58225.767835444,
128004.156092455, 6455.74953933247, 1399.72937329085, 1791.65071661348,
2548.88470543732, 5941.79076230161, 0, 1132.95013713932,
1355.22907294114, 2268.72957045473, 241.789424531994, 9199.9021721834,
4412.1096176349), sdcor = c(53.5672187199663, 74.3745413185916,
241.300161283502, 357.7766846686, 80.3476791160297, 37.4129572914365,
42.3278952537623, 50.4864804223598, 77.0830121511972, 0,
33.6593246684974, 36.8134360382339, 47.6311827530529, 15.5495795612613,
95.9161205021523, 66.4237127661116)), .Names = c("grp", "var1",
"var2", "vcov", "sdcor"), row.names = c(NA, -16L), class = "data.frame")
d2 <- structure(list(grp = c("new.gen", "one", "one.1", "one.2", "one.3",
"one.4", "one.5", "one.6", "one.7", "one.8", "one.9", "one.10",
"one.11", "one.12", "one.13", "Residual"), var1 = c("(Intercept)",
"r1:c3", "r1:c2", "r1:c1", "c8", "c6", "c4", "c3", "c2", "c1",
"r10", "r8", "r4", "r2", "r1", NA), var2 = c(NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_),
vcov = c(3228.41890564251, 7688.13916836557, 69747.5508913552,
107427.043198654, 6787.00354507896, 1636.12771714548, 12268.4603217744,
2686.30159414561, 7644.72994565782, 0.00122514315152732,
1975.50482871438, 1241.42852423718, 2811.24084391436, 928.227473340838,
10363.5849610346, 4126.83169047631), sdcor = c(56.8191772700249,
87.6820344675326, 264.097616216722, 327.760649252856, 82.3832722406616,
40.4490756031022, 110.763081944186, 51.8295436420735, 87.4341463368736,
0.0350020449620779, 44.4466514904597, 35.23391156595, 53.02113582256,
30.4668257838069, 101.801694293536, 64.2404210017051)), .Names = c("grp",
"var1", "var2", "vcov", "sdcor"), row.names = c(NA, -16L), class = "data.frame")
out <- cbind(d1[, c(2,4,5)], sep=" ",d2[,4:5])
names(out) <- c('term','vcov-bo','sdcor-bo','sep','vcov-ne','sdcor-ne')
print(out)
lucid(out, dig=4)
term var-boby std-boby var-neld std-neld
(Intercept) 2869.4469 53.56722 3.228419e+03 56.81917727
r1:c3 5531.5724 74.37454 7.688139e+03 87.68203447
r1:c2 58225.7678 241.30016 6.974755e+04 264.09761622
r1:c1 128004.1561 357.77668 1.074270e+05 327.76064925
c8 6455.7495 80.34768 6.787004e+03 82.38327224
c6 1399.7294 37.41296 1.636128e+03 40.44907560
c4 1791.6507 42.32790 1.226846e+04 110.76308194
c3 2548.8847 50.48648 2.686302e+03 51.82954364
c2 5941.7908 77.08301 7.644730e+03 87.43414634
c1 0.0000 0.00000 1.225143e-03 0.03500204
r10 1132.9501 33.65932 1.975505e+03 44.44665149
r8 1355.2291 36.81344 1.241429e+03 35.23391157
r4 2268.7296 47.63118 2.811241e+03 53.02113582
r2 241.7894 15.54958 9.282275e+02 30.46682578
r1 9199.9022 95.91612 1.036358e+04 101.80169429
<NA> 4412.1096 66.42371 4.126832e+03 64.24042100
term var-boby std-boby var-neld std-neld
(Intercept) 2869 53.57 3228 56.82
r1:c3 5532 74.37 7688 87.68
r1:c2 58230 241.3 69750 264.1
r1:c1 128000 357.8 107400 327.8
c8 6456 80.35 6787 82.38
c6 1400 37.41 1636 40.45
c4 1792 42.33 12270 110.8
c3 2549 50.49 2686 51.83
c2 5942 77.08 7645 87.43
c1 0 0 0 0.035
r10 1133 33.66 1976 44.45
r8 1355 36.81 1241 35.23
r4 2269 47.63 2811 53.02
r2 241.8 15.55 928.2 30.47
r1 9200 95.92 10360 101.8
<NA> 4412 66.42 4127 64.24
# ----------------------------------------------------------------------------
head(state.x77)
## Population Income Illiteracy Life Exp Murder HS Grad Frost Area
## Alabama 3615 3624 2.1 69.05 15.1 41.3 20 50708
## Alaska 365 6315 1.5 69.31 11.3 66.7 152 566432
## Arizona 2212 4530 1.8 70.55 7.8 58.1 15 113417
## Arkansas 2110 3378 1.9 70.66 10.1 39.9 65 51945
## California 21198 5114 1.1 71.71 10.3 62.6 20 156361
## Colorado 2541 4884 0.7 72.06 6.8 63.9 166 103766
lib(dplyr)
# ----------------------------------------------------------------------------
dat <- state.x77
dat <- as.data.frame(dat)
dat$State <- rownames(state.x77)
dat <- mutate(dat, Density = Population/Area)
set.seed(1)
d1 <- dat[sample(1:50),c('State','Population','Area','Density')]
d1[,c('State','Density')] %>% print
d1[,c('State','Density')] %>% lucid
R> d1[,c('State','Density')] %>% print
State Density
14 Indiana 0.1471867468
19 Maine 0.0342173351
28 Nevada 0.0053690542
43 Texas 0.0466822312
10 Georgia 0.0849103714
41 South Dakota 0.0089658350
42 Tennessee 0.1009727062
29 New Hampshire 0.0899523651
27 Nebraska 0.0201874926
3 Arizona 0.0195032491
9 Florida 0.1530227399
7 Connecticut 0.6375976964
44 Utah 0.0146535763
15 Iowa 0.0511431687
48 West Virginia 0.0747403407
18 Louisiana 0.0847095482
25 Missouri 0.0690919632
33 North Carolina 0.1115004713
13 Illinois 0.2008502547
34 North Dakota 0.0091955019
47 Washington 0.0534625207
39 Rhode Island 0.8875119161
49 Wisconsin 0.0842574912
4 Arkansas 0.0406198864
30 New Jersey 0.9750033240
46 Virginia 0.1252136752
1 Alabama 0.0712905261
40 South Carolina 0.0931679074
20 Maryland 0.4167424932
8 Delaware 0.2921291625
31 New Mexico 0.0094224624
12 Idaho 0.0098334482
23 Minnesota 0.0494520047
38 Pennsylvania 0.2637548370
50 Wyoming 0.0038681934
11 Hawaii 0.1350972763
36 Oklahoma 0.0394725364
2 Alaska 0.0006443845
35 Ohio 0.2619890177
5 California 0.1355708904
16 Kansas 0.0278772910
6 Colorado 0.0244877898
26 Montana 0.0051240839
17 Kentucky 0.0854224464
21 Massachusetts 0.7429082545
22 Michigan 0.1603569354
24 Mississippi 0.0494967862
32 New York 0.3779139052
45 Vermont 0.0509334197
37 Oregon 0.0237461532
R> d1[,c('State','Density')] %>% lucid
State Density
14 Indiana 0.147
19 Maine 0.0342
28 Nevada 0.00537
43 Texas 0.0467
10 Georgia 0.0849
41 South Dakota 0.00897
42 Tennessee 0.101
29 New Hampshire 0.09
27 Nebraska 0.0202
3 Arizona 0.0195
9 Florida 0.153
7 Connecticut 0.638
44 Utah 0.0147
15 Iowa 0.0511
48 West Virginia 0.0747
18 Louisiana 0.0847
25 Missouri 0.0691
33 North Carolina 0.112
13 Illinois 0.201
34 North Dakota 0.0092
47 Washington 0.0535
39 Rhode Island 0.888
49 Wisconsin 0.0843
4 Arkansas 0.0406
30 New Jersey 0.975
46 Virginia 0.125
1 Alabama 0.0713
40 South Carolina 0.0932
20 Maryland 0.417
8 Delaware 0.292
31 New Mexico 0.00942
12 Idaho 0.00983
23 Minnesota 0.0495
38 Pennsylvania 0.264
50 Wyoming 0.00387
11 Hawaii 0.135
36 Oklahoma 0.0395
2 Alaska 0.000644
35 Ohio 0.262
5 California 0.136
16 Kansas 0.0279
6 Colorado 0.0245
26 Montana 0.00512
17 Kentucky 0.0854
21 Massachusetts 0.743
22 Michigan 0.16
24 Mississippi 0.0495
32 New York 0.378
45 Vermont 0.0509
37 Oregon 0.0237
kwstat/lucid documentation built on June 14, 2025, 4:38 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# lucid: An R package for pretty printing floating point numbers
Abstract
Real numbers can be efficiently stored in computer memory with the floating
point format, but printed floating point numbers can be difficult for people
to interpret, especially when the exponents are not all the same. The R
package "lucid" is useful for formatting vectors and tables of floating-point
numbers in a human-friendly format. Examples are given to illustrate the
benefits with variance components from mixed models.
Keywords: R, floating-point, tables
Abstract Number 319969 has been submitted.
Draft manuscript emailed 5.11.16
---
Try to connect to the theme. Think about a prop like a big diamond or magnifying glass. Have 50 ? copies of the poster printed on 8.5x11 paper or paper strips linking to vignette.
Presentations may be given on any topic of statistical interest; however, authors are encouraged to submit papers on the theme set by 2016 ASA President Jessica Utts, "The Extraordinary Power of Statistics." Additionally, abstracts with a primary focus on statistical applications are encouraged.
JSM registraiton: 123013
password: jsm2016
A six-foot table, an 8 x 4 foot display board, and push pins are provided.
# ----------------------------------------------------------------------------
The sequence of steps used by lucid() is:
1. Zap small numbers to zero.
2. Round using 3 significant digits.
3. Drop trailing zeros.
4. Align numbers at the decimal point.
5. Format as text.
One recommendation for improving the display of tables of numbers
is to round numbers to 2 [@wainer1997improving]
or 3 [@feinberg2011extracting] digits for the following reasons:
1. We cannot comprehend more than three digits very easily.
2. We seldom care about accuracy of more than three digits.
3. We can rarely justify more than three digits of accuracy statistically.
# ----------------------------------------------------------------------------
df1 <- data.frame(effect=c(113.5, -13.5, 4.5, 24.5, 6.927792e-14, -1.75, 16.5))
rownames(df1) <- c("(Intercept)","A","B","C","C1","C2","D")
print(df1)
lucid(df1)
round(df1,4)
signif(df1,4)
R> print(df1)
effect
(Intercept) 1.135000e+02
A -1.350000e+01
B 4.500000e+00
C 2.450000e+01
C1 6.927792e-14
C2 -1.750000e+00
D 1.650000e+01
R> lucid(df1)
effect
(Intercept) 114
A -13.5
B 4.5
C 24.5
C1 0
C2 -1.75
D 16.5
# ----------------------------------------------------------------------------
# Compare lucid to round, signif
d4 <- cbind(default=format(antibiotic$penicillin),
signif=format(signif(antibiotic$penicillin)),
round=format(round(antibiotic$penicillin,3)),
lucid=format(lucid(antibiotic$penicillin)))
as.data.frame(d4)
default signif round lucid
870.000 870.000 870.000 870
1.000 1.000 1.000 1
0.001 0.001 0.001 0.001
0.005 0.005 0.005 0.005
100.000 100.000 100.000 100
850.000 850.000 850.000 850
800.000 800.000 800.000 800
3.000 3.000 3.000 3
850.000 850.000 850.000 850
1.000 1.000 1.000 1
10.000 10.000 10.000 10
0.007 0.007 0.007 0.007
0.030 0.030 0.030 0.03
1.000 1.000 1.000 1
0.001 0.001 0.001 0.001
0.005 0.005 0.005 0.005
# ----------------------------------------------------------------------------
print(antibiotic)
lucid(antibiotic)
bacteria penicillin streptomycin neomycin stain
Aerobacter aerogenes 870.000 1.00 1.600 neg
Brucella abortus 1.000 2.00 0.020 neg
Escherichia coli 100.000 0.40 0.100 neg
Klebsiella pneumoniae 850.000 1.20 1.000 neg
Mycobacterium tuberculosis 800.000 5.00 2.000 neg
Proteus vulgaris 3.000 0.10 0.100 neg
Pseudomonas aeruginosa 850.000 2.00 0.400 neg
Salmonella typhosa 1.000 0.40 0.008 neg
Salmonella schottmuelleri 10.000 0.80 0.090 neg
Bacillis anthracis 0.001 0.01 0.007 pos
Diplococcus pneumoniae 0.005 11.00 10.000 pos
Staphylococcus albus 0.007 0.10 0.001 pos
Staphylococcus aureus 0.030 0.03 0.001 pos
Streptococcus fecalis 1.000 1.00 0.100 pos
Streptococcus hemolyticus 0.001 14.00 10.000 pos
Streptococcus viridans 0.005 10.00 40.000 pos
bacteria penicillin streptomycin neomycin stain
Aerobacter aerogenes 870 1 1.6 neg
Brucella abortus 1 2 0.02 neg
Escherichia coli 100 0.4 0.1 neg
Klebsiella pneumoniae 850 1.2 1 neg
Mycobacterium tuberculosis 800 5 2 neg
Proteus vulgaris 3 0.1 0.1 neg
Pseudomonas aeruginosa 850 2 0.4 neg
Salmonella typhosa 1 0.4 0.008 neg
Salmonella schottmuelleri 10 0.8 0.09 neg
Bacillis anthracis 0.001 0.01 0.007 pos
Diplococcus pneumoniae 0.005 11 10 pos
Staphylococcus albus 0.007 0.1 0.001 pos
Staphylococcus aureus 0.03 0.03 0.001 pos
Streptococcus fecalis 1 1 0.1 pos
Streptococcus hemolyticus 0.001 14 10 pos
Streptococcus viridans 0.005 10 40 pos
# ----------------------------------------------------------------------------
# Orange tree
require(dplyr)
require(broom)
require(lattice)
require(lucid)
xyplot(circumference ~ age, data=Orange, group=Tree,
auto.key=list(columns=5), type=c('p','r'), main="Orange")
Orange %>% group_by(Tree) %>% do(tidy(lm(circumference ~ age, data=.)))
Orange %>% group_by(Tree) %>% do(tidy(lm(circumference ~ age, data=.))) %>% lucid
Tree term estimate std.error statistic p.value
<ord> <chr> <dbl> <dbl> <dbl> <dbl>
3 (Intercept) 19.20353638 5.863410215 3.275148 2.207255e-02
3 age 0.08111158 0.005628105 14.411881 2.901046e-05
1 (Intercept) 24.43784664 6.543311039 3.734783 1.350409e-02
1 age 0.08147716 0.006280721 12.972581 4.851902e-05
5 (Intercept) 8.75834459 8.176436207 1.071169 3.330518e-01
5 age 0.11102891 0.007848307 14.146861 3.177093e-05
2 (Intercept) 19.96090337 9.352361105 2.134317 8.593318e-02
2 age 0.12506176 0.008977041 13.931291 3.425041e-05
4 (Intercept) 14.63762022 11.233762751 1.303002 2.493507e-01
4 age 0.13517222 0.010782940 12.535748 5.733090e-05
Tree term estimate std.error statistic p.value
<ord> <chr> <chr> <chr> <chr> <chr>
3 (Intercept) 19.2 5.86 3.28 0.0221
3 age 0.0811 0.00563 14.4 0.000029
1 (Intercept) 24.4 6.54 3.73 0.0135
1 age 0.0815 0.00628 13 0.0000485
5 (Intercept) 8.76 8.18 1.07 0.333
5 age 0.111 0.00785 14.1 0.0000318
2 (Intercept) 20 9.35 2.13 0.0859
2 age 0.125 0.00898 13.9 0.0000343
4 (Intercept) 14.6 11.2 1.3 0.249
4 age 0.135 0.0108 12.5 0.0000573
# ----------------------------------------------------------------------------
df1 <- data.frame(effect=c(-13.5, 4.5, 24.5, 6.927792e-14, -1.75,
16.5, 113.5000))
rownames(df1) <- c("A","B","C","C1","C2","D","(Intercept)")
print(df1)
lucid(df1)
effect
(Intercept) 1.135000e+02
A -1.350000e+01
B 4.500000e+00
C 2.450000e+01
C1 6.927792e-14
C2 -1.750000e+00
D 1.650000e+01
effect
(Intercept) 114
A -13.5
B 4.5
C 24.5
C1 0
C2 -1.75
D 16.5
# ----------------------------------------------------------------------------
d1 <- structure(list(grp = c("new.gen", "one", "one.1", "one.2", "one.3",
"one.4", "one.5", "one.6", "one.7", "one.8", "one.9", "one.10",
"one.11", "one.12", "one.13", "Residual"), var1 = c("(Intercept)",
"r1:c3", "r1:c2", "r1:c1", "c8", "c6", "c4", "c3", "c2", "c1",
"r10", "r8", "r4", "r2", "r1", NA), var2 = c(NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_),
vcov = c(2869.44692139271, 5531.57239635089, 58225.767835444,
128004.156092455, 6455.74953933247, 1399.72937329085, 1791.65071661348,
2548.88470543732, 5941.79076230161, 0, 1132.95013713932,
1355.22907294114, 2268.72957045473, 241.789424531994, 9199.9021721834,
4412.1096176349), sdcor = c(53.5672187199663, 74.3745413185916,
241.300161283502, 357.7766846686, 80.3476791160297, 37.4129572914365,
42.3278952537623, 50.4864804223598, 77.0830121511972, 0,
33.6593246684974, 36.8134360382339, 47.6311827530529, 15.5495795612613,
95.9161205021523, 66.4237127661116)), .Names = c("grp", "var1",
"var2", "vcov", "sdcor"), row.names = c(NA, -16L), class = "data.frame")
d2 <- structure(list(grp = c("new.gen", "one", "one.1", "one.2", "one.3",
"one.4", "one.5", "one.6", "one.7", "one.8", "one.9", "one.10",
"one.11", "one.12", "one.13", "Residual"), var1 = c("(Intercept)",
"r1:c3", "r1:c2", "r1:c1", "c8", "c6", "c4", "c3", "c2", "c1",
"r10", "r8", "r4", "r2", "r1", NA), var2 = c(NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_),
vcov = c(3228.41890564251, 7688.13916836557, 69747.5508913552,
107427.043198654, 6787.00354507896, 1636.12771714548, 12268.4603217744,
2686.30159414561, 7644.72994565782, 0.00122514315152732,
1975.50482871438, 1241.42852423718, 2811.24084391436, 928.227473340838,
10363.5849610346, 4126.83169047631), sdcor = c(56.8191772700249,
87.6820344675326, 264.097616216722, 327.760649252856, 82.3832722406616,
40.4490756031022, 110.763081944186, 51.8295436420735, 87.4341463368736,
0.0350020449620779, 44.4466514904597, 35.23391156595, 53.02113582256,
30.4668257838069, 101.801694293536, 64.2404210017051)), .Names = c("grp",
"var1", "var2", "vcov", "sdcor"), row.names = c(NA, -16L), class = "data.frame")
out <- cbind(d1[, c(2,4,5)], sep=" ",d2[,4:5])
names(out) <- c('term','vcov-bo','sdcor-bo','sep','vcov-ne','sdcor-ne')
print(out)
lucid(out, dig=4)
term var-boby std-boby var-neld std-neld
(Intercept) 2869.4469 53.56722 3.228419e+03 56.81917727
r1:c3 5531.5724 74.37454 7.688139e+03 87.68203447
r1:c2 58225.7678 241.30016 6.974755e+04 264.09761622
r1:c1 128004.1561 357.77668 1.074270e+05 327.76064925
c8 6455.7495 80.34768 6.787004e+03 82.38327224
c6 1399.7294 37.41296 1.636128e+03 40.44907560
c4 1791.6507 42.32790 1.226846e+04 110.76308194
c3 2548.8847 50.48648 2.686302e+03 51.82954364
c2 5941.7908 77.08301 7.644730e+03 87.43414634
c1 0.0000 0.00000 1.225143e-03 0.03500204
r10 1132.9501 33.65932 1.975505e+03 44.44665149
r8 1355.2291 36.81344 1.241429e+03 35.23391157
r4 2268.7296 47.63118 2.811241e+03 53.02113582
r2 241.7894 15.54958 9.282275e+02 30.46682578
r1 9199.9022 95.91612 1.036358e+04 101.80169429
<NA> 4412.1096 66.42371 4.126832e+03 64.24042100
term var-boby std-boby var-neld std-neld
(Intercept) 2869 53.57 3228 56.82
r1:c3 5532 74.37 7688 87.68
r1:c2 58230 241.3 69750 264.1
r1:c1 128000 357.8 107400 327.8
c8 6456 80.35 6787 82.38
c6 1400 37.41 1636 40.45
c4 1792 42.33 12270 110.8
c3 2549 50.49 2686 51.83
c2 5942 77.08 7645 87.43
c1 0 0 0 0.035
r10 1133 33.66 1976 44.45
r8 1355 36.81 1241 35.23
r4 2269 47.63 2811 53.02
r2 241.8 15.55 928.2 30.47
r1 9200 95.92 10360 101.8
<NA> 4412 66.42 4127 64.24
# ----------------------------------------------------------------------------
head(state.x77)
## Population Income Illiteracy Life Exp Murder HS Grad Frost Area
## Alabama 3615 3624 2.1 69.05 15.1 41.3 20 50708
## Alaska 365 6315 1.5 69.31 11.3 66.7 152 566432
## Arizona 2212 4530 1.8 70.55 7.8 58.1 15 113417
## Arkansas 2110 3378 1.9 70.66 10.1 39.9 65 51945
## California 21198 5114 1.1 71.71 10.3 62.6 20 156361
## Colorado 2541 4884 0.7 72.06 6.8 63.9 166 103766
lib(dplyr)
# ----------------------------------------------------------------------------
dat <- state.x77
dat <- as.data.frame(dat)
dat$State <- rownames(state.x77)
dat <- mutate(dat, Density = Population/Area)
set.seed(1)
d1 <- dat[sample(1:50),c('State','Population','Area','Density')]
d1[,c('State','Density')] %>% print
d1[,c('State','Density')] %>% lucid
R> d1[,c('State','Density')] %>% print
State Density
14 Indiana 0.1471867468
19 Maine 0.0342173351
28 Nevada 0.0053690542
43 Texas 0.0466822312
10 Georgia 0.0849103714
41 South Dakota 0.0089658350
42 Tennessee 0.1009727062
29 New Hampshire 0.0899523651
27 Nebraska 0.0201874926
3 Arizona 0.0195032491
9 Florida 0.1530227399
7 Connecticut 0.6375976964
44 Utah 0.0146535763
15 Iowa 0.0511431687
48 West Virginia 0.0747403407
18 Louisiana 0.0847095482
25 Missouri 0.0690919632
33 North Carolina 0.1115004713
13 Illinois 0.2008502547
34 North Dakota 0.0091955019
47 Washington 0.0534625207
39 Rhode Island 0.8875119161
49 Wisconsin 0.0842574912
4 Arkansas 0.0406198864
30 New Jersey 0.9750033240
46 Virginia 0.1252136752
1 Alabama 0.0712905261
40 South Carolina 0.0931679074
20 Maryland 0.4167424932
8 Delaware 0.2921291625
31 New Mexico 0.0094224624
12 Idaho 0.0098334482
23 Minnesota 0.0494520047
38 Pennsylvania 0.2637548370
50 Wyoming 0.0038681934
11 Hawaii 0.1350972763
36 Oklahoma 0.0394725364
2 Alaska 0.0006443845
35 Ohio 0.2619890177
5 California 0.1355708904
16 Kansas 0.0278772910
6 Colorado 0.0244877898
26 Montana 0.0051240839
17 Kentucky 0.0854224464
21 Massachusetts 0.7429082545
22 Michigan 0.1603569354
24 Mississippi 0.0494967862
32 New York 0.3779139052
45 Vermont 0.0509334197
37 Oregon 0.0237461532
R> d1[,c('State','Density')] %>% lucid
State Density
14 Indiana 0.147
19 Maine 0.0342
28 Nevada 0.00537
43 Texas 0.0467
10 Georgia 0.0849
41 South Dakota 0.00897
42 Tennessee 0.101
29 New Hampshire 0.09
27 Nebraska 0.0202
3 Arizona 0.0195
9 Florida 0.153
7 Connecticut 0.638
44 Utah 0.0147
15 Iowa 0.0511
48 West Virginia 0.0747
18 Louisiana 0.0847
25 Missouri 0.0691
33 North Carolina 0.112
13 Illinois 0.201
34 North Dakota 0.0092
47 Washington 0.0535
39 Rhode Island 0.888
49 Wisconsin 0.0843
4 Arkansas 0.0406
30 New Jersey 0.975
46 Virginia 0.125
1 Alabama 0.0713
40 South Carolina 0.0932
20 Maryland 0.417
8 Delaware 0.292
31 New Mexico 0.00942
12 Idaho 0.00983
23 Minnesota 0.0495
38 Pennsylvania 0.264
50 Wyoming 0.00387
11 Hawaii 0.135
36 Oklahoma 0.0395
2 Alaska 0.000644
35 Ohio 0.262
5 California 0.136
16 Kansas 0.0279
6 Colorado 0.0245
26 Montana 0.00512
17 Kentucky 0.0854
21 Massachusetts 0.743
22 Michigan 0.16
24 Mississippi 0.0495
32 New York 0.378
45 Vermont 0.0509
37 Oregon 0.0237
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.