pava: isotonic regression
In twostageTE: Two-Stage Threshold Estimation
Description
Usage
Arguments
Value
Author(s)
References
Examples
Description
This is an internal function not meant to be called directly.
Wrapper for gpava in package isotone to apply the pava algorithm for isotonic regression
Usage
1
Arguments
explanatory
Explanatory sample points
response
Observed responses at the explanatory sample points
X_0
can ignore
Y_0
can ignore
w
weights if given repeated observations at same explanatory point
Value
return(list(x = explanatory, y = response_fit, response_obs = response))
List with
x
Explanatory sample points
y
estimated isotonic regression values
response_obs
Observed responses at the explanatory sample points
Author(s)
Shawn Mankad
References
de Leeuw J, Hornik K, Mair P (2009). 'Isotone Optimization in R: Pool-Adjacent-Violators
Algorithm (PAVA) and Active Set Methods.' Journal of Statistical Software, 32(5), 1-24.
ISSN 1548-7660. URL http://www.jstatsoft.org/v32/i05.
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
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76
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84
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86
87
88
89
X=runif(25, 0,1)
Y=X^2+rnorm(n=length(X), sd=0.1)
pava(X, Y, 0.25, 0.5)
## The function is currently defined as
function (explanatory, response, X_0 = NA, Y_0 = NA, w = NA)
{
require(isotone)
if (is.na(w))
w = rep(1, length(explanatory))
ind = order(explanatory, decreasing = FALSE)
if (sum(diff(ind) < 0) != 0) {
explanatory = explanatory[ind]
response = response[ind]
}
if (is.na(X_0) && is.na(Y_0)) {
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (is.na(X_0) || is.na(Y_0)) {
warning("Only X_0 or only Y_0 was supplied. Please check arguments.")
}
else {
n = length(explanatory)
if (sum(response < Y_0) == n && sum(explanatory < X_0) ==
n) {
warning("Warning: X_0 and Y_0 are outside observed region")
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (sum(response < Y_0) == n && sum(explanatory <
X_0) == 0) {
warning("Warning: X_0 and Y_0 are outside observed region")
return(list(x = explanatory, y = rep(Y_0, n), y_compressed = rep(Y_0,
n)))
}
else if (sum(response < Y_0) == n) {
warning("Warning: Y_0 is outside observed region")
n2 = n - sum(explanatory < X_0)
y1 = response[explanatory < X_0]
x1 = explanatory[explanatory < X_0]
fit = gpava(x1, y1)
response_fit = c(sapply(fit$x, min, Y_0), rep(Y_0,
n2))
}
else if (sum(response >= Y_0) == n && sum(explanatory <
X_0) == n) {
warning("Warning: X_0 and Y_0 are outside observed region")
return(list(x = explanatory, y = rep(Y_0, n), y_compressed = rep(Y_0,
n)))
}
else if (sum(response >= Y_0) == n && sum(explanatory <
X_0) == 0) {
warning("Warning: X_0 and Y_0 are outside observed region")
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (sum(response >= Y_0) == n) {
warning("Warning: Y_0 is outside observed region")
n2 = n - sum(explanatory > X_0)
y1 = response[explanatory > X_0]
x1 = explanatory[explanatory > X_0]
fit = gpava(x1, y1)
response_fit = c(rep(Y_0, n2), sapply(fit$x, max,
Y_0))
}
else if (sum(explanatory < X_0) == n) {
warning("Warning: X_0 is outside observed region")
fit = gpava(explanatory, response)
response_fit = sapply(fit$x, min, Y_0)
}
else if (sum(explanatory < X_0) == 0) {
warning("Warning: X_0 is outside observed region")
fit = gpava(explanatory, response)
response_fit = sapply(fit$x, max, Y_0)
}
else {
y1 = response[explanatory < X_0]
x1 = explanatory[explanatory < X_0]
y2 = response[explanatory >= X_0]
x2 = explanatory[explanatory >= X_0]
fit1 = gpava(x1, y1)
fit2 = gpava(x2, y2)
response_fit = c(sapply(fit1$x, min, Y_0), sapply(fit2$x,
max, Y_0))
}
}
return(list(x = explanatory, y = response_fit, response_obs = response))
}
Example output
Loading required package: isotone
$x
[1] 0.09837523 0.11732857 0.16941527 0.26140676 0.31938000 0.33134820
[7] 0.36670614 0.38522139 0.42364887 0.43459143 0.44945278 0.45937503
[13] 0.51292358 0.51798004 0.55556051 0.58622866 0.64202220 0.67656152
[19] 0.68063562 0.70394799 0.73561398 0.76137701 0.76450524 0.79436492
[25] 0.90731847
$y
[1] 0.09180087 0.09180087 0.09180087 0.50000000 0.50000000 0.50000000
[7] 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000
[13] 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000
[19] 0.50000000 0.50000000 0.50000000 0.50000000 0.62069256 0.68618993
[25] 0.86576155
$response_obs
[1] 0.12483306 0.12689096 0.02367858 0.27737997 0.20951037 0.15251393
[7] 0.06484818 -0.01302850 0.09690818 0.23783948 0.15379018 0.26563976
[13] 0.16622279 0.29769942 0.17718547 0.29525158 0.34381040 0.43833899
[19] 0.27000112 0.59416043 0.40025357 0.43564189 0.62069256 0.68618993
[25] 0.86576155
function (explanatory, response, X_0 = NA, Y_0 = NA, w = NA)
{
require(isotone)
if (is.na(w))
w = rep(1, length(explanatory))
ind = order(explanatory, decreasing = FALSE)
if (sum(diff(ind) < 0) != 0) {
explanatory = explanatory[ind]
response = response[ind]
}
if (is.na(X_0) && is.na(Y_0)) {
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (is.na(X_0) || is.na(Y_0)) {
warning("Only X_0 or only Y_0 was supplied. Please check arguments.")
}
else {
n = length(explanatory)
if (sum(response < Y_0) == n && sum(explanatory < X_0) ==
n) {
warning("Warning: X_0 and Y_0 are outside observed region")
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (sum(response < Y_0) == n && sum(explanatory <
X_0) == 0) {
warning("Warning: X_0 and Y_0 are outside observed region")
return(list(x = explanatory, y = rep(Y_0, n), y_compressed = rep(Y_0,
n)))
}
else if (sum(response < Y_0) == n) {
warning("Warning: Y_0 is outside observed region")
n2 = n - sum(explanatory < X_0)
y1 = response[explanatory < X_0]
x1 = explanatory[explanatory < X_0]
fit = gpava(x1, y1)
response_fit = c(sapply(fit$x, min, Y_0), rep(Y_0,
n2))
}
else if (sum(response >= Y_0) == n && sum(explanatory <
X_0) == n) {
warning("Warning: X_0 and Y_0 are outside observed region")
return(list(x = explanatory, y = rep(Y_0, n), y_compressed = rep(Y_0,
n)))
}
else if (sum(response >= Y_0) == n && sum(explanatory <
X_0) == 0) {
warning("Warning: X_0 and Y_0 are outside observed region")
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (sum(response >= Y_0) == n) {
warning("Warning: Y_0 is outside observed region")
n2 = n - sum(explanatory > X_0)
y1 = response[explanatory > X_0]
x1 = explanatory[explanatory > X_0]
fit = gpava(x1, y1)
response_fit = c(rep(Y_0, n2), sapply(fit$x, max,
Y_0))
}
else if (sum(explanatory < X_0) == n) {
warning("Warning: X_0 is outside observed region")
fit = gpava(explanatory, response)
response_fit = sapply(fit$x, min, Y_0)
}
else if (sum(explanatory < X_0) == 0) {
warning("Warning: X_0 is outside observed region")
fit = gpava(explanatory, response)
response_fit = sapply(fit$x, max, Y_0)
}
else {
y1 = response[explanatory < X_0]
x1 = explanatory[explanatory < X_0]
y2 = response[explanatory >= X_0]
x2 = explanatory[explanatory >= X_0]
fit1 = gpava(x1, y1)
fit2 = gpava(x2, y2)
response_fit = c(sapply(fit1$x, min, Y_0), sapply(fit2$x,
max, Y_0))
}
}
return(list(x = explanatory, y = response_fit, response_obs = response))
}
twostageTE documentation built on May 1, 2019, 9:18 p.m.
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Description Usage Arguments Value Author(s) References Examples
Description
This is an internal function not meant to be called directly. Wrapper for gpava in package isotone to apply the pava algorithm for isotonic regression
Usage
1 |
Arguments
explanatory |
Explanatory sample points |
response |
Observed responses at the explanatory sample points |
X_0 |
can ignore |
Y_0 |
can ignore |
w |
weights if given repeated observations at same explanatory point |
Value
return(list(x = explanatory, y = response_fit, response_obs = response)) List with
x |
Explanatory sample points |
y |
estimated isotonic regression values |
response_obs |
Observed responses at the explanatory sample points |
Author(s)
Shawn Mankad
References
de Leeuw J, Hornik K, Mair P (2009). 'Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods.' Journal of Statistical Software, 32(5), 1-24. ISSN 1548-7660. URL http://www.jstatsoft.org/v32/i05.
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 81 82 83 84 85 86 87 88 89 | X=runif(25, 0,1)
Y=X^2+rnorm(n=length(X), sd=0.1)
pava(X, Y, 0.25, 0.5)
## The function is currently defined as
function (explanatory, response, X_0 = NA, Y_0 = NA, w = NA)
{
require(isotone)
if (is.na(w))
w = rep(1, length(explanatory))
ind = order(explanatory, decreasing = FALSE)
if (sum(diff(ind) < 0) != 0) {
explanatory = explanatory[ind]
response = response[ind]
}
if (is.na(X_0) && is.na(Y_0)) {
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (is.na(X_0) || is.na(Y_0)) {
warning("Only X_0 or only Y_0 was supplied. Please check arguments.")
}
else {
n = length(explanatory)
if (sum(response < Y_0) == n && sum(explanatory < X_0) ==
n) {
warning("Warning: X_0 and Y_0 are outside observed region")
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (sum(response < Y_0) == n && sum(explanatory <
X_0) == 0) {
warning("Warning: X_0 and Y_0 are outside observed region")
return(list(x = explanatory, y = rep(Y_0, n), y_compressed = rep(Y_0,
n)))
}
else if (sum(response < Y_0) == n) {
warning("Warning: Y_0 is outside observed region")
n2 = n - sum(explanatory < X_0)
y1 = response[explanatory < X_0]
x1 = explanatory[explanatory < X_0]
fit = gpava(x1, y1)
response_fit = c(sapply(fit$x, min, Y_0), rep(Y_0,
n2))
}
else if (sum(response >= Y_0) == n && sum(explanatory <
X_0) == n) {
warning("Warning: X_0 and Y_0 are outside observed region")
return(list(x = explanatory, y = rep(Y_0, n), y_compressed = rep(Y_0,
n)))
}
else if (sum(response >= Y_0) == n && sum(explanatory <
X_0) == 0) {
warning("Warning: X_0 and Y_0 are outside observed region")
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (sum(response >= Y_0) == n) {
warning("Warning: Y_0 is outside observed region")
n2 = n - sum(explanatory > X_0)
y1 = response[explanatory > X_0]
x1 = explanatory[explanatory > X_0]
fit = gpava(x1, y1)
response_fit = c(rep(Y_0, n2), sapply(fit$x, max,
Y_0))
}
else if (sum(explanatory < X_0) == n) {
warning("Warning: X_0 is outside observed region")
fit = gpava(explanatory, response)
response_fit = sapply(fit$x, min, Y_0)
}
else if (sum(explanatory < X_0) == 0) {
warning("Warning: X_0 is outside observed region")
fit = gpava(explanatory, response)
response_fit = sapply(fit$x, max, Y_0)
}
else {
y1 = response[explanatory < X_0]
x1 = explanatory[explanatory < X_0]
y2 = response[explanatory >= X_0]
x2 = explanatory[explanatory >= X_0]
fit1 = gpava(x1, y1)
fit2 = gpava(x2, y2)
response_fit = c(sapply(fit1$x, min, Y_0), sapply(fit2$x,
max, Y_0))
}
}
return(list(x = explanatory, y = response_fit, response_obs = response))
}
|
Example output
Loading required package: isotone
$x
[1] 0.09837523 0.11732857 0.16941527 0.26140676 0.31938000 0.33134820
[7] 0.36670614 0.38522139 0.42364887 0.43459143 0.44945278 0.45937503
[13] 0.51292358 0.51798004 0.55556051 0.58622866 0.64202220 0.67656152
[19] 0.68063562 0.70394799 0.73561398 0.76137701 0.76450524 0.79436492
[25] 0.90731847
$y
[1] 0.09180087 0.09180087 0.09180087 0.50000000 0.50000000 0.50000000
[7] 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000
[13] 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000
[19] 0.50000000 0.50000000 0.50000000 0.50000000 0.62069256 0.68618993
[25] 0.86576155
$response_obs
[1] 0.12483306 0.12689096 0.02367858 0.27737997 0.20951037 0.15251393
[7] 0.06484818 -0.01302850 0.09690818 0.23783948 0.15379018 0.26563976
[13] 0.16622279 0.29769942 0.17718547 0.29525158 0.34381040 0.43833899
[19] 0.27000112 0.59416043 0.40025357 0.43564189 0.62069256 0.68618993
[25] 0.86576155
function (explanatory, response, X_0 = NA, Y_0 = NA, w = NA)
{
require(isotone)
if (is.na(w))
w = rep(1, length(explanatory))
ind = order(explanatory, decreasing = FALSE)
if (sum(diff(ind) < 0) != 0) {
explanatory = explanatory[ind]
response = response[ind]
}
if (is.na(X_0) && is.na(Y_0)) {
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (is.na(X_0) || is.na(Y_0)) {
warning("Only X_0 or only Y_0 was supplied. Please check arguments.")
}
else {
n = length(explanatory)
if (sum(response < Y_0) == n && sum(explanatory < X_0) ==
n) {
warning("Warning: X_0 and Y_0 are outside observed region")
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (sum(response < Y_0) == n && sum(explanatory <
X_0) == 0) {
warning("Warning: X_0 and Y_0 are outside observed region")
return(list(x = explanatory, y = rep(Y_0, n), y_compressed = rep(Y_0,
n)))
}
else if (sum(response < Y_0) == n) {
warning("Warning: Y_0 is outside observed region")
n2 = n - sum(explanatory < X_0)
y1 = response[explanatory < X_0]
x1 = explanatory[explanatory < X_0]
fit = gpava(x1, y1)
response_fit = c(sapply(fit$x, min, Y_0), rep(Y_0,
n2))
}
else if (sum(response >= Y_0) == n && sum(explanatory <
X_0) == n) {
warning("Warning: X_0 and Y_0 are outside observed region")
return(list(x = explanatory, y = rep(Y_0, n), y_compressed = rep(Y_0,
n)))
}
else if (sum(response >= Y_0) == n && sum(explanatory <
X_0) == 0) {
warning("Warning: X_0 and Y_0 are outside observed region")
fit = gpava(explanatory, response)
response_fit = fit$x
}
else if (sum(response >= Y_0) == n) {
warning("Warning: Y_0 is outside observed region")
n2 = n - sum(explanatory > X_0)
y1 = response[explanatory > X_0]
x1 = explanatory[explanatory > X_0]
fit = gpava(x1, y1)
response_fit = c(rep(Y_0, n2), sapply(fit$x, max,
Y_0))
}
else if (sum(explanatory < X_0) == n) {
warning("Warning: X_0 is outside observed region")
fit = gpava(explanatory, response)
response_fit = sapply(fit$x, min, Y_0)
}
else if (sum(explanatory < X_0) == 0) {
warning("Warning: X_0 is outside observed region")
fit = gpava(explanatory, response)
response_fit = sapply(fit$x, max, Y_0)
}
else {
y1 = response[explanatory < X_0]
x1 = explanatory[explanatory < X_0]
y2 = response[explanatory >= X_0]
x2 = explanatory[explanatory >= X_0]
fit1 = gpava(x1, y1)
fit2 = gpava(x2, y2)
response_fit = c(sapply(fit1$x, min, Y_0), sapply(fit2$x,
max, Y_0))
}
}
return(list(x = explanatory, y = response_fit, response_obs = response))
}
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