Description Usage Arguments Details Value Author(s) Examples
Interfaces to robustbase
functions that can be used
in a pipeline implemented by magrittr
.
1 2 3 4 5 | ntbt_adjbox(data, ...)
ntbt_glmrob(data, ...)
ntbt_lmrob(data, ...)
ntbt_ltsReg(data, ...)
ntbt_nlrob(data, ...)
|
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Interfaces call their corresponding interfaced function.
Object returned by interfaced function.
Roberto Bertolusso
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 | ## Not run:
library(intubate)
library(magrittr)
library(robustbase)
## ntbt_adjbox: Plot an Adjusted Boxplot for Skew Distributions
## Original function to interface
adjbox(len ~ dose, data = ToothGrowth)
## The interface puts data as first parameter
ntbt_adjbox(ToothGrowth, len ~ dose)
## so it can be used easily in a pipeline.
ToothGrowth %>%
ntbt_adjbox(len ~ dose)
## ntbt_glmrob: Robust Fitting of Generalized Linear Models
data(carrots)
## Original function to interface
glmrob(cbind(success, total-success) ~ logdose + block,
family = binomial, data = carrots, method= "Mqle",
control= glmrobMqle.control(tcc=1.2))
## The interface puts data as first parameter
ntbt_glmrob(carrots, cbind(success, total-success) ~ logdose + block,
family = binomial, method= "Mqle",
control= glmrobMqle.control(tcc=1.2))
## so it can be used easily in a pipeline.
carrots %>%
ntbt_glmrob(cbind(success, total-success) ~ logdose + block,
family = binomial, method= "Mqle",
control= glmrobMqle.control(tcc=1.2))
## ntbt_lmrob: MM-type Estimators for Linear Regression
data(coleman)
## Original function to interface
set.seed(0)
lmrob(Y ~ ., data = coleman, setting = "KS2011")
## The interface puts data as first parameter
ntbt_lmrob(coleman, Y ~ ., setting = "KS2011")
## so it can be used easily in a pipeline.
coleman %>%
ntbt_lmrob(Y ~ ., setting = "KS2011")
## ntbt_ltsReg: Least Trimmed Squares Robust (High Breakdown) Regression
data(stackloss)
## Original function to interface
ltsReg(stack.loss ~ ., data = stackloss)
## The interface puts data as first parameter
ntbt_ltsReg(stackloss, stack.loss ~ .)
## so it can be used easily in a pipeline.
stackloss %>%
ntbt_ltsReg(stack.loss ~ .)
## ntbt_nlrob: Robust Fitting of Nonlinear Regression Models
DNase1 <- DNase[ DNase$Run == 1, ]
## Original function to interface
nlrob(density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
data = DNase1, trace = TRUE,
start = list( Asym = 3, xmid = 0, scal = 1 ))
## The interface puts data as first parameter
ntbt_nlrob(DNase1, density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
trace = TRUE,
start = list( Asym = 3, xmid = 0, scal = 1 ))
## so it can be used easily in a pipeline.
DNase1 %>%
ntbt_nlrob(density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
trace = TRUE,
start = list( Asym = 3, xmid = 0, scal = 1 ))
## End(Not run)
|
$stats
[,1] [,2] [,3]
[1,] 4.20 13.60 18.50
[2,] 7.15 16.00 23.45
[3,] 9.85 19.25 25.95
[4,] 13.00 23.45 28.35
[5,] 21.50 27.30 33.90
$n
[1] 20 20 20
$conf
[,1] [,2] [,3]
[1,] 7.783202 16.61792 24.21884
[2,] 11.916798 21.88208 27.68116
$fence
[,1] [,2] [,3]
[1,] 2.461836 9.006864 16.1
[2,] 27.042045 39.332877 35.7
$out
numeric(0)
$group
numeric(0)
$names
[1] "0.5" "1" "2"
$stats
[,1] [,2] [,3]
[1,] 4.20 13.60 18.50
[2,] 7.15 16.00 23.45
[3,] 9.85 19.25 25.95
[4,] 13.00 23.45 28.35
[5,] 21.50 27.30 33.90
$n
[1] 20 20 20
$conf
[,1] [,2] [,3]
[1,] 7.783202 16.61792 24.21884
[2,] 11.916798 21.88208 27.68116
$fence
[,1] [,2] [,3]
[1,] 2.461836 9.006864 16.1
[2,] 27.042045 39.332877 35.7
$out
numeric(0)
$group
numeric(0)
$names
[1] "0.5" "1" "2"
Call: glmrob(formula = cbind(success, total - success) ~ logdose + block, family = binomial, data = carrots, method = "Mqle", control = glmrobMqle.control(tcc = 1.2))
Coefficients:
(Intercept) logdose blockB2 blockB3
2.3883 -2.0491 0.2351 -0.4496
Number of observations: 24
Fitted by method 'Mqle'
Call: glmrob(formula = cbind(success, total - success) ~ logdose + block, family = binomial, data = carrots, method = "Mqle", control = glmrobMqle.control(tcc = 1.2))
Coefficients:
(Intercept) logdose blockB2 blockB3
2.3883 -2.0491 0.2351 -0.4496
Number of observations: 24
Fitted by method 'Mqle'
Call: glmrob(formula = cbind(success, total - success) ~ logdose + block, family = binomial, data = ., method = "Mqle", control = glmrobMqle.control(tcc = 1.2))
Coefficients:
(Intercept) logdose blockB2 blockB3
2.3883 -2.0491 0.2351 -0.4496
Number of observations: 24
Fitted by method 'Mqle'
Call:
lmrob(formula = Y ~ ., data = coleman, setting = "KS2011")
\--> method = "SMDM"
Coefficients:
(Intercept) salaryP fatherWc sstatus teacherSc motherLev
30.43635 -1.67840 0.08504 0.66706 1.17113 -4.13766
Call:
lmrob(formula = Y ~ ., data = coleman, setting = "KS2011")
\--> method = "SMDM"
Coefficients:
(Intercept) salaryP fatherWc sstatus teacherSc motherLev
30.43635 -1.67840 0.08504 0.66706 1.17113 -4.13766
Call:
lmrob(formula = Y ~ ., data = ., setting = "KS2011")
\--> method = "SMDM"
Coefficients:
(Intercept) salaryP fatherWc sstatus teacherSc motherLev
30.43635 -1.67840 0.08504 0.66706 1.17113 -4.13766
Call:
ltsReg.formula(formula = stack.loss ~ ., data = stackloss)
Coefficients:
Intercept Air.Flow Water.Temp Acid.Conc.
-37.65246 0.79769 0.57734 -0.06706
Scale estimate 1.922
Call:
ltsReg.formula(formula = stack.loss ~ ., data = stackloss)
Coefficients:
Intercept Air.Flow Water.Temp Acid.Conc.
-37.65246 0.79769 0.57734 -0.06706
Scale estimate 1.922
Call:
ltsReg.formula(formula = stack.loss ~ ., data = .)
Coefficients:
Intercept Air.Flow Water.Temp Acid.Conc.
-37.65246 0.79769 0.57734 -0.06706
Scale estimate 1.922
robust iteration 1
14.32279 : 3 0 1
0.4542698 : 2.1152456 0.8410193 1.2000640
0.05869602 : 2.446376 1.747516 1.189515
0.005663523 : 2.294087 1.412198 1.020463
0.004791528 : 2.341429 1.479688 1.040758
0.004789569 : 2.345135 1.483047 1.041439
0.004789569 : 2.345179 1.483089 1.041454
--> irls.delta(previous, resid) = 0.999803 -- *not* converged
robust iteration 2
0.003971483 : 2.345179 1.483089 1.041454
0.003954569 : 2.356445 1.495544 1.043788
0.003954564 : 2.356586 1.495650 1.043815
--> irls.delta(previous, resid) = 0.0614627 -- *not* converged
robust iteration 3
0.003934724 : 2.356586 1.495650 1.043815
0.00393411 : 2.358633 1.498205 1.044647
0.00393411 : 2.358657 1.498229 1.044655
--> irls.delta(previous, resid) = 0.0121515 -- *not* converged
robust iteration 4
0.003930685 : 2.358657 1.498229 1.044655
0.00393062 : 2.359307 1.499046 1.044928
0.00393062 : 2.359314 1.499053 1.044931
--> irls.delta(previous, resid) = 0.00395064 -- *not* converged
robust iteration 5
0.00392958 : 2.359314 1.499053 1.044931
0.003929573 : 2.359525 1.499320 1.045020
--> irls.delta(previous, resid) = 0.00128452 -- *not* converged
robust iteration 6
0.003929244 : 2.359525 1.499320 1.045020
0.003929244 : 2.359596 1.499409 1.045050
--> irls.delta(previous, resid) = 0.000422973 -- *not* converged
robust iteration 7
0.003929132 : 2.359596 1.499409 1.045050
0.003929132 : 2.359620 1.499438 1.045060
--> irls.delta(previous, resid) = 0.000139345 -- *not* converged
robust iteration 8
0.003929095 : 2.359620 1.499438 1.045060
0.003929095 : 2.359628 1.499448 1.045063
--> irls.delta(previous, resid) = 4.5846e-05 -- *not* converged
robust iteration 9
0.003929083 : 2.359628 1.499448 1.045063
0.003929083 : 2.359630 1.499451 1.045064
--> irls.delta(previous, resid) = 1.51503e-05 -- *not* converged
robust iteration 10
0.003929079 : 2.359630 1.499451 1.045064
Robustly fitted nonlinear regression model
model: density ~ Asym/(1 + exp((xmid - log(conc))/scal))
data: DNase1
Asym xmid scal
2.359630 1.499451 1.045064
status: converged
robust iteration 1
14.32279 : 3 0 1
0.4542698 : 2.1152456 0.8410193 1.2000640
0.05869602 : 2.446376 1.747516 1.189515
0.005663523 : 2.294087 1.412198 1.020463
0.004791528 : 2.341429 1.479688 1.040758
0.004789569 : 2.345135 1.483047 1.041439
0.004789569 : 2.345179 1.483089 1.041454
--> irls.delta(previous, resid) = 0.999803 -- *not* converged
robust iteration 2
0.003971483 : 2.345179 1.483089 1.041454
0.003954569 : 2.356445 1.495544 1.043788
0.003954564 : 2.356586 1.495650 1.043815
--> irls.delta(previous, resid) = 0.0614627 -- *not* converged
robust iteration 3
0.003934724 : 2.356586 1.495650 1.043815
0.00393411 : 2.358633 1.498205 1.044647
0.00393411 : 2.358657 1.498229 1.044655
--> irls.delta(previous, resid) = 0.0121515 -- *not* converged
robust iteration 4
0.003930685 : 2.358657 1.498229 1.044655
0.00393062 : 2.359307 1.499046 1.044928
0.00393062 : 2.359314 1.499053 1.044931
--> irls.delta(previous, resid) = 0.00395064 -- *not* converged
robust iteration 5
0.00392958 : 2.359314 1.499053 1.044931
0.003929573 : 2.359525 1.499320 1.045020
--> irls.delta(previous, resid) = 0.00128452 -- *not* converged
robust iteration 6
0.003929244 : 2.359525 1.499320 1.045020
0.003929244 : 2.359596 1.499409 1.045050
--> irls.delta(previous, resid) = 0.000422973 -- *not* converged
robust iteration 7
0.003929132 : 2.359596 1.499409 1.045050
0.003929132 : 2.359620 1.499438 1.045060
--> irls.delta(previous, resid) = 0.000139375 -- *not* converged
robust iteration 8
0.003929095 : 2.359620 1.499438 1.045060
0.003929095 : 2.359628 1.499448 1.045063
--> irls.delta(previous, resid) = 4.58912e-05 -- *not* converged
robust iteration 9
0.003929083 : 2.359628 1.499448 1.045063
0.003929083 : 2.359630 1.499451 1.045064
--> irls.delta(previous, resid) = 1.50981e-05 -- *not* converged
robust iteration 10
0.003929079 : 2.359630 1.499451 1.045064
Robustly fitted nonlinear regression model
model: density ~ Asym/(1 + exp((xmid - log(conc))/scal))
data: DNase1
Asym xmid scal
2.359630 1.499451 1.045064
status: converged
robust iteration 1
14.32279 : 3 0 1
0.4542698 : 2.1152456 0.8410193 1.2000640
0.05869602 : 2.446376 1.747516 1.189515
0.005663523 : 2.294087 1.412198 1.020463
0.004791528 : 2.341429 1.479688 1.040758
0.004789569 : 2.345135 1.483047 1.041439
0.004789569 : 2.345179 1.483089 1.041454
--> irls.delta(previous, resid) = 0.999803 -- *not* converged
robust iteration 2
0.003971483 : 2.345179 1.483089 1.041454
0.003954569 : 2.356445 1.495544 1.043788
0.003954564 : 2.356586 1.495650 1.043815
--> irls.delta(previous, resid) = 0.0614627 -- *not* converged
robust iteration 3
0.003934724 : 2.356586 1.495650 1.043815
0.00393411 : 2.358633 1.498205 1.044647
0.00393411 : 2.358657 1.498229 1.044655
--> irls.delta(previous, resid) = 0.0121515 -- *not* converged
robust iteration 4
0.003930685 : 2.358657 1.498229 1.044655
0.00393062 : 2.359307 1.499046 1.044928
0.00393062 : 2.359314 1.499053 1.044931
--> irls.delta(previous, resid) = 0.00395064 -- *not* converged
robust iteration 5
0.00392958 : 2.359314 1.499053 1.044931
0.003929573 : 2.359525 1.499320 1.045020
--> irls.delta(previous, resid) = 0.00128452 -- *not* converged
robust iteration 6
0.003929244 : 2.359525 1.499320 1.045020
0.003929244 : 2.359596 1.499409 1.045050
--> irls.delta(previous, resid) = 0.000422973 -- *not* converged
robust iteration 7
0.003929132 : 2.359596 1.499409 1.045050
0.003929132 : 2.359620 1.499438 1.045060
--> irls.delta(previous, resid) = 0.000139375 -- *not* converged
robust iteration 8
0.003929095 : 2.359620 1.499438 1.045060
0.003929095 : 2.359628 1.499448 1.045063
--> irls.delta(previous, resid) = 4.58912e-05 -- *not* converged
robust iteration 9
0.003929083 : 2.359628 1.499448 1.045063
0.003929083 : 2.359630 1.499451 1.045064
--> irls.delta(previous, resid) = 1.50981e-05 -- *not* converged
robust iteration 10
0.003929079 : 2.359630 1.499451 1.045064
Robustly fitted nonlinear regression model
model: density ~ Asym/(1 + exp((xmid - log(conc))/scal))
data: .
Asym xmid scal
2.359630 1.499451 1.045064
status: converged
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