MASS: Interfaces for MASS package for data science pipelines.
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Interfaces to MASS functions that can be used
in a pipeline implemented by magrittr.
Usage
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ntbt_corresp(data, ...)
ntbt_glm.nb(data, ...)
ntbt_lda(data, ...)
ntbt_lm.gls(data, ...)
ntbt_lm.ridge(data, ...)
ntbt_loglm(data, ...)
ntbt_logtrans(data, ...)
ntbt_polr(data, ...)
ntbt_qda(data, ...)
ntbt_rlm(data, ...)
Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
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## Not run:
library(intubate)
library(magrittr)
library(MASS)
## corresp
## Original function to interface
corresp(~ Age + Eth, data = quine)
## The interface reverses the order of data and formula
ntbt_corresp(data = quine, ~ Age + Eth)
## so it can be used easily in a pipeline.
quine %>%
ntbt_corresp(~ Age + Eth)
## glm.nb
## Original function to interface
glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)
## The interface reverses the order of data and formula
ntbt_glm.nb(data = quine, Days ~ Sex/(Age + Eth*Lrn))
## so it can be used easily in a pipeline.
quine %>%
ntbt_glm.nb(Days ~ Sex/(Age + Eth*Lrn))
## lda
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
Sp = rep(c("s","c","v"), rep(50,3)))
## Original function to interface
lda(Sp ~ ., Iris)
## The interface reverses the order of data and formula
ntbt_lda(Iris, Sp ~ .)
## so it can be used easily in a pipeline.
Iris %>%
ntbt_lda(Sp ~ .)
stackloss %>%
ntbt_lda(stack.loss ~ .) %>%
summary()
## lm.gls
## Original function to interface
lm.gls(conc ~ uptake, CO2, W = diag(nrow(CO2)))
## The interface reverses the order of data and formula
ntbt_lm.gls(CO2, conc ~ uptake, W = diag(nrow(CO2)))
## so it can be used easily in a pipeline.
CO2 %>%
ntbt_lm.gls(conc ~ uptake, W = diag(nrow(CO2)))
## lm.ridge
## Original function to interface
lm.ridge(GNP.deflator ~ ., longley)
## The interface reverses the order of data and formula
ntbt_lm.ridge(longley, GNP.deflator ~ .)
## so it can be used easily in a pipeline.
longley %>%
ntbt_lm.ridge(GNP.deflator ~ .)
## loglm
## Original function to interface
xtCars93 <- xtabs(~ Type + Origin, Cars93)
loglm(~ Type + Origin, xtCars93)
## The interface reverses the order of data and formula
xtCars93 <- ntbt_xtabs(Cars93, ~ Type + Origin)
ntbt_loglm(xtCars93, ~ Type + Origin)
## so it can be used easily in a pipeline.
Cars93 %>%
ntbt_xtabs(~ Type + Origin) %>%
ntbt_loglm(~ Type + Origin)
## logtrans
## Original function to interface
logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine,
alpha = seq(0.75, 6.5, len=20))
## The interface reverses the order of data and formula
ntbt_logtrans(data = quine, Days ~ Age*Sex*Eth*Lrn,
alpha = seq(0.75, 6.5, len=20))
## so it can be used easily in a pipeline.
quine %>%
ntbt_logtrans(Days ~ Age*Sex*Eth*Lrn,
alpha = seq(0.75, 6.5, len=20))
## polr
op <- options(contrasts = c("contr.treatment", "contr.poly"))
## Original function to interface
polr(Sat ~ Infl + Type + Cont, housing)
## The interface reverses the order of data and formula
ntbt_polr(housing, Sat ~ Infl + Type + Cont)
## so it can be used easily in a pipeline.
housing %>%
ntbt_polr(Sat ~ Infl + Type + Cont)
options(op)
## qda
set.seed(123) ## make reproducible
tr <- sample(1:50, 25)
iris3df <- data.frame(cl = factor(c(rep("s",25), rep("c",25), rep("v",25))),
train = rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]))
## Original function to interface
qda(cl ~ ., iris3df)
## The interface reverses the order of data and formula
ntbt_qda(iris3df, cl ~ .)
## so it can be used easily in a pipeline.
iris3df %>%
ntbt_qda(cl ~ .)
## rlm
## Original function to interface
rlm(stack.loss ~ ., stackloss)
## The interface reverses the order of data and formula
ntbt_rlm(stackloss, stack.loss ~ .)
## so it can be used easily in a pipeline.
stackloss %>%
ntbt_rlm(stack.loss ~ .) %>%
summary()
stackloss %>%
ntbt_rlm(stack.loss ~ ., psi = psi.hampel, init = "lts") %>%
summary()
stackloss %>%
ntbt_rlm(stack.loss ~ ., psi = psi.bisquare) %>%
summary()
## End(Not run)
Example output
First canonical correlation(s): 0.05317534
Age scores:
F0 F1 F2 F3
-0.3344445 1.4246090 -1.0320002 -0.4612728
Eth scores:
A N
-1.0563816 0.9466276
First canonical correlation(s): 0.05317534
Age scores:
F0 F1 F2 F3
-0.3344445 1.4246090 -1.0320002 -0.4612728
Eth scores:
A N
-1.0563816 0.9466276
First canonical correlation(s): 0.05317534
Age scores:
F0 F1 F2 F3
-0.3344445 1.4246090 -1.0320002 -0.4612728
Eth scores:
A N
-1.0563816 0.9466276
Call: glm.nb(formula = Days ~ Sex/(Age + Eth * Lrn), data = quine,
init.theta = 1.597990733, link = log)
Coefficients:
(Intercept) SexM SexF:AgeF1 SexM:AgeF1
3.01919 -0.47541 -0.70887 -0.72373
SexF:AgeF2 SexM:AgeF2 SexF:AgeF3 SexM:AgeF3
-0.61486 0.62842 -0.34235 1.15084
SexF:EthN SexM:EthN SexF:LrnSL SexM:LrnSL
-0.07312 -0.67899 0.94358 0.23891
SexF:EthN:LrnSL SexM:EthN:LrnSL
-1.35849 0.76142
Degrees of Freedom: 145 Total (i.e. Null); 132 Residual
Null Deviance: 234.6
Residual Deviance: 167.6 AIC: 1093
Call: glm.nb(formula = Days ~ Sex/(Age + Eth * Lrn), init.theta = 1.597990733,
link = log)
Coefficients:
(Intercept) SexM SexF:AgeF1 SexM:AgeF1
3.01919 -0.47541 -0.70887 -0.72373
SexF:AgeF2 SexM:AgeF2 SexF:AgeF3 SexM:AgeF3
-0.61486 0.62842 -0.34235 1.15084
SexF:EthN SexM:EthN SexF:LrnSL SexM:LrnSL
-0.07312 -0.67899 0.94358 0.23891
SexF:EthN:LrnSL SexM:EthN:LrnSL
-1.35849 0.76142
Degrees of Freedom: 145 Total (i.e. Null); 132 Residual
Null Deviance: 234.6
Residual Deviance: 167.6 AIC: 1093
Call: glm.nb(formula = Days ~ Sex/(Age + Eth * Lrn), init.theta = 1.597990733,
link = log)
Coefficients:
(Intercept) SexM SexF:AgeF1 SexM:AgeF1
3.01919 -0.47541 -0.70887 -0.72373
SexF:AgeF2 SexM:AgeF2 SexF:AgeF3 SexM:AgeF3
-0.61486 0.62842 -0.34235 1.15084
SexF:EthN SexM:EthN SexF:LrnSL SexM:LrnSL
-0.07312 -0.67899 0.94358 0.23891
SexF:EthN:LrnSL SexM:EthN:LrnSL
-1.35849 0.76142
Degrees of Freedom: 145 Total (i.e. Null); 132 Residual
Null Deviance: 234.6
Residual Deviance: 167.6 AIC: 1093
Call:
lda(Sp ~ ., data = Iris)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
Sepal.L. Sepal.W. Petal.L. Petal.W.
c 5.936 2.770 4.260 1.326
s 5.006 3.428 1.462 0.246
v 6.588 2.974 5.552 2.026
Coefficients of linear discriminants:
LD1 LD2
Sepal.L. -0.8293776 0.02410215
Sepal.W. -1.5344731 2.16452123
Petal.L. 2.2012117 -0.93192121
Petal.W. 2.8104603 2.83918785
Proportion of trace:
LD1 LD2
0.9912 0.0088
Call:
lda(Sp ~ ., data = Iris)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
Sepal.L. Sepal.W. Petal.L. Petal.W.
c 5.936 2.770 4.260 1.326
s 5.006 3.428 1.462 0.246
v 6.588 2.974 5.552 2.026
Coefficients of linear discriminants:
LD1 LD2
Sepal.L. -0.8293776 0.02410215
Sepal.W. -1.5344731 2.16452123
Petal.L. 2.2012117 -0.93192121
Petal.W. 2.8104603 2.83918785
Proportion of trace:
LD1 LD2
0.9912 0.0088
Call:
lda(Sp ~ ., data = .)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
Sepal.L. Sepal.W. Petal.L. Petal.W.
c 5.936 2.770 4.260 1.326
s 5.006 3.428 1.462 0.246
v 6.588 2.974 5.552 2.026
Coefficients of linear discriminants:
LD1 LD2
Sepal.L. -0.8293776 0.02410215
Sepal.W. -1.5344731 2.16452123
Petal.L. 2.2012117 -0.93192121
Petal.W. 2.8104603 2.83918785
Proportion of trace:
LD1 LD2
0.9912 0.0088
Length Class Mode
prior 14 -none- numeric
counts 14 -none- numeric
means 42 -none- numeric
scaling 9 -none- numeric
lev 14 -none- character
svd 3 -none- numeric
N 1 -none- numeric
call 3 -none- call
terms 3 terms call
xlevels 0 -none- list
$coefficients
(Intercept) uptake
73.71000 13.27633
$residuals
[1] -191.131260 -302.310396 -285.726242 -217.589432 -42.364407 80.857911
[7] 399.219746 -159.268071 -261.153776 -316.261799 -278.660544 -112.728950
[13] 51.649987 338.148634 -193.786526 -328.863053 -358.746051 -282.643443
[19] -143.264506 18.459165 322.217039 -167.233868 -218.669524 -225.982763
[25] -183.070977 -5.190686 131.307960 412.496075 -102.179857 -261.153776
[31] -288.381508 -238.831558 -86.176292 103.427670 363.373658 -179.182564
[37] -177.512905 -329.538128 -175.105179 -90.159191 75.547379 376.649987
[43] -119.439085 -153.615513 -171.549814 -121.999864 16.051440 171.136947
[49] 454.980327 -138.025945 -190.789233 -229.965661 -145.897256 -3.863053
[55] 188.396174 508.085643 -128.732515 -156.270778 -166.239283 -94.119573
[61] 47.914629 228.225161 557.208059 -118.111452 -96.527299 -64.011551
[67] 25.367386 167.401589 306.555501 635.538400 -80.937731 -50.060148
[73] 12.991156 103.697726 260.335891 419.404296 735.110866 -119.439085
[79] -137.683918 -61.356285 38.643715 188.643715 350.367386 662.091057
$effects
(Intercept) uptake
-3986.8408546 1308.0368214 100.4113518 -49.5886482 -149.5886482
-225.6474908 -227.2931404 637.4708418 319.8827398 157.5888507
2.2946378 -90.2934642 -155.7638810 -196.5867059 558.0576489
229.8811212 83.4698707 -60.1770739 -151.7063333 -192.8233710
-226.2342979 495.5859372 167.4094095 -11.8259608 -155.4729054
-233.2372114 -240.4712868 -234.7050384 456.4087621 135.6441323
-53.1208211 -196.7677657 -284.0616548 -268.0011936 -242.1169364
414.0550592 121.8791789 -37.2381825 -177.7085994 -237.4725816
-238.3536016 -227.2931404 351.5833475 45.6425137 -121.9455883
-220.0623022 -363.4748477 -257.4127679 -274.9410562 340.9949217
67.8782077 -118.7690605 -270.8867457 -330.6507280 -324.1198499
-213.5281870 380.1720969 90.1139018 -54.1796637 -226.4153577
-280.8851271 -290.2368876 -265.4114730 308.1708020 0.1122832
-164.2992911 -305.8285506 -386.7693843 -378.1208211 -286.5883245
320.8769129 26.5833475 -139.9459120 -302.6520228 -352.8864220
-324.1198499 -259.0584176 369.5836712 49.8778840 -83.8272557
-253.9452645 -328.5330428 -356.9439697 -284.4706393
$rank
[1] 2
$fitted.values
[1] 286.1313 477.3104 535.7262 567.5894 542.3644 594.1421 600.7803 254.2681
[9] 436.1538 566.2618 628.6605 612.7289 623.3500 661.8514 288.7865 503.8631
[17] 608.7461 632.6434 643.2645 656.5408 677.7830 262.2339 393.6695 475.9828
[25] 533.0710 505.1907 543.6920 587.5039 197.1799 436.1538 538.3815 588.8316
[33] 586.1763 571.5723 636.6263 274.1826 352.5129 579.5381 525.1052 590.1592
[41] 599.4526 623.3500 214.4391 328.6155 421.5498 471.9999 483.9486 503.8631
[49] 545.0197 233.0259 365.7892 479.9657 495.8973 503.8631 486.6038 491.9144
[57] 223.7325 331.2708 416.2393 444.1196 452.0854 446.7748 442.7919 213.1115
[65] 271.5273 314.0116 324.6326 332.5984 368.4445 364.4616 175.9377 225.0601
[73] 237.0088 246.3023 239.6641 255.5957 264.8891 214.4391 312.6839 311.3563
[81] 311.3563 311.3563 324.6326 337.9089
$assign
NULL
$qr
$qr
(Intercept) uptake
[1,] -9.1651514 -2.494121e+02
[2,] 0.1091089 9.852398e+01
[3,] 0.1091089 8.722411e-02
[4,] 0.1091089 8.722411e-02
[5,] 0.1091089 8.722411e-02
[6,] 0.1091089 8.620913e-02
[7,] 0.1091089 1.613177e-01
[8,] 0.1091089 1.227485e-01
[9,] 0.1091089 1.298533e-01
[10,] 0.1091089 1.420331e-01
[11,] 0.1091089 1.369582e-01
[12,] 0.1091089 1.440631e-01
[13,] 0.1091089 1.531979e-01
[14,] 0.1091089 1.907522e-01
[15,] 0.1091089 4.662486e-02
[16,] 0.1091089 4.357992e-02
[17,] 0.1091089 7.098441e-02
[18,] 0.1091089 7.707430e-02
[19,] 0.1091089 8.519415e-02
[20,] 0.1091089 1.176736e-01
[21,] 0.1091089 1.623327e-01
[22,] 0.1091089 -1.325903e-02
[23,] 0.1091089 -1.630398e-02
[24,] 0.1091089 -2.036390e-02
[25,] 0.1091089 -1.427401e-02
[26,] 0.1091089 7.040592e-03
[27,] 0.1091089 7.199939e-02
[28,] 0.1091089 1.542129e-01
[29,] 0.1091089 -5.081334e-02
[30,] 0.1091089 -4.675342e-02
[31,] 0.1091089 -5.994817e-02
[32,] 0.1091089 -5.385828e-02
[33,] 0.1091089 -4.167851e-02
[34,] 0.1091089 4.560988e-02
[35,] 0.1091089 1.471080e-01
[36,] 0.1091089 -9.141259e-02
[37,] 0.1091089 -5.994817e-02
[38,] 0.1091089 -4.472345e-02
[39,] 0.1091089 -3.558862e-02
[40,] 0.1091089 2.980667e-03
[41,] 0.1091089 7.402936e-02
[42,] 0.1091089 1.613177e-01
[43,] 0.1091089 -1.512965e-01
[44,] 0.1091089 -1.330268e-01
[45,] 0.1091089 -1.259220e-01
[46,] 0.1091089 -7.618787e-02
[47,] 0.1091089 -1.178021e-01
[48,] 0.1091089 5.575969e-02
[49,] 0.1091089 1.156436e-01
[50,] 0.1091089 -1.614463e-01
[51,] 0.1091089 -1.117122e-01
[52,] 0.1091089 -1.228770e-01
[53,] 0.1091089 -1.249070e-01
[54,] 0.1091089 -8.633769e-02
[55,] 0.1091089 -8.184127e-03
[56,] 0.1091089 1.745125e-01
[57,] 0.1091089 -1.238920e-01
[58,] 0.1091089 -9.039761e-02
[59,] 0.1091089 -6.096315e-02
[60,] 0.1091089 -8.227776e-02
[61,] 0.1091089 -3.863357e-02
[62,] 0.1091089 2.429527e-02
[63,] 0.1091089 1.247784e-01
[64,] 0.1091089 -1.929107e-01
[65,] 0.1091089 -1.766710e-01
[66,] 0.1091089 -1.665212e-01
[67,] 0.1091089 -1.584014e-01
[68,] 0.1091089 -1.401317e-01
[69,] 0.1091089 -5.994817e-02
[70,] 0.1091089 1.044788e-01
[71,] 0.1091089 -1.807309e-01
[72,] 0.1091089 -1.512965e-01
[73,] 0.1091089 -1.431766e-01
[74,] 0.1091089 -1.553564e-01
[75,] 0.1091089 -1.076523e-01
[76,] 0.1091089 -8.184127e-03
[77,] 0.1091089 1.308683e-01
[78,] 0.1091089 -1.340418e-01
[79,] 0.1091089 -1.289669e-01
[80,] 0.1091089 -8.938263e-02
[81,] 0.1091089 -1.086673e-01
[82,] 0.1091089 -8.430772e-02
[83,] 0.1091089 -3.964855e-02
[84,] 0.1091089 1.065088e-01
$qraux
[1] 1.109109 1.077074
$pivot
[1] 1 2
$tol
[1] 1e-07
$rank
[1] 2
attr(,"class")
[1] "qr"
$df.residual
[1] 82
$terms
conc ~ uptake
attr(,"variables")
list(conc, uptake)
attr(,"factors")
uptake
conc 0
uptake 1
attr(,"term.labels")
[1] "uptake"
attr(,"order")
[1] 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
attr(,"predvars")
list(conc, uptake)
attr(,"dataClasses")
conc uptake
"numeric" "numeric"
$call
lm.gls(formula = conc ~ uptake, data = CO2, W = diag(nrow(CO2)))
$xlevels
named list()
attr(,"class")
[1] "lm.gls"
$coefficients
(Intercept) uptake
73.71000 13.27633
$residuals
[1] -191.131260 -302.310396 -285.726242 -217.589432 -42.364407 80.857911
[7] 399.219746 -159.268071 -261.153776 -316.261799 -278.660544 -112.728950
[13] 51.649987 338.148634 -193.786526 -328.863053 -358.746051 -282.643443
[19] -143.264506 18.459165 322.217039 -167.233868 -218.669524 -225.982763
[25] -183.070977 -5.190686 131.307960 412.496075 -102.179857 -261.153776
[31] -288.381508 -238.831558 -86.176292 103.427670 363.373658 -179.182564
[37] -177.512905 -329.538128 -175.105179 -90.159191 75.547379 376.649987
[43] -119.439085 -153.615513 -171.549814 -121.999864 16.051440 171.136947
[49] 454.980327 -138.025945 -190.789233 -229.965661 -145.897256 -3.863053
[55] 188.396174 508.085643 -128.732515 -156.270778 -166.239283 -94.119573
[61] 47.914629 228.225161 557.208059 -118.111452 -96.527299 -64.011551
[67] 25.367386 167.401589 306.555501 635.538400 -80.937731 -50.060148
[73] 12.991156 103.697726 260.335891 419.404296 735.110866 -119.439085
[79] -137.683918 -61.356285 38.643715 188.643715 350.367386 662.091057
$effects
(Intercept) uptake
-3986.8408546 1308.0368214 100.4113518 -49.5886482 -149.5886482
-225.6474908 -227.2931404 637.4708418 319.8827398 157.5888507
2.2946378 -90.2934642 -155.7638810 -196.5867059 558.0576489
229.8811212 83.4698707 -60.1770739 -151.7063333 -192.8233710
-226.2342979 495.5859372 167.4094095 -11.8259608 -155.4729054
-233.2372114 -240.4712868 -234.7050384 456.4087621 135.6441323
-53.1208211 -196.7677657 -284.0616548 -268.0011936 -242.1169364
414.0550592 121.8791789 -37.2381825 -177.7085994 -237.4725816
-238.3536016 -227.2931404 351.5833475 45.6425137 -121.9455883
-220.0623022 -363.4748477 -257.4127679 -274.9410562 340.9949217
67.8782077 -118.7690605 -270.8867457 -330.6507280 -324.1198499
-213.5281870 380.1720969 90.1139018 -54.1796637 -226.4153577
-280.8851271 -290.2368876 -265.4114730 308.1708020 0.1122832
-164.2992911 -305.8285506 -386.7693843 -378.1208211 -286.5883245
320.8769129 26.5833475 -139.9459120 -302.6520228 -352.8864220
-324.1198499 -259.0584176 369.5836712 49.8778840 -83.8272557
-253.9452645 -328.5330428 -356.9439697 -284.4706393
$rank
[1] 2
$fitted.values
[1] 286.1313 477.3104 535.7262 567.5894 542.3644 594.1421 600.7803 254.2681
[9] 436.1538 566.2618 628.6605 612.7289 623.3500 661.8514 288.7865 503.8631
[17] 608.7461 632.6434 643.2645 656.5408 677.7830 262.2339 393.6695 475.9828
[25] 533.0710 505.1907 543.6920 587.5039 197.1799 436.1538 538.3815 588.8316
[33] 586.1763 571.5723 636.6263 274.1826 352.5129 579.5381 525.1052 590.1592
[41] 599.4526 623.3500 214.4391 328.6155 421.5498 471.9999 483.9486 503.8631
[49] 545.0197 233.0259 365.7892 479.9657 495.8973 503.8631 486.6038 491.9144
[57] 223.7325 331.2708 416.2393 444.1196 452.0854 446.7748 442.7919 213.1115
[65] 271.5273 314.0116 324.6326 332.5984 368.4445 364.4616 175.9377 225.0601
[73] 237.0088 246.3023 239.6641 255.5957 264.8891 214.4391 312.6839 311.3563
[81] 311.3563 311.3563 324.6326 337.9089
$assign
NULL
$qr
$qr
(Intercept) uptake
[1,] -9.1651514 -2.494121e+02
[2,] 0.1091089 9.852398e+01
[3,] 0.1091089 8.722411e-02
[4,] 0.1091089 8.722411e-02
[5,] 0.1091089 8.722411e-02
[6,] 0.1091089 8.620913e-02
[7,] 0.1091089 1.613177e-01
[8,] 0.1091089 1.227485e-01
[9,] 0.1091089 1.298533e-01
[10,] 0.1091089 1.420331e-01
[11,] 0.1091089 1.369582e-01
[12,] 0.1091089 1.440631e-01
[13,] 0.1091089 1.531979e-01
[14,] 0.1091089 1.907522e-01
[15,] 0.1091089 4.662486e-02
[16,] 0.1091089 4.357992e-02
[17,] 0.1091089 7.098441e-02
[18,] 0.1091089 7.707430e-02
[19,] 0.1091089 8.519415e-02
[20,] 0.1091089 1.176736e-01
[21,] 0.1091089 1.623327e-01
[22,] 0.1091089 -1.325903e-02
[23,] 0.1091089 -1.630398e-02
[24,] 0.1091089 -2.036390e-02
[25,] 0.1091089 -1.427401e-02
[26,] 0.1091089 7.040592e-03
[27,] 0.1091089 7.199939e-02
[28,] 0.1091089 1.542129e-01
[29,] 0.1091089 -5.081334e-02
[30,] 0.1091089 -4.675342e-02
[31,] 0.1091089 -5.994817e-02
[32,] 0.1091089 -5.385828e-02
[33,] 0.1091089 -4.167851e-02
[34,] 0.1091089 4.560988e-02
[35,] 0.1091089 1.471080e-01
[36,] 0.1091089 -9.141259e-02
[37,] 0.1091089 -5.994817e-02
[38,] 0.1091089 -4.472345e-02
[39,] 0.1091089 -3.558862e-02
[40,] 0.1091089 2.980667e-03
[41,] 0.1091089 7.402936e-02
[42,] 0.1091089 1.613177e-01
[43,] 0.1091089 -1.512965e-01
[44,] 0.1091089 -1.330268e-01
[45,] 0.1091089 -1.259220e-01
[46,] 0.1091089 -7.618787e-02
[47,] 0.1091089 -1.178021e-01
[48,] 0.1091089 5.575969e-02
[49,] 0.1091089 1.156436e-01
[50,] 0.1091089 -1.614463e-01
[51,] 0.1091089 -1.117122e-01
[52,] 0.1091089 -1.228770e-01
[53,] 0.1091089 -1.249070e-01
[54,] 0.1091089 -8.633769e-02
[55,] 0.1091089 -8.184127e-03
[56,] 0.1091089 1.745125e-01
[57,] 0.1091089 -1.238920e-01
[58,] 0.1091089 -9.039761e-02
[59,] 0.1091089 -6.096315e-02
[60,] 0.1091089 -8.227776e-02
[61,] 0.1091089 -3.863357e-02
[62,] 0.1091089 2.429527e-02
[63,] 0.1091089 1.247784e-01
[64,] 0.1091089 -1.929107e-01
[65,] 0.1091089 -1.766710e-01
[66,] 0.1091089 -1.665212e-01
[67,] 0.1091089 -1.584014e-01
[68,] 0.1091089 -1.401317e-01
[69,] 0.1091089 -5.994817e-02
[70,] 0.1091089 1.044788e-01
[71,] 0.1091089 -1.807309e-01
[72,] 0.1091089 -1.512965e-01
[73,] 0.1091089 -1.431766e-01
[74,] 0.1091089 -1.553564e-01
[75,] 0.1091089 -1.076523e-01
[76,] 0.1091089 -8.184127e-03
[77,] 0.1091089 1.308683e-01
[78,] 0.1091089 -1.340418e-01
[79,] 0.1091089 -1.289669e-01
[80,] 0.1091089 -8.938263e-02
[81,] 0.1091089 -1.086673e-01
[82,] 0.1091089 -8.430772e-02
[83,] 0.1091089 -3.964855e-02
[84,] 0.1091089 1.065088e-01
$qraux
[1] 1.109109 1.077074
$pivot
[1] 1 2
$tol
[1] 1e-07
$rank
[1] 2
attr(,"class")
[1] "qr"
$df.residual
[1] 82
$terms
conc ~ uptake
attr(,"variables")
list(conc, uptake)
attr(,"factors")
uptake
conc 0
uptake 1
attr(,"term.labels")
[1] "uptake"
attr(,"order")
[1] 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: 0x470b3e0>
attr(,"predvars")
list(conc, uptake)
attr(,"dataClasses")
conc uptake
"numeric" "numeric"
$call
lm.gls(formula = conc ~ uptake, W = diag(nrow(CO2)))
$xlevels
named list()
attr(,"class")
[1] "lm.gls"
$coefficients
(Intercept) uptake
73.71000 13.27633
$residuals
[1] -191.131260 -302.310396 -285.726242 -217.589432 -42.364407 80.857911
[7] 399.219746 -159.268071 -261.153776 -316.261799 -278.660544 -112.728950
[13] 51.649987 338.148634 -193.786526 -328.863053 -358.746051 -282.643443
[19] -143.264506 18.459165 322.217039 -167.233868 -218.669524 -225.982763
[25] -183.070977 -5.190686 131.307960 412.496075 -102.179857 -261.153776
[31] -288.381508 -238.831558 -86.176292 103.427670 363.373658 -179.182564
[37] -177.512905 -329.538128 -175.105179 -90.159191 75.547379 376.649987
[43] -119.439085 -153.615513 -171.549814 -121.999864 16.051440 171.136947
[49] 454.980327 -138.025945 -190.789233 -229.965661 -145.897256 -3.863053
[55] 188.396174 508.085643 -128.732515 -156.270778 -166.239283 -94.119573
[61] 47.914629 228.225161 557.208059 -118.111452 -96.527299 -64.011551
[67] 25.367386 167.401589 306.555501 635.538400 -80.937731 -50.060148
[73] 12.991156 103.697726 260.335891 419.404296 735.110866 -119.439085
[79] -137.683918 -61.356285 38.643715 188.643715 350.367386 662.091057
$effects
(Intercept) uptake
-3986.8408546 1308.0368214 100.4113518 -49.5886482 -149.5886482
-225.6474908 -227.2931404 637.4708418 319.8827398 157.5888507
2.2946378 -90.2934642 -155.7638810 -196.5867059 558.0576489
229.8811212 83.4698707 -60.1770739 -151.7063333 -192.8233710
-226.2342979 495.5859372 167.4094095 -11.8259608 -155.4729054
-233.2372114 -240.4712868 -234.7050384 456.4087621 135.6441323
-53.1208211 -196.7677657 -284.0616548 -268.0011936 -242.1169364
414.0550592 121.8791789 -37.2381825 -177.7085994 -237.4725816
-238.3536016 -227.2931404 351.5833475 45.6425137 -121.9455883
-220.0623022 -363.4748477 -257.4127679 -274.9410562 340.9949217
67.8782077 -118.7690605 -270.8867457 -330.6507280 -324.1198499
-213.5281870 380.1720969 90.1139018 -54.1796637 -226.4153577
-280.8851271 -290.2368876 -265.4114730 308.1708020 0.1122832
-164.2992911 -305.8285506 -386.7693843 -378.1208211 -286.5883245
320.8769129 26.5833475 -139.9459120 -302.6520228 -352.8864220
-324.1198499 -259.0584176 369.5836712 49.8778840 -83.8272557
-253.9452645 -328.5330428 -356.9439697 -284.4706393
$rank
[1] 2
$fitted.values
[1] 286.1313 477.3104 535.7262 567.5894 542.3644 594.1421 600.7803 254.2681
[9] 436.1538 566.2618 628.6605 612.7289 623.3500 661.8514 288.7865 503.8631
[17] 608.7461 632.6434 643.2645 656.5408 677.7830 262.2339 393.6695 475.9828
[25] 533.0710 505.1907 543.6920 587.5039 197.1799 436.1538 538.3815 588.8316
[33] 586.1763 571.5723 636.6263 274.1826 352.5129 579.5381 525.1052 590.1592
[41] 599.4526 623.3500 214.4391 328.6155 421.5498 471.9999 483.9486 503.8631
[49] 545.0197 233.0259 365.7892 479.9657 495.8973 503.8631 486.6038 491.9144
[57] 223.7325 331.2708 416.2393 444.1196 452.0854 446.7748 442.7919 213.1115
[65] 271.5273 314.0116 324.6326 332.5984 368.4445 364.4616 175.9377 225.0601
[73] 237.0088 246.3023 239.6641 255.5957 264.8891 214.4391 312.6839 311.3563
[81] 311.3563 311.3563 324.6326 337.9089
$assign
NULL
$qr
$qr
(Intercept) uptake
[1,] -9.1651514 -2.494121e+02
[2,] 0.1091089 9.852398e+01
[3,] 0.1091089 8.722411e-02
[4,] 0.1091089 8.722411e-02
[5,] 0.1091089 8.722411e-02
[6,] 0.1091089 8.620913e-02
[7,] 0.1091089 1.613177e-01
[8,] 0.1091089 1.227485e-01
[9,] 0.1091089 1.298533e-01
[10,] 0.1091089 1.420331e-01
[11,] 0.1091089 1.369582e-01
[12,] 0.1091089 1.440631e-01
[13,] 0.1091089 1.531979e-01
[14,] 0.1091089 1.907522e-01
[15,] 0.1091089 4.662486e-02
[16,] 0.1091089 4.357992e-02
[17,] 0.1091089 7.098441e-02
[18,] 0.1091089 7.707430e-02
[19,] 0.1091089 8.519415e-02
[20,] 0.1091089 1.176736e-01
[21,] 0.1091089 1.623327e-01
[22,] 0.1091089 -1.325903e-02
[23,] 0.1091089 -1.630398e-02
[24,] 0.1091089 -2.036390e-02
[25,] 0.1091089 -1.427401e-02
[26,] 0.1091089 7.040592e-03
[27,] 0.1091089 7.199939e-02
[28,] 0.1091089 1.542129e-01
[29,] 0.1091089 -5.081334e-02
[30,] 0.1091089 -4.675342e-02
[31,] 0.1091089 -5.994817e-02
[32,] 0.1091089 -5.385828e-02
[33,] 0.1091089 -4.167851e-02
[34,] 0.1091089 4.560988e-02
[35,] 0.1091089 1.471080e-01
[36,] 0.1091089 -9.141259e-02
[37,] 0.1091089 -5.994817e-02
[38,] 0.1091089 -4.472345e-02
[39,] 0.1091089 -3.558862e-02
[40,] 0.1091089 2.980667e-03
[41,] 0.1091089 7.402936e-02
[42,] 0.1091089 1.613177e-01
[43,] 0.1091089 -1.512965e-01
[44,] 0.1091089 -1.330268e-01
[45,] 0.1091089 -1.259220e-01
[46,] 0.1091089 -7.618787e-02
[47,] 0.1091089 -1.178021e-01
[48,] 0.1091089 5.575969e-02
[49,] 0.1091089 1.156436e-01
[50,] 0.1091089 -1.614463e-01
[51,] 0.1091089 -1.117122e-01
[52,] 0.1091089 -1.228770e-01
[53,] 0.1091089 -1.249070e-01
[54,] 0.1091089 -8.633769e-02
[55,] 0.1091089 -8.184127e-03
[56,] 0.1091089 1.745125e-01
[57,] 0.1091089 -1.238920e-01
[58,] 0.1091089 -9.039761e-02
[59,] 0.1091089 -6.096315e-02
[60,] 0.1091089 -8.227776e-02
[61,] 0.1091089 -3.863357e-02
[62,] 0.1091089 2.429527e-02
[63,] 0.1091089 1.247784e-01
[64,] 0.1091089 -1.929107e-01
[65,] 0.1091089 -1.766710e-01
[66,] 0.1091089 -1.665212e-01
[67,] 0.1091089 -1.584014e-01
[68,] 0.1091089 -1.401317e-01
[69,] 0.1091089 -5.994817e-02
[70,] 0.1091089 1.044788e-01
[71,] 0.1091089 -1.807309e-01
[72,] 0.1091089 -1.512965e-01
[73,] 0.1091089 -1.431766e-01
[74,] 0.1091089 -1.553564e-01
[75,] 0.1091089 -1.076523e-01
[76,] 0.1091089 -8.184127e-03
[77,] 0.1091089 1.308683e-01
[78,] 0.1091089 -1.340418e-01
[79,] 0.1091089 -1.289669e-01
[80,] 0.1091089 -8.938263e-02
[81,] 0.1091089 -1.086673e-01
[82,] 0.1091089 -8.430772e-02
[83,] 0.1091089 -3.964855e-02
[84,] 0.1091089 1.065088e-01
$qraux
[1] 1.109109 1.077074
$pivot
[1] 1 2
$tol
[1] 1e-07
$rank
[1] 2
attr(,"class")
[1] "qr"
$df.residual
[1] 82
$terms
conc ~ uptake
attr(,"variables")
list(conc, uptake)
attr(,"factors")
uptake
conc 0
uptake 1
attr(,"term.labels")
[1] "uptake"
attr(,"order")
[1] 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: 0x47ab1e8>
attr(,"predvars")
list(conc, uptake)
attr(,"dataClasses")
conc uptake
"numeric" "numeric"
$call
lm.gls(formula = conc ~ uptake, W = diag(nrow(CO2)))
$xlevels
named list()
attr(,"class")
[1] "lm.gls"
GNP Unemployed Armed.Forces Population
2946.85636017 0.26352725 0.03648291 0.01116105 -1.73702984
Year Employed
-1.41879853 0.23128785
GNP Unemployed Armed.Forces Population
2946.85636017 0.26352725 0.03648291 0.01116105 -1.73702984
Year Employed
-1.41879853 0.23128785
GNP Unemployed Armed.Forces Population
2946.85636017 0.26352725 0.03648291 0.01116105 -1.73702984
Year Employed
-1.41879853 0.23128785
Call:
loglm(formula = ~Type + Origin, data = xtCars93)
Statistics:
X^2 df P(> X^2)
Likelihood Ratio 18.36179 5 0.00252554
Pearson 14.07985 5 0.01511005
Call:
loglm(formula = ~Type + Origin, data = xtCars93)
Statistics:
X^2 df P(> X^2)
Likelihood Ratio 18.36179 5 0.00252554
Pearson 14.07985 5 0.01511005
Call:
loglm(formula = ~Type + Origin, data = .)
Statistics:
X^2 df P(> X^2)
Likelihood Ratio 18.36179 5 0.00252554
Pearson 14.07985 5 0.01511005
$x
[1] 0.7500000 0.8080808 0.8661616 0.9242424 0.9823232 1.0404040 1.0984848
[8] 1.1565657 1.2146465 1.2727273 1.3308081 1.3888889 1.4469697 1.5050505
[15] 1.5631313 1.6212121 1.6792929 1.7373737 1.7954545 1.8535354 1.9116162
[22] 1.9696970 2.0277778 2.0858586 2.1439394 2.2020202 2.2601010 2.3181818
[29] 2.3762626 2.4343434 2.4924242 2.5505051 2.6085859 2.6666667 2.7247475
[36] 2.7828283 2.8409091 2.8989899 2.9570707 3.0151515 3.0732323 3.1313131
[43] 3.1893939 3.2474747 3.3055556 3.3636364 3.4217172 3.4797980 3.5378788
[50] 3.5959596 3.6540404 3.7121212 3.7702020 3.8282828 3.8863636 3.9444444
[57] 4.0025253 4.0606061 4.1186869 4.1767677 4.2348485 4.2929293 4.3510101
[64] 4.4090909 4.4671717 4.5252525 4.5833333 4.6414141 4.6994949 4.7575758
[71] 4.8156566 4.8737374 4.9318182 4.9898990 5.0479798 5.1060606 5.1641414
[78] 5.2222222 5.2803030 5.3383838 5.3964646 5.4545455 5.5126263 5.5707071
[85] 5.6287879 5.6868687 5.7449495 5.8030303 5.8611111 5.9191919 5.9772727
[92] 6.0353535 6.0934343 6.1515152 6.2095960 6.2676768 6.3257576 6.3838384
[99] 6.4419192 6.5000000
$y
[1] -686.8651 -686.3368 -685.8576 -685.4242 -685.0337 -684.6829 -684.3690
[8] -684.0884 -683.8376 -683.6132 -683.4116 -683.2294 -683.0643 -682.9154
[15] -682.7814 -682.6612 -682.5538 -682.4581 -682.3734 -682.2987 -682.2334
[22] -682.1765 -682.1274 -682.0855 -682.0504 -682.0217 -681.9990 -681.9817
[29] -681.9696 -681.9624 -681.9597 -681.9613 -681.9668 -681.9761 -681.9889
[36] -682.0050 -682.0243 -682.0464 -682.0713 -682.0989 -682.1289 -682.1612
[43] -682.1957 -682.2323 -682.2708 -682.3112 -682.3533 -682.3971 -682.4425
[50] -682.4893 -682.5375 -682.5870 -682.6378 -682.6898 -682.7429 -682.7971
[57] -682.8523 -682.9084 -682.9654 -683.0233 -683.0820 -683.1414 -683.2016
[64] -683.2624 -683.3239 -683.3860 -683.4487 -683.5119 -683.5756 -683.6398
[71] -683.7045 -683.7696 -683.8351 -683.9009 -683.9672 -684.0337 -684.1006
[78] -684.1677 -684.2352 -684.3029 -684.3708 -684.4389 -684.5073 -684.5758
[85] -684.6445 -684.7134 -684.7824 -684.8515 -684.9208 -684.9901 -685.0596
[92] -685.1291 -685.1988 -685.2685 -685.3382 -685.4080 -685.4779 -685.5477
[99] -685.6176 -685.6875
$x
[1] 0.7500000 0.8080808 0.8661616 0.9242424 0.9823232 1.0404040 1.0984848
[8] 1.1565657 1.2146465 1.2727273 1.3308081 1.3888889 1.4469697 1.5050505
[15] 1.5631313 1.6212121 1.6792929 1.7373737 1.7954545 1.8535354 1.9116162
[22] 1.9696970 2.0277778 2.0858586 2.1439394 2.2020202 2.2601010 2.3181818
[29] 2.3762626 2.4343434 2.4924242 2.5505051 2.6085859 2.6666667 2.7247475
[36] 2.7828283 2.8409091 2.8989899 2.9570707 3.0151515 3.0732323 3.1313131
[43] 3.1893939 3.2474747 3.3055556 3.3636364 3.4217172 3.4797980 3.5378788
[50] 3.5959596 3.6540404 3.7121212 3.7702020 3.8282828 3.8863636 3.9444444
[57] 4.0025253 4.0606061 4.1186869 4.1767677 4.2348485 4.2929293 4.3510101
[64] 4.4090909 4.4671717 4.5252525 4.5833333 4.6414141 4.6994949 4.7575758
[71] 4.8156566 4.8737374 4.9318182 4.9898990 5.0479798 5.1060606 5.1641414
[78] 5.2222222 5.2803030 5.3383838 5.3964646 5.4545455 5.5126263 5.5707071
[85] 5.6287879 5.6868687 5.7449495 5.8030303 5.8611111 5.9191919 5.9772727
[92] 6.0353535 6.0934343 6.1515152 6.2095960 6.2676768 6.3257576 6.3838384
[99] 6.4419192 6.5000000
$y
[1] -686.8651 -686.3368 -685.8576 -685.4242 -685.0337 -684.6829 -684.3690
[8] -684.0884 -683.8376 -683.6132 -683.4116 -683.2294 -683.0643 -682.9154
[15] -682.7814 -682.6612 -682.5538 -682.4581 -682.3734 -682.2987 -682.2334
[22] -682.1765 -682.1274 -682.0855 -682.0504 -682.0217 -681.9990 -681.9817
[29] -681.9696 -681.9624 -681.9597 -681.9613 -681.9668 -681.9761 -681.9889
[36] -682.0050 -682.0243 -682.0464 -682.0713 -682.0989 -682.1289 -682.1612
[43] -682.1957 -682.2323 -682.2708 -682.3112 -682.3533 -682.3971 -682.4425
[50] -682.4893 -682.5375 -682.5870 -682.6378 -682.6898 -682.7429 -682.7971
[57] -682.8523 -682.9084 -682.9654 -683.0233 -683.0820 -683.1414 -683.2016
[64] -683.2624 -683.3239 -683.3860 -683.4487 -683.5119 -683.5756 -683.6398
[71] -683.7045 -683.7696 -683.8351 -683.9009 -683.9672 -684.0337 -684.1006
[78] -684.1677 -684.2352 -684.3029 -684.3708 -684.4389 -684.5073 -684.5758
[85] -684.6445 -684.7134 -684.7824 -684.8515 -684.9208 -684.9901 -685.0596
[92] -685.1291 -685.1988 -685.2685 -685.3382 -685.4080 -685.4779 -685.5477
[99] -685.6176 -685.6875
Call:
polr(formula = Sat ~ Infl + Type + Cont, data = housing)
Coefficients:
InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace
1.438398e-12 1.442159e-12 1.081215e-12 1.080646e-12 1.077125e-12
ContHigh
2.158385e-12
Intercepts:
Low|Medium Medium|High
-0.6931472 0.6931472
Residual Deviance: 158.2002
AIC: 174.2002
Call:
polr(formula = Sat ~ Infl + Type + Cont)
Coefficients:
InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace
1.438398e-12 1.442159e-12 1.081215e-12 1.080646e-12 1.077125e-12
ContHigh
2.158385e-12
Intercepts:
Low|Medium Medium|High
-0.6931472 0.6931472
Residual Deviance: 158.2002
AIC: 174.2002
Call:
polr(formula = Sat ~ Infl + Type + Cont)
Coefficients:
InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace
1.438398e-12 1.442159e-12 1.081215e-12 1.080646e-12 1.077125e-12
ContHigh
2.158385e-12
Intercepts:
Low|Medium Medium|High
-0.6931472 0.6931472
Residual Deviance: 158.2002
AIC: 174.2002
Call:
qda(cl ~ ., data = iris3df)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
train.Sepal.L. train.Sepal.W. train.Petal.L. train.Petal.W.
c 5.892 2.708 4.140 1.276
s 4.996 3.324 1.484 0.272
v 6.628 2.980 5.504 2.000
Call:
qda(cl ~ ., data = iris3df)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
train.Sepal.L. train.Sepal.W. train.Petal.L. train.Petal.W.
c 5.892 2.708 4.140 1.276
s 4.996 3.324 1.484 0.272
v 6.628 2.980 5.504 2.000
Call:
qda(cl ~ ., data = .)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
train.Sepal.L. train.Sepal.W. train.Petal.L. train.Petal.W.
c 5.892 2.708 4.140 1.276
s 4.996 3.324 1.484 0.272
v 6.628 2.980 5.504 2.000
Call:
rlm(formula = stack.loss ~ ., data = stackloss)
Converged in 9 iterations
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-41.0265311 0.8293739 0.9261082 -0.1278492
Degrees of freedom: 21 total; 17 residual
Scale estimate: 2.44
Call:
rlm(formula = stack.loss ~ ., data = stackloss)
Converged in 9 iterations
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-41.0265311 0.8293739 0.9261082 -0.1278492
Degrees of freedom: 21 total; 17 residual
Scale estimate: 2.44
Call: rlm(formula = stack.loss ~ ., data = .)
Residuals:
Min 1Q Median 3Q Max
-8.91753 -1.73127 0.06187 1.54306 6.50163
Coefficients:
Value Std. Error t value
(Intercept) -41.0265 9.8073 -4.1832
Air.Flow 0.8294 0.1112 7.4597
Water.Temp 0.9261 0.3034 3.0524
Acid.Conc. -0.1278 0.1289 -0.9922
Residual standard error: 2.441 on 17 degrees of freedom
Call: rlm(formula = stack.loss ~ ., data = ., psi = psi.hampel, init = "lts")
Residuals:
Min 1Q Median 3Q Max
-7.6599 -1.7635 -0.3594 2.3209 5.7864
Coefficients:
Value Std. Error t value
(Intercept) -40.4748 11.8929 -3.4033
Air.Flow 0.7411 0.1348 5.4967
Water.Temp 1.2251 0.3679 3.3296
Acid.Conc. -0.1455 0.1563 -0.9313
Residual standard error: 3.088 on 17 degrees of freedom
Warning message:
In is.na(y) : is.na() applied to non-(list or vector) of type 'language'
Call: rlm(formula = stack.loss ~ ., data = ., psi = psi.bisquare)
Residuals:
Min 1Q Median 3Q Max
-10.4356 -1.7065 -0.2392 0.8797 6.9326
Coefficients:
Value Std. Error t value
(Intercept) -42.2853 9.5316 -4.4363
Air.Flow 0.9275 0.1081 8.5841
Water.Temp 0.6507 0.2949 2.2068
Acid.Conc. -0.1123 0.1252 -0.8970
Residual standard error: 2.282 on 17 degrees of freedom
intubate documentation built on May 2, 2019, 2:46 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Author(s) Examples
Description
Interfaces to MASS functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 3 4 5 6 7 8 9 10 | ntbt_corresp(data, ...)
ntbt_glm.nb(data, ...)
ntbt_lda(data, ...)
ntbt_lm.gls(data, ...)
ntbt_lm.ridge(data, ...)
ntbt_loglm(data, ...)
ntbt_logtrans(data, ...)
ntbt_polr(data, ...)
ntbt_qda(data, ...)
ntbt_rlm(data, ...)
|
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
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 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | ## Not run:
library(intubate)
library(magrittr)
library(MASS)
## corresp
## Original function to interface
corresp(~ Age + Eth, data = quine)
## The interface reverses the order of data and formula
ntbt_corresp(data = quine, ~ Age + Eth)
## so it can be used easily in a pipeline.
quine %>%
ntbt_corresp(~ Age + Eth)
## glm.nb
## Original function to interface
glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)
## The interface reverses the order of data and formula
ntbt_glm.nb(data = quine, Days ~ Sex/(Age + Eth*Lrn))
## so it can be used easily in a pipeline.
quine %>%
ntbt_glm.nb(Days ~ Sex/(Age + Eth*Lrn))
## lda
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
Sp = rep(c("s","c","v"), rep(50,3)))
## Original function to interface
lda(Sp ~ ., Iris)
## The interface reverses the order of data and formula
ntbt_lda(Iris, Sp ~ .)
## so it can be used easily in a pipeline.
Iris %>%
ntbt_lda(Sp ~ .)
stackloss %>%
ntbt_lda(stack.loss ~ .) %>%
summary()
## lm.gls
## Original function to interface
lm.gls(conc ~ uptake, CO2, W = diag(nrow(CO2)))
## The interface reverses the order of data and formula
ntbt_lm.gls(CO2, conc ~ uptake, W = diag(nrow(CO2)))
## so it can be used easily in a pipeline.
CO2 %>%
ntbt_lm.gls(conc ~ uptake, W = diag(nrow(CO2)))
## lm.ridge
## Original function to interface
lm.ridge(GNP.deflator ~ ., longley)
## The interface reverses the order of data and formula
ntbt_lm.ridge(longley, GNP.deflator ~ .)
## so it can be used easily in a pipeline.
longley %>%
ntbt_lm.ridge(GNP.deflator ~ .)
## loglm
## Original function to interface
xtCars93 <- xtabs(~ Type + Origin, Cars93)
loglm(~ Type + Origin, xtCars93)
## The interface reverses the order of data and formula
xtCars93 <- ntbt_xtabs(Cars93, ~ Type + Origin)
ntbt_loglm(xtCars93, ~ Type + Origin)
## so it can be used easily in a pipeline.
Cars93 %>%
ntbt_xtabs(~ Type + Origin) %>%
ntbt_loglm(~ Type + Origin)
## logtrans
## Original function to interface
logtrans(Days ~ Age*Sex*Eth*Lrn, data = quine,
alpha = seq(0.75, 6.5, len=20))
## The interface reverses the order of data and formula
ntbt_logtrans(data = quine, Days ~ Age*Sex*Eth*Lrn,
alpha = seq(0.75, 6.5, len=20))
## so it can be used easily in a pipeline.
quine %>%
ntbt_logtrans(Days ~ Age*Sex*Eth*Lrn,
alpha = seq(0.75, 6.5, len=20))
## polr
op <- options(contrasts = c("contr.treatment", "contr.poly"))
## Original function to interface
polr(Sat ~ Infl + Type + Cont, housing)
## The interface reverses the order of data and formula
ntbt_polr(housing, Sat ~ Infl + Type + Cont)
## so it can be used easily in a pipeline.
housing %>%
ntbt_polr(Sat ~ Infl + Type + Cont)
options(op)
## qda
set.seed(123) ## make reproducible
tr <- sample(1:50, 25)
iris3df <- data.frame(cl = factor(c(rep("s",25), rep("c",25), rep("v",25))),
train = rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]))
## Original function to interface
qda(cl ~ ., iris3df)
## The interface reverses the order of data and formula
ntbt_qda(iris3df, cl ~ .)
## so it can be used easily in a pipeline.
iris3df %>%
ntbt_qda(cl ~ .)
## rlm
## Original function to interface
rlm(stack.loss ~ ., stackloss)
## The interface reverses the order of data and formula
ntbt_rlm(stackloss, stack.loss ~ .)
## so it can be used easily in a pipeline.
stackloss %>%
ntbt_rlm(stack.loss ~ .) %>%
summary()
stackloss %>%
ntbt_rlm(stack.loss ~ ., psi = psi.hampel, init = "lts") %>%
summary()
stackloss %>%
ntbt_rlm(stack.loss ~ ., psi = psi.bisquare) %>%
summary()
## End(Not run)
|
Example output
First canonical correlation(s): 0.05317534
Age scores:
F0 F1 F2 F3
-0.3344445 1.4246090 -1.0320002 -0.4612728
Eth scores:
A N
-1.0563816 0.9466276
First canonical correlation(s): 0.05317534
Age scores:
F0 F1 F2 F3
-0.3344445 1.4246090 -1.0320002 -0.4612728
Eth scores:
A N
-1.0563816 0.9466276
First canonical correlation(s): 0.05317534
Age scores:
F0 F1 F2 F3
-0.3344445 1.4246090 -1.0320002 -0.4612728
Eth scores:
A N
-1.0563816 0.9466276
Call: glm.nb(formula = Days ~ Sex/(Age + Eth * Lrn), data = quine,
init.theta = 1.597990733, link = log)
Coefficients:
(Intercept) SexM SexF:AgeF1 SexM:AgeF1
3.01919 -0.47541 -0.70887 -0.72373
SexF:AgeF2 SexM:AgeF2 SexF:AgeF3 SexM:AgeF3
-0.61486 0.62842 -0.34235 1.15084
SexF:EthN SexM:EthN SexF:LrnSL SexM:LrnSL
-0.07312 -0.67899 0.94358 0.23891
SexF:EthN:LrnSL SexM:EthN:LrnSL
-1.35849 0.76142
Degrees of Freedom: 145 Total (i.e. Null); 132 Residual
Null Deviance: 234.6
Residual Deviance: 167.6 AIC: 1093
Call: glm.nb(formula = Days ~ Sex/(Age + Eth * Lrn), init.theta = 1.597990733,
link = log)
Coefficients:
(Intercept) SexM SexF:AgeF1 SexM:AgeF1
3.01919 -0.47541 -0.70887 -0.72373
SexF:AgeF2 SexM:AgeF2 SexF:AgeF3 SexM:AgeF3
-0.61486 0.62842 -0.34235 1.15084
SexF:EthN SexM:EthN SexF:LrnSL SexM:LrnSL
-0.07312 -0.67899 0.94358 0.23891
SexF:EthN:LrnSL SexM:EthN:LrnSL
-1.35849 0.76142
Degrees of Freedom: 145 Total (i.e. Null); 132 Residual
Null Deviance: 234.6
Residual Deviance: 167.6 AIC: 1093
Call: glm.nb(formula = Days ~ Sex/(Age + Eth * Lrn), init.theta = 1.597990733,
link = log)
Coefficients:
(Intercept) SexM SexF:AgeF1 SexM:AgeF1
3.01919 -0.47541 -0.70887 -0.72373
SexF:AgeF2 SexM:AgeF2 SexF:AgeF3 SexM:AgeF3
-0.61486 0.62842 -0.34235 1.15084
SexF:EthN SexM:EthN SexF:LrnSL SexM:LrnSL
-0.07312 -0.67899 0.94358 0.23891
SexF:EthN:LrnSL SexM:EthN:LrnSL
-1.35849 0.76142
Degrees of Freedom: 145 Total (i.e. Null); 132 Residual
Null Deviance: 234.6
Residual Deviance: 167.6 AIC: 1093
Call:
lda(Sp ~ ., data = Iris)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
Sepal.L. Sepal.W. Petal.L. Petal.W.
c 5.936 2.770 4.260 1.326
s 5.006 3.428 1.462 0.246
v 6.588 2.974 5.552 2.026
Coefficients of linear discriminants:
LD1 LD2
Sepal.L. -0.8293776 0.02410215
Sepal.W. -1.5344731 2.16452123
Petal.L. 2.2012117 -0.93192121
Petal.W. 2.8104603 2.83918785
Proportion of trace:
LD1 LD2
0.9912 0.0088
Call:
lda(Sp ~ ., data = Iris)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
Sepal.L. Sepal.W. Petal.L. Petal.W.
c 5.936 2.770 4.260 1.326
s 5.006 3.428 1.462 0.246
v 6.588 2.974 5.552 2.026
Coefficients of linear discriminants:
LD1 LD2
Sepal.L. -0.8293776 0.02410215
Sepal.W. -1.5344731 2.16452123
Petal.L. 2.2012117 -0.93192121
Petal.W. 2.8104603 2.83918785
Proportion of trace:
LD1 LD2
0.9912 0.0088
Call:
lda(Sp ~ ., data = .)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
Sepal.L. Sepal.W. Petal.L. Petal.W.
c 5.936 2.770 4.260 1.326
s 5.006 3.428 1.462 0.246
v 6.588 2.974 5.552 2.026
Coefficients of linear discriminants:
LD1 LD2
Sepal.L. -0.8293776 0.02410215
Sepal.W. -1.5344731 2.16452123
Petal.L. 2.2012117 -0.93192121
Petal.W. 2.8104603 2.83918785
Proportion of trace:
LD1 LD2
0.9912 0.0088
Length Class Mode
prior 14 -none- numeric
counts 14 -none- numeric
means 42 -none- numeric
scaling 9 -none- numeric
lev 14 -none- character
svd 3 -none- numeric
N 1 -none- numeric
call 3 -none- call
terms 3 terms call
xlevels 0 -none- list
$coefficients
(Intercept) uptake
73.71000 13.27633
$residuals
[1] -191.131260 -302.310396 -285.726242 -217.589432 -42.364407 80.857911
[7] 399.219746 -159.268071 -261.153776 -316.261799 -278.660544 -112.728950
[13] 51.649987 338.148634 -193.786526 -328.863053 -358.746051 -282.643443
[19] -143.264506 18.459165 322.217039 -167.233868 -218.669524 -225.982763
[25] -183.070977 -5.190686 131.307960 412.496075 -102.179857 -261.153776
[31] -288.381508 -238.831558 -86.176292 103.427670 363.373658 -179.182564
[37] -177.512905 -329.538128 -175.105179 -90.159191 75.547379 376.649987
[43] -119.439085 -153.615513 -171.549814 -121.999864 16.051440 171.136947
[49] 454.980327 -138.025945 -190.789233 -229.965661 -145.897256 -3.863053
[55] 188.396174 508.085643 -128.732515 -156.270778 -166.239283 -94.119573
[61] 47.914629 228.225161 557.208059 -118.111452 -96.527299 -64.011551
[67] 25.367386 167.401589 306.555501 635.538400 -80.937731 -50.060148
[73] 12.991156 103.697726 260.335891 419.404296 735.110866 -119.439085
[79] -137.683918 -61.356285 38.643715 188.643715 350.367386 662.091057
$effects
(Intercept) uptake
-3986.8408546 1308.0368214 100.4113518 -49.5886482 -149.5886482
-225.6474908 -227.2931404 637.4708418 319.8827398 157.5888507
2.2946378 -90.2934642 -155.7638810 -196.5867059 558.0576489
229.8811212 83.4698707 -60.1770739 -151.7063333 -192.8233710
-226.2342979 495.5859372 167.4094095 -11.8259608 -155.4729054
-233.2372114 -240.4712868 -234.7050384 456.4087621 135.6441323
-53.1208211 -196.7677657 -284.0616548 -268.0011936 -242.1169364
414.0550592 121.8791789 -37.2381825 -177.7085994 -237.4725816
-238.3536016 -227.2931404 351.5833475 45.6425137 -121.9455883
-220.0623022 -363.4748477 -257.4127679 -274.9410562 340.9949217
67.8782077 -118.7690605 -270.8867457 -330.6507280 -324.1198499
-213.5281870 380.1720969 90.1139018 -54.1796637 -226.4153577
-280.8851271 -290.2368876 -265.4114730 308.1708020 0.1122832
-164.2992911 -305.8285506 -386.7693843 -378.1208211 -286.5883245
320.8769129 26.5833475 -139.9459120 -302.6520228 -352.8864220
-324.1198499 -259.0584176 369.5836712 49.8778840 -83.8272557
-253.9452645 -328.5330428 -356.9439697 -284.4706393
$rank
[1] 2
$fitted.values
[1] 286.1313 477.3104 535.7262 567.5894 542.3644 594.1421 600.7803 254.2681
[9] 436.1538 566.2618 628.6605 612.7289 623.3500 661.8514 288.7865 503.8631
[17] 608.7461 632.6434 643.2645 656.5408 677.7830 262.2339 393.6695 475.9828
[25] 533.0710 505.1907 543.6920 587.5039 197.1799 436.1538 538.3815 588.8316
[33] 586.1763 571.5723 636.6263 274.1826 352.5129 579.5381 525.1052 590.1592
[41] 599.4526 623.3500 214.4391 328.6155 421.5498 471.9999 483.9486 503.8631
[49] 545.0197 233.0259 365.7892 479.9657 495.8973 503.8631 486.6038 491.9144
[57] 223.7325 331.2708 416.2393 444.1196 452.0854 446.7748 442.7919 213.1115
[65] 271.5273 314.0116 324.6326 332.5984 368.4445 364.4616 175.9377 225.0601
[73] 237.0088 246.3023 239.6641 255.5957 264.8891 214.4391 312.6839 311.3563
[81] 311.3563 311.3563 324.6326 337.9089
$assign
NULL
$qr
$qr
(Intercept) uptake
[1,] -9.1651514 -2.494121e+02
[2,] 0.1091089 9.852398e+01
[3,] 0.1091089 8.722411e-02
[4,] 0.1091089 8.722411e-02
[5,] 0.1091089 8.722411e-02
[6,] 0.1091089 8.620913e-02
[7,] 0.1091089 1.613177e-01
[8,] 0.1091089 1.227485e-01
[9,] 0.1091089 1.298533e-01
[10,] 0.1091089 1.420331e-01
[11,] 0.1091089 1.369582e-01
[12,] 0.1091089 1.440631e-01
[13,] 0.1091089 1.531979e-01
[14,] 0.1091089 1.907522e-01
[15,] 0.1091089 4.662486e-02
[16,] 0.1091089 4.357992e-02
[17,] 0.1091089 7.098441e-02
[18,] 0.1091089 7.707430e-02
[19,] 0.1091089 8.519415e-02
[20,] 0.1091089 1.176736e-01
[21,] 0.1091089 1.623327e-01
[22,] 0.1091089 -1.325903e-02
[23,] 0.1091089 -1.630398e-02
[24,] 0.1091089 -2.036390e-02
[25,] 0.1091089 -1.427401e-02
[26,] 0.1091089 7.040592e-03
[27,] 0.1091089 7.199939e-02
[28,] 0.1091089 1.542129e-01
[29,] 0.1091089 -5.081334e-02
[30,] 0.1091089 -4.675342e-02
[31,] 0.1091089 -5.994817e-02
[32,] 0.1091089 -5.385828e-02
[33,] 0.1091089 -4.167851e-02
[34,] 0.1091089 4.560988e-02
[35,] 0.1091089 1.471080e-01
[36,] 0.1091089 -9.141259e-02
[37,] 0.1091089 -5.994817e-02
[38,] 0.1091089 -4.472345e-02
[39,] 0.1091089 -3.558862e-02
[40,] 0.1091089 2.980667e-03
[41,] 0.1091089 7.402936e-02
[42,] 0.1091089 1.613177e-01
[43,] 0.1091089 -1.512965e-01
[44,] 0.1091089 -1.330268e-01
[45,] 0.1091089 -1.259220e-01
[46,] 0.1091089 -7.618787e-02
[47,] 0.1091089 -1.178021e-01
[48,] 0.1091089 5.575969e-02
[49,] 0.1091089 1.156436e-01
[50,] 0.1091089 -1.614463e-01
[51,] 0.1091089 -1.117122e-01
[52,] 0.1091089 -1.228770e-01
[53,] 0.1091089 -1.249070e-01
[54,] 0.1091089 -8.633769e-02
[55,] 0.1091089 -8.184127e-03
[56,] 0.1091089 1.745125e-01
[57,] 0.1091089 -1.238920e-01
[58,] 0.1091089 -9.039761e-02
[59,] 0.1091089 -6.096315e-02
[60,] 0.1091089 -8.227776e-02
[61,] 0.1091089 -3.863357e-02
[62,] 0.1091089 2.429527e-02
[63,] 0.1091089 1.247784e-01
[64,] 0.1091089 -1.929107e-01
[65,] 0.1091089 -1.766710e-01
[66,] 0.1091089 -1.665212e-01
[67,] 0.1091089 -1.584014e-01
[68,] 0.1091089 -1.401317e-01
[69,] 0.1091089 -5.994817e-02
[70,] 0.1091089 1.044788e-01
[71,] 0.1091089 -1.807309e-01
[72,] 0.1091089 -1.512965e-01
[73,] 0.1091089 -1.431766e-01
[74,] 0.1091089 -1.553564e-01
[75,] 0.1091089 -1.076523e-01
[76,] 0.1091089 -8.184127e-03
[77,] 0.1091089 1.308683e-01
[78,] 0.1091089 -1.340418e-01
[79,] 0.1091089 -1.289669e-01
[80,] 0.1091089 -8.938263e-02
[81,] 0.1091089 -1.086673e-01
[82,] 0.1091089 -8.430772e-02
[83,] 0.1091089 -3.964855e-02
[84,] 0.1091089 1.065088e-01
$qraux
[1] 1.109109 1.077074
$pivot
[1] 1 2
$tol
[1] 1e-07
$rank
[1] 2
attr(,"class")
[1] "qr"
$df.residual
[1] 82
$terms
conc ~ uptake
attr(,"variables")
list(conc, uptake)
attr(,"factors")
uptake
conc 0
uptake 1
attr(,"term.labels")
[1] "uptake"
attr(,"order")
[1] 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
attr(,"predvars")
list(conc, uptake)
attr(,"dataClasses")
conc uptake
"numeric" "numeric"
$call
lm.gls(formula = conc ~ uptake, data = CO2, W = diag(nrow(CO2)))
$xlevels
named list()
attr(,"class")
[1] "lm.gls"
$coefficients
(Intercept) uptake
73.71000 13.27633
$residuals
[1] -191.131260 -302.310396 -285.726242 -217.589432 -42.364407 80.857911
[7] 399.219746 -159.268071 -261.153776 -316.261799 -278.660544 -112.728950
[13] 51.649987 338.148634 -193.786526 -328.863053 -358.746051 -282.643443
[19] -143.264506 18.459165 322.217039 -167.233868 -218.669524 -225.982763
[25] -183.070977 -5.190686 131.307960 412.496075 -102.179857 -261.153776
[31] -288.381508 -238.831558 -86.176292 103.427670 363.373658 -179.182564
[37] -177.512905 -329.538128 -175.105179 -90.159191 75.547379 376.649987
[43] -119.439085 -153.615513 -171.549814 -121.999864 16.051440 171.136947
[49] 454.980327 -138.025945 -190.789233 -229.965661 -145.897256 -3.863053
[55] 188.396174 508.085643 -128.732515 -156.270778 -166.239283 -94.119573
[61] 47.914629 228.225161 557.208059 -118.111452 -96.527299 -64.011551
[67] 25.367386 167.401589 306.555501 635.538400 -80.937731 -50.060148
[73] 12.991156 103.697726 260.335891 419.404296 735.110866 -119.439085
[79] -137.683918 -61.356285 38.643715 188.643715 350.367386 662.091057
$effects
(Intercept) uptake
-3986.8408546 1308.0368214 100.4113518 -49.5886482 -149.5886482
-225.6474908 -227.2931404 637.4708418 319.8827398 157.5888507
2.2946378 -90.2934642 -155.7638810 -196.5867059 558.0576489
229.8811212 83.4698707 -60.1770739 -151.7063333 -192.8233710
-226.2342979 495.5859372 167.4094095 -11.8259608 -155.4729054
-233.2372114 -240.4712868 -234.7050384 456.4087621 135.6441323
-53.1208211 -196.7677657 -284.0616548 -268.0011936 -242.1169364
414.0550592 121.8791789 -37.2381825 -177.7085994 -237.4725816
-238.3536016 -227.2931404 351.5833475 45.6425137 -121.9455883
-220.0623022 -363.4748477 -257.4127679 -274.9410562 340.9949217
67.8782077 -118.7690605 -270.8867457 -330.6507280 -324.1198499
-213.5281870 380.1720969 90.1139018 -54.1796637 -226.4153577
-280.8851271 -290.2368876 -265.4114730 308.1708020 0.1122832
-164.2992911 -305.8285506 -386.7693843 -378.1208211 -286.5883245
320.8769129 26.5833475 -139.9459120 -302.6520228 -352.8864220
-324.1198499 -259.0584176 369.5836712 49.8778840 -83.8272557
-253.9452645 -328.5330428 -356.9439697 -284.4706393
$rank
[1] 2
$fitted.values
[1] 286.1313 477.3104 535.7262 567.5894 542.3644 594.1421 600.7803 254.2681
[9] 436.1538 566.2618 628.6605 612.7289 623.3500 661.8514 288.7865 503.8631
[17] 608.7461 632.6434 643.2645 656.5408 677.7830 262.2339 393.6695 475.9828
[25] 533.0710 505.1907 543.6920 587.5039 197.1799 436.1538 538.3815 588.8316
[33] 586.1763 571.5723 636.6263 274.1826 352.5129 579.5381 525.1052 590.1592
[41] 599.4526 623.3500 214.4391 328.6155 421.5498 471.9999 483.9486 503.8631
[49] 545.0197 233.0259 365.7892 479.9657 495.8973 503.8631 486.6038 491.9144
[57] 223.7325 331.2708 416.2393 444.1196 452.0854 446.7748 442.7919 213.1115
[65] 271.5273 314.0116 324.6326 332.5984 368.4445 364.4616 175.9377 225.0601
[73] 237.0088 246.3023 239.6641 255.5957 264.8891 214.4391 312.6839 311.3563
[81] 311.3563 311.3563 324.6326 337.9089
$assign
NULL
$qr
$qr
(Intercept) uptake
[1,] -9.1651514 -2.494121e+02
[2,] 0.1091089 9.852398e+01
[3,] 0.1091089 8.722411e-02
[4,] 0.1091089 8.722411e-02
[5,] 0.1091089 8.722411e-02
[6,] 0.1091089 8.620913e-02
[7,] 0.1091089 1.613177e-01
[8,] 0.1091089 1.227485e-01
[9,] 0.1091089 1.298533e-01
[10,] 0.1091089 1.420331e-01
[11,] 0.1091089 1.369582e-01
[12,] 0.1091089 1.440631e-01
[13,] 0.1091089 1.531979e-01
[14,] 0.1091089 1.907522e-01
[15,] 0.1091089 4.662486e-02
[16,] 0.1091089 4.357992e-02
[17,] 0.1091089 7.098441e-02
[18,] 0.1091089 7.707430e-02
[19,] 0.1091089 8.519415e-02
[20,] 0.1091089 1.176736e-01
[21,] 0.1091089 1.623327e-01
[22,] 0.1091089 -1.325903e-02
[23,] 0.1091089 -1.630398e-02
[24,] 0.1091089 -2.036390e-02
[25,] 0.1091089 -1.427401e-02
[26,] 0.1091089 7.040592e-03
[27,] 0.1091089 7.199939e-02
[28,] 0.1091089 1.542129e-01
[29,] 0.1091089 -5.081334e-02
[30,] 0.1091089 -4.675342e-02
[31,] 0.1091089 -5.994817e-02
[32,] 0.1091089 -5.385828e-02
[33,] 0.1091089 -4.167851e-02
[34,] 0.1091089 4.560988e-02
[35,] 0.1091089 1.471080e-01
[36,] 0.1091089 -9.141259e-02
[37,] 0.1091089 -5.994817e-02
[38,] 0.1091089 -4.472345e-02
[39,] 0.1091089 -3.558862e-02
[40,] 0.1091089 2.980667e-03
[41,] 0.1091089 7.402936e-02
[42,] 0.1091089 1.613177e-01
[43,] 0.1091089 -1.512965e-01
[44,] 0.1091089 -1.330268e-01
[45,] 0.1091089 -1.259220e-01
[46,] 0.1091089 -7.618787e-02
[47,] 0.1091089 -1.178021e-01
[48,] 0.1091089 5.575969e-02
[49,] 0.1091089 1.156436e-01
[50,] 0.1091089 -1.614463e-01
[51,] 0.1091089 -1.117122e-01
[52,] 0.1091089 -1.228770e-01
[53,] 0.1091089 -1.249070e-01
[54,] 0.1091089 -8.633769e-02
[55,] 0.1091089 -8.184127e-03
[56,] 0.1091089 1.745125e-01
[57,] 0.1091089 -1.238920e-01
[58,] 0.1091089 -9.039761e-02
[59,] 0.1091089 -6.096315e-02
[60,] 0.1091089 -8.227776e-02
[61,] 0.1091089 -3.863357e-02
[62,] 0.1091089 2.429527e-02
[63,] 0.1091089 1.247784e-01
[64,] 0.1091089 -1.929107e-01
[65,] 0.1091089 -1.766710e-01
[66,] 0.1091089 -1.665212e-01
[67,] 0.1091089 -1.584014e-01
[68,] 0.1091089 -1.401317e-01
[69,] 0.1091089 -5.994817e-02
[70,] 0.1091089 1.044788e-01
[71,] 0.1091089 -1.807309e-01
[72,] 0.1091089 -1.512965e-01
[73,] 0.1091089 -1.431766e-01
[74,] 0.1091089 -1.553564e-01
[75,] 0.1091089 -1.076523e-01
[76,] 0.1091089 -8.184127e-03
[77,] 0.1091089 1.308683e-01
[78,] 0.1091089 -1.340418e-01
[79,] 0.1091089 -1.289669e-01
[80,] 0.1091089 -8.938263e-02
[81,] 0.1091089 -1.086673e-01
[82,] 0.1091089 -8.430772e-02
[83,] 0.1091089 -3.964855e-02
[84,] 0.1091089 1.065088e-01
$qraux
[1] 1.109109 1.077074
$pivot
[1] 1 2
$tol
[1] 1e-07
$rank
[1] 2
attr(,"class")
[1] "qr"
$df.residual
[1] 82
$terms
conc ~ uptake
attr(,"variables")
list(conc, uptake)
attr(,"factors")
uptake
conc 0
uptake 1
attr(,"term.labels")
[1] "uptake"
attr(,"order")
[1] 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: 0x470b3e0>
attr(,"predvars")
list(conc, uptake)
attr(,"dataClasses")
conc uptake
"numeric" "numeric"
$call
lm.gls(formula = conc ~ uptake, W = diag(nrow(CO2)))
$xlevels
named list()
attr(,"class")
[1] "lm.gls"
$coefficients
(Intercept) uptake
73.71000 13.27633
$residuals
[1] -191.131260 -302.310396 -285.726242 -217.589432 -42.364407 80.857911
[7] 399.219746 -159.268071 -261.153776 -316.261799 -278.660544 -112.728950
[13] 51.649987 338.148634 -193.786526 -328.863053 -358.746051 -282.643443
[19] -143.264506 18.459165 322.217039 -167.233868 -218.669524 -225.982763
[25] -183.070977 -5.190686 131.307960 412.496075 -102.179857 -261.153776
[31] -288.381508 -238.831558 -86.176292 103.427670 363.373658 -179.182564
[37] -177.512905 -329.538128 -175.105179 -90.159191 75.547379 376.649987
[43] -119.439085 -153.615513 -171.549814 -121.999864 16.051440 171.136947
[49] 454.980327 -138.025945 -190.789233 -229.965661 -145.897256 -3.863053
[55] 188.396174 508.085643 -128.732515 -156.270778 -166.239283 -94.119573
[61] 47.914629 228.225161 557.208059 -118.111452 -96.527299 -64.011551
[67] 25.367386 167.401589 306.555501 635.538400 -80.937731 -50.060148
[73] 12.991156 103.697726 260.335891 419.404296 735.110866 -119.439085
[79] -137.683918 -61.356285 38.643715 188.643715 350.367386 662.091057
$effects
(Intercept) uptake
-3986.8408546 1308.0368214 100.4113518 -49.5886482 -149.5886482
-225.6474908 -227.2931404 637.4708418 319.8827398 157.5888507
2.2946378 -90.2934642 -155.7638810 -196.5867059 558.0576489
229.8811212 83.4698707 -60.1770739 -151.7063333 -192.8233710
-226.2342979 495.5859372 167.4094095 -11.8259608 -155.4729054
-233.2372114 -240.4712868 -234.7050384 456.4087621 135.6441323
-53.1208211 -196.7677657 -284.0616548 -268.0011936 -242.1169364
414.0550592 121.8791789 -37.2381825 -177.7085994 -237.4725816
-238.3536016 -227.2931404 351.5833475 45.6425137 -121.9455883
-220.0623022 -363.4748477 -257.4127679 -274.9410562 340.9949217
67.8782077 -118.7690605 -270.8867457 -330.6507280 -324.1198499
-213.5281870 380.1720969 90.1139018 -54.1796637 -226.4153577
-280.8851271 -290.2368876 -265.4114730 308.1708020 0.1122832
-164.2992911 -305.8285506 -386.7693843 -378.1208211 -286.5883245
320.8769129 26.5833475 -139.9459120 -302.6520228 -352.8864220
-324.1198499 -259.0584176 369.5836712 49.8778840 -83.8272557
-253.9452645 -328.5330428 -356.9439697 -284.4706393
$rank
[1] 2
$fitted.values
[1] 286.1313 477.3104 535.7262 567.5894 542.3644 594.1421 600.7803 254.2681
[9] 436.1538 566.2618 628.6605 612.7289 623.3500 661.8514 288.7865 503.8631
[17] 608.7461 632.6434 643.2645 656.5408 677.7830 262.2339 393.6695 475.9828
[25] 533.0710 505.1907 543.6920 587.5039 197.1799 436.1538 538.3815 588.8316
[33] 586.1763 571.5723 636.6263 274.1826 352.5129 579.5381 525.1052 590.1592
[41] 599.4526 623.3500 214.4391 328.6155 421.5498 471.9999 483.9486 503.8631
[49] 545.0197 233.0259 365.7892 479.9657 495.8973 503.8631 486.6038 491.9144
[57] 223.7325 331.2708 416.2393 444.1196 452.0854 446.7748 442.7919 213.1115
[65] 271.5273 314.0116 324.6326 332.5984 368.4445 364.4616 175.9377 225.0601
[73] 237.0088 246.3023 239.6641 255.5957 264.8891 214.4391 312.6839 311.3563
[81] 311.3563 311.3563 324.6326 337.9089
$assign
NULL
$qr
$qr
(Intercept) uptake
[1,] -9.1651514 -2.494121e+02
[2,] 0.1091089 9.852398e+01
[3,] 0.1091089 8.722411e-02
[4,] 0.1091089 8.722411e-02
[5,] 0.1091089 8.722411e-02
[6,] 0.1091089 8.620913e-02
[7,] 0.1091089 1.613177e-01
[8,] 0.1091089 1.227485e-01
[9,] 0.1091089 1.298533e-01
[10,] 0.1091089 1.420331e-01
[11,] 0.1091089 1.369582e-01
[12,] 0.1091089 1.440631e-01
[13,] 0.1091089 1.531979e-01
[14,] 0.1091089 1.907522e-01
[15,] 0.1091089 4.662486e-02
[16,] 0.1091089 4.357992e-02
[17,] 0.1091089 7.098441e-02
[18,] 0.1091089 7.707430e-02
[19,] 0.1091089 8.519415e-02
[20,] 0.1091089 1.176736e-01
[21,] 0.1091089 1.623327e-01
[22,] 0.1091089 -1.325903e-02
[23,] 0.1091089 -1.630398e-02
[24,] 0.1091089 -2.036390e-02
[25,] 0.1091089 -1.427401e-02
[26,] 0.1091089 7.040592e-03
[27,] 0.1091089 7.199939e-02
[28,] 0.1091089 1.542129e-01
[29,] 0.1091089 -5.081334e-02
[30,] 0.1091089 -4.675342e-02
[31,] 0.1091089 -5.994817e-02
[32,] 0.1091089 -5.385828e-02
[33,] 0.1091089 -4.167851e-02
[34,] 0.1091089 4.560988e-02
[35,] 0.1091089 1.471080e-01
[36,] 0.1091089 -9.141259e-02
[37,] 0.1091089 -5.994817e-02
[38,] 0.1091089 -4.472345e-02
[39,] 0.1091089 -3.558862e-02
[40,] 0.1091089 2.980667e-03
[41,] 0.1091089 7.402936e-02
[42,] 0.1091089 1.613177e-01
[43,] 0.1091089 -1.512965e-01
[44,] 0.1091089 -1.330268e-01
[45,] 0.1091089 -1.259220e-01
[46,] 0.1091089 -7.618787e-02
[47,] 0.1091089 -1.178021e-01
[48,] 0.1091089 5.575969e-02
[49,] 0.1091089 1.156436e-01
[50,] 0.1091089 -1.614463e-01
[51,] 0.1091089 -1.117122e-01
[52,] 0.1091089 -1.228770e-01
[53,] 0.1091089 -1.249070e-01
[54,] 0.1091089 -8.633769e-02
[55,] 0.1091089 -8.184127e-03
[56,] 0.1091089 1.745125e-01
[57,] 0.1091089 -1.238920e-01
[58,] 0.1091089 -9.039761e-02
[59,] 0.1091089 -6.096315e-02
[60,] 0.1091089 -8.227776e-02
[61,] 0.1091089 -3.863357e-02
[62,] 0.1091089 2.429527e-02
[63,] 0.1091089 1.247784e-01
[64,] 0.1091089 -1.929107e-01
[65,] 0.1091089 -1.766710e-01
[66,] 0.1091089 -1.665212e-01
[67,] 0.1091089 -1.584014e-01
[68,] 0.1091089 -1.401317e-01
[69,] 0.1091089 -5.994817e-02
[70,] 0.1091089 1.044788e-01
[71,] 0.1091089 -1.807309e-01
[72,] 0.1091089 -1.512965e-01
[73,] 0.1091089 -1.431766e-01
[74,] 0.1091089 -1.553564e-01
[75,] 0.1091089 -1.076523e-01
[76,] 0.1091089 -8.184127e-03
[77,] 0.1091089 1.308683e-01
[78,] 0.1091089 -1.340418e-01
[79,] 0.1091089 -1.289669e-01
[80,] 0.1091089 -8.938263e-02
[81,] 0.1091089 -1.086673e-01
[82,] 0.1091089 -8.430772e-02
[83,] 0.1091089 -3.964855e-02
[84,] 0.1091089 1.065088e-01
$qraux
[1] 1.109109 1.077074
$pivot
[1] 1 2
$tol
[1] 1e-07
$rank
[1] 2
attr(,"class")
[1] "qr"
$df.residual
[1] 82
$terms
conc ~ uptake
attr(,"variables")
list(conc, uptake)
attr(,"factors")
uptake
conc 0
uptake 1
attr(,"term.labels")
[1] "uptake"
attr(,"order")
[1] 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: 0x47ab1e8>
attr(,"predvars")
list(conc, uptake)
attr(,"dataClasses")
conc uptake
"numeric" "numeric"
$call
lm.gls(formula = conc ~ uptake, W = diag(nrow(CO2)))
$xlevels
named list()
attr(,"class")
[1] "lm.gls"
GNP Unemployed Armed.Forces Population
2946.85636017 0.26352725 0.03648291 0.01116105 -1.73702984
Year Employed
-1.41879853 0.23128785
GNP Unemployed Armed.Forces Population
2946.85636017 0.26352725 0.03648291 0.01116105 -1.73702984
Year Employed
-1.41879853 0.23128785
GNP Unemployed Armed.Forces Population
2946.85636017 0.26352725 0.03648291 0.01116105 -1.73702984
Year Employed
-1.41879853 0.23128785
Call:
loglm(formula = ~Type + Origin, data = xtCars93)
Statistics:
X^2 df P(> X^2)
Likelihood Ratio 18.36179 5 0.00252554
Pearson 14.07985 5 0.01511005
Call:
loglm(formula = ~Type + Origin, data = xtCars93)
Statistics:
X^2 df P(> X^2)
Likelihood Ratio 18.36179 5 0.00252554
Pearson 14.07985 5 0.01511005
Call:
loglm(formula = ~Type + Origin, data = .)
Statistics:
X^2 df P(> X^2)
Likelihood Ratio 18.36179 5 0.00252554
Pearson 14.07985 5 0.01511005
$x
[1] 0.7500000 0.8080808 0.8661616 0.9242424 0.9823232 1.0404040 1.0984848
[8] 1.1565657 1.2146465 1.2727273 1.3308081 1.3888889 1.4469697 1.5050505
[15] 1.5631313 1.6212121 1.6792929 1.7373737 1.7954545 1.8535354 1.9116162
[22] 1.9696970 2.0277778 2.0858586 2.1439394 2.2020202 2.2601010 2.3181818
[29] 2.3762626 2.4343434 2.4924242 2.5505051 2.6085859 2.6666667 2.7247475
[36] 2.7828283 2.8409091 2.8989899 2.9570707 3.0151515 3.0732323 3.1313131
[43] 3.1893939 3.2474747 3.3055556 3.3636364 3.4217172 3.4797980 3.5378788
[50] 3.5959596 3.6540404 3.7121212 3.7702020 3.8282828 3.8863636 3.9444444
[57] 4.0025253 4.0606061 4.1186869 4.1767677 4.2348485 4.2929293 4.3510101
[64] 4.4090909 4.4671717 4.5252525 4.5833333 4.6414141 4.6994949 4.7575758
[71] 4.8156566 4.8737374 4.9318182 4.9898990 5.0479798 5.1060606 5.1641414
[78] 5.2222222 5.2803030 5.3383838 5.3964646 5.4545455 5.5126263 5.5707071
[85] 5.6287879 5.6868687 5.7449495 5.8030303 5.8611111 5.9191919 5.9772727
[92] 6.0353535 6.0934343 6.1515152 6.2095960 6.2676768 6.3257576 6.3838384
[99] 6.4419192 6.5000000
$y
[1] -686.8651 -686.3368 -685.8576 -685.4242 -685.0337 -684.6829 -684.3690
[8] -684.0884 -683.8376 -683.6132 -683.4116 -683.2294 -683.0643 -682.9154
[15] -682.7814 -682.6612 -682.5538 -682.4581 -682.3734 -682.2987 -682.2334
[22] -682.1765 -682.1274 -682.0855 -682.0504 -682.0217 -681.9990 -681.9817
[29] -681.9696 -681.9624 -681.9597 -681.9613 -681.9668 -681.9761 -681.9889
[36] -682.0050 -682.0243 -682.0464 -682.0713 -682.0989 -682.1289 -682.1612
[43] -682.1957 -682.2323 -682.2708 -682.3112 -682.3533 -682.3971 -682.4425
[50] -682.4893 -682.5375 -682.5870 -682.6378 -682.6898 -682.7429 -682.7971
[57] -682.8523 -682.9084 -682.9654 -683.0233 -683.0820 -683.1414 -683.2016
[64] -683.2624 -683.3239 -683.3860 -683.4487 -683.5119 -683.5756 -683.6398
[71] -683.7045 -683.7696 -683.8351 -683.9009 -683.9672 -684.0337 -684.1006
[78] -684.1677 -684.2352 -684.3029 -684.3708 -684.4389 -684.5073 -684.5758
[85] -684.6445 -684.7134 -684.7824 -684.8515 -684.9208 -684.9901 -685.0596
[92] -685.1291 -685.1988 -685.2685 -685.3382 -685.4080 -685.4779 -685.5477
[99] -685.6176 -685.6875
$x
[1] 0.7500000 0.8080808 0.8661616 0.9242424 0.9823232 1.0404040 1.0984848
[8] 1.1565657 1.2146465 1.2727273 1.3308081 1.3888889 1.4469697 1.5050505
[15] 1.5631313 1.6212121 1.6792929 1.7373737 1.7954545 1.8535354 1.9116162
[22] 1.9696970 2.0277778 2.0858586 2.1439394 2.2020202 2.2601010 2.3181818
[29] 2.3762626 2.4343434 2.4924242 2.5505051 2.6085859 2.6666667 2.7247475
[36] 2.7828283 2.8409091 2.8989899 2.9570707 3.0151515 3.0732323 3.1313131
[43] 3.1893939 3.2474747 3.3055556 3.3636364 3.4217172 3.4797980 3.5378788
[50] 3.5959596 3.6540404 3.7121212 3.7702020 3.8282828 3.8863636 3.9444444
[57] 4.0025253 4.0606061 4.1186869 4.1767677 4.2348485 4.2929293 4.3510101
[64] 4.4090909 4.4671717 4.5252525 4.5833333 4.6414141 4.6994949 4.7575758
[71] 4.8156566 4.8737374 4.9318182 4.9898990 5.0479798 5.1060606 5.1641414
[78] 5.2222222 5.2803030 5.3383838 5.3964646 5.4545455 5.5126263 5.5707071
[85] 5.6287879 5.6868687 5.7449495 5.8030303 5.8611111 5.9191919 5.9772727
[92] 6.0353535 6.0934343 6.1515152 6.2095960 6.2676768 6.3257576 6.3838384
[99] 6.4419192 6.5000000
$y
[1] -686.8651 -686.3368 -685.8576 -685.4242 -685.0337 -684.6829 -684.3690
[8] -684.0884 -683.8376 -683.6132 -683.4116 -683.2294 -683.0643 -682.9154
[15] -682.7814 -682.6612 -682.5538 -682.4581 -682.3734 -682.2987 -682.2334
[22] -682.1765 -682.1274 -682.0855 -682.0504 -682.0217 -681.9990 -681.9817
[29] -681.9696 -681.9624 -681.9597 -681.9613 -681.9668 -681.9761 -681.9889
[36] -682.0050 -682.0243 -682.0464 -682.0713 -682.0989 -682.1289 -682.1612
[43] -682.1957 -682.2323 -682.2708 -682.3112 -682.3533 -682.3971 -682.4425
[50] -682.4893 -682.5375 -682.5870 -682.6378 -682.6898 -682.7429 -682.7971
[57] -682.8523 -682.9084 -682.9654 -683.0233 -683.0820 -683.1414 -683.2016
[64] -683.2624 -683.3239 -683.3860 -683.4487 -683.5119 -683.5756 -683.6398
[71] -683.7045 -683.7696 -683.8351 -683.9009 -683.9672 -684.0337 -684.1006
[78] -684.1677 -684.2352 -684.3029 -684.3708 -684.4389 -684.5073 -684.5758
[85] -684.6445 -684.7134 -684.7824 -684.8515 -684.9208 -684.9901 -685.0596
[92] -685.1291 -685.1988 -685.2685 -685.3382 -685.4080 -685.4779 -685.5477
[99] -685.6176 -685.6875
Call:
polr(formula = Sat ~ Infl + Type + Cont, data = housing)
Coefficients:
InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace
1.438398e-12 1.442159e-12 1.081215e-12 1.080646e-12 1.077125e-12
ContHigh
2.158385e-12
Intercepts:
Low|Medium Medium|High
-0.6931472 0.6931472
Residual Deviance: 158.2002
AIC: 174.2002
Call:
polr(formula = Sat ~ Infl + Type + Cont)
Coefficients:
InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace
1.438398e-12 1.442159e-12 1.081215e-12 1.080646e-12 1.077125e-12
ContHigh
2.158385e-12
Intercepts:
Low|Medium Medium|High
-0.6931472 0.6931472
Residual Deviance: 158.2002
AIC: 174.2002
Call:
polr(formula = Sat ~ Infl + Type + Cont)
Coefficients:
InflMedium InflHigh TypeApartment TypeAtrium TypeTerrace
1.438398e-12 1.442159e-12 1.081215e-12 1.080646e-12 1.077125e-12
ContHigh
2.158385e-12
Intercepts:
Low|Medium Medium|High
-0.6931472 0.6931472
Residual Deviance: 158.2002
AIC: 174.2002
Call:
qda(cl ~ ., data = iris3df)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
train.Sepal.L. train.Sepal.W. train.Petal.L. train.Petal.W.
c 5.892 2.708 4.140 1.276
s 4.996 3.324 1.484 0.272
v 6.628 2.980 5.504 2.000
Call:
qda(cl ~ ., data = iris3df)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
train.Sepal.L. train.Sepal.W. train.Petal.L. train.Petal.W.
c 5.892 2.708 4.140 1.276
s 4.996 3.324 1.484 0.272
v 6.628 2.980 5.504 2.000
Call:
qda(cl ~ ., data = .)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
train.Sepal.L. train.Sepal.W. train.Petal.L. train.Petal.W.
c 5.892 2.708 4.140 1.276
s 4.996 3.324 1.484 0.272
v 6.628 2.980 5.504 2.000
Call:
rlm(formula = stack.loss ~ ., data = stackloss)
Converged in 9 iterations
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-41.0265311 0.8293739 0.9261082 -0.1278492
Degrees of freedom: 21 total; 17 residual
Scale estimate: 2.44
Call:
rlm(formula = stack.loss ~ ., data = stackloss)
Converged in 9 iterations
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-41.0265311 0.8293739 0.9261082 -0.1278492
Degrees of freedom: 21 total; 17 residual
Scale estimate: 2.44
Call: rlm(formula = stack.loss ~ ., data = .)
Residuals:
Min 1Q Median 3Q Max
-8.91753 -1.73127 0.06187 1.54306 6.50163
Coefficients:
Value Std. Error t value
(Intercept) -41.0265 9.8073 -4.1832
Air.Flow 0.8294 0.1112 7.4597
Water.Temp 0.9261 0.3034 3.0524
Acid.Conc. -0.1278 0.1289 -0.9922
Residual standard error: 2.441 on 17 degrees of freedom
Call: rlm(formula = stack.loss ~ ., data = ., psi = psi.hampel, init = "lts")
Residuals:
Min 1Q Median 3Q Max
-7.6599 -1.7635 -0.3594 2.3209 5.7864
Coefficients:
Value Std. Error t value
(Intercept) -40.4748 11.8929 -3.4033
Air.Flow 0.7411 0.1348 5.4967
Water.Temp 1.2251 0.3679 3.3296
Acid.Conc. -0.1455 0.1563 -0.9313
Residual standard error: 3.088 on 17 degrees of freedom
Warning message:
In is.na(y) : is.na() applied to non-(list or vector) of type 'language'
Call: rlm(formula = stack.loss ~ ., data = ., psi = psi.bisquare)
Residuals:
Min 1Q Median 3Q Max
-10.4356 -1.7065 -0.2392 0.8797 6.9326
Coefficients:
Value Std. Error t value
(Intercept) -42.2853 9.5316 -4.4363
Air.Flow 0.9275 0.1081 8.5841
Water.Temp 0.6507 0.2949 2.2068
Acid.Conc. -0.1123 0.1252 -0.8970
Residual standard error: 2.282 on 17 degrees of freedom
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