Last updated on 2025-03-09 07:53:49 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.0-29 | 6.22 | 388.13 | 394.35 | OK | |
| r-devel-linux-x86_64-debian-gcc | 2.0-29 | 3.80 | 309.91 | 313.71 | OK | |
| r-devel-linux-x86_64-fedora-clang | 2.0-29 | 593.01 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0-29 | 581.63 | ERROR | |||
| r-devel-macos-arm64 | 2.0-29 | 160.00 | OK | |||
| r-devel-macos-x86_64 | 2.0-29 | 354.00 | OK | |||
| r-devel-windows-x86_64 | 2.0-29 | 9.00 | 331.00 | 340.00 | OK | |
| r-patched-linux-x86_64 | 2.0-29 | 7.53 | 355.14 | 362.67 | OK | |
| r-release-linux-x86_64 | 2.0-29 | 6.10 | 357.32 | 363.42 | OK | |
| r-release-macos-arm64 | 2.0-29 | 159.00 | OK | |||
| r-release-macos-x86_64 | 2.0-29 | 232.00 | OK | |||
| r-release-windows-x86_64 | 2.0-29 | 8.00 | 359.00 | 367.00 | OK | |
| r-oldrel-macos-arm64 | 2.0-29 | OK | ||||
| r-oldrel-macos-x86_64 | 2.0-29 | 357.00 | OK | |||
| r-oldrel-windows-x86_64 | 2.0-29 | 9.00 | 429.00 | 438.00 | OK |
Version: 2.0-29
Check: tests
Result: ERROR
Running ‘testthat.R’ [143s/347s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-5
Super Learner
Version: 2.0-29
Package created on 2024-02-06
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.100027 0.73787905
SL.xgboost_All 1.532526 0.01813194
SL.xgb.1_All 1.290361 0.24398901
SL.xgb.2_All 1.176793 0.00000000
SL.xgb.3_All 1.321512 0.00000000
SL.xgb.4_All 1.223680 0.00000000
SL.xgb.5_All 1.593716 0.00000000
SL.xgb.6_All 1.544470 0.00000000
SL.xgb.7_All 1.600138 0.00000000
SL.xgb.8_All 1.555782 0.00000000
SL.xgb.9_All 1.642955 0.00000000
SL.xgb.10_All 1.637269 0.00000000
SL.xgb.11_All 1.643616 0.00000000
SL.xgb.12_All 1.638586 0.00000000
Warning: The response y is integer, bartMachine will run regression.
Warning: The response y is integer, bartMachine will run regression.
Warning: The response y is integer, bartMachine will run regression.
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Y
0 1
53 47
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
$params$ntree
[1] 100
[1] "SL.randomForest_1" "X" "Y"
[4] "create_rf" "data"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.045984 1
$grid
mtry
1 1
2 4
3 20
$names
[1] "SL.randomForest_1" "SL.randomForest_2" "SL.randomForest_3"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.06729890 0.93195369
SL.randomForest_2_All 0.07219426 0.00000000
SL.randomForest_3_All 0.07243423 0.06804631
$grid
alpha
1 0.00
2 0.25
3 0.50
4 0.75
5 1.00
$names
[1] "SL.glmnet_0" "SL.glmnet_0.25" "SL.glmnet_0.5" "SL.glmnet_0.75"
[5] "SL.glmnet_1"
$base_learner
[1] "SL.glmnet"
$params
list()
[1] "SL.glmnet_0" "SL.glmnet_0.25" "SL.glmnet_0.5" "SL.glmnet_0.75"
[5] "SL.glmnet_1"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = ls(learners),
cvControl = list(V = 2), env = learners)
Risk Coef
SL.glmnet_0_All 0.08849610 0
SL.glmnet_0.25_All 0.08116755 0
SL.glmnet_0.5_All 0.06977106 1
SL.glmnet_0.75_All 0.07686953 0
SL.glmnet_1_All 0.07730595 0
Call:
SuperLearner(Y = Y, X = X_clean, family = binomial(), SL.library = c("SL.mean",
svm$names), cvControl = list(V = 3))
Risk Coef
SL.mean_All 0.25711218 0.0000000
SL.svm_polynomial_All 0.08463484 0.1443046
SL.svm_radial_All 0.06530910 0.0000000
SL.svm_sigmoid_All 0.05716227 0.8556954
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Got an error, as expected.
<simpleError in predict.glmnet(object$glmnet.fit, newx, s = lambda, ...): The number of variables in newx must be 8>
Got an error, as expected.
<simpleError in predict.glmnet(object$glmnet.fit, newx, s = lambda, ...): The number of variables in newx must be 8>
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.6696 0.02186479
SL.lm_All 24.3340 0.97813521
V1
Min. :-3.695
1st Qu.:17.557
Median :22.128
Mean :22.533
3rd Qu.:27.303
Max. :44.189
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2349366 0
SL.lm_All 0.1125027 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.1986827 0.31226655
SL.glmnet_All 0.1803963 0.66105261
SL.mean_All 0.2534500 0.02668084
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.1921176 0.08939677
SL.glmnet_All 0.1635548 0.91060323
SL.mean_All 0.2504500 0.00000000
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2279346 0.05397859
SL.glmnet_All 0.1670620 0.94602141
SL.mean_All 0.2504500 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5804469 0.1760951
SL.glmnet_All 0.5010294 0.8239049
SL.mean_All 0.6964542 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0.1338597
SL.glmnet_All 0.5027498 0.8661403
SL.mean_All 0.7000679 0.0000000
SL.bad_algorithm_All NA 0.0000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2033781 0.16438434
SL.glmnet_All 0.1740498 0.82391928
SL.mean_All 0.2516500 0.01169638
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 295.8455 0.3333333
SL.glmnet_All 205.3289 0.3333333
SL.mean_All 277.1389 0.3333333
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Error in (function (Y, X, newX, ...) : bad algorithm
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 212.5569 0.25
SL.glmnet_All 193.9384 0.25
SL.mean_All 277.1389 0.25
SL.bad_algorithm_All NA 0.25
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2533780 0.3333333
SL.glmnet_All 0.1869683 0.3333333
SL.mean_All 0.5550495 0.3333333
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2467721 0.2982123
SL.glmnet_All 0.1705535 0.3508938
SL.mean_All 0.5150135 0.3508938
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Y
0 1
62 38
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = sl_lib, cvControl = list(V = 2))
Risk Coef
SL.randomForest_All 0.0384594 0.98145221
SL.mean_All 0.2356000 0.01854779
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.05215472 1
SL.randomForest_1 <- function(...) SL.randomForest(...)
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1"
[1] 1
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.04151372 1
$grid
mtry
1 1
2 2
$names
[1] "SL.randomForest_1" "SL.randomForest_2"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1" "SL.randomForest_2"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.05852161 0.8484752
SL.randomForest_2_All 0.05319324 0.1515248
$grid
mtry
1 1
2 2
$names
[1] "SL.randomForest_1" "SL.randomForest_2"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1" "SL.randomForest_2"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.04540374 0.2120815
SL.randomForest_2_All 0.03931360 0.7879185
$grid
mtry nodesize maxnodes
1 1 NULL NULL
2 2 NULL NULL
$names
[1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_NULL_NULL_All 0.05083433 0.2589592
SL.randomForest_2_NULL_NULL_All 0.04697238 0.7410408
$grid
mtry maxnodes
1 1 5
2 2 5
3 1 10
4 2 10
5 1 NULL
6 2 NULL
$names
[1] "SL.randomForest_1_5" "SL.randomForest_2_5" "SL.randomForest_1_10"
[4] "SL.randomForest_2_10" "SL.randomForest_1_NULL" "SL.randomForest_2_NULL"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_5_All 0.04597977 0.0000000
SL.randomForest_2_5_All 0.03951320 0.0000000
SL.randomForest_1_10_All 0.04337471 0.1117946
SL.randomForest_2_10_All 0.03898477 0.8882054
SL.randomForest_1_NULL_All 0.04395171 0.0000000
SL.randomForest_2_NULL_All 0.03928269 0.0000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_5_All 0.05330062 0.4579034
SL.randomForest_2_5_All 0.05189278 0.0000000
SL.randomForest_1_10_All 0.05263432 0.1614643
SL.randomForest_2_10_All 0.05058144 0.0000000
SL.randomForest_1_NULL_All 0.05415397 0.0000000
SL.randomForest_2_NULL_All 0.05036643 0.3806323
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_5_All 0.05978213 0
SL.randomForest_2_5_All 0.05628852 0
SL.randomForest_1_10_All 0.05751494 0
SL.randomForest_2_10_All 0.05889935 0
SL.randomForest_1_NULL_All 0.05629605 1
SL.randomForest_2_NULL_All 0.05807645 0
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39743
R squared (OOB): 0.8770796
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08374536
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.74731
R squared (OOB): 0.8729433
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08326064
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 4 | WARN 22 | SKIP 0 | PASS 64 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
min(sl_bad$SL.predict) is not more than 0. Difference: NA
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
max(sl_bad$SL.predict) is not less than 1. Difference: NA
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
min(pred$pred) is not more than 0. Difference: NA
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
max(pred$pred) is not less than 1. Difference: NA
[ FAIL 4 | WARN 22 | SKIP 0 | PASS 64 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 2.0-29
Check: tests
Result: ERROR
Running ‘testthat.R’ [142s/514s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-5
Super Learner
Version: 2.0-29
Package created on 2024-02-06
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.100027 0.73787905
SL.xgboost_All 1.532526 0.01813194
SL.xgb.1_All 1.290361 0.24398901
SL.xgb.2_All 1.176793 0.00000000
SL.xgb.3_All 1.321512 0.00000000
SL.xgb.4_All 1.223680 0.00000000
SL.xgb.5_All 1.593716 0.00000000
SL.xgb.6_All 1.544470 0.00000000
SL.xgb.7_All 1.600138 0.00000000
SL.xgb.8_All 1.555782 0.00000000
SL.xgb.9_All 1.642955 0.00000000
SL.xgb.10_All 1.637269 0.00000000
SL.xgb.11_All 1.643616 0.00000000
SL.xgb.12_All 1.638586 0.00000000
Warning: The response y is integer, bartMachine will run regression.
Warning: The response y is integer, bartMachine will run regression.
Warning: The response y is integer, bartMachine will run regression.
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Y
0 1
53 47
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
$params$ntree
[1] 100
[1] "SL.randomForest_1" "X" "Y"
[4] "create_rf" "data"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.045984 1
$grid
mtry
1 1
2 4
3 20
$names
[1] "SL.randomForest_1" "SL.randomForest_2" "SL.randomForest_3"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.06729890 0.93195369
SL.randomForest_2_All 0.07219426 0.00000000
SL.randomForest_3_All 0.07243423 0.06804631
$grid
alpha
1 0.00
2 0.25
3 0.50
4 0.75
5 1.00
$names
[1] "SL.glmnet_0" "SL.glmnet_0.25" "SL.glmnet_0.5" "SL.glmnet_0.75"
[5] "SL.glmnet_1"
$base_learner
[1] "SL.glmnet"
$params
list()
[1] "SL.glmnet_0" "SL.glmnet_0.25" "SL.glmnet_0.5" "SL.glmnet_0.75"
[5] "SL.glmnet_1"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = ls(learners),
cvControl = list(V = 2), env = learners)
Risk Coef
SL.glmnet_0_All 0.08849610 0
SL.glmnet_0.25_All 0.08116755 0
SL.glmnet_0.5_All 0.06977106 1
SL.glmnet_0.75_All 0.07686953 0
SL.glmnet_1_All 0.07730595 0
Call:
SuperLearner(Y = Y, X = X_clean, family = binomial(), SL.library = c("SL.mean",
svm$names), cvControl = list(V = 3))
Risk Coef
SL.mean_All 0.25711218 0.0000000
SL.svm_polynomial_All 0.08463484 0.1443046
SL.svm_radial_All 0.06530910 0.0000000
SL.svm_sigmoid_All 0.05716227 0.8556954
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Got an error, as expected.
<simpleError in predict.glmnet(object$glmnet.fit, newx, s = lambda, ...): The number of variables in newx must be 8>
Got an error, as expected.
<simpleError in predict.glmnet(object$glmnet.fit, newx, s = lambda, ...): The number of variables in newx must be 8>
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.6696 0.02186479
SL.lm_All 24.3340 0.97813521
V1
Min. :-3.695
1st Qu.:17.557
Median :22.128
Mean :22.533
3rd Qu.:27.303
Max. :44.189
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2349366 0
SL.lm_All 0.1125027 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.1986827 0.31226655
SL.glmnet_All 0.1803963 0.66105261
SL.mean_All 0.2534500 0.02668084
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.1921176 0.08939677
SL.glmnet_All 0.1635548 0.91060323
SL.mean_All 0.2504500 0.00000000
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2279346 0.05397859
SL.glmnet_All 0.1670620 0.94602141
SL.mean_All 0.2504500 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5804469 0.1760951
SL.glmnet_All 0.5010294 0.8239049
SL.mean_All 0.6964542 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0.1338597
SL.glmnet_All 0.5027498 0.8661403
SL.mean_All 0.7000679 0.0000000
SL.bad_algorithm_All NA 0.0000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2033781 0.16438434
SL.glmnet_All 0.1740498 0.82391928
SL.mean_All 0.2516500 0.01169638
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 295.8455 0.3333333
SL.glmnet_All 205.3289 0.3333333
SL.mean_All 277.1389 0.3333333
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Error in (function (Y, X, newX, ...) : bad algorithm
Error: nlopt_add_equality_mconstraint returned NLOPT_INVALID_ARGS.
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 212.5569 0.25
SL.glmnet_All 193.9384 0.25
SL.mean_All 277.1389 0.25
SL.bad_algorithm_All NA 0.25
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2533780 0.3333333
SL.glmnet_All 0.1869683 0.3333333
SL.mean_All 0.5550495 0.3333333
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2467721 0.2982123
SL.glmnet_All 0.1705535 0.3508938
SL.mean_All 0.5150135 0.3508938
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Y
0 1
62 38
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = sl_lib, cvControl = list(V = 2))
Risk Coef
SL.randomForest_All 0.0384594 0.98145221
SL.mean_All 0.2356000 0.01854779
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.05215472 1
SL.randomForest_1 <- function(...) SL.randomForest(...)
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1"
[1] 1
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.04151372 1
$grid
mtry
1 1
2 2
$names
[1] "SL.randomForest_1" "SL.randomForest_2"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1" "SL.randomForest_2"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.05852161 0.8484752
SL.randomForest_2_All 0.05319324 0.1515248
$grid
mtry
1 1
2 2
$names
[1] "SL.randomForest_1" "SL.randomForest_2"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1" "SL.randomForest_2"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.04540374 0.2120815
SL.randomForest_2_All 0.03931360 0.7879185
$grid
mtry nodesize maxnodes
1 1 NULL NULL
2 2 NULL NULL
$names
[1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_NULL_NULL_All 0.05083433 0.2589592
SL.randomForest_2_NULL_NULL_All 0.04697238 0.7410408
$grid
mtry maxnodes
1 1 5
2 2 5
3 1 10
4 2 10
5 1 NULL
6 2 NULL
$names
[1] "SL.randomForest_1_5" "SL.randomForest_2_5" "SL.randomForest_1_10"
[4] "SL.randomForest_2_10" "SL.randomForest_1_NULL" "SL.randomForest_2_NULL"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_5_All 0.04597977 0.0000000
SL.randomForest_2_5_All 0.03951320 0.0000000
SL.randomForest_1_10_All 0.04337471 0.1117946
SL.randomForest_2_10_All 0.03898477 0.8882054
SL.randomForest_1_NULL_All 0.04395171 0.0000000
SL.randomForest_2_NULL_All 0.03928269 0.0000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_5_All 0.05330062 0.4579034
SL.randomForest_2_5_All 0.05189278 0.0000000
SL.randomForest_1_10_All 0.05263432 0.1614643
SL.randomForest_2_10_All 0.05058144 0.0000000
SL.randomForest_1_NULL_All 0.05415397 0.0000000
SL.randomForest_2_NULL_All 0.05036643 0.3806323
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_5_All 0.05978213 0
SL.randomForest_2_5_All 0.05628852 0
SL.randomForest_1_10_All 0.05751494 0
SL.randomForest_2_10_All 0.05889935 0
SL.randomForest_1_NULL_All 0.05629605 1
SL.randomForest_2_NULL_All 0.05807645 0
Growing trees.. Progress: 61%. Estimated remaining time: 19 seconds.
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.57547
R squared (OOB): 0.8749748
Growing trees.. Progress: 81%. Estimated remaining time: 7 seconds.
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08262419
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.46443
R squared (OOB): 0.8762876
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08395011
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 4 | WARN 22 | SKIP 0 | PASS 64 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
min(sl_bad$SL.predict) is not more than 0. Difference: NA
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
max(sl_bad$SL.predict) is not less than 1. Difference: NA
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
min(pred$pred) is not more than 0. Difference: NA
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
max(pred$pred) is not less than 1. Difference: NA
[ FAIL 4 | WARN 22 | SKIP 0 | PASS 64 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc