+
-
+
+
+
-
- -
-
-
-
+
-
-
+
-
-
+ inst/CITATION
+
+ inst/CITATION
Suchard MA, Simpson SE, Zorych I, Ryan P, Madigan D (2013). -“Massive parallelization of serial inference algorithms for complex generalized linear models.” +“Massive parallelization of serial inference algorithms for complex generalized linear models.” ACM Transactions on Modeling and Computer Simulation, 23, 10. -https://dl.acm.org/doi/10.1145/2414416.2414791. +https://dl.acm.org/doi/10.1145/2414416.2414791.
@Article{,
author = {M. A. Suchard and S. E. Simpson and I. Zorych and P. Ryan and D. Madigan},
@@ -132,79 +119,26 @@ Citation
url = {https://dl.acm.org/doi/10.1145/2414416.2414791},
}
- Marc A. Suchard. Author, maintainer. -
-Martijn J. Schuemie. Author. -
-Trevor R. Shaddox. Author. -
-Yuxi Tian. Author. -
-Jianxiao Yang. Author. -
-Eric Kawaguchi. Author. -
-Sushil Mittal. Contributor. -
-Observational Health Data Sciences and Informatics. Copyright holder. -
-Marcus Geelnard. Copyright holder, contributor.
-
provided the TinyThread library
Rutgers University. Copyright holder, contributor.
-
provided the HParSearch routine
R Development Core Team. Copyright holder, contributor.
-
provided the ZeroIn routine
+ hadesLogo
Cyclops is part of the HADES.
-Cyclops (Cyclic coordinate descent for logistic, Poisson and survival analysis) is an R package for performing large scale regularized regressions.
- library(Cyclops)
- cyclopsData <- createCyclopsDataFrame(formula)
- cyclopsFit <- fitCyclopsModel(cyclopsData) library(Cyclops)
+ cyclopsData <- createCyclopsDataFrame(formula)
+ cyclopsFit <- fitCyclopsModel(cyclopsData)Cyclops in an R package, with most functionality implemented in C++. Cyclops uses cyclic coordinate descent to optimize the likelihood function, which makes use of the sparse nature of the data.
Requires R (version 3.1.0 or higher). Compilation on Windows requires RTools >= 3.4.
+Requires R (version 3.1.0 or higher). Compilation on Windows requires RTools >= 3.4.
In R, to install the latest stable version, install from CRAN:
-install.packages("Cyclops")install.packages("Cyclops")To install the latest development version, install from GitHub. Note that this will require RTools to be installed.
-install.packages("devtools")
-devtools::install_github("OHDSI/Cyclops")Documentation can be found on the package website.
-PDF versions of the documentation are also available:
+install.packages("devtools")
+devtools::install_github("OHDSI/Cyclops")Documentation can be found on the package website.
+PDF versions of the documentation are also available: * Package manual: Cyclops manual
+Read here how you can contribute to this package.
Read here how you can contribute to this package.
-Cyclops is licensed under Apache License 2.0. Cyclops contains the TinyThread libray.
-The TinyThread library is licensed under the zlib/libpng license as described here.
+The TinyThread library is licensed under the zlib/libpng license as described here.
+ Changes:
-Changes:
-
nocenter in survival package when testing predicted hazard functionBH
+auto option to cvRepetitions
+dbplyr:::$.tbl_lazy
+dbplyr v2.4.0
+Changes:
-computeAsymptoticPrecisionMatrix(); value was priorType::Rf_error()
+minValues
+Changes:
+Changes:
+Changes:
+Changes:
+nocenter in survival package when testing predicted hazard functionChanges:
+getCyclopsProfileLogLikelihood when starting with extreme coefficientsChanges:
-Andromeda from ff to hold large datasets. This change breaks APIAndromeda from ff to hold large datasets. This change breaks APIChanges:
-std::exit
+std::exit
Changes:
-R 4.0 factorsChanges:
-RNGversion("3.5.0") in unit-tests to reproduce old RNG behaviorRNGversion("3.5.0") in unit-tests to reproduce old RNG behaviorChanges:
-ModelData.cpp and ModelSpecifics.hpp
+ModelData.cpp and ModelSpecifics.hpp
Changes:
-RcppParallel (until TBB is again R-compliant)Changes:
-MASS and microbenchmarks
+MASS and microbenchmarks
AbstractModelSpecifics
Changes:
-<complex> header, needed for R 3.5 builds<complex> header, needed for R 3.5 buildspragma statements used to quiet RcppEigen and RcppParallel
Changes:
-.checkCovariates when excluding covariates from regularization.checkCovariates when excluding covariates from regularizationChanges:
-R/Cyclops-package.R
+ Cyclops-package.RdThis model fitting tool incorporates cyclic coordinate descent and majorization-minimization approaches to fit a variety of regression models found in large-scale observational healthcare data. Implementations focus on computational optimization and fine-scale parallelization to yield efficient inference in massive datasets. Please see: Suchard, Simpson, Zorych, Ryan and Madigan (2013) doi:10.1145/2414416.2414791 +.
+Useful links:
Report bugs at https://github.com/ohdsi/cyclops/issues
+ Multitype creates a multitype outcome object, usually used as a response variable in a
+
Multitype creates a multitype outcome object, usually used as a response variable in a
hierarchical Cyclops model fit.
Multitype(y, type)- -
| y | -Numeric: Response count(s) |
-
|---|---|
| type | -Numeric or factor: Response type |
-
An object of class Multitype with length equal to the length of y and type.
+#> y type -#> [1,] 0 1 -#> [2,] 1 1 -#> [3,] 0 2 -#> attr(,"contrasts") -#> B -#> A 0 -#> B 1 -#> attr(,"class") -#> [1] "Multitype"-
Multitype(y, type)Numeric: Response count(s)
Numeric or factor: Response type
An object of class Multitype with length equal to the length of y and type.
+ R/ModelFit.R
+ Source: R/ModelFit.R
aconfint.Rdaconfint( - object, - parm, - level = 0.95, - control, - overrideNoRegularization = FALSE, - ... -)- -
| object | -A fitted Cyclops model object |
-
|---|---|
| parm | -A specification of which parameters require confidence intervals, -either a vector of numbers of covariateId names |
-
| level | -Numeric: confidence level required |
-
| control | -A |
-
| overrideNoRegularization | -Logical: Enable confidence interval estimation for regularized parameters |
-
| ... | -Additional argument(s) for methods |
-
A matrix with columns reporting lower and upper confidence limits for each parameter. -These columns are labelled as (1-level) / 2 and 1 - (1 - level) / 2 in -(by default 2.5
+aconfint(
+ object,
+ parm,
+ level = 0.95,
+ control,
+ overrideNoRegularization = FALSE,
+ ...
+)A fitted Cyclops model object
A specification of which parameters require confidence intervals, +either a vector of numbers of covariateId names
Numeric: confidence level required
A "cyclopsControl" object constructed by createControl
Logical: Enable confidence interval estimation for regularized parameters
Additional argument(s) for methods
A matrix with columns reporting lower and upper confidence limits for each parameter. +These columns are labelled as (1-level) / 2 and 1 - (1 - level) / 2 in % +(by default 2.5% and 97.5%)
+
+ R/DataManagement.R
+ Source: R/DataManagement.R
appendSqlCyclopsData.RdappendSqlCyclopsData appends data to an OHDSI data object.
appendSqlCyclopsData( - object, - oStratumId, - oRowId, - oY, - oTime, - cRowId, - cCovariateId, - cCovariateValue -)- -
| object | -OHDSI Cyclops data object to append entries |
-
|---|---|
| oStratumId | -Integer vector (optional): non-unique stratum identifier for each row in outcomes table |
-
| oRowId | -Integer vector: unique row identifier for each row in outcomes table |
-
| oY | -Numeric vector: model outcome variable for each row in outcomes table |
-
| oTime | -Numeric vector (optional): exposure interval or censoring time for each row in outcomes table |
-
| cRowId | -Integer vector: non-unique row identifier for each row in covariates table that matches a single outcomes table entry |
-
| cCovariateId | -Integer vector: covariate identifier |
-
| cCovariateValue | -Numeric vector: covariate value |
-
appendSqlCyclopsData(
+ object,
+ oStratumId,
+ oRowId,
+ oY,
+ oTime,
+ cRowId,
+ cCovariateId,
+ cCovariateValue
+)OHDSI Cyclops data object to append entries
Integer vector (optional): non-unique stratum identifier for each row in outcomes table
Integer vector: unique row identifier for each row in outcomes table
Numeric vector: model outcome variable for each row in outcomes table
Numeric vector (optional): exposure interval or censoring time for each row in outcomes table
Integer vector: non-unique row identifier for each row in covariates table that matches a single outcomes table entry
Integer vector: covariate identifier
Numeric vector: covariate value
Append data using two tables. The outcomes table is dense and contains ... The covariates table is sparse and contains ... -All entries in the outcome table must be sorted in increasing order by oStratumId, oRowId. All entries in the covariate table -must be sorted in increasing order by cRowId. Each cRowId value must match exactly one oRowId value.
+All entries in the outcome table must be sorted in increasing order by (oStratumId, oRowId). All entries in the covariate table +must be sorted in increasing order by (cRowId). Each cRowId value must match exactly one oRowId value. +
+ coef.cyclopsFit extracts model coefficients from an Cyclops model fit object
# S3 method for cyclopsFit -coef(object, rescale = FALSE, ignoreConvergence = FALSE, ...)- -
| object | -Cyclops model fit object |
-
|---|---|
| rescale | -Boolean: rescale coefficients for unnormalized covariate values |
-
| ignoreConvergence | -Boolean: return coefficients even if fit object did not converge |
-
| ... | -Other arguments |
-
Named numeric vector of model coefficients.
+# S3 method for cyclopsFit
+coef(object, rescale = FALSE, ignoreConvergence = FALSE, ...)Cyclops model fit object
Boolean: rescale coefficients for unnormalized covariate values
Boolean: return coefficients even if fit object did not converge
Other arguments
Named numeric vector of model coefficients.
+
+ R/ModelFit.R
+ Source: R/ModelFit.R
confint.cyclopsFit.Rd# S3 method for cyclopsFit -confint( - object, - parm, - level = 0.95, - overrideNoRegularization = FALSE, - includePenalty = TRUE, - rescale = FALSE, - ... -)+
# S3 method for cyclopsFit
+confint(
+ object,
+ parm,
+ level = 0.95,
+ overrideNoRegularization = FALSE,
+ includePenalty = TRUE,
+ rescale = FALSE,
+ ...
+)A fitted Cyclops model object
A specification of which parameters require confidence intervals, +either a vector of numbers of covariateId names
Numeric: confidence level required
| object | -A fitted Cyclops model object |
-
|---|---|
| parm | -A specification of which parameters require confidence intervals, -either a vector of numbers of covariateId names |
-
| level | -Numeric: confidence level required |
-
| overrideNoRegularization | -Logical: Enable confidence interval estimation for regularized parameters |
-
| includePenalty | -Logical: Include regularized covariate penalty in profile |
-
| rescale | -Boolean: rescale coefficients for unnormalized covariate values |
-
| ... | -Additional argument(s) for methods |
-
Logical: Enable confidence interval estimation for regularized parameters
A matrix with columns reporting lower and upper confidence limits for each parameter. +
Logical: Include regularized covariate penalty in profile
Boolean: rescale coefficients for unnormalized covariate values
Additional argument(s) for methods
A matrix with columns reporting lower and upper confidence limits for each parameter. These columns are labelled as (1-level) / 2 and 1 - (1 - level) / 2 in percent (by default 2.5 percent and 97.5 percent)
++#Generate some simulated data: -sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5, - model = "poisson") -#> Sparseness = 74.45 %cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr", - addIntercept = TRUE) -#> Sorting covariates by covariateId and rowId-#Define the prior and control objects to use cross-validation for finding the -#optimal hyperparameter: -prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE) -control <- createControl(cvType = "auto", noiseLevel = "quiet") - -#Fit the model -fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control) -#> Using cross-validation selector type byRow -#> Performing 10-fold cross-validation [seed = 1623936644] with data partitions of sizes 100 100 100 100 100 100 100 100 100 100 -#> Using 1 thread(s) -#> Starting var = 0.2555 (default) -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #1 Rep #1 pred log like = 375.98 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #2 Rep #1 pred log like = 358.921 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #3 Rep #1 pred log like = 402.461 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #4 Rep #1 pred log like = 224.91 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #5 Rep #1 pred log like = 416.062 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #6 Rep #1 pred log like = 381.177 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #7 Rep #1 pred log like = 410.934 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #8 Rep #1 pred log like = 363.449 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #9 Rep #1 pred log like = 380.333 -#> Running at Laplace(2.79782) None Grid-point #1 at 0.2555 Fold #10 Rep #1 pred log like = 355.956 -#> AvgPred = 367.018 with stdev = 51.4233 -#> Completed at 0.2555 -#> Next point at 2.555 with value 0 and continue = 1 -#> search[ 0.2555 ] = 367.018(51.4233) -#> -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #1 Rep #1 pred log like = 375.99 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #2 Rep #1 pred log like = 358.892 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #3 Rep #1 pred log like = 402.439 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #4 Rep #1 pred log like = 224.885 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #5 Rep #1 pred log like = 416.043 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #6 Rep #1 pred log like = 381.179 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #7 Rep #1 pred log like = 410.904 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #8 Rep #1 pred log like = 363.425 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #9 Rep #1 pred log like = 380.301 -#> Running at Laplace(0.884748) None Grid-point #2 at 2.555 Fold #10 Rep #1 pred log like = 355.924 -#> AvgPred = 366.998 with stdev = 51.4251 -#> Completed at 2.555 -#> Next point at 0.02555 with value 0 and continue = 1 -#> search[ 0.2555 ] = 367.018(51.4233) -#> search[ 2.555 ] = 366.998(51.4251) -#> -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #1 Rep #1 pred log like = 375.97 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #2 Rep #1 pred log like = 358.995 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #3 Rep #1 pred log like = 402.483 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #4 Rep #1 pred log like = 224.985 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #5 Rep #1 pred log like = 416.12 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #6 Rep #1 pred log like = 381.169 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #7 Rep #1 pred log like = 411.069 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #8 Rep #1 pred log like = 363.513 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #9 Rep #1 pred log like = 380.385 -#> Running at Laplace(8.84748) None Grid-point #3 at 0.02555 Fold #10 Rep #1 pred log like = 356.053 -#> AvgPred = 367.074 with stdev = 51.4186 -#> Completed at 0.02555 -#> Next point at 0.002555 with value 0 and continue = 1 -#> search[ 0.02555 ] = 367.074(51.4186) -#> search[ 0.2555 ] = 367.018(51.4233) -#> search[ 2.555 ] = 366.998(51.4251) -#> -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #1 Rep #1 pred log like = 375.97 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #2 Rep #1 pred log like = 359.05 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #3 Rep #1 pred log like = 402.485 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #4 Rep #1 pred log like = 225.084 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #5 Rep #1 pred log like = 416.178 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #6 Rep #1 pred log like = 381.169 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #7 Rep #1 pred log like = 411.373 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #8 Rep #1 pred log like = 363.543 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #9 Rep #1 pred log like = 380.399 -#> Running at Laplace(27.9782) None Grid-point #4 at 0.002555 Fold #10 Rep #1 pred log like = 356.206 -#> AvgPred = 367.146 with stdev = 51.4189 -#> Completed at 0.002555 -#> Next point at 0.0002555 with value 0 and continue = 1 -#> search[ 0.002555 ] = 367.146(51.4189) -#> search[ 0.02555 ] = 367.074(51.4186) -#> search[ 0.2555 ] = 367.018(51.4233) -#> search[ 2.555 ] = 366.998(51.4251) -#> -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #1 Rep #1 pred log like = 375.97 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #2 Rep #1 pred log like = 359.05 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #3 Rep #1 pred log like = 402.485 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #4 Rep #1 pred log like = 225.084 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #5 Rep #1 pred log like = 416.178 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #6 Rep #1 pred log like = 381.169 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #7 Rep #1 pred log like = 411.373 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #8 Rep #1 pred log like = 363.543 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #9 Rep #1 pred log like = 380.399 -#> Running at Laplace(88.4748) None Grid-point #5 at 0.0002555 Fold #10 Rep #1 pred log like = 356.206 -#> AvgPred = 367.146 with stdev = 51.4189 -#> Completed at 0.0002555 -#> Next point at 2.16648e-14 with value 367.335 and continue = 0 -#> search[ 0.0002555 ] = 367.146(51.4189) -#> search[ 0.002555 ] = 367.146(51.4189) -#> search[ 0.02555 ] = 367.074(51.4186) -#> search[ 0.2555 ] = 367.018(51.4233) -#> search[ 2.555 ] = 366.998(51.4251) -#> -#> -#> Maximum predicted log likelihood (367.335) estimated at: -#> 2.16648e-14 (variance) -#> 9.6081e+06 (lambda) -#> -#> Fitting model at optimal hyperparameter -#> Using prior: Laplace(9.6081e+06) None#> [1] 2.166483e-14#> 'log Lik.' -1973.343 (df=3)#> (Intercept) 1 2 -#> -3.937029 0.000000 0.000000-#We can only retrieve the confidence interval for unregularized coefficients: -confint(fit, c(0)) -#> Using 1 thread(s)#> covariate 2.5 % 97.5 % evaluations -#> [1,] 0 -3.965463 -3.908849 24
#Generate some simulated data:
+sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5,
+ model = "poisson")
+#> Sparseness = 77 %
+cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr",
+ addIntercept = TRUE)
+#> Sorting covariates by covariateId and rowId
+
+#Define the prior and control objects to use cross-validation for finding the
+#optimal hyperparameter:
+prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
+control <- createControl(cvType = "auto", noiseLevel = "quiet")
+
+#Fit the model
+fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)
+#> Using cross-validation selector type byRow
+#> Performing 10-fold cross-validation [seed = 1698757931] with data partitions of sizes 100 100 100 100 100 100 100 100 100 100
+#> Using 1 thread(s)
+#> Starting var = 0.23 (default)
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #1 Rep #1 pred log like = 267.858
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #2 Rep #1 pred log like = 210.218
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #3 Rep #1 pred log like = 247.642
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #4 Rep #1 pred log like = 268.625
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #5 Rep #1 pred log like = 234.92
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #6 Rep #1 pred log like = 256.935
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #7 Rep #1 pred log like = 237.206
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #8 Rep #1 pred log like = 203.575
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #9 Rep #1 pred log like = 268.979
+#> Running at Laplace(2.94884) None Grid-point #1 at 0.23 Fold #10 Rep #1 pred log like = 313.499
+#> AvgPred = 250.946 with stdev = 30.3841
+#> Completed at 0.23
+#> Next point at 2.3 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#>
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #1 Rep #1 pred log like = 267.817
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #2 Rep #1 pred log like = 210.18
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #3 Rep #1 pred log like = 247.667
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #4 Rep #1 pred log like = 268.65
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #5 Rep #1 pred log like = 234.965
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #6 Rep #1 pred log like = 256.945
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #7 Rep #1 pred log like = 237.151
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #8 Rep #1 pred log like = 203.606
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #9 Rep #1 pred log like = 268.987
+#> Running at Laplace(0.932505) None Grid-point #2 at 2.3 Fold #10 Rep #1 pred log like = 313.505
+#> AvgPred = 250.947 with stdev = 30.3849
+#> Completed at 2.3
+#> Next point at 23 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#>
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #1 Rep #1 pred log like = 267.803
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #2 Rep #1 pred log like = 210.168
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #3 Rep #1 pred log like = 247.674
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #4 Rep #1 pred log like = 268.657
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #5 Rep #1 pred log like = 234.98
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #6 Rep #1 pred log like = 256.948
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #7 Rep #1 pred log like = 237.134
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #8 Rep #1 pred log like = 203.616
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #9 Rep #1 pred log like = 268.988
+#> Running at Laplace(0.294884) None Grid-point #3 at 23 Fold #10 Rep #1 pred log like = 313.506
+#> AvgPred = 250.947 with stdev = 30.3852
+#> Completed at 23
+#> Next point at 230 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#>
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #1 Rep #1 pred log like = 267.799
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #2 Rep #1 pred log like = 210.165
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #3 Rep #1 pred log like = 247.677
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #4 Rep #1 pred log like = 268.66
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #5 Rep #1 pred log like = 234.984
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #6 Rep #1 pred log like = 256.948
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #7 Rep #1 pred log like = 237.128
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #8 Rep #1 pred log like = 203.619
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(0.0932505) None Grid-point #4 at 230 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 230
+#> Next point at 2300 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #1 Rep #1 pred log like = 267.798
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #4 Rep #1 pred log like = 268.66
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #5 Rep #1 pred log like = 234.985
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #7 Rep #1 pred log like = 237.126
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(0.0294884) None Grid-point #5 at 2300 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2300
+#> Next point at 23000 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #7 Rep #1 pred log like = 237.126
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(0.00932505) None Grid-point #6 at 23000 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 23000
+#> Next point at 230000 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #7 Rep #1 pred log like = 237.126
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(0.00294884) None Grid-point #7 at 230000 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 230000
+#> Next point at 2.3e+06 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(0.000932505) None Grid-point #8 at 2.3e+06 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+06
+#> Next point at 2.3e+07 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(0.000294884) None Grid-point #9 at 2.3e+07 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+07
+#> Next point at 2.3e+08 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(9.32505e-05) None Grid-point #10 at 2.3e+08 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+08
+#> Next point at 2.3e+09 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(2.94884e-05) None Grid-point #11 at 2.3e+09 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+09
+#> Next point at 2.3e+10 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(9.32505e-06) None Grid-point #12 at 2.3e+10 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+10
+#> Next point at 2.3e+11 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(2.94884e-06) None Grid-point #13 at 2.3e+11 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+11
+#> Next point at 2.3e+12 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(9.32505e-07) None Grid-point #14 at 2.3e+12 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+12
+#> Next point at 2.3e+13 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(2.94884e-07) None Grid-point #15 at 2.3e+13 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+13
+#> Next point at 2.3e+14 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#> search[ 2.3e+13 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(9.32505e-08) None Grid-point #16 at 2.3e+14 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+14
+#> Next point at 2.3e+15 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#> search[ 2.3e+13 ] = 250.948(30.3853)
+#> search[ 2.3e+14 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(2.94884e-08) None Grid-point #17 at 2.3e+15 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+15
+#> Next point at 2.3e+16 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#> search[ 2.3e+13 ] = 250.948(30.3853)
+#> search[ 2.3e+14 ] = 250.948(30.3853)
+#> search[ 2.3e+15 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(9.32505e-09) None Grid-point #18 at 2.3e+16 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+16
+#> Next point at 2.3e+17 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#> search[ 2.3e+13 ] = 250.948(30.3853)
+#> search[ 2.3e+14 ] = 250.948(30.3853)
+#> search[ 2.3e+15 ] = 250.948(30.3853)
+#> search[ 2.3e+16 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(2.94884e-09) None Grid-point #19 at 2.3e+17 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+17
+#> Next point at 2.3e+18 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#> search[ 2.3e+13 ] = 250.948(30.3853)
+#> search[ 2.3e+14 ] = 250.948(30.3853)
+#> search[ 2.3e+15 ] = 250.948(30.3853)
+#> search[ 2.3e+16 ] = 250.948(30.3853)
+#> search[ 2.3e+17 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(9.32505e-10) None Grid-point #20 at 2.3e+18 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+18
+#> Next point at 2.3e+19 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#> search[ 2.3e+13 ] = 250.948(30.3853)
+#> search[ 2.3e+14 ] = 250.948(30.3853)
+#> search[ 2.3e+15 ] = 250.948(30.3853)
+#> search[ 2.3e+16 ] = 250.948(30.3853)
+#> search[ 2.3e+17 ] = 250.948(30.3853)
+#> search[ 2.3e+18 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(2.94884e-10) None Grid-point #21 at 2.3e+19 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+19
+#> Next point at 2.3e+20 with value 0 and continue = 1
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#> search[ 2.3e+13 ] = 250.948(30.3853)
+#> search[ 2.3e+14 ] = 250.948(30.3853)
+#> search[ 2.3e+15 ] = 250.948(30.3853)
+#> search[ 2.3e+16 ] = 250.948(30.3853)
+#> search[ 2.3e+17 ] = 250.948(30.3853)
+#> search[ 2.3e+18 ] = 250.948(30.3853)
+#> search[ 2.3e+19 ] = 250.948(30.3853)
+#>
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #1 Rep #1 pred log like = 267.797
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #2 Rep #1 pred log like = 210.163
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #3 Rep #1 pred log like = 247.678
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #4 Rep #1 pred log like = 268.661
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #5 Rep #1 pred log like = 234.986
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #6 Rep #1 pred log like = 256.949
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #7 Rep #1 pred log like = 237.125
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #8 Rep #1 pred log like = 203.62
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #9 Rep #1 pred log like = 268.989
+#> Running at Laplace(9.32505e-11) None Grid-point #22 at 2.3e+20 Fold #10 Rep #1 pred log like = 313.507
+#> AvgPred = 250.948 with stdev = 30.3853
+#> Completed at 2.3e+20
+#> Next point at 2.41831e+12 with value 250.948 and continue = 0
+#> search[ 0.23 ] = 250.946(30.3841)
+#> search[ 2.3 ] = 250.947(30.3849)
+#> search[ 23 ] = 250.947(30.3852)
+#> search[ 230 ] = 250.948(30.3853)
+#> search[ 2300 ] = 250.948(30.3853)
+#> search[ 23000 ] = 250.948(30.3853)
+#> search[ 230000 ] = 250.948(30.3853)
+#> search[ 2.3e+06 ] = 250.948(30.3853)
+#> search[ 2.3e+07 ] = 250.948(30.3853)
+#> search[ 2.3e+08 ] = 250.948(30.3853)
+#> search[ 2.3e+09 ] = 250.948(30.3853)
+#> search[ 2.3e+10 ] = 250.948(30.3853)
+#> search[ 2.3e+11 ] = 250.948(30.3853)
+#> search[ 2.3e+12 ] = 250.948(30.3853)
+#> search[ 2.3e+13 ] = 250.948(30.3853)
+#> search[ 2.3e+14 ] = 250.948(30.3853)
+#> search[ 2.3e+15 ] = 250.948(30.3853)
+#> search[ 2.3e+16 ] = 250.948(30.3853)
+#> search[ 2.3e+17 ] = 250.948(30.3853)
+#> search[ 2.3e+18 ] = 250.948(30.3853)
+#> search[ 2.3e+19 ] = 250.948(30.3853)
+#> search[ 2.3e+20 ] = 250.948(30.3853)
+#>
+#>
+#> Maximum predicted log likelihood (250.948) estimated at:
+#> 2.41831e+12 (variance)
+#> 9.09409e-07 (lambda)
+#>
+#> Fitting model at optimal hyperparameter
+#> Using prior: Laplace(9.09409e-07) None
+
+#Find out what the optimal hyperparameter was:
+getHyperParameter(fit)
+#> [1] 2.418308e+12
+
+#Extract the current log-likelihood, and coefficients
+logLik(fit)
+#> 'log Lik.' -1953.765 (df=3)
+coef(fit)
+#> (Intercept) 1 2
+#> -4.19783727 0.30746087 0.02797239
+
+#We can only retrieve the confidence interval for unregularized coefficients:
+confint(fit, c(0))
+#> Using 1 thread(s)
+#> covariate 2.5 % 97.5 % evaluations
+#> (Intercept) 0 -4.236526 -4.159602 22
+R/NewDataConversion.R
+ Source: R/NewDataConversion.R
convertToCyclopsData.RdconvertToCyclopsData loads data from two data frames or ffdf objects, and inserts it into a Cyclops data object.
convertToCyclopsData( - outcomes, - covariates, - modelType = "lr", - addIntercept = TRUE, - checkSorting = NULL, - checkRowIds = TRUE, - normalize = NULL, - quiet = FALSE, - floatingPoint = 64 -) - -# S3 method for data.frame -convertToCyclopsData( - outcomes, - covariates, - modelType = "lr", - addIntercept = TRUE, - checkSorting = NULL, - checkRowIds = TRUE, - normalize = NULL, - quiet = FALSE, - floatingPoint = 64 -) - -# S3 method for tbl_dbi -convertToCyclopsData( - outcomes, - covariates, - modelType = "lr", - addIntercept = TRUE, - checkSorting = NULL, - checkRowIds = TRUE, - normalize = NULL, - quiet = FALSE, - floatingPoint = 64 -)- -
| outcomes | -A data frame or ffdf object containing the outcomes with predefined columns (see below). |
-
|---|---|
| covariates | -A data frame or ffdf object containing the covariates with predefined columns (see below). |
-
| modelType | -Cyclops model type. Current supported types are "pr", "cpr", lr", "clr", or "cox" |
-
| addIntercept | -Add an intercept to the model? |
-
| checkSorting | -(DEPRECATED) Check if the data are sorted appropriately, and if not, sort. |
-
| checkRowIds | -Check if all rowIds in the covariates appear in the outcomes. |
-
| normalize | -String: Name of normalization for all non-indicator covariates (possible values: stdev, max, median) |
-
| quiet | -If true, (warning) messages are suppressed. |
-
| floatingPoint | -Specified floating-point representation size (32 or 64) |
-
An object of type cyclopsData
-These columns are expected in the outcome object:
stratumId | (integer) | (optional) Stratum ID for conditional regression models |
rowId | (integer) | Row ID is used to link multiple covariates (x) to a single outcome (y) |
y | (real) | The outcome variable |
time | (real) | For models that use time (e.g. Poisson or Cox regression) this contains time |
| (e.g. number of days) | ||
weights | (real) | (optional) Non-negative weights to apply to outcome |
censorWeights | (real) | (optional) Non-negative censoring weights for competing risk model; will be computed if not provided. |
These columns are expected in the covariates object:
stratumId | (integer) | (optional) Stratum ID for conditional regression models |
rowId | (integer) | Row ID is used to link multiple covariates (x) to a single outcome (y) |
covariateId | (integer) | A numeric identifier of a covariate |
covariateValue | (real) | The value of the specified covariate |
convertToCyclopsData(
+ outcomes,
+ covariates,
+ modelType = "lr",
+ addIntercept = TRUE,
+ checkSorting = NULL,
+ checkRowIds = TRUE,
+ normalize = NULL,
+ quiet = FALSE,
+ floatingPoint = 64
+)
+
+# S3 method for data.frame
+convertToCyclopsData(
+ outcomes,
+ covariates,
+ modelType = "lr",
+ addIntercept = TRUE,
+ checkSorting = NULL,
+ checkRowIds = TRUE,
+ normalize = NULL,
+ quiet = FALSE,
+ floatingPoint = 64
+)
+
+# S3 method for tbl_dbi
+convertToCyclopsData(
+ outcomes,
+ covariates,
+ modelType = "lr",
+ addIntercept = TRUE,
+ checkSorting = NULL,
+ checkRowIds = TRUE,
+ normalize = NULL,
+ quiet = FALSE,
+ floatingPoint = 64
+)A data frame or ffdf object containing the outcomes with predefined columns (see below).
A data frame or ffdf object containing the covariates with predefined columns (see below).
Cyclops model type. Current supported types are "pr", "cpr", lr", "clr", or "cox"
Add an intercept to the model?
(DEPRECATED) Check if the data are sorted appropriately, and if not, sort.
Check if all rowIds in the covariates appear in the outcomes.
String: Name of normalization for all non-indicator covariates (possible values: stdev, max, median)
If true, (warning) messages are suppressed.
Specified floating-point representation size (32 or 64)
data.frame: Convert data from two data.frame
tbl_dbi: Convert data from two Andromeda tables
+ +#Convert infert dataset to Cyclops format: -covariates <- data.frame(stratumId = rep(infert$stratum, 2), - rowId = rep(1:nrow(infert), 2), - covariateId = rep(1:2, each = nrow(infert)), - covariateValue = c(infert$spontaneous, infert$induced)) -outcomes <- data.frame(stratumId = infert$stratum, - rowId = 1:nrow(infert), - y = infert$case) -#Make sparse: -covariates <- covariates[covariates$covariateValue != 0, ] - -#Create Cyclops data object: -cyclopsData <- convertToCyclopsData(outcomes, covariates, modelType = "clr", - addIntercept = FALSE) -#> Sorting outcomes by stratumId and rowId -#> Sorting covariates by covariateId, stratumId, and rowId
An object of type cyclopsData
+These columns are expected in the outcome object:
stratumId | (integer) | (optional) Stratum ID for conditional regression models |
rowId | (integer) | Row ID is used to link multiple covariates (x) to a single outcome (y) |
y | (real) | The outcome variable |
time | (real) | For models that use time (e.g. Poisson or Cox regression) this contains time |
| (e.g. number of days) | ||
weights | (real) | (optional) Non-negative weights to apply to outcome |
censorWeights | (real) | (optional) Non-negative censoring weights for competing risk model; will be computed if not provided. |
These columns are expected in the covariates object:
stratumId | (integer) | (optional) Stratum ID for conditional regression models |
rowId | (integer) | Row ID is used to link multiple covariates (x) to a single outcome (y) |
covariateId | (integer) | A numeric identifier of a covariate |
covariateValue | (real) | The value of the specified covariate |
convertToCyclopsData(data.frame): Convert data from two data.frame
convertToCyclopsData(tbl_dbi): Convert data from two Andromeda tables
#Convert infert dataset to Cyclops format:
+covariates <- data.frame(stratumId = rep(infert$stratum, 2),
+ rowId = rep(1:nrow(infert), 2),
+ covariateId = rep(1:2, each = nrow(infert)),
+ covariateValue = c(infert$spontaneous, infert$induced))
+outcomes <- data.frame(stratumId = infert$stratum,
+ rowId = 1:nrow(infert),
+ y = infert$case)
+#Make sparse:
+covariates <- covariates[covariates$covariateValue != 0, ]
+
+#Create Cyclops data object:
+cyclopsData <- convertToCyclopsData(outcomes, covariates, modelType = "clr",
+ addIntercept = FALSE)
+#> Sorting outcomes by stratumId and rowId
+#> Sorting covariates by covariateId, stratumId, and rowId
+
+#Fit model:
+fit <- fitCyclopsModel(cyclopsData, prior = createPrior("none"))
+
+R/ModelFit.R
+ Source: R/ModelFit.R
convertToCyclopsVariance.Rdglmnet into a prior variance.
convertToCyclopsVariance(lambda, nobs)+
convertToCyclopsVariance(lambda, nobs)Prior variance under a Laplace() prior
+
+ R/ModelFit.R
+ Source: R/ModelFit.R
convertToGlmnetLambda.RdCyclops into the regularization parameter lambda.
convertToGlmnetLambda(variance, nobs)+
convertToGlmnetLambda(variance, nobs)R/TimeEffects.R
+ convertToTimeVaryingCoef.RdconvertToTimeVaryingCoef convert short sparse covariate table to long sparse covariate table for time-varying coefficients.
convertToTimeVaryingCoef(shortCov, longOut, timeVaryCoefId)A data frame containing the covariate with predefined columns (see below).
A data frame containing the outcomes with predefined columns (see below), output of splitTime.
Integer: A numeric identifier of a time-varying coefficient
A long sparse covariate table for time-varying coefficients.
+These columns are expected in the shortCov object:
rowId | (integer) | Row ID is used to link multiple covariates (x) to a single outcome (y) |
covariateId | (integer) | A numeric identifier of a covariate |
covariateValue | (real) | The value of the specified covariate |
These columns are expected in the longOut object:
stratumId | (integer) | Stratum ID for time-varying models |
subjectId | (integer) | Subject ID is used to link multiple covariates (x) at different time intervals to a single subject |
rowId | (integer) | Row ID is used to link multiple covariates (x) to a single outcome (y) |
y | (real) | The outcome variable |
time | (real) | For models that use time (e.g. Poisson or Cox regression) this contains time |
| (e.g. number of days) |
+ coverage computes the coverage on confidence intervals
coverage(goldStandard, lowerBounds, upperBounds)- -
| goldStandard | -Numeric vector |
-
|---|---|
| lowerBounds | -Numeric vector. Lower bound of the confidence intervals |
-
| upperBounds | -Numeric vector. Upper bound of the confidence intervals |
-
The proportion of times goldStandard falls between lowerBound and upperBound
coverage(goldStandard, lowerBounds, upperBounds)Numeric vector
Numeric vector. Lower bound of the confidence intervals
Numeric vector. Upper bound of the confidence intervals
The proportion of times goldStandard falls between lowerBound and upperBound
+ R/CrossValidationControl.R
+ Source: R/CrossValidationControl.R
createAutoGridCrossValidationControl.RdcreateCrossValidationControl creates a Cyclops control object for use with fitCyclopsModel
+
createCrossValidationControl creates a Cyclops control object for use with fitCyclopsModel
that supports multiple hyperparameters through an auto-search in one dimension and a grid-search over the remaining
dimensions
createAutoGridCrossValidationControl( - outerGrid, - autoPosition = 1, - refitAtMaximum = TRUE, - cvType = "auto", - initialValue = 1, - ... -)- -
| outerGrid | -List or data.frame of grid parameters to explore |
-
|---|---|
| autoPosition | -Vector position for auto-search parameter (concatenated into outerGrid) |
-
| refitAtMaximum | -Logical: re-fit Cyclops object at maximal cross-validation parameters |
-
| cvType | -Must equal "auto" |
-
| initialValue | -Initial value for auto-search parameter |
-
| ... | -Additional parameters passed through to |
-
A Cyclops prior object of class inheriting from "cyclopsPrior" and "cyclopsFunctionalPrior"
-for use with fitCyclopsModel.
createAutoGridCrossValidationControl(
+ outerGrid,
+ autoPosition = 1,
+ refitAtMaximum = TRUE,
+ cvType = "auto",
+ initialValue = 1,
+ ...
+)List or data.frame of grid parameters to explore
Vector position for auto-search parameter (concatenated into outerGrid)
Logical: re-fit Cyclops object at maximal cross-validation parameters
Must equal "auto"
Initial value for auto-search parameter
Additional parameters passed through to createControl
A Cyclops prior object of class inheriting from "cyclopsPrior" and "cyclopsFunctionalPrior"
for use with fitCyclopsModel.