FIMM-UTU DREAM winning implementation of an ensemble of Penalized Cox Regression models for mCPRC research (ePCR)

Description

FIMM-UTU DREAM winning implementation of an ensemble of Penalized Cox Regression models for mCPRC research (ePCR)

Usage

data(ePCRmodels)

Format

An object of class PEP of length 1.

Note

Notice that in order to save space, some slots in the S4 object have been set to null.

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

References

Guinney J, Wang T, Laajala TD, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol 2017; 18: 132-142.

Cox-Oakes extension of the Nelson-Aalen estimates for a Cox model

Description

Implementing the heuristic Cox and Oakes extension of the Nelson-Aalen estimate for Cox model to extract individual-specific survival. Time-to-event predictions are then given at the first time point at which an individual reaches an event probability of 50

Usage

NelsonAalen(
  b,
  Xold,
  Xnew,
  events,
  time,
  tpred = 0:round(max(time, na.rm = T), 0),
  plot = FALSE
)

Arguments

Value

Predicted times-to-event predictions either for the training data or an optional provided novel dataset

Note

See section 3.6 at http://data.princeton.edu/pop509/NonParametricSurvival.pdf for the reference

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

Examples

data(TYKSSIMU)
library(survival)
xdat <- as.matrix(xMEDISIMU)
ydat <- yMEDISIMU[,"surv"]

Penalized Ensemble Predictor (PEP) S4-class ensemble consisting of individual PSP-members

Description

This class constructs an ensemble of individual Penalized Ensemble Predictor (PSP-class) members. Each member contributes to the model output equally, and ensemble-level functions wrap up individual predictions into an averaged ensemble prediction. The user may define an arbitrary number of PSPs and tailor them to suit the particular needs, and then provide them as a list to the PEP-constructor. As such, constructing well tailored individual ensemble members (of PSP-class) in order to produce a powerful ensemble (of PEP-class) is important on both levels.

Slots

PSPs

List of PSP-objects that will be treated as equal members of the ensemble

description

A character string describing the structure or purpose of the ensemble

features

A character list of variable/feature names

dictionary

A named list of above variables/features and their more precise description

predens

A function for compiling all predictions from the PSPs into consensus prediction

prednorm

A function for normalizing the predictions e.g. to risk scores in [0,1]

Examples

## Not run: 
# The PEP-construction is wrapped in NOT RUN, because cross-validating multiple PSPs
# is very time consuming especially if a tight grid of alpha/lambda is to be explored.
# The simulated data from Turku University Hospital (TYKS) is used as an example:
data(TYKSSIMU)

# Two cohorts and corresponding data matrices:
head(xMEDISIMU)
head(xTEXTSIMU)
# Two survival responses:
head(yMEDISIMU)
head(xTEXTSIMU)

# Search L1/L2 norm alpha-grid with 10 values between [0,1]
aseq <- seq(from=0, to=1, by=0.1)
# Lambda sequence penalization is of 100 length conditional for each alpha
nlamb <- 100

library(survival)
# Create three ensemble members; one for MEDI cohort, one for TEXT cohort,
# and finally one member that combines both cohorts simultaneously in a coxnet
psp1 <- new("PSP", x = rbind(xMEDISIMU, xTEXTSIMU), 
y = Surv(rbind(yMEDISIMU, yTEXTSIMU)[,"surv"]),
plot = TRUE, alphaseq = aseq, scorefunc = score.cindex, seed = 1,
folds = 10, nlambda = nlamb)
psp2 <- new("PSP", x = xMEDISIMU, 
y = Surv(yMEDISIMU[,"surv"]),
plot = TRUE, alphaseq = aseq, scorefunc = score.cindex, seed = 1,
folds = 10, nlambda = nlamb)
psp3 <- new("PSP", x = xTEXTSIMU, 
	y = Surv(yTEXTSIMU[,"surv"]),
plot = TRUE, alphaseq = aseq, scorefunc = score.cindex, seed = 1,
folds = 10, nlambda = nlamb)
par(mfrow=c(1,3))
plot(psp1); plot(psp2); plot(psp3); # Inspect the alpha/lambda surfaces

# Create an ensemble of the above 3 members
simuens <- new("PEP", PSPs = list(psp1, psp2, psp3))
simuens
# Ready PEP-object can be used for novel predictions etc


## End(Not run)

# Run example predictions from a previously optimized PEP-model
data(ePCRmodels)
data(TYKSSIMU)

# Perform risk predictions from the joint cohort ensemble member as an example
MEDIpred <- predict(TYKS@PSPs[[1]]@fit, s=TYKS@PSPs[[1]]@optimum["Lambda"], 
newx = conforminput(TYKS@PSPs[[1]], xMEDISIMU))[,1]
TEXTpred <- predict(TYKS@PSPs[[1]]@fit, s=TYKS@PSPs[[1]]@optimum["Lambda"], 
newx = conforminput(TYKS@PSPs[[1]], xTEXTSIMU))[,1]

# Risk scores obtained for the new patients (arbitrary unit as per Cox regression)
head(MEDIpred)
head(TEXTpred)

PEP-methods

Description

PEP-methods

print.PEP: Print a PEP object to the console

predict.PEP: Predict for a novel patient from current PEP-ensemble

Usage

## S4 method for signature 'PEP'
print(x, ...)

## S4 method for signature 'PEP'
predict(object, type = "response", newx, x.expand)

Arguments

Penalized Single Predictor (PSP) S4-class as a member of PEP-ensembles

Description

PSP is a single penalized Cox regression model, where an alpha/lambda grid has been optimized using cross-validation and a chosen prediction metric. PSPs are single entities that will compile together into PEPs, the ensemble objects that will average over multiple PSPs to generate an ensemble prediction. Typically a single PSP models a part of the data, such as a cohort strata.

Slots

description

A general user-provided string describing the PSP

features

A character vector indicating feature names

strata

Information whether data matrix x included substrata (will be used in plotting functions etc)

alphaseq

The sequence of alpha values to test, ranging between [0,1]; alpha = 0 being ridge regression, 0 < alpha < 1 being elastic net and alpha = 1 being LASSO

cvfolds

The number of cross-validation folds to utilize; by default 10

nlambda

The amount of lambda values utilized in each regularization path; by default 100 as in glmnet-package

cvmean

A matrix indicating the mean CV performance in alpha/lambda grid (preferred over median)

cvmedian

A matrix indicating the median CV performance in alpha/lambda grid

cvstdev

A matrix indicating the standard deviation in CV performance over the folds in the alpha/lambda grid

cvmin

A matrix indicating minimum CV performance in alpha/lambda grid

cvmax

A matrix indicating maximum CV performance in alpha/lambda grid

score

The scoring function, user-defined or one provided by ePCR package such as score.cindex or score.iAUC

cvrepeat

Number of cross-validation procedures to run multiple times and then average over, in order to reduce the effect of binning samples

impute

The imputation function used if provided matrix 'x' includes missing values; by default the impute.knn-function from BioConductor package 'impute'

optimum

The optimum in alpha/lambda grid, with optimal alpha and similarly for lambda

seed

The initial random seed used for cross-validation

x

The input data matrix

x.expand

A function that allows expansion of matrix 'x' to include interactions between variables; if no such are desired, this should be an identity function

y

The Surv-object as in survival-package, which serves as the response y

fit

The glmnet coxnet-object obtained with optimal alpha

criterion

The optimizing criterion; by default "min" for minimizing CV-error

dictionary

A list of discriptions for each variable

regAUC

A numeric vector for the AUC under regularization curve as computed by integrateRegCurve-function

Examples

# As an example, illustrate a naive PSP built on the small medication cohort
data(TYKSSIMU)
library(survival)
# Minimal example with much fewer patients and variables
psp_ex <- new("PSP", alphaseq=c(0.2, 0.8), nlambda=20, folds=3,
	x = xMEDISIMU[1:80,c(1:20,40:50)], y = yMEDISIMU[1:80,"surv"],
seeds = 1, score=score.cindex)

plot(psp_ex) # Optimization surface of alpha/lambda

# Illustrate the use of some PSP-methods:
PSP.KM(psp_ex, cutoff = 0.5) # Kaplan-Meier
PSP.PCA(psp_ex) # PCA plot of training data
PSP.BOX(psp_ex) # Boxplots, here for the first training variable
PSP.CSP(psp_ex) # Cumulative survival probabilities for the training data
invisible(PSP.NA(psp_ex)) # Time-to-event Nelson-Aalen heuristic algorithm

## Not run: 
# Computationally intensive novel PSP-fitting is omitted from the test runs
# Functions for readily fitted PSP-objects are illustrated above
data(TYKSSIMU)
library(survival)
psp_meditext <- new("PSP", x = rbind(xMEDISIMU, xTEXTSIMU), 
y = Surv(rbind(yMEDISIMU, yTEXTSIMU)[,"surv"]),
plot = TRUE, alphaseq = seq(0, 1, by=.01), scorefunc = score.cindex, 
seed = 1, folds = 10, nlambda = 100)
plot(psp_meditext)

## End(Not run)

PSP-methods

Description

PSP-methods

print.PSP: Print general information of PSPs contents to the terminal

plot.PSP: By default the mean CV surface in terms of alpha/lambda is plotted using hamlet-package's hmap-function

coef.PSP: Default PSP coef-function extracts only the optimum parameters, not whole lambda-range

predict.PSP: Predict for a novel patient from current PSP

PSP.KM: Kaplan-Meier with division at a given cutoff point within [0,1]

PSP.PCA: Principal Component Plot of a single PSP, showing 2 principal axes with a colouring if strata have been indicated; newx can also be plotted in relation to fitting data

PSP.BOX: Boxplot of a single variable in a PSP in respect to strata, for outlier detection and observing variable distributions

PSP.CSP: Cumulative survival probabilities

PSP.NA: Nelson-Aalen with time-to-event prediction at point t = F^-1(0.5)

Usage

## S4 method for signature 'PSP'
print(x, ...)

plot(x, y, ...)

## S4 method for signature 'PSP'
plot(x, y, bias = 0.1, ...)

## S4 method for signature 'PSP'
coef(object)

## S4 method for signature 'PSP'
predict(object, type = "response", newx, verb = 0)

PSP.KM(object, ...)

## S4 method for signature 'PSP'
PSP.KM(object, cutoff = 0.5)

PSP.PCA(object, ...)

## S4 method for signature 'PSP'
PSP.PCA(
  object,
  newx,
  expanded = TRUE,
  type = "all",
  shuffle = TRUE,
  z = TRUE,
  cex = 1,
  col = c("aquamarine", "coral", "royalblue", "black"),
  pch = 16
)

PSP.BOX(object, ...)

## S4 method for signature 'PSP'
PSP.BOX(object, newx, var = colnames(object@x)[1], expanded = FALSE)

PSP.CSP(object, ...)

## S4 method for signature 'PSP'
PSP.CSP(object, newx, t = seq(from = 1, to = 36 * 30.5, by = 1), plot = FALSE)

PSP.NA(object, ...)

## S4 method for signature 'PSP'
PSP.NA(object, newx, plot = TRUE)

Arguments

Note

Please refer to the PSP-class examples for applying these PSP-methods

ePCR model fitted to the Turku University Hospital cohorts (all features)

Description

ePCR model fitted to the Turku University Hospital cohorts (all features)

Usage

data(ePCRmodels)

Format

An object of class PEP of length 1.

Note

Notice that in order to save space, some slots in the S4 object have been set to null.

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

References

Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to a real-world patient cohort. Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to a real-world patient cohort. Bioinformatics. 2018 Jun 15. doi: 10.1093/bioinformatics/bty477.

TYKSSIMU - simulated data matrices and survival responses from Turku University Hospital

Description

TYKSSIMU - simulated data matrices and survival responses from Turku University Hospital

xMEDISIMU: Simulated prostate cancer data from Turku University Hospital (data matrix x, Medication-cohort)

xTEXTSIMU: Simulated prostate cancer data from Turku University Hospital (data matrix x, Text-cohort)

yMEDISIMU: Simulated prostate cancer data from Turku University Hospital (survival response y, Medication-cohort)

yTEXTSIMU: Simulated prostate cancer data from Turku University Hospital (survival response y, Text-cohort)

Usage

data(TYKSSIMU)

xMEDISIMU

xTEXTSIMU

yMEDISIMU

yTEXTSIMU

Format

xTEXTSIMU:

xMEDISIMU:

yTEXTSIMU:

yMEDISIMU:

An object of class data.frame with 150 rows and 101 columns.

An object of class data.frame with 500 rows and 101 columns.

An object of class data.frame with 150 rows and 3 columns.

An object of class data.frame with 500 rows and 3 columns.

Author(s)

Teemu Daniel Laajala teelaa@utu.fi, Mika Murtojärvi mianmu2@hotmail.com

References

Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to a real-world patient cohort. Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to a real-world patient cohort. Bioinformatics. 2018 Jun 15. doi: 10.1093/bioinformatics/bty477.

Examples

data(TYKSSIMU)
head(xTEXTSIMU)
head(xMEDISIMU)
head(yTEXTSIMU)
head(yMEDISIMU)
dim(xTEXTSIMU)
dim(xMEDISIMU)

ePCR model fitted to the Turku University Hospital cohorts (features derived from text mining only)

Description

ePCR model fitted to the Turku University Hospital cohorts (features derived from text mining only)

Usage

data(ePCRmodels)

Format

An object of class PEP of length 1.

Note

Notice that in order to save space, some slots in the S4 object have been set to null.

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

References

Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to a real-world patient cohort. Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to a real-world patient cohort. Bioinformatics. 2018 Jun 15. doi: 10.1093/bioinformatics/bty477.

Predict cumulative survival probabilities for new data at given time points

Description

Given a readily fitted regularized Cox regression model, this function predicts the cumulative survival probabilities for new data at time points determined by the user. The function uses c060-package's functionality for computing base hazard, and then performs linear predictions for new observations using the fitted regularized Cox regression model.

Usage

TimeSurvProb(
  fit,
  time,
  event,
  olddata,
  newdata,
  s,
  times = c(1:36) * 30.5,
  plot = FALSE
)

Arguments

Value

Cumulative survival probabilities at the chosen time points

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

Bootstrapped testing of regression coefficients in a penalized model

Description

The purpose of this function is to evaluate a p-value-like statistic for penalized regression coefficients. A fixed number of bootstrapped datasets are generated, and the model coefficients are fitted to these bootstrapped datasets using the pre-determined lambda.

Usage

bootstrapRegCoefs(fit, lambda, boot = 1000, epsilon = 10^-6)

Arguments

Value

Significance values for regression coefficients, defined as the proportion of bootstrapped model fits where coefficient did not shrink within epsilon of zero or where it did not flip sign.

Note

Notice that this is a highly experimental function, and that many statisticians argue that computing p-values does not make sense for penalized models. The null hypothesis is not well defined, as the bias (regularization) pushes the regression coefficients towards zero. Therefore the null hypothesis is not known and the interpretation is not the conventional regression coefficient p-value.

Examples

## Not run: 
# Computationally too intensive to run bootstrapped fits <5s
data(TYKSSIMU)
library(survival)
x <- as.matrix(xMEDISIMU)
y <- yMEDISIMU[,"surv"]
nlambda <- 30
psp1 <- new("PSP", alphaseq=c(0, 0.5, 1), nlambda = nlambda, folds = 3, x = x, y = y, seeds = 1)
.Object <- psp1
alphaopt <- psp1@optimum["Alpha"]
bs <- bootstrapRegCoefs(fit = psp1@fit, lambda = psp1@optimum["Lambda"], boot = 100)
# Histogram of bootstrapped ps
hist(bs$ps, breaks=100)

## End(Not run)

Conform the dimensions of a new input data matrix to a readily fitted PEP or PSP object

Description

Conform the dimensions of a new input data matrix to a readily fitted PEP or PSP object

Usage

conforminput(object, newx)

Arguments

Value

An expanded data matrix for which the dimensions conform to the regression coefficients in the PSP or PEP

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

Function that creates customized cross-validation folds

Description

The function creates two matrices and returns them as members of a list. 'train' holds training sample indices, columns are cv-folds and rows are sample indices to hold as training samples in each fold. 'test' holds test/validation sample indices, columns are cv-folds and rows are sample indices to hold as test samples in each fold

Usage

cv(x, fold = 10, strata = rep(1, times = nrow(x)), shuffle = TRUE, seed = NULL)

Arguments

Examples

data(TYKSSIMU)
cvfolds <- cv(x = xMEDISIMU, fold = 3)
cvfolds$train
cvfolds$test

Cross-validation runs for risk predition at a single value of alpha

Description

Run n-fold cross-validation for a chosen prediction metric at a single value of the L1/L2 norm alpha. A suitable lambda sequence is determined by glmnet, and the cross-validation returns a prediction matrix over the folds over various lambda. This function is mostly called by the higher hierarchy functions, such as cv.grid, which allows varying also the alpha-parameter.

Usage

cv.alpha(
  x,
  y,
  folds = 10,
  alpha = 0.5,
  nlamb = 100,
  verb = 0,
  scorefunc,
  plot = FALSE
)

Arguments

Value

A matrix of cross-validation scores, where rows correspond to CV folds and columns to various lambda values chosen by glmnet

Examples

data(TYKSSIMU)
library(survival)
ydat <- Surv(event = yMEDISIMU[,"DEATH"], time = yMEDISIMU[,"LKADT_P"])
set.seed(1)
cvs <- cv.alpha(x = xMEDISIMU, y = ydat, alpha = 0.5, folds = 5, 
	nlamb = 50, verb = 1, scorefunc = score.cindex, plot = TRUE)
cvs

Cross-validation runs for risk predition for a grid of predetermined alpha values and their conditional lambda values

Description

Expanded Cross-Validation function to run the whole CV in the lambda/alpha grid instead of just lambda-sequence with a pre-specified alpha

Usage

cv.grid(
  alphaseq = seq(from = 0, to = 1, by = 0.1),
  seed,
  x,
  y,
  folds = 10,
  nlamb = 100,
  verb = 0,
  scorefunc,
  plot = FALSE
)

Arguments

Value

List of matrices of cross-validation performance values over the alpha/lambda grid for mean/median/min/max/stdev of the chosen performance metric, with rows indicating various alpha-values and columns indicating lambda-values.

Examples

data(TYKSSIMU)
library(survival)
ydat <- Surv(event = yMEDISIMU[,"DEATH"], time = yMEDISIMU[,"LKADT_P"])
cvs <- cv.grid(x = xMEDISIMU, y = ydat, folds = 3, nlamb = 30, alphaseq = seq(0, 1, by=5), 
	scorefunc = score.iAUC, plot = TRUE, seed = 1)
cvs

Ensemble Penalized Cox Regression Modeling for Overall Survival and Time-to-Event Prediction in Advanced Prostate Cancer

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

References

Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to a real-world patient cohort. Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to a real-world patient cohort. Bioinformatics. 2018 Jun 15. doi: 10.1093/bioinformatics/bty477.

Guinney J, Wang T, Laajala TD, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol 2017; 18: 132-142.

Laajala TD, Guinney J, Costello JC. Community mining of open clinical trial data. Oncotarget 2017; 8: 81721-81722. doi: 10.18632/oncotarget.20853.

Plot a heatmap of the prediction performance statistic as a function of lambda and alpha combinations

Description

This function plots a heatmap of cross-validation results by varying the penalization/regularization parameter (lambda, x-axis), together with the corresponding L1/L2 norm parameter alpha (i.e. LASSO, elastic net, ridge regression). The optimal spot in the parameter grid gives insight into the behavior of the regularization in respect to the norms, but note that the lambda-parameter on x-axis is not constant given a conditional alpha-parameter; rather it is a suitable vector chosen by the glmnet-package.

Usage

heatcv(
  psp,
  bias = 0.1,
  by.rownames = 1,
  by.colnames = 1,
  paletcol = c("cyan", "blue", "black", "red", "orange"),
  paletncol = 1000,
  xlab = "Alpha-dependent log-Lambda",
  ylab = "Alpha",
  main = "",
  plot.opt = TRUE,
  plot.1sd = FALSE,
  ...
)

Arguments

Note

The heatmap plotting is compatible with the default plot-region in a R graphic canvas. The function hmap from the same author's hmap-package can be highly customized to fit more specific needs.

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

Examples

data(ePCRmodels)
par(mfrow=c(1,3))
heatcv(DREAM@PSPs[[1]], main=DREAM@PSPs[[1]]@description, by.rownames=10, by.colnames=10)
heatcv(DREAM@PSPs[[2]], main=DREAM@PSPs[[2]]@description, by.rownames=10, by.colnames=10)
heatcv(DREAM@PSPs[[3]], main=DREAM@PSPs[[3]]@description, by.rownames=10, by.colnames=10)

Integrate the area over/under the regularization path of a penalized regression model

Description

This function evaluates the overall significance of a regularized regression coefficient in a penalized Cox model. It takes into account the whole range of lambda-penalization parameter, and computes the area over or under the regularization curve. This gives more insight into the importance of a regression coefficient over the whole range of lambda, instead of evaluating it at a single optimal lambda point determined typically using cross-validation.

Usage

integrateRegCurve(fit, weighted = FALSE)

Arguments

Value

Integrated area over or under a regularization curve using the trapezoid method from the pracma-package

Examples

# Exemplify one PSP of the readily fitted ensembles
data(ePCRmodels)
RegAUC <- cbind(
integrateRegCurve(fit = DREAM@PSPs[[1]]@fit),
integrateRegCurve(fit = DREAM@PSPs[[2]]@fit),
integrateRegCurve(fit = DREAM@PSPs[[3]]@fit)
)
SortRegAUC <- RegAUC[order(apply(RegAUC, MARGIN=1, 
	FUN=function(z) abs(mean(z)) ), decreasing=TRUE),]
colnames(SortRegAUC) <- c(DREAM@PSPs[[1]]@description, 
DREAM@PSPs[[2]]@description,
DREAM@PSPs[[3]]@description)
SortRegAUC[1:10,] # Top 10 coefficients according to (absolute) regularization curve auc

Compute all pairwise interactions between the columns of a data matrix

Description

The function multiplies the columns (variables) of a matrix or a data.frame with each other, and produces a new matrix where all pairwise interactions are present. This also includes multiplying a column with its self, thus effectively returning a squared column.

Usage

interact.all(input)

Arguments

Value

A matrix where columns of the original data matrix have been multiplied, indicating column names coupled with a colon in-between

Examples

set.seed(1)
somedata <- data.frame(a = rnorm(10), b = rnorm(10), c = runif(10), d = runif(10))
somedata
allinteract <- interact.all(somedata)
allinteract

Compute a chosen set of pairwise interactions between two sets of columns in a data matrix

Description

Similar to interact.all-function, but here user provides two sets of variables, and each pairwise combination between these two sets is multiplied. These pairwise interactions are then returned as a new data matrix, with a colon indicating which variables were multiplied.

Usage

interact.part(input, first, second)

Arguments

Value

A data matrix with multiplied columns as indicated using the sets 'first' and 'second'

Examples

set.seed(1)
somedata <- data.frame(a = rnorm(10), b = rnorm(10), c = runif(10), d = runif(10))
somedata
someinteract <- interact.part(somedata, first = c("a", "b"), second = c("c", "d"))
someinteract

Compute mean of predicted risk ranks for an ePCR ensemble

Description

Compute mean of predicted risk ranks for an ePCR ensemble

Usage

meanrank(x)

Arguments

Value

An averaged predicted risk rank over all the ensemble members

Note

Extensively called by the 'predict'-function for PEP-objects when risk predictions are performed over the ensemble

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

Normalize ensemble risk scores to ranks and then to uniform range

Description

Normalize ensemble risk scores to ranks and then to uniform range

Usage

normriskrank(x)

Arguments

Value

An averaged predicted risk rank over all the ensemble members that has been normalized to the range [0,1] based on: (x - min(x)) / (max(x) - min(x)) -> [0,1]

Note

Normalizes 'predict'-function calls for PEP-objects after calling 'meanrank'-function

Author(s)

Teemu Daniel Laajala teelaa@utu.fi

Scoring function for evaluating survival prediction through concordance index (c-index)

Description

C-index (Concordance index) of the predicted vs. true answer, i.e. proportion of pairs that go in correct direction over all pairwise comparisons

Usage

score.cindex(pred, time, event, real)

Arguments

Value

Corcordance index (c-index) of the prediction

Examples

# A random prediction ought to be near 0.5
# c-index is not sensitive to time scale, as it tests pairwise prediction accuracy
set.seed(1); prediction <- sample(1:20)
time <- seq(from=1000, to=50, by=-50)
event <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1)
library(survival)
score.cindex(pred = prediction, real = Surv(time=time, event=event))

Scoring function for evaluating survival prediction by time-wise integrated AUC

Description

Time-wise integrated prediction for survival is performed by this scoring function using the timeROC-package. It's offered as an alternative to the score.cindex-function with the difference that time-wise integrated AUC is sensitive to the choice of time-window. By default (as similar to DREAM 9.5 mCRPC challenge), the AUCs are determined at 6 to 30 months, and the AUC is then normalized to a score within [0,1]. Notice that for studies shorter or longer than this proposed time window, the scoring function should be adjusted accordingly.

Usage

score.iAUC(pred, time, event, real, times = seq(6, 30, by = 1) * 30.5)

Arguments

Value

The integrated area under the ROC-curve over time

Examples

# A random prediction ought to be near 0.5 
# iAUC is sensitive to the choice of time points to test AUC at
set.seed(1); prediction <- sample(1:20)
time <- seq(from=1000, to=50, by=-50)
event <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1)
library(survival)
score.iAUC(pred = prediction, real = Surv(time=time, event=event))

Extended function for z-transformation, filling non-finite values and changes column names at will

Description

An extended function of the standard z-score standardization of a vector in R (i.e. function 'scale'). Supports filling in non-finite values as well as re-naming variables to distinguish them from non-standardized variables.

Usage

zt(x, fillfinite = 0, addz = T, saveattr = T)

Arguments

Value

z-score standardized values (zero mean and unit variation), with non-finite values imputed by zero by default.

Examples

somedata <- cbind(rnorm(100), runif(100))
normdata <- zt(somedata)
head(normdata)
apply(normdata, MARGIN=2, FUN=mean)
apply(normdata, MARGIN=2, FUN=sd)