Description Usage Arguments Details Author(s) See Also Examples

Performs cross-validation of predictive k-means clustering and cluster prediction.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
predkmeansCVest(
X,
R,
K,
cv.groups = 10,
sigma2 = 0,
sigma2fixed = FALSE,
scale = TRUE,
covarnames = colnames(R),
PCA = FALSE,
PCAcontrol = list(covarnames = colnames(R), ncomps = 5),
TPRS = FALSE,
TPRScontrol = list(df = 5, xname = "x", yname = "y"),
returnAll = FALSE,
...
)
predkmeansCVpred(
object,
X = object$X,
R = object$R,
method = c("ML", "MixExp", "SVM"),
...
)
``` |

`X` |
Outcome data |

`R` |
Covariates. Coerced to data frame. |

`K` |
Number of clusters |

`cv.groups` |
A list providing the cross-validation groups for splitting the data. groups for splitting the data. Alternatively, a single number giving the number of groups into which the data are randomly split. A value of '0' implies leave-one-out. Defaults to 10. |

`sigma2` |
starting value of sigma2. Setting |

`sigma2fixed` |
Logical indicating whether sigma2 should be held fixed. If FALSE, then sigma2 is estimated using Maximum Likelihood. |

`scale` |
Should the outcomes be re-scaled within each training group? |

`covarnames` |
Names of covariates to be included directly. |

`PCA` |
Logical indicator for whether PCA components should be computed from R. |

`PCAcontrol` |
Arguments passed to |

`TPRS` |
Logical indicator for whether thin-plate regression splines should be created and added to covariates. |

`TPRScontrol` |
Arguments passed to |

`returnAll` |
A list containing all |

`...` |
Additional arguments passed to either |

`object` |
A |

`method` |
Character string indicating which prediciton method should be used. Optins are |

These wrappers are designed to simplify cross-validation of a dataset. For models including thin-plate regression splines (TPRS) or principal component analysis (PCA) scores, these functions will re-evaluate the TPRS basis or PCA decomposition on each training set.

Joshua Keller

`predkmeans`

, `createPCAmodelmatrix`

, `createTPRSmodelmatrix`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
n <- 200
r1 <- rnorm(n)
r2 <- rnorm(n)
u1 <- rbinom(n, size=1,prob=0)
cluster <- ifelse(r1<0, ifelse(u1, "A", "B"), ifelse(r2<0, "C", "D"))
mu1 <- c(A=2, B=2, C=-2, D=-2)
mu2 <- c(A=1, B=-1, C=-1, D=-1)
x1 <- rnorm(n, mu1[cluster], 4)
x2 <- rnorm(n, mu2[cluster], 4)
R <- model.matrix(~r1 + r2)
X <- cbind(x1, x2)
pkmcv <- predkmeansCVest(X=cbind(x1, x2),
R=R, K=4, nStarts=4, cv.groups= 5,
TPRS=FALSE, PCA=FALSE, covarnames=colnames(R))
pkmcv
``` |

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