Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/functions_predict.R

Predicts cluster membership using either multinomial logistic regression or SVMs.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
## S3 method for class 'predkmeans'
predictML(
object = NULL,
centers = object$centers,
K = nrow(centers),
R,
Rstar,
Xstar = NULL,
tr.assign = object$cluster,
muStart = "random",
maxitMlogit = 500,
verbose = 1,
nMlogitStarts = 1,
mlogit.control = list(suppressFittedWarning = TRUE),
...
)
## S3 method for class 'predkmeans'
predictSVM(
object = NULL,
centers = object$centers,
R,
Rstar,
K = nrow(centers),
Xstar = NULL,
tr.assign = object$cluster,
svm.control = list(gamma = c(1/(2:1), 2), cost = seq(20, 100, by = 20)),
...
)
## S3 method for class 'predkmeans'
predictMixExp(object, R, Rstar = NULL, ...)
``` |

`object` |
A predkmeans object, from which the cluster centers will be extracted. |

`centers` |
Matrix of cluster centers, assumed to be K-by-p |

`K` |
Number of clusters |

`R` |
matrix of covariates for observations to be predicted at. |

`Rstar` |
matrix of covariates at training locations |

`Xstar` |
matrix of observation at training locations. Either this or |

`tr.assign` |
vector of cluster assignments at training locations. By default, extracted from |

`muStart` |
starting value for cluster centers in mlogit optimization (IDEA: change to pull from predkmeans object?). If not provided, starting values are selected randomly. |

`maxitMlogit` |
Maximum number of iterations for |

`verbose` |
integer indicating amount of output to be displayed |

`nMlogitStarts` |
number of mlogit starts to use in estimation of parameters |

`mlogit.control` |
list of control parameters to be passes to |

`...` |
Unused additional arguments |

`svm.control` |
list of options for |

Function for predicting cluster membership in clusters identified by k-means or predictive k-means using multinomial logistic regression or support vector machines (SVMs). For multinomial logitic regression, parameter estimation is handled by `mlogit`

. The SVMs are fit using `best.svm`

from `e1071`

package.

Because this prediction includes return information about cluster assignment
and prediction model parameters, this method is deliberately distinct from
the generic `predict`

functions.

The `predictMixExp`

funciton provides predictions from
the 'working' cluster assignments created as part of the
mixture of experts algorithm from `predkmeans`

.

A list containing some or all of the following elements:

`tr.assign` |
Cluster assignments at training locations |

`mlfit` |
A subset of the mlogit object returned by the function of that name |

`beta` |
Estimated model parameters |

`test.pred` |
Predicted cluster assignments at test locations |

Joshua Keller

`mlogit`

, `predkmeans`

, `predictionMetrics`

Other methods for predkmeans objects:
`relevel.predkmeans()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
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 <- cbind(1, r1, r2)
X <- cbind(x1, x2)
pkm <- predkmeans(X=cbind(x1, x2), R=R, K=4)
n_pred <- 50
Rnew <- cbind(1, r1=rnorm(n_pred), r2=rnorm(n_pred))
pkmPred <- predictML(pkm, R=Rnew, Rstar=R)
pkmPred$test.pred
``` |

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