# contriubutions

### Description

Contributions of Variates to Model Predictions

### Usage

1 2 3 4 5 6 7 8 9 10 | ```
contributions(object, newdata, dims = TRUE, ...)
## S3 method for class 'lda'
contributions(object, newdata,
dims = TRUE, prior = object$prior, ...)
## S3 method for class 'pcalda'
contributions(object,
newdata = stop("newdata is required: ", "pcalda models do not store the original data"),
dims = TRUE, ...)
``` |

### Arguments

`object` |
a model |

`newdata` |
data to use for calculating contributions |

`dims` |
dimensions in model space of which the contributions should be calculated |

`...` |
further parameters |

`prior` |
For |

### Details

Linear models procduce scores according to

*S = (X - center) x
coefficients*

from the data matrix X, the center of the model and the model coefficients (scaling, rotation, loadings, latent variables, ...)

To study the behaviour of the model it is often useful to
calculate the element-wise product of centered data and
the for each latent variable (component, discriminant
function, ...). This is done by `contributions`

### Value

an array of dimension (`nrow (newdata)`

x ```
ncol
(newdata)
```

x `length (dims)`

)

### Author(s)

Claudia Beleites

### See Also

`lda`

`pcalda`

### Examples

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 | ```
## contributions 1st discriminant function
model <- lda (Species ~ ., data = iris)
contrib.LD1 <- contributions (model, dims = 1)
contrib.LD1[1:6,,]
dim (contrib.LD1)
layout (1:3, 3, 1)
for (class in levels (iris$Species))
boxplot (contrib.LD1 [iris$Species == class,,], ylim = range (contrib.LD1))
## all contributions
contrib.LD <- contributions (model)
dim (contrib.LD)
contrib.LD[1:6,,]
if (require ("reshape2")) {
contrib.df <- melt (contrib.LD, value.name = "contribution")
} else {
contrib.df <- array2df (contrib.LD, label.x = "contribution")
}
contrib.df$Species <- iris$Species [contrib.df$row]
head (contrib.df)
if (require ("lattice")){
bwplot (contribution ~ Species | variate, data = contrib.df, layout = c (4, 1))
}
## sum contributions to get scores
diff <- predict (model)$x - apply (contrib.LD, c (1, 3), sum)
summary (diff)
boxplot (diff)
1+1
``` |

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