View source: R/predict_ffmanova.R

predict.ffmanova | R Documentation |

The same predictions as `lm`

can be obtained. With some variables missing in input,
adjusted means or mean predictions are computed (Langsrud et al., 2007).
Linear combinations of such predictions, with standard errors,
can also be obtained.

## S3 method for class 'ffmanova' predict(object, newdata = NULL, linComb = NULL, nonEstimableAsNA = TRUE, ...)

`object` |
Output from |

`newdata` |
Data frame or list. Missing values and missing variables are possible. |

`linComb` |
A matrix defining linear combinations. |

`nonEstimableAsNA` |
When TRUE missing values are retuned when predictions cannot be made.
When FALSE predictions are made anyway, but the logical vector, |

`...` |
further arguments (not used) |

A list of two matrices:

`YnewPred` |
Predictions, mean predictions, adjusted means or linear combinations of such predictions. |

`YnewStd` |
Corresponding standard errors. |

Langsrud, Ø., Jørgensen, K., Ofstad, R. and Næs, T. (2007):
“Analyzing Designed Experiments with Multiple Responses”,
*Journal of Applied Statistics*, **34**, 1275-1296.

# Generate data x1 <- 1:6 x2 <- rep(c(100, 200), each = 3) y1 <- x1 + rnorm(6)/10 y2 <- y1 + x2 + rnorm(6)/10 # Create ffmanova object ff <- ffmanova(cbind(y1, y2) ~ x1 + x2) # Predictions from the input data predict(ff) # Rows 1 and 5 from above predictions predict(ff, data.frame(x1 = c(1, 5), x2 = c(100, 200))) # Rows 1 as above and row 2 different predict(ff, data.frame(x1 = c(1, 5), x2 = 100)) # Three ways of making the same mean predictions predict(ff, data.frame(x1 = c(1, 5), x2 = 150)) predict(ff, data.frame(x1 = c(1, 5), x2 = NA)) predict(ff, data.frame(x1 = c(1, 5))) # Using linComb input specified to produce regression coefficients # with std. As produced by summary(lm(cbind(y1, y2) ~ x1 + x2)) predict(ff, data.frame(x1 = c(1, 2)), matrix(c(-1, 1), 1, 2)) predict(ff, data.frame(x2 = c(101, 102)), matrix(c(-1, 1), 1, 2)) # Above results by a 2*4 linComb matrix and with rownames lC <- t(matrix(c(-1, 1, 0, 0, 0, 0, -1, 1), 4, 2)) rownames(lC) <- c("x1", "x2") predict(ff, data.frame(x1 = c(1, 2, 1, 1), x2 = c(100, 100, 101, 102)), lC)

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