Description Usage Arguments Details Value Author(s) See Also Examples
Predicted values for models of class 'drc'.
1 2 3 4 5 |
object |
an object of class 'drc'. |
newdata |
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. |
se.fit |
logical. If TRUE standard errors are required. |
interval |
character string. Type of interval calculation: "none", "confidence", "prediction", or "ssd". |
level |
Tolerance/confidence level. |
na.action |
function determining what should be done with missing values in 'newdata'. The default is to predict 'NA'. |
od |
logical. If TRUE adjustment for over-dispersion is used. |
vcov. |
function providing the variance-covariance matrix. |
ssdSEfct |
specifies the function for interpolating standard errors between observed standard errors. The default is linear interpolation on log-log scale (back-transformed). See Details for more explanation. |
constrain |
logical. If TRUE (default) predicted values are truncated within meaningful limits, i.e., 0 and, possibly, 1. |
checkND |
logical indicating whether or not names in "newdata" data frame match the names in the original data frame (used for fitting the model). Default is TRUE. |
... |
further arguments passed to or from other methods. |
For the built-in log-logistic, log-normal, and Weibull-type models standard errors and confidence/prediction intervals can be calculated. For other built-in models it may not yet be implemented (drop us an e-mail if you need them).
The function for interpolating standard errors of estimates, which may be used when fitting an SSD, should have 3 arguments: observed estimates and corresponding standard errors and future estimates and should return interpolated standard errors corresponding to the future estimates provided.
A matrix with as many rows as there are dose values provided in 'newdata' or in the original dataset (in case 'newdata' is not specified) and, at most, 4 columns containing fitted, standard errors, lower and upper limits of confidence/prediction intervals.
Christian Ritz
For details are found in the help page for predict.lm
.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Fitting a model
spinach.model1 <- drm(SLOPE~DOSE, CURVE, data = spinach, fct = LL.4())
## Predicting values a dose=2 (with standard errors)
predict(spinach.model1, data.frame(dose=2, CURVE=c("1", "2", "3")), se.fit = TRUE)
## Getting confidence intervals
predict(spinach.model1, data.frame(dose=2, CURVE=c("1", "2", "3")),
interval = "confidence")
## Getting prediction intervals
predict(spinach.model1, data.frame(dose=2, CURVE=c("1", "2", "3")),
interval = "prediction")
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