Prediction

Predicted values for models of class 'drc'.

Usage

# S3 method for class 'drc'
predict(
  object,
  newdata,
  se.fit = FALSE,
  interval = c("none", "confidence", "prediction", "ssd"),
  level = 0.95,
  na.action = na.pass,
  od = FALSE,
  vcov. = vcov,
  ssdSEfct = NULL,
  constrain = TRUE,
  checkND = TRUE,
  ...
)

Arguments

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. vcov is the default, but sandwich is also an option (for obtaining robust standard errors).

ssdSEfct

specifies the function for interpolating standard errors between observed standard errors. The default is linear interpolation on log-log scale (back-transformed).

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.

Value

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 values, standard errors, lower and upper limits of confidence/prediction intervals.

See also

For details see the help page for predict.lm.

Author

Christian Ritz

Examples

## Fitting a model
spinach.model1 <- drm(SLOPE~DOSE, CURVE, data = spinach, fct = LL.4())

## Predicting values at dose=2 (with standard errors)
predict(spinach.model1, data.frame(dose=2, CURVE=c("1", "2", "3")), se.fit = TRUE)
#>      Prediction         SE
#> [1,]  0.9048476 0.02496135
#> [2,]  0.4208307 0.02924987
#> [3,]  0.5581673 0.03067170

## Getting confidence intervals
predict(spinach.model1, data.frame(dose=2, CURVE=c("1", "2", "3")),
interval = "confidence")
#>      Prediction     Lower     Upper
#> [1,]  0.9048476 0.8552178 0.9544775
#> [2,]  0.4208307 0.3626741 0.4789873
#> [3,]  0.5581673 0.4971838 0.6191509

## Getting prediction intervals
predict(spinach.model1, data.frame(dose=2, CURVE=c("1", "2", "3")),
interval = "prediction")
#>      Prediction     Lower     Upper
#> [1,]  0.9048476 0.7504590 1.0592363
#> [2,]  0.4208307 0.2634937 0.5781677
#> [3,]  0.5581673 0.3997636 0.7165710