Predict probability of success for given tumour size measurements.
This method simply forwards to prob_success.
## S3 method for class 'augbin_2t_1a_fit' predict( object, y1_lower = -Inf, y1_upper = Inf, y2_lower = -Inf, y2_upper = log(0.7), probs = c(0.025, 0.975), newdata = NULL, ... )
object |
Object of class |
y1_lower |
numeric, minimum threshold to constitute success, scrutinising the log of the tumour size ratio comparing time 1 to baseline. Defaults to negative infinity. |
y1_upper |
numeric, maximum threshold to constitute success, scrutinising the log of the tumour size ratio comparing time 1 to baseline. Defaults to positive infinity. |
y2_lower |
numeric, minimum threshold to constitute success, scrutinising the log of the tumour size ratio comparing time 2 to baseline. |
y2_upper |
numeric, maximum threshold to constitute success, scrutinising the log of the tumour size ratio comparing time 2 to baseline. Defaults to log(0.7). |
probs |
pair of probabilities to use to calculate the credible interval for the probability of success. |
newdata |
data for which to infer the probability of success.
A dataframe-like object with baseline tumour sizes in first column, and first
and second post-baseline tumour sizes in columns 2 and 3. Omitted by default.
When omitted, newdata is set to be the |
... |
Extra args passed onwards. |
Object of class tibble
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.