| predict.sclr {sclr} | R Documentation |
Predict method for scaled logit model x.
Description
Returns only the protection estimates. The only supported interval is a confidence interval (i.e. the interval for the estimated expected value).
Usage
## S3 method for class 'sclr'
predict(object, newdata, ci_lvl = 0.95, ...)
Arguments
object |
Object returned by |
newdata |
A dataframe with all covariates. Names should be as they
appear in the formula in the call to |
ci_lvl |
Confidence level for the calculated interval. |
... |
Not used. Needed to match generic signature. |
Details
The model is
P(Y = 1) = \lambda(1 - logit^{-1}(\beta_0 +
\beta_1X_1 + \beta_2X_2 + ... + \beta_kX_k))
Where Y is the binary
outcome indicator, (e.g. 1 - infected, 0 - not infected). X -
covariate.
k - number of covariates.
This function calculates
\beta_0 + \beta_1X_1 + \beta_2X_2 + ..
. + \beta_kX_k
transformations at the covariate values found in
newdata as well as the variance-covariance matrices of those
transformations. This is used to calculate the confidence intervals at the
given parameter values. The inverse logit transformation is then applied
to point estimates and interval bounds.
Value
A tibble obtained by adding the following
columns to newdata:
prot_point_lin prot_l_lin prot_u_lin |
Point estimate, low and high bounds of the linear transformation. |
prot_sd_lin |
Estimated standard deviation of the linear transformation. |
prot_point prot_l prot_u |
Inverse logit-transformed point estimate, low and high bounds of the linear transformation. |