Methods for efp fits

Methods to summarise and extract information from a model fitted using efp.

Usage

# S3 method for class 'efp'
fitted(object, type = "p", ...)

# S3 method for class 'efp'
vcov(object, ...)

# S3 method for class 'efp'
confint(object, parm, level = 0.95, ...)

# S3 method for class 'efp'
logLik(object, ...)

# S3 method for class 'efp'
formula(x, ...)

# S3 method for class 'efp'
summary(object, ...)

# S3 method for class 'summary.efp'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

# S3 method for class 'efp'
residuals(object, ...)

# S3 method for class 'efp'
anova(object, ...)

# S3 method for class 'efp'
plot(x, ...)

# S3 method for class 'efp'
model.matrix(object, ...)

# S3 method for class 'efp'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

object, x

an efp fitted model

type

the type of fitted value: "p" for the capture probability in each pass, "pi" for the probability that a fish is first caught in each pass, "both" for a list containing both and "lpmatrix" for the design matrix.

...

further arguments, ignored unless stated

parm

the parameters to compute confidence intervals for, either a vector of numbers or names. The default is all fixed effects.

level

the confidence level required

digits

the number of significant digits to print

Value

fitted returns a vector, list or matrix depending on type; model.matrix a matrix; vcov a matrix; confint a matrix with columns giving the lower and upper confidence limits; logLik an object of class logLik; formula the model formula; summary an object of class summary.efp, a list containing the coefficient table (coefficients) and the table of random effect standard deviations (sigma). The print methods return their argument invisibly.

Details

The variance matrix returned by vcov is for the fixed effects only, the joint variance matrix of the fixed and random effects is available as object$Vp. Confidence intervals are Wald intervals based on this variance matrix.

The log likelihood is the marginal log likelihood when the model contains random effects or penalised smooths, and the degrees of freedom are the number of fixed effects plus the number of standard deviations.

Residuals, analysis of deviance tables and diagnostic plots are not available for efp fits, and the corresponding methods return an error.

Examples

data(ef_data)
ef_data$sampleID <- factor(paste(ef_data$date, ef_data$siteID,
                                 ef_data$lifestage, ef_data$species))
m1 <- efp(count ~ lifestage, data = ef_data, pass = pass, id = sampleID)

summary(m1)
#> 
#> Call:
#> efp(formula = count ~ lifestage, data = ef_data, pass = pass, 
#>     id = sampleID)
#> 
#> Formula:
#> count ~ lifestage
#> <environment: 0x55888cab0750>
#> 
#> No random effects
#> 
#> Coefficients:
#>               Estimate Std. Error z value Pr(>|z^2|)
#> (Intercept)     0.6820    0.07119   9.580  9.704e-22
#> lifestageParr   0.6212    0.10291   6.036  1.579e-09
#> 
#> Degrees of Freedom: 90 Total (i.e. Null);  88 Residual
#> AIC: 3192
vcov(m1)
#>                (Intercept) lifestageParr
#> (Intercept)    0.005067977  -0.005067977
#> lifestageParr -0.005067977   0.010590042
confint(m1)
#>                   2.5 %    97.5 %
#> (Intercept)   0.5424690 0.8215276
#> lifestageParr 0.4194697 0.8228614
logLik(m1)
#> 'log Lik.' -1594.226 (df=2)
head(fitted(m1))
#> [1] 0.7863670 0.6641846 0.7863670 0.6641846 0.7863670 0.6641846