Data fit using glmer from lme4 with family poisson to fit the dataset with the given formula.
Arguments
- data
The observed data to be fitted.
- response
The response
- fixef
The fixed effects
- ranef
The random effects
- dist_family
Family
- offset
The offsets.
Value
The model fit of the data with additional attributes offset, response and fit_fix. Offset and response are the same as in the input and fit_fix is the linear model of the fix effects.
For more details see the help vignette:
vignette("intro", package="attrib")
Examples
response <- "deaths_n"
fixef <- "ili_isoweekmean7_13_pr100 +
sin(2 * pi * (isoweek - 1) / 52) +
cos(2 * pi * (isoweek - 1) / 52)"
ranef <- " (ili_isoweekmean7_13_pr100| season)"
offset <- "log(pop_jan1_n)"
data <- attrib::data_fake_attrib_nation
fit_attrib(data = data, response = response, fixef = fixef, ranef = ranef, offset = offset)
#> boundary (singular) fit: see help('isSingular')
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#> Approximation) [glmerMod]
#> Family: poisson ( log )
#> Formula: deaths_n ~ ili_isoweekmean7_13_pr100 + sin(2 * pi * (isoweek -
#> 1)/52) + cos(2 * pi * (isoweek - 1)/52) + offset(log(pop_jan1_n)) +
#> (ili_isoweekmean7_13_pr100 | season)
#> Data: data
#> AIC BIC logLik deviance df.resid
#> 5584.788 5615.256 -2785.394 5570.788 567
#> Random effects:
#> Groups Name Std.Dev. Corr
#> season (Intercept) 0.000000
#> ili_isoweekmean7_13_pr100 0.003655 NaN
#> Number of obs: 574, groups: season, 11
#> Fixed Effects:
#> (Intercept) ili_isoweekmean7_13_pr100
#> -8.800873 0.062266
#> sin(2 * pi * (isoweek - 1)/52) cos(2 * pi * (isoweek - 1)/52)
#> -0.005386 0.033550
#> optimizer (Nelder_Mead) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warnings