If there are no positive observations for a contrast, it is generally not estimible. However, for the purposes of testing we can replace it with the least favorable value with respect to the contrasts that are defined.
impute(object, groupby)Arguments
- object
Output of predict
- groupby
Variables (column names in predict) to group by for imputation (facets of the plot)
Value
data.table
Examples
##See stat_ell
example(stat_ell)
#>
#> stt_ll> data(vbetaFA)
#>
#> stt_ll> library(ggplot2)
#> RStudio Community is a great place to get help:
#> https://community.rstudio.com/c/tidyverse
#>
#> stt_ll> zlmCond <- zlm(~Stim.Condition, vbetaFA[1:10,])
#>
#> Done!
#>
#> stt_ll> MM <- model.matrix(~Stim.Condition,unique(colData(vbetaFA)[,c("Stim.Condition"),drop=FALSE]))
#>
#> stt_ll> predicted <- predict(zlmCond,modelmatrix=MM)
#>
#> stt_ll> plt <- ggplot(predicted)+aes(x=invlogit(etaD),y=muC,xse=seD,yse=seC,col=sample)+
#> stt_ll+ facet_wrap(~primerid,scales="free_y")+theme_linedraw()+
#> stt_ll+ geom_point(size=0.5)+scale_x_continuous("Proportion expression")+
#> stt_ll+ scale_y_continuous("Estimated Mean")+
#> stt_ll+ stat_ell(aes(x=etaD,y=muC),level=0.95, invert='x')
#>
#> stt_ll> ## plot with inverse logit transformed x-axis
#> stt_ll> print(plt)
#> Warning: Removed 3 rows containing non-finite values (stat_ell).
#> Warning: Removed 1 rows containing missing values (geom_point).
#>
#> stt_ll> # doesn't do anything in this case because there are no inestimable coefficients
#> stt_ll> predictI <- impute(predicted, groupby='primerid')
#> Warning: no non-missing arguments to max; returning -Inf
