Hisp | Yet another simple Scheme Interpreter

"Linear predictors" are not the same as "fitted values", unless a GLM is fitted with an identity link. In general the linear predictor is eta = b0 + b1*x1 + b2*x2 + ..., while the fitted value is mu = linkinv(eta), where linkinv is the inverse link function (e.g. logistic or inverse-logit in this case).

In general it's always safer to use accessor methods: that way you don't have to worry about internal definitions

You can solve it using group_by() and summarize(), as follows:

The margins package takes care of this automatically if you declare a variable to be a factor. See the subsetting section of the vignette or you can inspect the source code to see that marginal effects are computed as differences for factor variables.

Note that the default setting for margins is to compute the "average marginal effect", and not the "marginal effect at the mean". IMO, the default setting is best in most cases, but if you insist on considering a "synthetic" average observation, it is easy to do with the at argument of the margins function.

Code example. In the first case, vs is treated as a continuous variable. In the second, vs is treated as a binary variable.

Finally, I managed to know how it's done. The way we use @AssociationOverrides and @JoinTable in my use case to override the parent-defined relation is as the following:

The problem is simply that you are drawing the gray background with your call to geom_rect, which by default is gray. You can either make this white, or better still, remove it and use scales and themes to give your plot the desired look.

To remove the color guide from the legend, you can add + scale_color_discrete(guide = guide_none()) to your plot.

The symbols are being clipped (and don't align perfectly with the labels) because each of the facets is actually preserving a tiny space for all the groups. You therefore need to specify scales = "free_y" to level everything out, give your error bars greater width and prevent the symbols from clipping.

You can also choose a global theme that requires less individual tweaks to the theme parameters, and you may prefer the look of making the strip labels right-aligned and external to the y axis line.

You could try using a prop.test of a table of each column, which gives you the proportion as well as 95% confidence intervals. Just multiply these by 100 to get percentages:

To delete vertices where Race=="NatAm", you can indicate this in the vid argument of delete.vertices(). But beware--delete.vertices() modifies the network in place!

I think the issue is that your data males_part is a tibble, but you don't have the tibble package loaded until you attach haven. If you don't have tibble loaded, then you won't have any methods for the tibble classes "tbl_df" and "tbl", and it will act exactly like a data frame. Once tibble is loaded, it will start to act like a tibble.

This is an issue because tibbles and data frames aren't identical, but the class of a tibble includes "data.frame". I'd guess what's happening is that plm assumes that extracting a single column from a data frame gives a vector, but with a tibble, it gives another tibble.

The workaround for you is pretty simple. Just use males_part <- as.data.frame(males_part) to remove the tibble class, and then haven won't matter.

Conceivably this is worth reporting to the maintainer of plm. It's a design flaw in tibble that is causing the problem (if tibbles inherit from data.frame, they should act like data frames), but tibbles are pretty common nowadays, and that design is unlikely to change. The plm function could protect itself against this by putting data <- as.data.frame(data) early in the pdata.frame function, or protecting every column extraction with drop = TRUE.

I think you are doing it wrong, since no input data is given I am using iris. If you run the same command on iris , the error which you shown can be replicated as well.

From documentation:

The first argument(ellipsis) only accepts these things:

... one or more model objects (for regression analysis tables) or data frames/vectors/matrices (for summary statistics, or direct output of content). They can also be included as lists (or even lists within lists).

you should do it like this: