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This is the appropriate command for the full contingency table. It fits a log-linear model to the nine cell counts, with logarithmic link function and no interaction:With baseline constraints on $g_i,d_j$, there are five parameters. The factorized means express independence of treatment group and damage category. The maximum-likelihood estimates areAlthough the experiment fixes each row total at 30, conditioning these independent Poisson random variables on those totals produces the appropriate row-wise multinomial distribution. The Poisson trick therefore gives the correct likelihood inference for this independence log-linear model for a two-way contingency table; the margins are nuisance parameters. It tests whether treatment changes the distribution of damage, rather than modelling a binary outcome.

This is not a valid ordinary R glm family specification. A multinomial distribution is natural for the three damage counts conditional on the row total, but base R's generalized linear model interface does not supply a family named multinomial. Furthermore, the written response is one cell count, not a three-category response or grouped multinomial vector. A properly specified multinomial logistic regression could model category probabilities by group, but it is not the model fit by this command. The independent-count log-linear model in part (a), conditional on the margins, is the appropriate supplied alternative.

The count weights correct the loss of frequencies for a deliberately coarsened binary response, but do not supply a third outcome category. Thus this remains inappropriate for the full contingency table. With the base R factor convention, it could fit the chosen first damage level versus the other two combined, using a logistic regression and the frequencies as weights. That is a different scientific question. The supplied log-linear model retains all three categories and their counts without this information loss.

Let $Y_{go}$ be the count for treatment(Control, LD, or SD) and outcome(Better or Worse), and write ${\mu }_{go}=\mathbb{E}{Y}_{go}$. Both fits take the six cell counts to be independent Poisson variables.

The additive log-linear model fitted by fit1 iswith reference constraints ${\alpha }_C=0$ and ${\beta }_B=0$. Thus treatment changes the overall group count but not the relative frequencies of the two outcomes.

The interaction model fitted by fit2 iswhere, under reference coding,and the two free interactions are ${\gamma }_{LW}$ and ${\gamma }_{SW}$. This is the saturated log-linear model for the $3\times 2$ table.

In the log-linear model fit1 has only three main effects, so its fitted mean factors into a species factor, a diameter factor, and a height factor: mutual independence. Fit2 adds the species-diameter interaction, allowing those variables to associate, while asserting $\text{Height}\perp (\text{Species},\text{Diameter})$.

The likelihood-ratio deviance drop $12.606$ on one degree of freedom has $p=0.0003845$, strong evidence against species-diameter independence. Test fit2's remaining independence claim by comparing it with a model adding both Species:Height and Diameter:Height interactions (equivalently compare fit2 with the saturated model); its residual deviance $12.431$ is assessed against ${\chi }_3^2$.