# multinomial

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##### Multinomial Logit Model

Fits a multinomial logit model to a (preferably unordered) factor response.

Keywords
models, regression
##### Usage
multinomial(zero = NULL, parallel = FALSE, nointercept = NULL,
refLevel = "(Last)", whitespace = FALSE)
##### Arguments
zero

Can be an integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. Any values must be from the set {1,2,…,$M$}. The default value means none are modelled as intercept-only terms. See CommonVGAMffArguments for more information.

parallel

A logical, or formula specifying which terms have equal/unequal coefficients.

nointercept, whitespace

See CommonVGAMffArguments for more details.

refLevel

Either a (1) single positive integer or (2) a value of the factor or (3) a character string. If inputted as an integer then it specifies which column of the response matrix is the reference or baseline level. The default is the last one (the $(M+1)$th one). If used, this argument will be usually assigned the value 1. If inputted as a value of a factor then beware of missing values of certain levels of the factor (drop.unused.levels = TRUE or drop.unused.levels = FALSE). See the example below. If inputted as a character string then this should be equal to (A) one of the levels of the factor response, else (B) one of the column names of the matrix response of counts; e.g., vglm(cbind(normal, mild, severe) ~ let, multinomial(refLevel = "severe"), data = pneumo) if it was (incorrectly because the response is ordinal) applied to the pneumo data set. Another example is vglm(ethnicity ~ age, multinomial(refLevel = "European"), data = xs.nz) if it was applied to the xs.nz data set.

##### Details

In this help file the response $Y$ is assumed to be a factor with unordered values $1,2,\dots,M+1$, so that $M$ is the number of linear/additive predictors $\eta_j$.

The default model can be written $$\eta_j = \log(P[Y=j]/ P[Y=M+1])$$ where $\eta_j$ is the $j$th linear/additive predictor. Here, $j=1,\ldots,M$, and $\eta_{M+1}$ is 0 by definition. That is, the last level of the factor, or last column of the response matrix, is taken as the reference level or baseline---this is for identifiability of the parameters. The reference or baseline level can be changed with the refLevel argument.

In almost all the literature, the constraint matrices associated with this family of models are known. For example, setting parallel = TRUE will make all constraint matrices (including the intercept) equal to a vector of $M$ 1's; to suppress the intercepts from being parallel then set parallel = FALSE ~ 1. If the constraint matrices are unknown and to be estimated, then this can be achieved by fitting the model as a reduced-rank vector generalized linear model (RR-VGLM; see rrvglm). In particular, a multinomial logit model with unknown constraint matrices is known as a stereotype model (Anderson, 1984), and can be fitted with rrvglm.

##### Value

An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.

##### Note

The response should be either a matrix of counts (with row sums that are all positive), or a factor. In both cases, the y slot returned by vglm/vgam/rrvglm is the matrix of sample proportions.

The multinomial logit model is more appropriate for a nominal (unordered) factor response than for an ordinal (ordered) factor response. Models more suited for the latter include those based on cumulative probabilities, e.g., cumulative.

multinomial is prone to numerical difficulties if the groups are separable and/or the fitted probabilities are close to 0 or 1. The fitted values returned are estimates of the probabilities $P[Y=j]$ for $j=1,\ldots,M+1$. See safeBinaryRegression for the logistic regression case.

Here is an example of the usage of the parallel argument. If there are covariates x2, x3 and x4, then parallel = TRUE ~ x2 + x3 - 1 and parallel = FALSE ~ x4 are equivalent. This would constrain the regression coefficients for x2 and x3 to be equal; those of the intercepts and x4 would be different.

In Example 4 below, a conditional logit model is fitted to an artificial data set that explores how cost and travel time affect people's decision about how to travel to work. Walking is the baseline group. The variable Cost.car is the difference between the cost of travel to work by car and walking, etc. The variable Time.car is the difference between the travel duration/time to work by car and walking, etc. For other details about the xij argument see vglm.control and fill.

The multinom function in the nnet package uses the first level of the factor as baseline, whereas the last level of the factor is used here. Consequently the estimated regression coefficients differ.

##### Warning

No check is made to verify that the response is nominal.

See CommonVGAMffArguments for more warnings.

##### References

Yee, T. W. (2010) The VGAM package for categorical data analysis. Journal of Statistical Software, 32, 1--34. http://www.jstatsoft.org/v32/i10/.

Yee, T. W. and Hastie, T. J. (2003) Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15--41.

McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, 2nd ed. London: Chapman & Hall.

Agresti, A. (2013) Categorical Data Analysis, 3rd ed. Hoboken, NJ, USA: Wiley.

Hastie, T. J., Tibshirani, R. J. and Friedman, J. H. (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd ed. New York, USA: Springer-Verlag.

Simonoff, J. S. (2003) Analyzing Categorical Data, New York, USA: Springer-Verlag.

Anderson, J. A. (1984) Regression and ordered categorical variables. Journal of the Royal Statistical Society, Series B, Methodological, 46, 1--30.

Tutz, G. (2012) Regression for Categorical Data, Cambridge University Press.

margeff, cumulative, acat, cratio, sratio, dirichlet, dirmultinomial, rrvglm, fill1, Multinomial, multilogitlink, iris. The author's homepage has further documentation about categorical data analysis using VGAM.

• multinomial
##### Examples
# NOT RUN {
# Example 1: fit a multinomial logit model to Edgar Anderson's iris data
data(iris)
# }
# NOT RUN {
fit <- vglm(Species ~ ., multinomial, iris)
coef(fit, matrix = TRUE)
# }
# NOT RUN {
# Example 2a: a simple example
ycounts <- t(rmultinom(10, size = 20, prob = c(0.1, 0.2, 0.8)))  # Counts
fit <- vglm(ycounts ~ 1, multinomial)
fit@prior.weights   # NOT recommended for extraction of prior weights
weights(fit, type = "prior", matrix = FALSE)  # The better method
depvar(fit)         # Sample proportions; same as fit@y
constraints(fit)    # Constraint matrices

# Example 2b: Different reference level used as the baseline
fit2 <- vglm(ycounts ~ 1, multinomial(refLevel = 2))
coef(fit2, matrix = TRUE)
coef(fit , matrix = TRUE)  # Easy to reconcile this output with fit2

# Example 3: The response is a factor.
nn <- 10
dframe3 <- data.frame(yfac = gl(3, nn, labels = c("Ctrl", "Trt1", "Trt2")),
x2   = runif(3 * nn))
myrefLevel <- with(dframe3, yfac[12])
fit3a <- vglm(yfac ~ x2, multinomial(refLevel = myrefLevel), dframe3)
fit3b <- vglm(yfac ~ x2, multinomial(refLevel = 2), dframe3)
coef(fit3a, matrix = TRUE)  # "Trt1" is the reference level
coef(fit3b, matrix = TRUE)  # "Trt1" is the reference level
margeff(fit3b)

# Example 4: Fit a rank-1 stereotype model
fit4 <- rrvglm(Country ~ Width + Height + HP, multinomial, data = car.all)
coef(fit4)  # Contains the C matrix
constraints(fit4)$HP # The A matrix coef(fit4, matrix = TRUE) # The B matrix Coef(fit4)@C # The C matrix concoef(fit4) # Better to get the C matrix this way Coef(fit4)@A # The A matrix svd(coef(fit4, matrix = TRUE)[-1, ])$d  # This has rank 1; = C %*% t(A)
# Classification (but watch out for NAs in some of the variables):
apply(fitted(fit4), 1, which.max)  # Classification
colnames(fitted(fit4))[apply(fitted(fit4), 1, which.max)]  # Classification
apply(predict(fit4, car.all, type = "response"), 1, which.max)  # Ditto

# Example 5: The use of the xij argument (aka conditional logit model)
set.seed(111)
nn <- 100  # Number of people who travel to work
M <- 3  # There are M+1 models of transport to go to work
ycounts <- matrix(0, nn, M+1)
ycounts[cbind(1:nn, sample(x = M+1, size = nn, replace = TRUE))] = 1
dimnames(ycounts) <- list(NULL, c("bus","train","car","walk"))
gotowork <- data.frame(cost.bus  = runif(nn), time.bus  = runif(nn),
cost.train= runif(nn), time.train= runif(nn),
cost.car  = runif(nn), time.car  = runif(nn),
cost.walk = runif(nn), time.walk = runif(nn))
gotowork <- round(gotowork, digits = 2)  # For convenience
gotowork <- transform(gotowork,
Cost.bus   = cost.bus   - cost.walk,
Cost.car   = cost.car   - cost.walk,
Cost.train = cost.train - cost.walk,
Cost       = cost.train - cost.walk,  # for labelling
Time.bus   = time.bus   - time.walk,
Time.car   = time.car   - time.walk,
Time.train = time.train - time.walk,
Time       = time.train - time.walk)  # for labelling
fit <- vglm(ycounts ~ Cost + Time,
multinomial(parall = TRUE ~ Cost + Time - 1),
xij = list(Cost ~ Cost.bus + Cost.train + Cost.car,
Time ~ Time.bus + Time.train + Time.car),
form2 =  ~ Cost + Cost.bus + Cost.train + Cost.car +
Time + Time.bus + Time.train + Time.car,
data = gotowork, trace = TRUE)
head(model.matrix(fit, type = "lm"))   # LM model matrix
head(model.matrix(fit, type = "vlm"))  # Big VLM model matrix
coef(fit)
coef(fit, matrix = TRUE)
constraints(fit)
summary(fit)
max(abs(predict(fit) - predict(fit, new = gotowork)))  # Should be 0
# }

Documentation reproduced from package VGAM, version 1.1-1, License: GPL-3

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