glmval - Generalized linear model values

Syntax

yhat = glmval(b,X,link) [yhat,dylo,dyhi] = glmval(b,X,link,stats) [...] = glmval(...,param1,val1,param2,val2,...)

Description

yhat = glmval(b,X,link) computes predicted values for the generalized linear model with link function link and predictors X. Distinct predictor variables should appear in different columns of X. b is a vector of coefficient estimates as returned by the glmfit function. link can be any of the strings used as values for the link parameter in the glmfit function.

Note By default, glmval adds a first column of 1s to X, corresponding to a constant term in the model. Do not enter a column of 1s directly into X. You can change the default behavior of glmval using the 'constant' parameter, below.

[yhat,dylo,dyhi] = glmval(b,X,link,stats) also computes 95% confidence bounds for the predicted values. When the stats structure output of the glmfit function is specified, dylo and dyhi are also returned. dylo and dyhi define a lower confidence bound of yhat-dylo, and an upper confidence bound of yhat+dyhi. Confidence bounds are nonsimultaneous, and apply to the fitted curve, not to a new observation.

[...] = glmval(...,param1,val1,param2,val2,...) specifies optional parameter name/value pairs to control the predicted values. Acceptable parameters are:

Parameter	Value
`'confidence'` — the confidence level for the confidence bounds	A scalar between 0 and 1
`'size'` — the size parameter (N) for a binomial model	A scalar, or a vector with one value for each row of X
`'offset'` — used as an additional predictor variable, but with a coefficient value fixed at 1.0	A vector
`'constant'`	`'on'` — Includes a constant term in the model. The coefficient of the constant term is the first element of `b`. `'off'` — Omit the constant term

Examples

Fit a probit regression model for y on x. Each y(i) is the number of successes in n(i) trials.

x = [2100 2300 2500 2700 2900 3100 ...
     3300 3500 3700 3900 4100 4300]';
n = [48 42 31 34 31 21 23 23 21 16 17 21]';
y = [1 2 0 3 8 8 14 17 19 15 17 21]';
b = glmfit(x,[y n],'binomial','link','probit');
yfit = glmval(b,x,'probit','size',n);
plot(x, y./n,'o',x,yfit./n,'-','LineWidth',2)

References

[1] Dobson, A. J. An Introduction to Generalized Linear Models. New York: Chapman & Hall, 1990.

[2] McCullagh, P., and J. A. Nelder. Generalized Linear Models. New York: Chapman & Hall, 1990.

[3] Collett, D. Modeling Binary Data. New York: Chapman & Hall, 2002.

glmval - Generalized linear model values

Syntax

Description

Examples

References

See Also

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