betalimits(Term, level), Term a CHARACTER scalar, a positive integer or a variable in the most recent regression model, REAL positive scalar level < 1. |

You use betalimits() to compute confidence limits for a regression coefficient. Its use must be preceded by a GLM command, usually regress() or anova(). betalimits(Term, Level), where Term specifies a variable or a term in the ANOVA table, and Level is a REAL scalar between 0.5 and 1, returns upper and lower confidence limits for a regression coefficeint or ANOVA main effects or interactions. Term can be a quoted or unquoted predictor variable or term name, or CONSTANT or a positive integer specifying the number of the term. If the term is an iteraction it must be quoted. Level is the desired confidence coefficient. If level < .5, a warning message is given and the confidence coefficient is assumed to be 1 - level. After regress(), the result is vector(lower, upper), where lower and upper are the confidence limits. After anova(), when the term is a main effect with k levels, the result is k by 2 matrix hconcat(lower, upper). When the term is an interaction of factors with k1, k2, ... levels, the result is array(vector(upper,lower),k1,k2,...,2). Example: After regress("y=x1 + x2 + x3"), the following are all equivalent Cmd> betalimits(x2, .95) Cmd> betalimits("x2",.95) Cmd> betalimits(3,.95) #counting the constant, x2 is the third term They all return a vector of length 2. If a and b are factors with 3 and 4 levels, respectively, after anova("y = a*b") (equivalent to anova("y = a + b + a.b")) Cmd> betalimits("a.b",.95) # quotes are required and Cmd> betalimits(4, .95) # a.b is term 4 are equivalent and return a 3 by 4 by 2 array with a[i,j,] the upper and lower limits for the i,j interaction effect. In all these replacing .95 by .05 gives the same result except a warning message is printed. See also coefs(), secoefs(), regress()

Gary Oehlert 2003-01-15