X — NRX by NCOL matrix containing the data. (Input)

IRSP — Column number IRSP of X contains the data for the response (dependent) variable. (Input)

B — Vector of length NCOEF containing a least-squares solution

R — NCOEF by NCOEF upper triangular matrix containing the R matrix. (Input)
The R matrix can come from a regression fit based on a QR decomposition of the matrix of regressors or based on a Cholesky factorization RTR of the matrix of sums of squares and crossproducts of the regressors. Elements to the right of a diagonal element of R that is zero must also be zero. A zero row indicates a nonfull rank model. For an R matrix that comes from a regression fit with linear equality restrictions on the parameters, each row of R corresponding to a restriction must have a corresponding diagonal element that is negative. The remaining rows of R must have positive diagonal elements. Only the upper triangle of R is referenced.

DFE — Degrees of freedom for error. (Input)

SSE — Sum of squares for error. (Input)

CASE — NRX by 12 matrix containing the case statistics. (Output)
Columns 1 through 12 contain the following:

IDO — Processing option. (Input)
Default: IDO = 0.

NRX — Number of rows in X. (Input)
Default: NRX = size (X,1).

NCOL — Number of columns in X. (Input)
Default: NCOL = size (X,2).

LDX — Leading dimension of X exactly as specified in the dimension statement in the calling program. (Input)
Default: LDX = size (X,1).

INTCEP — Intercept option. (Input)
Default: INTCEP = 1.

IEF — Effect option. (Input)
Default: IEF = ‑1.
The absolute value of IEF is the number of effects (sources of variation) due to the model. The sign of IEF specifies the following options.

NCLVAR — Number of classification variables. (Input, if IEF is positive)

INDCL — Index vector of length NCLVAR containing the column numbers of X that are the classification variables. (Input, if IEF is positive)

NCLVAL — Vector of length NCLVAR containing the number of values taken on by each classification variable. (Input, if IEF is positive)
NCLVAL(I) is the number of distinct values for the I-th classification variable.

CLVAL — Vector of length NCLVAL(1) + NCLVAL(2) + … + NCLVAL(NCLVAR) containing the values of the classification variables. (Input, if IEF is positive)
The first NCLVAL(1) variables contain the values of the first classification variable; the next NCLVAL(2) variables contain the values of the second classification variable; and so on. The last NCLVAL(NCLVAR) variables contain the values of the last classification variable.

NVEF — Vector of length IEF containing the number of variables associated with each effect in the model. (Input, if IEF is positive)

INDEF — Index vector of length NVEF(1) + NVEF(2) + … + NVEF(IEF). (Input, if IEF is positive)
The first NVEF(1) elements give the column numbers of X for each variable in the first effect; the next NVEF(2) elements give the column numbers for each variable in the second effect; and so on. The last NVEF(NEF) elements give the column numbers for each variable in the last effect.

IDUMMY — Dummy variable option. (Input, if IEF is positive)
Some indicator variables are defined for the I-th class variable as follows: Let J = NCLVAL(1) + NCLVAL(2) + … + NCLVAL(I ‑ 1). NCLVAL(I) indicator variables are defined such that for K = 1, 2, …, NCLVAL(I) the K-th indicator variable for row M of X takes the value 1.0 if X(M, INDCL(I)) = CLVAL(J + K) and equals 0.0 otherwise. Dummy variables are generated from these indicator variables in one of the three following ways:

IWT — Weighting option. (Input)
IWT = 0 means that all weights are 1.0. For positive IWT, column number IWT of X contains the weights, and the computed prediction interval uses SSE/(DFE * X(I, IWT)) for the estimated variance of a future response.
Default: IWT = 0.

IPRED — Prediction interval option. (Input)
IPRED = 0 means that prediction intervals are desired for a single future response. For positive IPRED, column number IPRED of X contains the number of future responses for which a prediction interval is desired on the average of the future responses.
Default: IPRED = 0.

CONPCM — Confidence level for two-sided interval estimates on the mean, in percent. (Input)
CONPCM percent confidence intervals are computed, hence, CONPCM must be greater than or equal to 0.0 and less than 100.0. CONPCM often will be 90.0, 95.0, or 99.0. For one-sided intervals with confidence level ONECL, where ONECL is greater than or equal to 50.0 and less than 100.0, set CONPCM = 100.0 ‑ 2.0 * (100.0 ‑ ONECL).
Default: CONPCM = 95.0.

CONPCP — Confidence level for two-sided prediction intervals, in percent. (Input)
CONPCP percent prediction intervals are computed, hence, CONPCP must be greater than or equal to 0.0 and less than 100.0. CONPCP often will be 90.0, 95.0, or 99.0. For one-sided intervals with confidence level ONECL, where ONECL is greater than or equal to 50.0 and less than 100.0, set CONPCP = 100.0 ‑ 2.0 * (100.0 ‑ ONECL).
Default: CONPCP = 95.0.

PRINT — Printing option. (Input)
Default: PRINT = ‘N’.
PRINT is a character string indicating what is to be printed. The PRINT string is composed of one-character print codes to control printing. These print codes are given as follows:

The concatenated print codes ‘A’, ‘N’, ‘1’, …, ‘P’ that comprise the PRINT string give the combination of statistics to be printed. Concatenation of these codes with print codes ‘X’ or ‘Y’ restricts printing to cases determined to be influential or outliers. Here are a few examples.

IOBS — Number of the observation corresponding to the first row of X. (Input)
This observation number is used only for printing the row labels for the individual case statistics.
Default: IOBS = size (X,1).

NCOEF — Number of regression coefficients in the model. (Input)
Default: NCOEF = size (B,1).

LDR — Leading dimension of R exactly as specified in the dimension statement in the calling program. (Input)
Default: LDR = size (R,1).

LDCASE — Leading dimension of CASE exactly as specified in the dimension statement in the calling program. (Input)
Default: LDCASE = size (CASE,1).

NRMISS — Number of rows of CASE containing NaN (not a number). (Output)
If any row of data contains NaN as a value of a variable other than the response variable, columns 3 through 12 of the corresponding row in CASE are set to NaN. If the response is missing, columns 1, 3, and 5 through 8 are set to NaN.

1. Workspace may be explicitly provided, if desired, by use of R2ASE/DR2ASE. The reference is:

2. Informational errors