boxglm | Power transformations by Box-Cox method for GLMs | boxglm |

York University

The influence plot also implements a score test for the power transformation due to Atkinson, which provides an alternative estimate of the power transformation. based on power = 1 - slope of the fitted line in the partial regression plot for a constructed variable.

The arguments may be listed within parentheses in any order, separated by commas. For example:

%boxglm(resp=responsevariable, model=predictors, ..., )

- RESP=
- The name of the response variable for analysis.
- MODEL=
- The independent variables in the model, i.e., the terms on the right side of the = sign in the MODEL statement for PROC GLM. The MODEL= argument should be spelled out completely, rather than using abbreviated 'bar' notation.
- CLASS=
- Specifies the MODEL= variables which are classification factors rather than continuous variables.
- DATA=_LAST_
- The name of the data set holding the response and predictor variables. (Default: most recently created)
- ID=
- The name of an ID variable for observations
- OUT=_DATA_
- The name of an output dataset to contain the transformed response. This dataset contains all original variables, with the transformed response replacing the original variable.
- OUTPLOT=_PLOT_
- The name of the output data set containing _RMSE_, and t-values for each effect in the model, with one observation for each power value tried. PPLOT=RMSE EFFECT INFL
- Which printer plots should be produced? One or more of RMSE, EFFECT, and INFL, or NONE.
- GPLOT=NONE
- Which high-resolution (PROC GPLOT) plots should be produced? One or more of RMSE, EFFECT, and INFL, or NONE.
- LOPOWER=-2
- low value for power
- HIPOWER=2
- high value for power
- NPOWER=21
- number of power values in the interval LOPOWER to HIPOWER
- CONF=.95
- confidence coefficient for the confidence interval for the power.

data poisons; input antidote $ poison @; label survival='Survival time'; length id $4; do rep = 1 to 4; id = trim(antidote)||trim(put(poison,1.))||'-'||put(rep,1.); input survival @; output; end; cards; A 1 .31 .45 .46 .43 A 2 .36 .29 .40 .23 A 3 .22 .21 .18 .23 B 1 .82 1.10 .88 .72 B 2 .92 .61 .49 1.24 B 3 .30 .37 .38 .29 C 1 .43 .45 .63 .76 C 2 .44 .35 .31 .40 C 3 .23 .25 .24 .22 D 1 .45 .71 .66 .62 D 2 .56 1.02 .71 .38 D 3 .30 .36 .31 .33 ; *include macros(boxglm); %boxglm(data=poisons, resp=Survival, model=antidote poison, class=antidote poison, id=id, gplot=RMSE EFFECT INFL, npower=17, conf=.99);The plot of RMSE vs. lambda (power) indicates power = -1 / sqrt(SURVIVAL) as the maximum likelihood estimate, but power = -1 / SURVIVAL == survival rate is within the confidence interval.

The EFFECT plot indicates that the significance of partial F-tests are unaffected by the choice of power.
The influence plot indicates that a few observations have a large leverage,
but none is influential in determining the choice of power.

boxtid Power transformations by Box-Tidwell method

outlier Robust multivariate outlier detection

resline Resistant line for bivariate data

stars Diagnostic plots for transformations to symmetry