Description Usage Arguments References See Also Examples
ilm
is used for fitting improper linear models
(unit-weighted regression, equal-weights models, model
with correlation weights, Z-score method, random linear
models). Univariate model reduces to univariate linear
regression. iglm
extends ilm
functionality
to generalized linear models.
1 2 3 4 5 6 7 8 9 | iglm(formula, data, family = gaussian, scaling = c("none", "zscore",
"minmax", "medmad"), weighting = 1)
iglm.fit(x, y, family = gaussian(), scaling = "none", weighting = 1)
ilm(formula, data, scaling = c("none", "zscore", "minmax", "medmad"),
weighting = 1)
ilm.fit(x, y, scaling = "none", weighting = 1)
|
formula |
an object of class |
data |
an optional data frame, list or environment
(or object coercible by |
family |
a description of the error distribution and link function
to be used in the model. For glm this can be a character
string naming a family function, a family function or the
result of a call to a family function. For glm.fit only the
third option is supported. (See |
scaling |
method of scaling to be used. Possible choices
are: "none" for no scaling; "zscore" for
transforming into Z-scores; "minmax" for
scaling into [0, 1] range; and "medmad" for
subtracting median and dividing by MAD. See:
|
weighting |
weights to be applied to all the variables
(notice: this is different than |
Dawes, Robyn M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34, 571-582.
Graefe, A. (2015). Improving forecasts using equally weighted predictors. Journal of Business Research, 68(8), 1792-1799.
Wainer, Howard (1976). Estimating coefficients in linear models: It don't make no nevermind. Psychological Bulletin 83(2), 213.
Dana, J. and Dawes, R.M. (2004). The Superiority of Simple Alternatives to Regression for Social Science Predictions. Journal of Educational and Behavioral Statistics, 29(3), 317-331.
lm
, lm.fit
,
glm
, glm.fit
,
scale
1 2 3 4 5 6 7 8 9 |
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