Good morning reader, I have encountered a, probably, simple issue with respect to the *formulae* of a *regression model* I want to use in my research. I’m researching alliances as part of my study Business Economics (focus Strategy) at the Vrije Universiteit in Amsterdam. In the research model I use a moderating variable, I’m looking for confirmation or help on the formulation of the model. The research model consist of 2 explanatory variables, a moderating variable and 1 response variable. The first explanatory variable is Culture, measured on a nominal scale and the second is Structure of the alliance, also measured on a nominal scale. The moderating variable in the relation towards Trust is Speed of Integration, measured as an interval. The response variable is Trust, measured on a nominal scale (highly likely a 5-point Likert scale). Given the research model and the measurement scales, I intent to use a ordered probit model, often used in Marketing models, to execute the regression modelling. I can’t seem to find confirmation on how to model the formulae. I have been reading and studying R! for a couple of weeks now, read a lot of books from the R! series in the past, but I can’t get a grasp on this seemingly simple formulae. I think I understand how to model multinomial regression (using the R-package MNP), how to execute a Principal Components Analysis and an Explanatory Factor analysis (obviously I’m using a questionnaire to collect my data), but the formulae itself seems to be to simple. I expect to use the interaction symbol: "Trust~Culture + Structure : Speed_of_Integration" for the formulae, but is it really this simple? Can anybody confirm this or help me, advise me on this issue? Kind regards, -- Met vriendelijke groet, Johan Haasnoot De Haan & Martojo Kerklaan 5 3645 ES Vinkeveen Telefoon: 0297-266354 Mobiel: 06-81827665 Email: johan.haasnoot@dh-m.nl Website: www.dehaanenmartojo.nl [[alternative HTML version deleted]]
Dear Johan,
Why should it be complicated? You have a very simple model, thus a very simple
formula. Isn't that great?
Your formula matches the model. Though Trust~Culture + Structure *
Speed_of_Integration is another option. The model fit is the same, the only
difference is the parameterization of the model.
Best regards,
Thierry
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx at inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more than
asking him to perform a post-mortem examination: he may be able to say what the
experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not ensure
that a reasonable answer can be extracted from a given body of data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Johan Haasnoot
Verzonden: vrijdag 10 augustus 2012 9:15
Aan: r-help at r-project.org
Onderwerp: [R] Simple question about formulae in R!?
Good morning reader,
I have encountered a, probably, simple issue with respect to the *formulae* of a
*regression model* I want to use in my research. I'm researching alliances
as part of my study Business Economics (focus Strategy) at the Vrije
Universiteit in Amsterdam. In the research model I use a moderating variable,
I'm looking for confirmation or help on the formulation of the model.
The research model consist of 2 explanatory variables, a moderating variable and
1 response variable. The first explanatory variable is Culture, measured on a
nominal scale and the second is Structure of the alliance, also measured on a
nominal scale. The moderating variable in the relation towards Trust is Speed of
Integration, measured as an interval.
The response variable is Trust, measured on a nominal scale (highly likely a
5-point Likert scale). Given the research model and the measurement scales, I
intent to use a ordered probit model, often used in Marketing models, to
execute the regression modelling. I can't seem to find confirmation on how
to model the formulae. I have been reading and studying R! for a couple of weeks
now, read a lot of books from the R! series in the past, but I can't get a
grasp on this seemingly simple formulae. I think I understand how to model
multinomial regression (using the R-package MNP), how to execute a Principal
Components Analysis and an Explanatory Factor analysis (obviously I'm using
a questionnaire to collect my data), but the formulae itself seems to be to
simple.
I expect to use the interaction symbol: "Trust~Culture + Structure :
Speed_of_Integration" for the formulae, but is it really this simple? Can
anybody confirm this or help me, advise me on this issue?
Kind regards,
--
Met vriendelijke groet,
Johan Haasnoot
De Haan & Martojo
Kerklaan 5
3645 ES Vinkeveen
Telefoon: 0297-266354
Mobiel: 06-81827665
Email: johan.haasnoot at dh-m.nl
Website: www.dehaanenmartojo.nl
[[alternative HTML version deleted]]
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On Fri, Aug 10, 2012 at 9:16 AM, S Ellison <S.Ellison at lgcgroup.com> wrote:>> > R in general tries hard to prohibit this behavior (i.e., including an >> > interaction but not the main effect). When removing a main effect and >> > leaving the interaction, the number of parameters is not reduced by >> > one (as would be expected) but stays the same, at least >> > when using model.matrix: > > Surely this behaviour is less to do with a dislike of interactions without both main effects (which we will necessarily use if we fit a simple two-factor nested model) than the need to avoid non-uniqueness of a model fitted with too many coefficients? > In a simple case, an intercept plus n coefficients for n factor levels gives us n+1 coefficients to find, and we only have n independent groups to estimate them from. In model matrix terms we would have one column that is a linear combination of others. For OLS normal equations that generates a zero determinant and for the numerical methods R uses the effect is the same; no useful fit. To avoid that and allow least squares fitting, R sets up the model matrix with only n-1 coefficients in addition to the intercept. As a result we end up with fewer model coefficients than we might have expected (and that annoyingly missing first level that always puzzles newcomers the first time we look at a linear model summary), but we have exactly the number of coefficients that we can estimate uniquely from the groups we have specified.N.B. Off topic. This is an incredibly nice feature of R. SAS overparameterizes the design matrix and employs the sweep algorithm to zero out redundant parameters. With one result being if you want to specify your own data to post multiply by the coefficient vector, you need to realize that the vector is larger than the number of nonmissing parameters. I struggle to imagine this is more computationally efficient than simply creating an appropriately parameterized design matrix, although I suppose in either case you need to check for less than full rank design matrices.> > S > > ******************************************************************* > This email and any attachments are confidential. Any u...{{dropped:17}}