In practical terms, there are two types of macros. The process of replacing a macro call with the processed copy of the body is called expansion of the macro call. If the macro accepts arguments, the actual arguments following the macro name are substituted for formal parameters in the macro body. The macro name is replaced by a copy of the macro body. When the name of a macro is recognized in the program source text, or in the arguments of certain other preprocessor commands, it's treated as a call to that macro. In this preprocessor documentation, only the term "macro" is used. Identifiers that represent statements or expressions are called macros. Identifiers that represent constants are sometimes called symbolic constants or manifest constants.
The #define directive is typically used to associate meaningful identifiers with constants, keywords, and commonly used statements or expressions. They test a constant expression or identifier to determine which text blocks to pass on to the compiler, and which ones to remove from the source file during preprocessing. Conditional compilation directives allow you to suppress compilation of parts of a source file.
It expands macros in parts of some directives that aren't skipped as part of a conditional compilation. ( It would help me immensely if these explanations were supported by R-codes.The preprocessor expands macros in all lines except preprocessor directives, lines that have a # as the first non-white-space character.
How do I apply the methodology of splines to solve my problem? How do I interpret the results? (I don't want to use the model as a predictive or forecasting model, but only to explain/describe within the time period of the data.) Now I would like to know if someone can kindly help me here.ĭoes a violation of the assumption mean that I am not allowed to use this model and thus the results could be wrong? Especially not for a logistic model estimation. Unfortunately I could not find any literature explaining this. I also read that I can counter the problem with the methodology of splines. I know that I can get around the assumption if I transform the metric independent variables to categorical variables. Several transformations of the independent variables did not help either.Īdditionally, my models failed the Hosmer-Lemeshow test. , familiy = binomial("logit"))įurthermore I tested the linearity assumption with the R function boxTidwell(model$linear.predictors ~ independent variable) for each variable separately.įor almost all variables, the test showed significance and thus a violation of the model assumption. Once with all variables in a logistic regression, where I regressed the original dependent variable on the independent variables and the product of the independent variables with the respective logarithmic transformation of the independent variables. To check this, I applied the Box-Tidwell test several times. (All other assumptions of logistic regression are not violated). This assumption is violated in all my models.
One assumption of logistic regression is that there is a linear relationship between the logit of the outcome and each independent metric variable. The model includes 30 metric independent variables (9 of them have both positive and negative observations). The dependent variable is a dichotomous variable (1 or 0). I have a problem with my logistic regression that I set up and I hope someone can help me.
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