[R] Could the Odds represent weight in Generalized Linear Model?

Tue Jan 30 15:37:07 CET 2018

Dear Lenny,

You can do this by using Age as an offset factor.

dataset$wAge <- dataset$Age * 1.02
glm(cbind(Yes,No) ~ offset(wAge) + Times + Type, family=binomial, data =
dataset)

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx op inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
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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.
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<https://www.inbo.be>

2018-01-30 11:14 GMT+01:00 contact retour-client <
retour.client.contact op gmail.com>:

> Hello all,
>
>
> I'm sorry if my question seems basic.
>
> Im studying a responses (Yes,No) in a survey and, thanks to GLM I obtain
> the following relation with my variables : (Yes,No)~ β0 + Age We note this
> this certain type of (Yes,No) response is linked to age (p<0.05 in glm) .
>
> After that we calculated :
>
> model1=glm(cbind(Yes,No) ~ Age + Times + Type, family=binomial)
> summary(model1)
> exp(model1$coefficients)
>
> exp(model1$coefficients)(Intercept)         Age       Times TypeRegular
>  0.01659381  1.02546748  1.01544154  1.70056425
>
> The odds of answering 'Yes' is multiplied with 1.02 for each additional
> year of age.
>
> My questions is :
>
> (1) it is possible to add to my model, (Yes,No)~ β0 + Age, the weight of
> the variable Age. Is it in fact the odd value ? Here is an example : is it
> ok to formulate my model as that (Yes,No)~ β0 + 1.02* Age: here 1.02 is
> what I call weight of age, in other words, I want to quantify its impact in
> the model.
>
> (2)suppose I want to model (Yes,No)~ β0 + Type with type a categorical
> data. odd value of TypeRegular is 1.70056425. But in my model it is simply
> Type that include Regular and Irregular. How to adapt this value to Type ?
>
> My data
>
> res=structure(list(Age = c(10, 14, 14, 15, 16, 16, 16, 17, 17, 17, 17,
> 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 20,
> 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 22, 22,
> 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23,
> 23, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26,
> 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27,
> 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29,
> 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31,
> 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33,
> 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35,
> 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37,
> 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38,
> 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40,
> 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42,
> 42, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43,
> 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 45,
> 45, 45, 45, 45, 46, 46, 46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47,
> 47, 47, 47, 48, 48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 49, 50,
> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
> 51, 51, 51, 51, 51, 51, 51, 51, 51, 52, 52, 52, 52, 52, 52, 52, 52,
> 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53,
> 53, 54, 54, 54, 54, 54, 54, 54, 54, 54, 55, 55, 55, 55, 55, 55, 55,
> 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56,
> 57, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 58, 58, 58, 59,
> 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60, 60, 61, 62, 62, 62, 62,
> 63, 64, 64, 65, 65, 67, 74), Times = c(6L, 6L, 16L, 6L, 9L, 23L, 33L,
> 6L, 14L, 17L, 36L, 4L, 9L, 15L, 20L, 26L, 28L, 30L, 33L, 6L, 11L, 14L,
> 20L, 26L, 28L, 30L, 32L, 4L, 4L, 6L, 9L, 17L, 26L, 28L, 30L, 33L, 44L,
> 47L, 4L, 6L, 23L, 26L, 32L, 4L, 9L, 11L, 11L, 14L, 14L, 15L, 17L, 18L,
> 20L, 23L, 26L, 36L, 44L, 50L, 4L, 9L, 28L, 30L, 32L, 4L, 17L, 23L, 4L,
> 6L, 9L, 9L, 11L, 14L, 25L, 33L, 33L, 51L, 4L, 6L, 14L, 17L, 18L, 26L,
> 28L, 30L, 32L, 33L, 44L, 50L, 6L, 9L, 9L, 11L, 14L, 17L, 22L, 23L,
> 30L, 4L, 9L, 11L, 14L, 15L, 20L, 23L, 28L, 29L, 36L, 39L, 43L, 51L,
> 58L, 14L, 20L, 23L, 26L, 28L, 36L, 51L, 4L, 6L, 9L, 16L, 17L, 18L,
> 23L, 33L, 37L, 51L, 9L, 11L, 14L, 18L, 23L, 26L, 28L, 58L, 9L, 17L,
> 33L, 36L, 37L, 58L, 4L, 6L, 9L, 9L, 11L, 17L, 20L, 26L, 28L, 32L, 33L,
> 47L, 4L, 6L, 9L, 15L, 23L, 28L, 4L, 9L, 9L, 15L, 17L, 18L, 20L, 23L,
> 28L, 30L, 30L, 4L, 6L, 6L, 9L, 17L, 18L, 33L, 36L, 4L, 6L, 11L, 14L,
> 15L, 17L, 23L, 26L, 28L, 36L, 4L, 6L, 9L, 11L, 17L, 18L, 23L, 25L,
> 28L, 30L, 6L, 9L, 11L, 14L, 14L, 17L, 20L, 23L, 28L, 35L, 44L, 4L, 6L,
> 9L, 14L, 17L, 44L, 6L, 9L, 14L, 17L, 22L, 26L, 28L, 29L, 33L, 36L,
> 50L, 4L, 6L, 6L, 17L, 20L, 23L, 28L, 30L, 36L, 51L, 58L, 4L, 9L, 9L,
> 14L, 15L, 17L, 23L, 26L, 28L, 30L, 36L, 38L, 6L, 6L, 9L, 17L, 23L,
> 26L, 28L, 43L, 44L, 4L, 15L, 17L, 17L, 25L, 26L, 28L, 36L, 44L, 51L,
> 58L, 6L, 9L, 16L, 25L, 28L, 32L, 44L, 58L, 4L, 9L, 17L, 28L, 30L, 36L,
> 43L, 44L, 6L, 11L, 14L, 16L, 26L, 30L, 44L, 15L, 20L, 23L, 26L, 28L,
> 52L, 4L, 6L, 9L, 9L, 11L, 14L, 16L, 17L, 20L, 23L, 26L, 28L, 30L, 33L,
> 35L, 37L, 50L, 51L, 6L, 9L, 14L, 17L, 18L, 18L, 26L, 44L, 50L, 9L,
> 14L, 14L, 15L, 18L, 20L, 23L, 28L, 33L, 36L, 43L, 44L, 50L, 4L, 9L,
> 11L, 14L, 18L, 26L, 28L, 29L, 30L, 32L, 43L, 44L, 52L, 6L, 9L, 20L,
> 23L, 28L, 30L, 33L, 36L, 43L, 4L, 9L, 11L, 14L, 16L, 20L, 23L, 26L,
> 28L, 36L, 50L, 51L, 4L, 6L, 9L, 14L, 18L, 23L, 26L, 30L, 36L, 43L,
> 44L, 52L, 6L, 9L, 17L, 18L, 23L, 26L, 28L, 30L, 35L, 9L, 14L, 20L,
> 32L, 33L, 36L, 44L, 6L, 9L, 23L, 25L, 36L, 51L, 9L, 17L, 17L, 18L,
> 20L, 33L, 58L, 9L, 23L, 26L, 28L, 36L, 6L, 20L, 28L, 20L, 23L, 4L,
> 15L), Type = c("Regular", "Regular", "Irregular", "Regular",
> "Regular", "Irregular", "Regular", "Irregular", "Irregular",
> "Irregular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Irregular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
> "Irregular", "Regular", "Irregular", "Regular", "Regular", "Regular",
> "Irregular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Irregular",
> "Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Irregular", "Regular", "Irregular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Irregular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Irregular", "Regular", "Irregular",
> "Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Irregular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Irregular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Irregular", "Irregular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Irregular", "Regular", "Irregular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Irregular", "Irregular", "Irregular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Irregular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Irregular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Irregular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Regular", "Regular", "Regular",
> "Irregular", "Regular", "Regular", "Regular", "Irregular", "Regular",
> "Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
> "Regular", "Regular", "Regular", "Irregular", "Regular", "Regular",
> "Regular", "Irregular", "Regular", "Regular"), Yes = c(0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L,
> 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
> 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
> 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
> 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), No = c(1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
> 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 0L, 1L, 1L, 1L, 1L,
> 2L, 1L, 1L, 1L, 1L, 1L, 3L, 0L, 1L, 1L, 1L, 1L, 1L, 5L, 1L, 1L, 1L,
> 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 3L, 1L, 2L, 1L, 1L,
> 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
> 3L, 1L, 2L, 2L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 0L, 1L, 1L, 1L, 2L,
> 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
> 1L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 0L, 0L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 5L, 1L, 1L, 0L, 3L,
> 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 2L, 0L, 1L, 1L, 1L, 0L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 0L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
> 0L, 1L, 1L, 1L, 0L, 1L, 2L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 0L, 0L, 1L, 1L, 1L, 2L, 1L, 1L, 0L, 1L, 2L, 1L, 2L, 1L, 1L,
> 1L, 2L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L,
> 1L, 1L, 1L, 1L, 3L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
> 1L, 1L, 0L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 0L, 2L, 4L, 1L, 3L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 2L, 2L, 2L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L)),
> .Names = c("Age", "Times", "Type", "Yes", "No"), row.names = c(NA,
> -426L), class = "data.frame")
>
> Thansk a lot for your help.
>
>
> Lenny
>
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