[R] what is the difference between the two logistic models?
SNN
s.nancy1 at yahoo.com
Thu Aug 13 00:04:33 CEST 2009
Hi All,
I have data with 400 individuals and the following information
Grade: pass or fail coded as 1 for pass and 0 for fail
Sex: male or female ( coded as 1 for male and 2 for female )
Age
Teaching.method : can be 1,2,3
I want to fit a logistic regression where the outcome if (1=pass or 0 for
fail) and the rest of the variables are the regressors.
My question is that I am not sure when to use “factor” for a variable.
In my example, Grade, sex, teaching method are categorial variables coded as
stated above.
Age is a continuous variable
I have tried the model both ways where in the first model I stick in the
word “factor” in front of the categorial variables, but in this case I do
not know how to interpret the output?
Can someone shed some light on the difference between model1 and model2 and
how to interpret them?
Below is my output
Thanks for your help
Call:
glm(formula = factor(Grade) ~ factor(sex) + age + factor(teaching.method),
family = binomial, data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8649 -1.1926 0.7494 1.0091 1.6659
Coefficients:
Estimate Std. Error z value
Pr(>|z|)
(Intercept) -2.77217 0.82182 -3.373 0.000743
***
factor(sex)2 -0.34751 0.22960 -1.514 0.130140
age 0.04544 0.01074 4.230
2.34e-05 ***
factor(teaching.method) 2 -0.07125 0.30123 -0.237 0.813023
factor(teaching.method)3 0.50058 0.33087 1.513 0.130303
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 465.18 on 344 degrees of freedom
Residual deviance: 438.91 on 340 degrees of freedom
AIC: 448.91
Number of Fisher Scoring iterations: 4
> model2<-glm(Grade~ sex + age +teaching.method, family=binomial,data=ndata)
> summary(model2)
Call:
glm(formula = Grade ~ sex + age +teaching.method, family = binomial,
data = ndata)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7959 -1.2122 0.7547 1.0043 1.5791
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.83988 0.94749 -2.997 0.00272 **
sex -0.33361 0.22867 -1.459 0.14458
age 0.04432 0.01065 4.160 3.18e-05 ***
teaching.method 0.28017 0.16181 1.731 0.08336 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 465.18 on 344 degrees of freedom
Residual deviance: 440.85 on 341 degrees of freedom
AIC: 448.85
Number of Fisher Scoring iterations: 4
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