Hi, I'm sure this is simple, but I haven't been able to find this in TFM,
say I have some data in R like this (pasted here:
http://pastebin.com/raw.php?i=sjS9Zkup):
> head(df)
gender age smokes disease Y
1 female 65 ever control 0.18
2 female 77 never control 0.12
3 male 40 state1 0.11
4 female 67 ever control 0.20
5 male 63 ever state1 0.16
6 female 26 never state1 0.13
where unique(disease) == c("control", "state1", "state2")
and unique(smokes) == c("ever", "never", "", "current")
I then fit a linear model like:
> model = lm(Y ~ smokes + disease + age + gender, data=df)
And I want to understand the difference between:
> print(summary(model))
Call:
lm(formula = Y ~ smokes + disease + age + gender, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.22311 -0.08108 -0.03483 0.05604 0.46507
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1206825 0.0521368 2.315 0.0211 *
smokescurrent 0.0150641 0.0444466 0.339 0.7348
smokesever 0.0498764 0.0326254 1.529 0.1271
smokesnever 0.0394109 0.0349142 1.129 0.2597
diseasestate1 0.0018739 0.0176817 0.106 0.9157
diseasestate2 -0.0009858 0.0178651 -0.055 0.9560
age 0.0002841 0.0006290 0.452 0.6518
gendermale 0.1164889 0.0128748 9.048 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1257 on 397 degrees of freedom
Multiple R-squared: 0.1933, Adjusted R-squared: 0.1791
F-statistic: 13.59 on 7 and 397 DF, p-value: 8.975e-16
and:
> anova(model)
Analysis of Variance Table
Response: Y
Df Sum Sq Mean Sq F value Pr(>F)
smokes 3 0.1536 0.05120 3.2397 0.02215 *
disease 2 0.0129 0.00647 0.4096 0.66420
age 1 0.0431 0.04310 2.7270 0.09946 .
gender 1 1.2937 1.29373 81.8634 < 2e-16 ***
Residuals 397 6.2740 0.01580
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
I understand (hopefully correctly) that anova() tests by adding each covariate
to the model in order it is specified in the formula.
More specific questions are:
1) How do the p-values for smokes* in summary(model) relate to the
Pr(>F) for smokes in anova
2) what do the p-values for each of those smokes* mean exactly?
3) the summary above shows the values for diseasestate1 and diseasestate2
how can I get the p-value for diseasecontrol? (or, e.g. genderfemale)
thanks.
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