r - One p-value for glm model -

i'm searching way 1 p-value describes goodness of fit glm-model. here modified example lm manpage:

 ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)  trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)  conf<- c(rnorm(mean=-1, sd=1, n=10), rnorm(mean=1, sd=1, n=10))    group <- gl(2,10,20, labels=c("ctl","trt"))  weight <- c(ctl, trt)  lm.d9 <- lm(weight ~ group + conf) 

with summary(lm.d9) 1 gets

call: lm(formula = weight ~ group + conf)  residuals:      min       1q   median       3q      max  -1.17619 -0.40373 -0.05262  0.24987  1.40777   coefficients:             estimate std. error t value pr(>|t|)     (intercept)  4.97416    0.25153  19.775  3.6e-13 *** grouptrt    -0.23724    0.41117  -0.577    0.572     conf        -0.07044    0.13725  -0.513    0.614     --- signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1   residual standard error: 0.7111 on 17 degrees of freedom multiple r-squared: 0.08722,    adjusted r-squared: -0.02017  f-statistic: 0.8122 on 2 , 17 df,  p-value: 0.4604  

if id same glm

glm.d9 <- glm(weight ~ group + conf) summary(glm.d9) 

i get

call: glm(formula = weight ~ group + conf)  deviance residuals:       min        1q    median        3q       max   -1.17619  -0.40373  -0.05262   0.24987   1.40777    coefficients:             estimate std. error t value pr(>|t|)     (intercept)  4.97416    0.25153  19.775  3.6e-13 *** grouptrt    -0.23724    0.41117  -0.577    0.572     conf        -0.07044    0.13725  -0.513    0.614     --- signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1   (dispersion parameter gaussian family taken 0.5056514)      null deviance: 9.4175  on 19  degrees of freedom residual deviance: 8.5961  on 17  degrees of freedom aic: 47.869  number of fisher scoring iterations: 2 

lm has f-statistics summary whole model, glm has not. again question: how can 1 p-value glm model describes fit?

thanks

you can calculate f statistics this:

glm.d9 <- glm(weight ~ group + conf)  glm.0 <- glm(weight ~ 1)  anova(glm.d9, glm.0, test="f")  # analysis of deviance table #  # model 1: weight ~ group + conf # model 2: weight ~ 1 #   resid. df resid. dev df deviance      f pr(>f) # 1        17     8.5868                           # 2        19     9.4175 -2  -0.8307 0.8223 0.4562 

see ?anova.glm details , other tests available.