Here are some data
dat = data.frame(y = c(9,7,7,7,5,6,4,6,3,5,1,5), x = c(1,1,2,2,3,3,4,4,5,5,6,6), color = rep(c('a','b'),6))
and the plot of these data if you wish
require(ggplot)
ggplot(dat, aes(x=x,y=y, color=color)) + geom_point() + geom_smooth(method='lm')
When running a model with the function MCMCglmm()
…
require(MCMCglmm)
summary(MCMCglmm(fixed = y~x/color, data=dat))
I get the lower and upper 95% interval for the estimate allowing me to know if the two slopes (color = a and color = b) are significantly different.
When looking at this output...
summary(glm(y~x/color, data=dat))
... I can't see the confidence interval!
My question is:
How can I have these lower and upper 95% interval confidence for the estimates when using the function glm()
?
use confint
mod = glm(y~x/color, data=dat) summary(mod) Call: glm(formula = y ~ x/color, data = dat) Deviance Residuals: Min 1Q Median 3Q Max -1.11722 -0.40952 -0.04908 0.32674 1.35531 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 8.8667 0.4782 18.540 0.0000000177 x -1.2220 0.1341 -9.113 0.0000077075 x:colorb 0.4725 0.1077 4.387 0.00175 (Dispersion parameter for gaussian family taken to be 0.5277981) Null deviance: 48.9167 on 11 degrees of freedom Residual deviance: 4.7502 on 9 degrees of freedom AIC: 30.934 Number of Fisher Scoring iterations: 2 confint(mod) Waiting for profiling to be done... 2.5 % 97.5 % (Intercept) 7.9293355 9.8039978 x -1.4847882 -0.9591679 x:colorb 0.2614333 0.6836217