Functions for Processing lm, glm, mer, polr and svyglm Output
This generic function gives a clean printout of lm, glm, mer, polr and svyglm objects.
display (object, ...) ## S4 method for signature 'lm' display(object, digits=2, detail=FALSE) ## S4 method for signature 'bayesglm' display(object, digits=2, detail=FALSE) ## S4 method for signature 'glm' display(object, digits=2, detail=FALSE) ## S4 method for signature 'merMod' display(object, digits=2, detail=FALSE) ## S4 method for signature 'polr' display(object, digits=2, detail=FALSE) ## S4 method for signature 'svyglm' display(object, digits=2, detail=FALSE)
object |
The output of a call to lm, glm, mer, polr, svyglm or related regressions function with n data points and k predictors. |
... |
further arguments passed to or from other methods. |
digits |
number of significant digits to display. |
detail |
defaul is |
This generic function gives a clean printout of lm, glm, mer and polr objects, focusing on the most pertinent pieces of information: the coefficients and their standard errors, the sample size, number of predictors, residual standard deviation, and R-squared. Note: R-squared is automatically displayed to 2 digits, and deviances are automatically displayed to 1 digit, no matter what.
Coefficients and their standard errors, the sample size, number of predictors, residual standard deviation, and R-squared
Output are the model, the regression coefficients and standard errors, and the residual sd and R-squared (for a linear model), or the null deviance and residual deviance (for a generalized linear model).
Andrew Gelman gelman@stat.columbia.edu; Yu-Sung Su suyusung@tsinghua.edu.cn; Maria Grazia Pittau grazia@stat.columbia.edu
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2006.
# Here's a simple example of a model of the form, y = a + bx + error,
# with 10 observations in each of 10 groups, and with both the
# intercept and the slope varying by group. First we set up the model and data.
group <- rep(1:10, rep(10,10))
group2 <- rep(1:10, 10)
mu.a <- 0
sigma.a <- 2
mu.b <- 3
sigma.b <- 4
rho <- 0.56
Sigma.ab <- array (c(sigma.a^2, rho*sigma.a*sigma.b,
rho*sigma.a*sigma.b, sigma.b^2), c(2,2))
sigma.y <- 1
ab <- mvrnorm (10, c(mu.a,mu.b), Sigma.ab)
a <- ab[,1]
b <- ab[,2]
d <- rnorm(10)
x <- rnorm (100)
y1 <- rnorm (100, a[group] + b*x, sigma.y)
y2 <- rbinom(100, 1, prob=invlogit(a[group] + b*x))
y3 <- rnorm (100, a[group] + b[group]*x + d[group2], sigma.y)
y4 <- rbinom(100, 1, prob=invlogit(a[group] + b*x + d[group2]))
# display a simple linear model
M1 <- lm (y1 ~ x)
display (M1)
M1.sim <- sim(M1, n.sims=2)
# display a simple logit model
M2 <- glm (y2 ~ x, family=binomial(link="logit"))
display (M2)
M2.sim <- sim(M2, n.sims=2)
# Then fit and display a simple varying-intercept model:
M3 <- lmer (y1 ~ x + (1|group))
display (M3)
M3.sim <- sim(M3, n.sims=2)
# Then the full varying-intercept, varying-slope model:
M4 <- lmer (y1 ~ x + (1 + x |group))
display (M4)
M4.sim <- sim(M4, n.sims=2)
# Then the full varying-intercept, logit model:
M5 <- glmer (y2 ~ x + (1|group), family=binomial(link="logit"))
display (M5)
M5.sim <- sim(M5, n.sims=2)
# Then the full varying-intercept, varying-slope logit model:
M6 <- glmer (y2 ~ x + (1|group) + (0 + x |group),
family=binomial(link="logit"))
display (M6)
M6.sim <- sim(M6, n.sims=2)
# Then non-nested varying-intercept, varying-slop model:
M7 <- lmer (y3 ~ x + (1 + x |group) + (1|group2))
display(M7)
M7.sim <- sim(M7, n.sims=2)
# Then the ordered logit model from polr
M8 <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
display(M8)
M9 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
display(M9)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.