arm (version 1.1-14)

display: Functions for Processing lm, glm, mer and polr Output

Description

This generic function gives a clean printout of lm, glm, mer, and polr objects.

Usage

display (object, ...)

## S3 method for class 'lm':
display(object, digits=2)
## S3 method for class 'bayesglm':
display(object, digits=2)
## S3 method for class 'bayesglm.h':
display(object, digits=2)
## S3 method for class 'glm':
display(object, digits=2)
## S3 method for class 'mer':
display(object, digits=2)
## S3 method for class 'polr':
display(object, digits=2)

Arguments

object

The output of a call to lm, glm, mer, polr, 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.

Value

• Coefficients and their standard errors, the sample size, number of predictors, residual standard deviation, and R-squared

Details

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.

References

Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2006.

See Also

summary, lm, glm, lmer, polr

Examples

# 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)
  

# display a simple logit model

   M2 <- glm (y2 ~ x, family=binomial(link="logit"))
   display (M2)

# Then fit and display a simple varying-intercept model:
 
   M3 <- lmer (y1 ~ x + (1|group))
   display (M3)
#   M3.sim <- mcsamp (M3)
#   print (M3.sim)
#   plot (M3.sim)
 
# Then the full varying-intercept, varying-slope model:
 
   M4 <- lmer (y1 ~ x + (1 + x |group))
   display (M4)
#   M4.sim <- mcsamp (M4)
#   print (M4.sim)
#   plot (M4.sim)
 
# Then the full varying-intercept, logit model:
 
   M5 <- glmer (y2 ~ x + (1|group), family=binomial(link="logit"))
   display (M5)
#   M5.sim <- mcsamp (M5)
#   print (M5.sim)
#   plot (M5.sim)
 
# 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 <- mcsamp (M6)
#   print (M6.sim)
#   plot (M6.sim)

# Then non-nested varying-intercept, varying-slop model:
   
   M7 <- lmer (y3 ~ x + (1 + x |group) + (1|group2))
   display(M7)
#   M7.sim <- mcsamp (M7)
#   print (M7.sim)
#   plot (M7.sim)

 
# 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)