R: Functions for Processing lm, glm, mer, polr and svyglm Output

display {arm} | R Documentation

## Functions for Processing lm, glm, mer, polr and svyglm Output

### Description

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

### Usage

    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)

### Arguments

`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 `FALSE`, if `TRUE`, display p-values or z-values

### 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.

### Value

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

### Note

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).

### Author(s)

Andrew Gelman gelman@stat.columbia.edu; Yu-Sung Su suyusung@tsinghua.edu.cn; Maria Grazia Pittau grazia@stat.columbia.edu

### 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`, `svyglm`

### 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)
       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)

[Package arm version 1.9-3 Index]