Generic function to preduce predictive margins

Description

Generic function to preduce predictive margins

Usage

predmarg(
  obj,
  settings,
  data,
  subset,
  type = NULL,
  groups = NULL,
  setup = NULL,
  cifunc = get_cifunc(obj),
  level = 0.95,
  parallel = FALSE,
  mc.cores = if (.Platform$OS.type == "windows") 1L else max.cores,
  ...
)

Arguments

obj

a model object, e.g. returned by lm, glm, etc.

settings

an optional data frame of settings for independent variables.

data

an optional data frame for which the predictive margins are computed. If ommited, an attempt is made to obtain the data from the model object.

subset

an optional logical vector that defines a subset for which a predictive margin is computed

type

an optional character string that specifies the type of predictions, e.g. probabilities or cumulative probabilites. For future versions only.

groups

a variable that defines groups for which predictive margines are computed. This variable has to have the same number of observations as the data to which the model was fitted.

setup

an optional expression that is evaluated for each setting, i.e. individually for each row of the settings data frame. Can be used to modify independent variables.

cifunc

a function to compute prediction intervals. By default it is the chosen by the function of the same name.

level

level of confidence intervals of predictions.

parallel

logical value that determines whether predictions for individual settings are computed in parallel. (Does not yet work on windows.)

mc.cores

number of CPU cores used for parallel processing.

...

optional vectors of values of independent variabls. These further arguments, if present, are used to create a data frame of settings, using expand.grid.

Value

a data frame with the following variables:

pred

the mean prediction for the setting of the independent variables

var.pred

the (estimated) variance of the mean prediction

se.pred

the standard error of prediction, i.e. the square root of the variance of the mean prediction

lower

lower prediction interval computed with qfunc

upper

upper prediction interval computed with qfunc

the independent variables for which values are set to create the predictions are also included in the resulting data frame.

Details

The generic function predmarg computes predictive margins for various settings of the independent variables. It is also possible to provide settings for independent variables that are included in the model, but that are used in the setup expression to transform independent variables. See the examples below.

Examples

library(magrittr)
Attaching package: 'magrittr'

The following object is masked from 'package:memisc':

    %$%
# Simple linear regression

fm <- lm(weight ~ poly(height, 2), data = women)
pm <-predmarg(fm,
             height=seq(from=58,to=72,
                        length=10))
str(pm)
'data.frame':        10 obs. of  6 variables:
 $ pred    : num  115 119 123 128 133 ...
 $ var.pred: num  0.0685 0.0298 0.0182 0.019 0.0218 ...
 $ se.pred : num  0.262 0.173 0.135 0.138 0.148 ...
 $ lower   : num  115 119 123 127 132 ...
 $ upper   : num  116 119 123 128 133 ...
 $ height  : num  58 59.6 61.1 62.7 64.2 ...
plot(pred~height,data=pm,
    type="l")
Loading required package: svglite
/software/mpred/manual/predmarg/predmarg_0001.svg
with(women, points(height,weight))
with(pm, lines(height,lower,lty=2))
with(pm, lines(height,upper,lty=2))

# Logistic regression

library(carData)
Chile %<>% within({
   vote2 <- factor(vote,levels=c("N","Y"))
   vote2 <- as.integer(vote2=="Y")
})
glm.Chile.1 <- glm(vote2~sex+age+income+education,
                  data=Chile,
                  family=binomial)
summary(glm.Chile.1)
Call:
glm(formula = vote2 ~ sex + age + income + education, family = binomial,
    data = Chile)

Coefficients:
              Estimate Std. Error z value Pr(>|z|)
(Intercept)  1.053e-01  1.786e-01   0.589 0.555615
sexM        -5.415e-01  1.009e-01  -5.365 8.10e-08 ***
age          1.203e-02  3.594e-03   3.347 0.000817 ***
income       4.481e-06  1.342e-06   3.339 0.000840 ***
educationPS -1.125e+00  1.656e-01  -6.791 1.12e-11 ***
educationS  -6.369e-01  1.207e-01  -5.276 1.32e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2361.7  on 1703  degrees of freedom
Residual deviance: 2239.0  on 1698  degrees of freedom
  (996 observations deleted due to missingness)
AIC: 2251

Number of Fisher Scoring iterations: 4
pm.Chile.1.income <- predmarg(glm.Chile.1,
                             income=seq(from=2500,to=200000,length=20))
plot(pred~income,data=pm.Chile.1.income,
    type="l")
/software/mpred/manual/predmarg/predmarg_0002.svg
# Baseline category logit

library(mclogit)
Loading required package: Matrix
library(MASS)
mb.Chile <- mblogit(vote~statusquo,
                   data=Chile)
Iteration 1 - deviance = 4528.43 - criterion = 0.22885
Iteration 2 - deviance = 4394.717 - criterion = 0.03042507
Iteration 3 - deviance = 4383.817 - criterion = 0.002486294
Iteration 4 - deviance = 4383.674 - criterion = 3.271481e-05
Iteration 5 - deviance = 4383.674 - criterion = 7.802726e-09
converged
pm.mb.Chile <- predmarg(mb.Chile,
                       statusquo=seq(from=-2,to=2,length=20))
str(pm.mb.Chile)
'data.frame':        80 obs. of  8 variables:
 $ pred     : num  0.018123 0.950795 0.030231 0.000851 0.025456 ...
 $ var.pred : num  1.38e-05 4.92e-05 2.21e-05 4.66e-08 2.10e-05 ...
 $ se.pred  : num  0.003713 0.007016 0.004698 0.000216 0.004578 ...
 $ lower    : num  0.011579 0.93617 0.021711 0.000482 0.017267 ...
 $ upper    : num  0.02608 0.96362 0.04009 0.00132 0.03516 ...
 $ eqnum    : int  1 2 3 4 1 2 3 4 1 2 ...
 $ statusquo: num  -2 -2 -2 -2 -1.79 ...
 $ response : Factor w/ 4 levels "A","N","U","Y": 1 2 3 4 1 2 3 4 1 2 ...
library(ggplot2)
Attaching package: 'ggplot2'

The following object is masked from 'package:memisc':

    syms
(ggplot(pm.mb.Chile,
      aes(x=statusquo,
          y=pred,
          fill=response
          )
      ) + geom_area())
/software/mpred/manual/predmarg/predmarg_0003.svg
(ggplot(pm.mb.Chile,
      aes(x=statusquo,
          y=pred,
          ymin=lower,
          ymax=upper
          )
      ) + geom_line() +geom_ribbon(alpha=.25) + facet_grid(~response))
/software/mpred/manual/predmarg/predmarg_0004.svg
mb.hs <- mblogit(Sat~Infl+Type+Cont,weights=Freq,
                data=housing)
Iteration 1 - deviance = 3493.764 - criterion = 0.9614469
Iteration 2 - deviance = 3470.111 - criterion = 0.00681597
Iteration 3 - deviance = 3470.084 - criterion = 7.82437e-06
Iteration 4 - deviance = 3470.084 - criterion = 7.469596e-11
converged
pm.mb.hs <- predmarg(mb.hs,
                    Infl=levels(Infl),
                    Type=levels(Type))
dodge <- position_dodge(width=.8)
(ggplot(pm.mb.hs)
   +facet_wrap(~Type)
   +geom_bar(
        aes(fill=response,
            x=Infl,
            y=pred),
        stat='identity',position=dodge,width=.8)
   +geom_errorbar(
        aes(x=Infl,
            ymin=lower,
            ymax=upper,group=response),
        position=dodge,width=.4))
/software/mpred/manual/predmarg/predmarg_0005.svg
# The following requires the most current 'mclogit' version on GitHub
# and fails with the CRAN version
# # Baseline category logit with random effects
#
# # Some artificial data
# exadata <- local({
#     B <- cbind(c(-.5,.3),
#                c(.5,-.5))
#     set.seed(42)
#     x <- rnorm(n=60)
#     X <- cbind(1,x)
#     Eta <- X %*% B
#     j <- rep(1:10,6)
#     jf <- as.factor(j)
#     u1 <- rnorm(n=10,sd=.8)
#     u2 <- rnorm(n=10,sd=.8)
#     Eta <- Eta + cbind(u1[j],x*u2[j])
#     expEta <- cbind(1,exp(Eta))
#     sum.expEta <- rowSums(expEta)
#     pi <- expEta/sum.expEta
#     Y <- t(apply(pi,1,rmultinom,n=1,size=300))
#     res <-data.frame(Y,x,j,jf)
#     names(res)[1:3] <- paste0("y",1:3)
#     res
# })
#
# # Baseline logit model with random intercepts and random slopes
# mbrsl <- mblogit(cbind(y1,y2,y3)~x,data=exadata,
#                  random = ~1+x|j)
# summary(mbrsl)
#
# # Predictive margins for values of x
# pm.mbrsl <- predmarg(mbrsl,x=seq(from=min(x),to=max(x),length=24))
# (ggplot(pm.mbrsl,
#        aes(x=x,
#            y=pred,
#            fill=response
#            )
#        ) + geom_area())
#
# # Predictive margins for the random effects
# pm.mbrsl.j <- predmarg(mbrsl,j=1:10)
# (ggplot(pm.mbrsl.j,
#        aes(x=j,
#            y=pred,
#            fill=response
#            )
#        ) + geom_bar(position="fill",stat="identity"))
#
# pm.mbrsl.jx <- predmarg(mbrsl,
#                     j=1:10,
#                     x=seq(from=min(x),to=max(x),
#                           length=24))
#
# (ggplot(pm.mbrsl.jx,
#        aes(x=x,
#            y=pred,
#            fill=response
#            )
#        ) + geom_area()
#     + facet_wrap(~j))
#     + facet_wrap(~j))
© Copyright 2022, Martin Elff. Last updated on 28 Aug 2024. Created using Sphinx 7.2.6. Page source