residuals.lrm

0th

Percentile

Residuals from a Logistic Regression Model Fit

For a binary logistic model fit, computes the following residuals, letting $P$ denote the predicted probability of the higher category of $Y$, $X$ denote the design matrix (with a column of 1s for the intercept), and $L$ denote the logit or linear predictors: ordinary ($Y-P$), score ($X (Y-P)$), pearson ($(Y-P)/\sqrt{P(1-P)}$), deviance (for $Y=0$ is $-\sqrt{2|\log(1-P)|}$, for $Y=1$ is $\sqrt{2|\log(P)|}$, pseudo dependent variable used in influence statistics ($L + (Y-P)/(P(1-P))$), and partial ($X_{i}\beta_{i} + (Y-P)/(P(1-P))$).

Will compute all these residuals for an ordinal logistic model, using as temporary binary responses dichotomizations of $Y$, along with the corresponding $P$, the probability that $Y \geq$ cutoff. For type="partial", all possible dichotomizations are used, and for type="score", the actual components of the first derivative of the log likelihood are used for an ordinal model. Alternatively, specify type="score.binary" to use binary model score residuals but for all cutpoints of $Y$ (plotted only, not returned). The score.binary, partial, and perhaps score residuals are useful for checking the proportional odds assumption. If the option pl=TRUE is used to plot the score or score.binary residuals, a score residual plot is made for each column of the design (predictor) matrix, with Y cutoffs on the x-axis and the mean +- 1.96 standard errors of the score residuals on the y-axis. You can instead use a box plot to display these residuals, for both score.binary and score. Proportional odds dictates a horizontal score.binary plot. Partial residual plots use smooth nonparametric estimates, separately for each cutoff of $Y$. One examines that plot for parallelism of the curves to check the proportional odds assumption, as well as to see if the predictor behaves linearly.

Also computes a variety of influence statistics and the le Cessie - van Houwelingen - Copas - Hosmer unweighted sum of squares test for global goodness of fit, done separately for each cutoff of $Y$ in the case of an ordinal model.

The plot.lrm.partial function computes partial residuals for a series of binary logistic model fits that all used the same predictors and that specified x=TRUE, y=TRUE. It then computes smoothed partial residual relationships (using lowess with iter=0) and plots them separately for each predictor, with residual plots from all model fits shown on the same plot for that predictor.

Usage
## S3 method for class 'lrm':
residuals(object, type=c("ordinary", "score", "score.binary",
                  "pearson", "deviance", "pseudo.dep", "partial",
                  "dfbeta","dfbetas","dffit","dffits","hat","gof","lp1"),
           pl=FALSE, xlim, ylim, kint, label.curves=TRUE, which, ...)

## S3 method for class 'lrm.partial': plot(\dots, labels, center=FALSE)

Arguments
object
object created by lrm
...
for residuals, applies to type="partial" when pl is not FALSE. These are extra arguments passed to the smoothing function. Can also be used to pass extra arguments to boxplot for ty
type
type of residual desired. Use type="lp1" to get approximate leave-out-1 linear predictors, derived by subtracting the dffit from the original linear predictor values.
pl
applies only to type="partial", "score", and "score.binary". For score residuals in an ordinal model, set pl=TRUE to get means and approximate 0.95 confidence bars vs. $Y$, separately for each $X$. A
xlim
plotting range for x-axis (default = whole range of predictor)
ylim
plotting range for y-axis (default = whole range of residuals, range of all confidence intervals for score or score.binary or range of all smoothed curves for partial if pl=TRUE, or 0.1 and 0.9 quantiles
kint
for an ordinal model for residuals other than partial, score, or score.binary, specifies the intercept (and the cutoff of $Y$) to use for the calculations. Specifying kint=2, for example, means to use $Y
label.curves
set to FALSE to suppress curve labels when type="partial". The default, TRUE, causes labcurve to be invoked to label curves where they are most separated. label.curves can be a list contai
which
a vector of integers specifying column numbers of the design matrix for which to compute or plot residuals, for type="partial","score","score.binary".
labels
for plot.lrm.partial this specifies a vector of character strings providing labels for the list of binary fits. By default, the names of the fit objects are used as labels. The labcurve function is used to label the curve with
center
for plot.lrm.partial this causes partial residuals for every model to have a mean of zero before smoothing and plotting
Details

For the goodness-of-fit test, the le Cessie-van Houwelingen normal test statistic for the unweighted sum of squared errors (Brier score times $n$) is used. For an ordinal response variable, the test for predicting the probability that $Y\geq j$ is done separately for all $j$ (except the first). Note that the test statistic can have strange behavior (i.e., it is far too large) if the model has no predictive value.

For most of the values of type, you must have specified x=TRUE, y=TRUE to lrm.

There is yet no literature on interpreting score residual plots for the ordinal model. Simulations when proportional odds is satisfied have still shown a U-shaped residual plot. The series of binary model score residuals for all cutoffs of $Y$ seems to better check the assumptions. See the last example.

Value

  • a matrix (type="partial","dfbeta","dfbetas","score"), test statistic (type="gof"), or a vector otherwise. For partial residuals from an ordinal model, the returned object is a 3-way array (rows of $X$ by columns of $X$ by cutoffs of $Y$), and NAs deleted during the fit are not re-inserted into the residuals. For score.binary, nothing is returned.

References

Landwehr, Pregibon, Shoemaker. JASA 79:61--83, 1984.

le Cessie S, van Houwelingen JC. Biometrics 47:1267--1282, 1991.

Hosmer DW, Hosmer T, Lemeshow S, le Cessie S, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat in Med 16:965--980, 1997.

Copas JB. Applied Statistics 38:71--80, 1989.

See Also

lrm, naresid, which.influence, loess, supsmu, lowess, boxplot, labcurve

Aliases
  • residuals.lrm
  • plot.lrm.partial
Examples
set.seed(1)
x1 <- runif(200, -1, 1)
x2 <- runif(200, -1, 1)
L  <- x1^2 - .5 + x2
y  <- ifelse(runif(200) <= plogis(L), 1, 0)
f <- lrm(y ~ x1 + x2, x=TRUE, y=TRUE)
resid(f)            #add rows for NAs back to data
resid(f, "score")   #also adds back rows
r <- resid(f, "partial")  #for checking transformations of X's
par(mfrow=c(1,2))
for(i in 1:2) {
  xx <- if(i==1)x1 else x2
  plot(xx, r[,i], xlab=c('x1','x2')[i])
  lines(lowess(xx,r[,i]))
}
resid(f, "partial", pl="loess")  #same as last 3 lines
resid(f, "partial", pl=TRUE) #plots for all columns of X using supsmu
resid(f, "gof")           #global test of goodness of fit
lp1 <- resid(f, "lp1")    #approx. leave-out-1 linear predictors
-2*sum(y*lp1 + log(1-plogis(lp1)))  #approx leave-out-1 deviance
                                    #formula assumes y is binary


# Simulate data from a population proportional odds model
set.seed(1)
n   <- 400
age <- rnorm(n, 50, 10)
blood.pressure <- rnorm(n, 120, 15)
L <- .05*(age-50) + .03*(blood.pressure-120)
p12 <- plogis(L)    # Pr(Y>=1)
p2  <- plogis(L-1)  # Pr(Y=2)
p   <- cbind(1-p12, p12-p2, p2)   # individual class probabilites
# Cumulative probabilities:
cp  <- matrix(cumsum(t(p)) - rep(0:(n-1), rep(3,n)), byrow=TRUE, ncol=3)
# simulate multinomial with varying probs:
y <- (cp < runif(n)) %*% rep(1,3)
y <- as.vector(y)
# Thanks to Dave Krantz for this trick
f <- lrm(y ~ age + blood.pressure, x=TRUE, y=TRUE)
par(mfrow=c(2,2))
resid(f, 'score.binary',   pl=TRUE)              #plot score residuals
resid(f, 'partial', pl=TRUE)                     #plot partial residuals
resid(f, 'gof')           #test GOF for each level separately


# Make a series of binary fits and draw 2 partial residual plots
#
f1 <- lrm(y>=1 ~ age + blood.pressure, x=TRUE, y=TRUE)
f2  <- update(f1, y==2 ~.)
par(mfrow=c(2,1))
plot.lrm.partial(f1, f2)


# Simulate data from both a proportional odds and a non-proportional
# odds population model.  Check how 3 kinds of residuals detect
# non-prop. odds
set.seed(71)
n <- 400
x <- rnorm(n)

par(mfrow=c(2,3))
for(j in 1:2) {     # 1: prop.odds   2: non-prop. odds
  if(j==1) 
    L <- matrix(c(1.4,.4,-.1,-.5,-.9),nrow=n,ncol=5,byrow=TRUE) + x/2 else {
	  # Slopes and intercepts for cutoffs of 1:5 :
	  slopes <- c(.7,.5,.3,.3,0)
	  ints   <- c(2.5,1.2,0,-1.2,-2.5)
      L <- matrix(ints,nrow=n,ncol=5,byrow=TRUE)+
           matrix(slopes,nrow=n,ncol=5,byrow=TRUE)*x
	}
  p <- plogis(L)
  # Cell probabilities
  p <- cbind(1-p[,1],p[,1]-p[,2],p[,2]-p[,3],p[,3]-p[,4],p[,4]-p[,5],p[,5])
  # Cumulative probabilities from left to right
  cp  <- matrix(cumsum(t(p)) - rep(0:(n-1), rep(6,n)), byrow=TRUE, ncol=6)
  y   <- (cp < runif(n)) %*% rep(1,6)


  f <- lrm(y ~ x, x=TRUE, y=TRUE)
  for(cutoff in 1:5)print(lrm(y>=cutoff ~ x)$coef)


  print(resid(f,'gof'))
  resid(f, 'score', pl=TRUE)
  # Note that full ordinal model score residuals exhibit a
  # U-shaped pattern even under prop. odds
  ti <- if(j==2) 'Non-Proportional Odds<nSlopes>=.7 .5 .3 .3 0' else
    'True Proportional Odds<nOrdinal>Model Score Residuals'
  title(ti)
  resid(f, 'score.binary', pl=TRUE)
  if(j==1) ti <- 'True Proportional Odds<nBinary>Score Residuals'
  title(ti)
  resid(f, 'partial', pl=TRUE)
  if(j==1) ti <- 'True Proportional Odds<nPartial>Residuals'
  title(ti)</nPartial>
par(mfrow=c(1,1))


# Get data used in Hosmer et al. paper and reproduce their calculations
v <- Cs(id, low, age, lwt, race, smoke, ptl, ht, ui, ftv, bwt)
d <- read.table("http://www-unix.oit.umass.edu/~statdata/data/lowbwt.dat",
                skip=6, col.names=v)
d <- upData(d, race=factor(race,1:3,c('white','black','other')))
f <- lrm(low ~ age + lwt + race + smoke, data=d, x=TRUE,y=TRUE)
f
resid(f, 'gof')
# Their Table 7 Line 2 found sum of squared errors=36.91, expected
# value under H0=36.45, variance=.065, P=.071
# We got 36.90, 36.45, SD=.26055 (var=.068), P=.085
# Note that two logistic regression coefficients differed a bit
# from their Table 1</nBinary>
<keyword>models</keyword>
<keyword>regression</keyword>
<concept>logistic regression model</concept>
<concept>model validation</concept></nOrdinal></nSlopes>
Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)

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