Diagnostic Analysis Using Forward Search Procedure for Various Models Diagnostic Analysis Using Forward Search Procedure for Various Models

Description

Identifies potential data outliers and their impact on estimates and analyses. Tool for evaluation of study credibility. Uses the forward search approach of Atkinson and Riani, "Robust Diagnostic Regression Analysis", 2000,<ISBN: o-387-95017-6> to prepare descriptive statistics of a dataset that is to be analyzed by functions lm {stats}, glm {stats}, nls {stats}, lme {nlme}, or coxph {survival}, or their equivalent in another language. Includes graphics functions to display the descriptive statistics.

Details

The DESCRIPTION file:

Index of help topics:

aStep1                  Create Set of Observation Numbers in Step 1 for
                        Linear Model Analysis
aStep2                  Update Observation Set in Step 2
bStep1                  Create Set of Observation Numbers in Step 1 for
                        Linear Mixed Effects Model Analysis
bStep2                  Update Observation Numbers in Step 2
cStep1                  Create Set of Observation Numbers in Step 1 for
                        Cox Proportional Hazards Model Analysis
cStep2                  Update Observation Set in Step 2
forsearch-package       Diagnostic Analysis Using Forward Search
                        Procedure for Various Models Diagnostic
                        Analysis Using Forward Search Procedure for
                        Various Models
forsearch_cph           Create Statistics of Forward Search in a Cox
                        Proportional Hazard Database
forsearch_glm           Create Statistics of Forward Search in a
                        Generalized Linear Model Database
forsearch_lm            Create Statistics Of Forward Search in a Linear
                        Model Database
forsearch_lme           Create Statistics Of Forward Search For a
                        Linear Mixed Effects Database
forsearch_nls           Create Statistics Of Forward Search in a
                        Nonlinear Model Database
identifyCoeffs          Index To Identify Fixed and Random Coefficients
                        To Appear Together on Plot
identifyFixedCoeffs     Index To Identify Fixed Coefficients To Appear
                        Together on Plot
logist3                 Three Parameter Logistic
plotdiag.AICX           Plot Diagnostic AIC Statistics
plotdiag.ANOX2          Plot Diagnostic Statistics of Analysis of
                        Variance Tables
plotdiag.Cook           Plot Diagnostic Statistics of Modified Cook's
                        Distance
plotdiag.Wald           Plot Diagnostic Statistics of Wald Test Output
                        of COXPH Function
plotdiag.allgraphs      Execute All Plotting Functions For a Select
                        Forsearch Object
plotdiag.blind.fixed    Plot Diagnostic Statistics of Fixed
                        Coefficients for Blinded Dataset
plotdiag.deviance.residuals
                        Plot Diagnostic Statistics Of Deviance
                        Residuals
plotdiag.deviances      Plot Diagnostic Deviance Statistics
plotdiag.fit3           Plot Diagnostic Statistics of AIC, BIC, and Log
                        Likelihood
plotdiag.leverage       Plot Diagnostic Statistics Of Leverage
plotdiag.loglik         Plot Diagnostic Statistics of LOGLIK Output of
                        COXPH Function
plotdiag.lrt            Plot Diagnostic Statistics of Likelihood Ratio
                        Test of COXPH Function
plotdiag.params.fixed   Plot Diagnostic Statistics of Fixed
                        Coefficients
plotdiag.params.random
                        Plot Diagnostic Statistics Of Random
                        Coefficients
plotdiag.phihatx        Plot Diagnostic PhiHat Statistics
plotdiag.residuals      Plot Diagnostic Statistics Of Residuals Or
                        Squared Residuals
plotdiag.s2             Plot Diagnostic Statistics Of Residual
                        Variation
plotdiag.tstats         Plot Diagnostic T Statistics
search.history          Create Tabular History Of Forward Search
showme                  Display Abbreviated Output of FORSEARCH_xxx
                        Function
variablelist            Identify Level(s) to Which Each Factor
                        Observation Belongs

Ensure that data frame has a leading column of observation numbers. Run forsearch_foo to create a file of diagnostic statistics to be used as input to such plotting functions as plotdiag.residuals, plotdiag.params.fixed, plotdiag.params.random, plotdiag.s2, plotdiag,leverage, and plotdiag.Cook. The file of diagnostic statistics can be voluminous, and the utility function showme displays the output more succinctly. Plotting of statistics for fixed and for random coefficients is limited by graphical restraints in some cases. The function identifyCoeffs provides a set of indexing codes so that plotdiag.params.random can display diagnostics for selected fixed or random model parameters. The function identifyFixedCoeffs does the same for lm models.

Author(s)

William R. Fairweather, Flower Valley Consulting, Inc., Silver Spring MD USA William Fairweather [aut, cre]

Maintainer: William Fairweather <wrf343@flowervalleyconsulting.com> William R. Fairweather <wrf343 AT flowervalleyconsulting DOT com>

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000. Pinheiro, JC and DM Bates. Mixed-Effects Models in S and S-Plus, Springer, New York, 2000.

Create Set of Observation Numbers in Step 1 for Linear Model Analysis

Description

Derives the first set of observation numbers for forsearch in linear models

Usage

aStep1(yesfactor, df1, df1.ls, inner.rank, initial.sample, formulaA, 
   nofactform, ycol, b.d)

Arguments

Details

Support function, usually not called independently

Value

Produces set of observation numbers for Step 1. Accounts for presence of factors in the dataset

Note

Presence of Observation column has no effect on outcome

Author(s)

William R. Fairweather

Update Observation Set in Step 2

Description

Derives the set of observation numbers for forsearch in Step 2 for linear models

Usage

aStep2(yesfactor, form.A2, finalm, rimbs, onlyfactor = FALSE, dfa2, finalm.ls,
          ycol,mstart, rnk, b.d)

Arguments

Details

Support function, usually not called independently

Value

Vector of integers corresponding to observation numbers

Author(s)

William R. Fairweather

Create Set of Observation Numbers in Step 1 for Linear Mixed Effects Model Analysis

Description

Derives the first set of observation numbers for forsearch in linear mixed effects models

Usage

bStep1(yesfactor, df1, df1.ls, groups, inner.rank, source, initial.sample, 
      nofactform = NULL, formulaA, randform, inc, ycol, b.d)

Arguments

Details

Support function, usually not called independently

Value

Produces set of observation numbers for Step 1. Accounts for presence of factors and groups in the dataset

Note

Presence of Observation column has no effect on outcome

Author(s)

William R. Fairweather

Update Observation Numbers in Step 2

Description

Derives the set of Step 2 observation numbers for forsearch in linear mixed effects models

Usage

bStep2(yf, f2, dfa2, randm2, onlyfactor = FALSE,ms, ycol, initn, inc, finalm, fbg, b.d)

Arguments

Details

Support function, usually not called independently

Value

List of expanding number sets corresponding to observation numbers

Author(s)

William R. Fairweather

Create Set of Observation Numbers in Step 1 for Cox Proportional Hazards Model Analysis

Description

Derives the first set of observation numbers for forsearch in Cox Proportional Hazards models

Usage

cStep1(df1, df1.ls, inner.rank, initial.sample, f.e, cphties, ycol, b.d)

Arguments

Details

Support function, usually not called independently

Value

Produces set of observation numbers for Step 1. Accounts for presence of factors in the dataset

Author(s)

William R. Fairweather

Update Observation Set in Step 2

Description

Derives the set of observation numbers for step 2 for forsearch in Cox proportional hazard models

Usage

cStep2(fe, finalm, rimbs, dfa2, onlyfactor=FALSE, ycol, cphties,mstart, rnk, b.d)

Arguments

Details

Support function; usually not called independently

Value

Vector of expanding number sets corresponding to observation numbers

Author(s)

William R. Fairweather

Create Statistics of Forward Search in a Cox Proportional Hazard Database

Description

Prepares summary statistics at each stage of forward search for subsequent plotting.

Usage

forsearch_cph(alldata, formula.rhs, nofactform, initial.sample=1000, 
      skip.step1=NULL, ties = "efron", maxdisturb=.01, proportion=TRUE, 
      unblinded=TRUE, begin.diagnose= 100, verbose=TRUE)

Arguments

Details

Step 1 requires one additional observation to be included in database to prevent failure of Wald statistic in Step 2 calculation of coxph function

Value

LIST

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Examples

Observation <- 1:60
xx1 <- runif(60)
xx2 <- runif(60)
treat <- c(rep("A",30),rep("B",20),rep("C",10))
treat <- as.factor(treat)
event.time <- trunc(100*runif(60))
statusx <- c(rep(1,22),rep(0,8),rep(1,17),rep(0,3),rep(1,6),rep(0,4))
index3 <- sample(1:60,60)
xindep <- data.frame(Observation,event.time,status=statusx,xx1,xx2,treat)
formula.rhs <- "xx1 + xx2 + treat"
coxph.out05 <- forsearch_cph(alldata=xindep, formula.rhs=formula.rhs, 
    nofactform="xx1 + xx2", skip.step1=NULL, ties="efron", initial.sample=500, 
    begin.diagnose=100)

Create Statistics of Forward Search in a Generalized Linear Model Database

Description

Prepares summary statistics at each stage of forward search for subsequent plotting. Forward search is conducted in three steps: Step 1 to identify minimal set of observations to estimate unknown parameters, and Step 2 to add one observation at each stage such that observations in the set are best fitting at that stage. A preliminary step (Step 0) contains code for pre-processing of the data.

Usage

forsearch_glm(initial.sample=1000, response.cols, indep.cols, family,  
   formula=NULL, binomialrhs=NULL, formula.cont.rhs, data, 
   estimate.phi = TRUE, wiggle=1, skip.step1=NULL, unblinded=TRUE, 
   begin.diagnose=100, verbose=TRUE)

Arguments

Details

Step 2 is determined by the results of Step 1, which itself is random. So, it is possible to reproduce the entire run by using the skip.step1 argument. Inner subgroups are produced by presence of categorical variables. Current version assumes independent variables are all continuous.

Value

LIST

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Examples

## Not run: 
# Train deaths (Atkinson and Riani, 2000) with Rolling Stock as a factor
Observation<-1:67
Month<-c(9,8,3,1,10,6,7,1,8,4,3,3,12,11,10,9,9,4,3,12,12,10,7,2,12,2,12,12,12,
    11,3,10,4,2,12,12,9,11,1,10,8,6,1,10,6,12,8,4,9,6,12,10,7,2,5,12,5,5,4,3,1,
    9,11,9,7,3,2)
Year<-c(97,96,96,95,94,94,91,91,90,89,89,89,88,88,87,86,86,86,86,84,84,84,84,84,
    83,83,82,81,81,80,80,79,79,79,78,78,77,76,76,75,75,75,75,74,74,73,73,73,72,
    72,71,71,71,71,70,69,69,69,69,69,69,68,67,67,67,67,67)
RollingStock<-c(2,2,3,2,1,1,1,1,2,3,1,1,1,2,1,2,1,3,2,2,1,2,2,3,1,2,1,1,2,3,1,
    1,1,1,1,1,1,3,3,2,3,1,2,3,1,1,1,3,3,1,3,3,1,1,1,2,1,1,2,1,1,1,1,1,1,1,1)
RollingStock <- as.factor(RollingStock)    
Traffic<-c(0.436,0.424,0.424,0.426,0.419,0.419,0.439,0.439,0.431,0.436,0.436,
    0.436,0.443,0.443,0.397,0.414,0.414,0.414,0.414,0.389,0.389,0.389,0.389,
    0.389,0.401,0.401,0.372,0.417,0.417,0.43,0.43,0.426,0.426,0.426,0.43,0.43,
    0.425,0.426,0.426,0.436,0.436,0.436,0.436,0.452,0.452,0.433,0.433,0.433,
    0.431,0.431,0.444,0.444,0.444,0.444,0.452,0.447,0.447,0.447,0.447,0.447,
    0.447,0.449,0.459,0.459,0.459,0.459,0.459)
Deaths<-c(7,1,1,1,5,2,4,2,1,1,2,5,35,1,4,1,2,1,1,3,1,3,13,2,1,1,1,4,1,2,1,5,7,
    1,1,3,2,1,2,1,2,6,1,1,1,10,5,1,1,6,3,1,2,1,2,1,1,6,2,2,4,2,49,1,7,5,9)
train2022 <- data.frame(Observation, Year, RollingStock, Traffic, Deaths)
test02<-forsearch_glm(initial.sample = 100, response.cols = 5, 
    indep.cols = 2:4, formula=Deaths~Year + RollingStock + Traffic,
    formula.cont.rhs="Year + Traffic", 
    family = poisson("log"), data = train2022, 
    estimate.phi = TRUE, skip.step1 = NULL, 
    unblinded = TRUE, begin.diagnose=100)

## End(Not run)

Create Statistics Of Forward Search in a Linear Model Database

Description

Prepares summary statistics at each stage of forward search for subsequent plotting. Forward search is conducted in two steps: Step 1 to identify minimal set of observations to estimate unknown parameters, and Step 2 to add one observation at each stage such that observations in the set are best fitting at that stage.

Usage

forsearch_lm(formula, nofactform, data, initial.sample=1000, 
                   skip.step1 = NULL, unblinded = TRUE, begin.diagnose = 100,
                   verbose = TRUE)

Arguments

Value

LIST

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Examples

## Not run: 
# Multiple regression
Observation <- 1:16
y <- runif(16)
x1 <- runif(16)
x2 <- runif(16)
x3 <- runif(16)
lmtest1 <- data.frame(Observation,y,x1,x2,x3)
test1 <- forsearch_lm(formula=y~x1+x2+x3, nofactform=y~x1+x2+x3, data=lmtest1, 
     initial.sample=200,begin.diagnose=100)

# Analysis of variance 
Observation <- 1:30
y <- runif(30)
AN1 <- as.factor(c(rep("A1",5),rep("A2",5),rep("A3",5)))
AN1 <- c(AN1,AN1)
AN2 <- as.factor(c(rep("B1",15),rep("B2",15)))
lmtest2 <- data.frame(Observation,y,AN1,AN2)
test2 <- forsearch_lm(formula=y~AN1*AN2, nofactform=y~1, data=lmtest2, 
       initial.sample=200, begin.diagnose=100)

# Analysis of covariance
Observation <- 1:60
y <- runif(60)
AN1 <- as.factor(c(rep("A1",10),rep("A2",10),rep("A3",10)))
AN1 <- c(AN1,AN1)
AN2 <- as.factor(c(rep("B1",30),rep("B2",30)))
COV <- runif(60)
lmtest3 <- data.frame(Observation,y,AN1,AN2,COV)
test3 <- forsearch_lm(formula=y~AN1*AN2+COV, nofactform=y~COV, data=lmtest3, 
      initial.sample=200,begin.diagnose=100)

# Polynomial regression
C1 <- 7*runif(60) + 1
y <- 4 + C1 - 6*C1^2 + 9*C1^3 + rnorm(60)
Observation <- 1:60
dfpoly <- data.frame(Observation,C1,y) 
test4 <- forsearch_lm(formula = y ~ C1 + I(C1^2) + I(C1^3), data = dfpoly,  
     nofactform=y ~ C1 + I(C1^2) + I(C1^3),initial.sample = 200, 
     begin.diagnose=100)

## End(Not run)

Create Statistics Of Forward Search For a Linear Mixed Effects Database

Description

Prepares summary statistics at each stage of forward search for subsequent plotting. Forward search is conducted in four steps: Step 0 to set up accounting for group structure, Step 1 to identify minimal set of observations to estimate unknown fixed parameters, Step 2 to identify the order of the remaining observations, and a final stage to extract the intermediate statistics based on increasing sample size.

Usage

forsearch_lme(fixedform, nofactform, alldata, randomform, groupname, randfactnames=NULL,
   initial.sample=1000, skip.step1=NULL, unblinded=TRUE, begin.diagnose = 100, 
   incCont=FALSE, verbose = TRUE)

Arguments

Details

data will be grouped within the function, regardless of initial layout. Step 2 is determined by the results of Step 1, which itself is random. So, it is possible to reproduce the entire run by using the skip.step1 argument. Variables in the randomform formula must be character variables, but *not* factors

Value

LIST

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000. Pinheiro, JC and DM Bates. Mixed-Effects Models in S and S-Plus, Springer, New York, 2000. https://CRAN.R-project.org/package=nlme

Examples

## Not run: 
# Multiple regression in grouped data
Observation <- 1:160
y <- runif(160)
x1 <- runif(160)
x2 <- runif(160)
x3 <- runif(160)
group <- rep(c("G1","G2"),each=80)
lmetest1 <- data.frame(Observation,y,x1,x2,x3,group)
forsearch_lme(fixedform=y~x1+x2+x3, nofactform=y~x1+x2+x3, alldata=lmetest1, 
   randomform= ~1|group, groupname="group", initial.sample=200)

# Analysis of variance in grouped data
Observation <- 1:60
y <- runif(60)
AN1 <- as.factor(c(rep("A1",5),rep("A2",5),rep("A3",5)))
AN1 <- c(AN1,AN1,AN1,AN1)
AN2 <- as.factor(c(rep("B1",15),rep("B2",15)))
AN2 <- c(AN2,AN2)
group <- rep(c("G1","G2"),each=30)
lmetest2 <- data.frame(Observation,y,AN1,AN2,group)
forsearch_lme(fixedform=y~AN1*AN2, nofactform=y~1, alldata=lmetest2, 
   randomform= ~1|group, groupname="group",initial.sample=500)

# Analysis of covariance in grouped data

Observation <- 1:120
y <- runif(120)
AN1 <- as.factor(c(rep("A1",10),rep("A2",10),rep("A3",10),rep("A4",10)))
AN1 <- c(AN1,AN1,AN1)
AN2 <- as.factor(c(rep("B1",5),rep("B2",5)))
AN2 <- c(AN2,AN2,AN2,AN2,AN2,AN2)
AN2 <- c(AN2,AN2)
COV <- runif(120)
group <- rep(c("G1","G2"),each=30)
group <- c(group,group)
lmetest3 <- data.frame(Observation,y,AN1,AN2,COV,group)
test3<-forsearch_lme(fixedform=y~AN1*AN2+COV,nofactform=y~COV,alldata=lmetest3,
        randomform= ~ 1 | group,groupname="group",initial.sample=500,
        begin.diagnose=100)

## End(Not run)

Create Statistics Of Forward Search in a Nonlinear Model Database

Description

Prepares summary statistics at each stage of forward search for subsequent plotting. Forward search is conducted in two steps: Step 1 to identify minimal set of observations to estimate unknown parameters, and Step 2 to add one observation at each stage such that observations in the set are best fitting at that stage.

Usage

forsearch_nls(nlsform, data, start, algorithm="default", 
   nls.control=FALSE, initial.sample=1000, skip.step1=NULL, begin.diagnose=100, 
   verbose=TRUE)

Arguments

Value

LIST

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000. Pinheiro, JC and DM Bates. Mixed Effects Models in S and S-PLUS, Springer, New York, 2000. https://cran.r-project.org/web/packages/nlstools/vignettes/vignetteJSS.pdf

Examples

## Not run: 
Observation <- 1:70
Section <- rep(c(1,1,1,1,1,2,2,2,2,2), times=7)
Tree <- rep(1:7, each=10)
age <- rep(c(1,3,5,7,9,11,13,15,17,19), times = 7) * 100
circum <- forsearch::logist3(age, a=170, b=7, c=500) + rnorm(70)*0.1
test02 <- data.frame(Observation, Section, Tree, age, circum) 
startPru <- list(Asym=170, xmid=7, scal=500)
formulaPru  <- circum ~ I(logist3(x=age, a=Asym, b=xmid, c=scal))
forsearch_nls(nlsform = formulaPru,  
           data=test02, start=startPru, nls.control=TRUE,  
           initial.sample = 179, skip.step1=NULL, begin.diagnose=100, verbose=TRUE)
           
t <- (0:35)/3
VO2<- c(377.1111, 333.3333, 352.1429, 328.7500, 369.8750 ,394.4000, 352.6667, 
       337.3333, 366.4286, 364.0000, 293.8889, 387.0000, 364.8889, 342.2222, 
       400.3000, 375.1111 ,320.5556, 385.1667)
VO2<- c(VO2,527.0714,688.6364,890.8182,1145.1538, 1254.9091, 1327.5000,1463.9000,
       1487.8333 ,1586.6667, 1619.1000, 1494.4167 ,1640.4545, 1643.3750,
       1583.6364, 1610.8000 ,1568.5000, 1464.5833, 1652.8000) 
test01 <- data.frame(t,VO2)       
Observation <- 1:36
Section <- c(rep(1,20),rep(2,8),rep(3,8))
test01 <- cbind(Observation, Section, test01)
pstart <- list(VO2rest=400, VO2peak = 1600, mu = 1)
pformula <- as.formula(VO2~(t<=5.883)*(VO2rest)+          
            (t>5.883)*(VO2rest+(VO2peak-VO2rest)*
            (1-exp(-(t-5.883)*I(1/mu)))))
test2 <- forsearch_nls(nlsform=pformula, data = test01, 
           start=pstart, nls.control=FALSE, initial.sample = 300, skip.step1=NULL, 
           begin.diagnose=100, verbose=TRUE)


## End(Not run)

Index To Identify Fixed and Random Coefficients To Appear Together on Plot

Description

Runs the defined, grouped linear mixed effects (lme) model. Displays the resulting fixed and random coefficients. Attaches codes for identifying them to the plotting functions of this package.

Usage

identifyCoeffs(fixed, data, random, 
    XmaxIter = 1000, XmsMaxIter = 1000, 
    Xtolerance = 0.01, XniterEM = 1000, XmsMaxEval = 400, XmsTol = 1e-05, 
    Xopt = "optim", verbose = TRUE)

Arguments

Details

Plotting functions cannot plot more than a few coefficients on one graph. This function prepares an index of the coefficients so that the user can more easily identify which ones should appear together in a plot.

Value

Index of fixed and random coefficients from forsearch_lme.

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Examples

info3 <- system.file("extdata","Machines.O.R",package="forsearch");
info3 <- source(info3);
info3 <- info3[[1]]; 
identifyCoeffs(fixed=score~1, data=info3, random= ~1 | Worker)

Index To Identify Fixed Coefficients To Appear Together on Plot

Description

Runs the defined linear (lm) model. Displays the resulting coefficients. Attaches codes for identifying them to the plotting functions of this package.

Usage

identifyFixedCoeffs(formula, data, verbose = TRUE)

Arguments

Details

Plotting functions cannot plot more than a few coefficients on one graph. This function prepares an index of the coefficients so that the user can more easily identify which ones should appear together in a plot.

Value

Index of coefficients from forsearch_lm.

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Examples

info3 <- system.file("extdata", "crossdata.R", package="forsearch");
crossdata <- source(info3);
crossdata <- crossdata[[1]];
identifyFixedCoeffs(formula=y~x1*x2, data=crossdata)

Three Parameter Logistic

Description

Calculates three parameter logistic function

Usage

logist3(x, a, b, c)

Arguments

Details

Calculates y = a/(1 + xx), where xx = exp(-(x - b)/c)

Value

Numerical output of the function described in details

Author(s)

William R. Fairweather

Plot Diagnostic AIC Statistics

Description

Plot output from forsearch_glm to show change in AIC statistics as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.AICX(forn, maintitle = "Put main title here",
   subtitle = "Put subtitle here", caption="Put caption title here",
   wmf = "Put_plot_file_title_here",
   Cairo=TRUE, printgraph=TRUE,addline="none",
   verbose = TRUE)

Arguments

Value

Process and plot AIC statistics from forsearch_glm

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics of Analysis of Variance Tables

Description

Plot output from forsearch_xxx to show change in anova p-values as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.ANOX2(forn, anova.rows=NULL, ylab.extend=c("proportionality","variance"), 
maintitle = "Put main title here", 
subtitle = "Put subtitle here", caption="Put caption here",wmf = "Put_stored_name_here", 
Cairo=TRUE, printgraph=TRUE,legend = "Dummy legend name",  
verbose = TRUE)

Arguments

Value

Process and plot anova test p values from forsearch_lm or forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics of Modified Cook's Distance

Description

Plot output from forsearch_lm or forsearch_lme to show change in Modified Cook's distance as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.Cook(forn, maintitle = "Put main title here", subtitle = "Put subtitle here", 
caption = "Put caption here", wmf = "Put_plot_file_title_here", 
Cairo=TRUE, printgraph=TRUE, addline = "none", verbose = TRUE)

Arguments

Value

Process and plot Cook distance statistics from forsearch_lm or forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics of Wald Test Output of COXPH Function

Description

Plot output from forsearch_cph to show change in Wald test as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.Wald(forn,  
maintitle= "Put main title here", 
subtitle= "Put subtitle here" , 
caption="Put caption here",
wmf = "Put_graph_filename_here", 
Cairo=TRUE,
printgraph = TRUE,
addline=c("none","loess","straight"),
verbose=TRUE)

Arguments

Value

Process and plot Wald Test statistics from forsearch_cph

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Execute All Plotting Functions For a Select Forsearch Object

Description

Executes all the plotting functions for a select analytical function such as lm or glm; default omits titles and subtitles and attempts to plot all fixed and random coefficients.

Usage

plotdiag.allgraphs(object, mt=" ", st=" ", cpt=" ", blind.label=FALSE, cc=NULL,
               ccrand = NULL,Cairo=TRUE)

Arguments

Value

Prints search history and creates graphical files in current subdirectory

Author(s)

William R. Fairweather

Plot Diagnostic Statistics of Fixed Coefficients for Blinded Dataset

Description

Plot output from forsearch_xxx to show change in fixed coefficients as the number of observations in the forward search procedure increases. Save plot in folder containing working directory. Run on blinded data only.

Usage

plotdiag.blind.fixed(forn, coeff.codenums=NULL, maintitle = "Put main title here", 
subtitle = "Put subtitle here", caption="Put caption here",wmf = "Put_stored_name_here", 
Cairo=TRUE, printgraph=TRUE,legend = "Dummy legend name",  
verbose = TRUE)

Arguments

Value

Process and plot fixed coefficient statistics from forsearch_lm or forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics Of Deviance Residuals

Description

Plot output from forsearch_glm to show change in deviance residuals or augmented deviance residuals, either of which can be squared, as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.deviance.residuals(forn, squared = FALSE, augmented=TRUE, hilos = c(1, 0), 
maintitle="Put main title here", subtitle="Put subtitle here", caption="Put caption here", 
wmf= "Put_graph_title_here", Cairo=TRUE,printgraph=TRUE,
legend = "Dummy legend name", verbose = TRUE)

Arguments

Details

We reserve the use of the term 'Deviance residuals' to deviance residuals of the observations that were used to create the model fit, and use the term 'Augmented deviance residuals' to refer to deviance residuals of all available observations. The latter are created by predicting the fit of the model to all observations.

Value

Process and plot changes in deviance residuals or squared deviance residuals from forsearch_glm

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Deviance Statistics

Description

Plot output from forsearch_glm to show change in deviances as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.deviances(forn, devtype, maintitle = "Put main title here", 
subtitle = "Put subtitle here", caption="Put caption here", 
wmf = "Put_plot_file_title_here", 
Cairo=TRUE, printgraph=TRUE,addline="none",
verbose = TRUE)

Arguments

Value

Process and plot deviances from forsearch_glm

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics of AIC, BIC, and Log Likelihood

Description

Plot output from forsearch_lme to show change in AIC, BIC, and log likelihood as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.fit3(forn, maintitle = "Put main title here", subtitle = "Put subtitle here", 
caption = "Put caption here", wmf = "Put_stored_name_here", 
Cairo=TRUE,printgraph=TRUE, legend="Dummy legend name",
verbose = TRUE)

Arguments

Value

Process and plot trends of AIC, BIC, and log likelihood statistics from forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics Of Leverage

Description

Plot output from forsearch_lm or forsearch_lme to show change in leverage of each observation as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.leverage(forn, hilos = c(1, 0), maintitle = "Put main title here", 
subtitle = "Put subtitle here", caption="Put caption here",wmf = "Put_graph_title_here", 
Cairo=TRUE, printgraph = TRUE, verbose = TRUE)

Arguments

Value

Process and plot Cook distance statistics from forsearch_lm or forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics of LOGLIK Output of COXPH Function

Description

Plot output from forsearch_cph to show change in loglik pairs as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.loglik(forn,  
     maintitle= "Put main title here", 
     subtitle= "Put subtitle here" , 
     caption="Put caption here",
     wmf = "Put_stored_name_here", 
     Cairo=TRUE,
     printgraph = TRUE,
     verbose=TRUE)

Arguments

Value

Process and plot Wald Test statistics from forsearch_cph

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics of Likelihood Ratio Test of COXPH Function

Description

Plot output from forsearch_cph to show change in likelihood ratio test as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.lrt(forn,  
     maintitle= "Put main title here", 
     subtitle= "Put subtitle here" , 
     caption="Put caption here",
     wmf = "Put_graph_filename_here", 
     Cairo=TRUE,
     printgraph = TRUE,
     addline=c("none","loess","straight"),
     verbose=TRUE)

Arguments

Value

Process and plot likelihood ratio test statistics from forsearch_cph

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics of Fixed Coefficients

Description

Plot output from forsearch_xxx to show change in fixed coefficients as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.params.fixed(forn, coeff.codenums=NULL, maintitle = "Put main title here", 
subtitle = "Put subtitle here", caption="Put caption here",wmf = "Put_stored_name_here", 
Cairo=TRUE, printgraph=TRUE,legend = "Dummy legend name",  
verbose = TRUE)

Arguments

Value

Process and plot fixed coefficient statistics from forsearch_lm or forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics Of Random Coefficients

Description

Plot output from forsearch_lme to show change in root mean squares of random coefficients as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.params.random(forn, coeff.codenums=NULL, asfacets=FALSE, facetdir=c("h","v"), 
maintitle = "Put maintitle here", subtitle = "Put subtitle here", 
caption = "Put caption here", wmf = "Put_stored_name_here", Cairo=TRUE,
printgraph = TRUE, legend = "Dummy legend name", verbose = TRUE)

Arguments

Value

Process and plot RMS of random coefficients from forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic PhiHat Statistics

Description

Plot output from forsearch_glm to show change in phiHat statistics as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.phihatx(forn, maintitle = "Put main title here", 
subtitle = "Put subtitle here", caption="Put caption here", 
wmf = "Put_graph_filename_here", 
Cairo=TRUE, printgraph=TRUE, addline="none",
verbose = TRUE)

Arguments

Value

Process and plot phiHat statistics from forsearch_glm

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics Of Residuals Or Squared Residuals

Description

Plot output from forsearch_lm or forsearch_lme to show change in residuals or squared residuals as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.residuals(forn, squared = FALSE, hilos = c(1, 0), maintitle, subtitle, 
caption, wmf, Cairo=TRUE,printgraph=TRUE,
legend = "Dummy legend name", verbose = TRUE)

Arguments

Value

Process and plot changes in residuals or squared residuals from forsearch_lm or forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic Statistics Of Residual Variation

Description

Plot output from forsearch_lm to show change in residual variation as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.s2(forn, maintitle = "Put main title here", subtitle = "Put subtitle here", 
caption = "Put caption here", wmf = "Put_graph_filename_here", 
Cairo=TRUE,printgraph=TRUE, addline = c("none","loess","straight"), 
verbose = TRUE)

Arguments

Value

Process and plot residual variation statistics from forsearch_lm

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Plot Diagnostic T Statistics

Description

Plot output from forsearch_lm or forsearch_lme to show change in t statistics as the number of observations in the forward search procedure increases. Save plot in folder containing working directory.

Usage

plotdiag.tstats(forn, coeff.codenums=NULL, maintitle = "Put main title here", 
subtitle = "Put subtitle here", caption="Put caption here", wmf = "Put_stored_name_here", 
Cairo=TRUE, printgraph=TRUE,legend = "Dummy legend name",  
verbose = TRUE)

Arguments

Value

Process and plot t statistics of fixed coefficients from forsearch_lm or forsearch_lme

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Create Tabular History Of Forward Search

Description

The forward search functions output a list of vectors, each of which indicates which observations are in the model at each stage of the search. This function processes that list to create a more easily understood matrix of the observation numbers that are newly entered into the model and any that were temporarily removed from the model over the course of the search.

Usage

search.history(list1, verbose = TRUE)

Arguments

Value

Printout of matrix showing evolution of observations to enter or leave the model during the course of the forward search

Author(s)

William R. Fairweather

Examples

info3 <- system.file("extdata", "crossdata.for1.R", package="forsearch");
info3 <- source(info3);
info3 <- info3[[1]];
search.history(list1=info3, verbose=TRUE)

Display Abbreviated Output of FORSEARCH_xxx Function

Description

Output of forsearch_xxx function can be voluminous. This function displays the output in an abbreviated format. Primarily for programmer use.

Usage

showme(x, verbose = TRUE)

Arguments

Value

Abbreviated printout of output of forsearch_lm function

Author(s)

William R. Fairweather

Identify Level(s) to Which Each Factor Observation Belongs

Description

For a data frame with factor variables V1, V2, V3, etc having levels n1, n2, n3, etc, lists the n1*n2*n3*... possible interaction levels and identifies which of the observations of the data frame belong in which of these interaction levels.

Usage

variablelist(datadf, prank)

Arguments

Details

Support function, usually not called independently

Value

List, each element is a data frame of 2 columns with code indicating the highest possible level of interaction to which each observation can belong

Author(s)

William R. Fairweather