INDEX
Author
Newsletter_Article Title Subject Macros
1.05 Aitkin Murray GLIM 3 facilities
SSRC research programme and Information
1.17 Aitkin Murray Algorithms and computational methods
GLIM and the expectation-maximisation algorithm
2.09 Aitkin Murray* and Francis Brian Survival analysis, Macros and the macro library
WEIBULL, extreme RESPLOTS
A GLIM macro for the fitting of the MODEL, value distribution
3.13 Aitkin Murray* and Francis Brian Survival analysis, Macros and the macro library
A GLIM macro for the fitting of the extreme value distribution
5.03 Altham Douglas G Book reviews conference reports, etc
Letter to the editors
11.18 Altham P M E* and Porteous B T Statistical modelling in GLIM
Markov chains : reversibility, equilibrium and GLIM
14.07 Altham P M E* and Farrington D P pairs problem with discrete data : a comparison o
A matched Statistical modelling in GLIM
21.02 Altham P M E Macros andALL macro library, Graphics
to
Fitting graphical models themulti way contingency tables in G
23.07 Altham P M E Statistical modelling in GLIM
Improving the precision of estimation by fitting a generalized
23.02 Andrews N J * and Swan A V GLIM 4 facilities and information
GRAPHS, NNCA, GLIM 4
A sample size investigation system using TABLES
9.08 Aranda-Ordaz F J Statistical modelling in GLIM
INST, LLIK, M1-M4
Fitting a generalised symmetric model to binary response da
9.09 Aranda-Ordaz F J generalisations of logistic models with GLIM
INAT, LLIK, M1-M4
AsymmetricStatistical modelling in GLIM
6.06 Armstrong W GLIM at the Polytechnic of North London
The Prism courseand teaching
6.17 Armstrong W Letter to the Editors
7.06 Armstrong W simulate the effect of experiments, four treatment
A macro to Analysis of SIM designed randomizing Macros and th
1.03 Baker R J GLIM
Bugs in release 3 3 facilities and Information
1.08 Baker R J* and Clarke M R B GLIM - facilities 4
The next episode 4 Release and information
1.01 Baker R J Graphics
Drawing histograms FREQ, HIST
1.11 Baker R J log-linear models
InterpretingStatistical modelling in GLIM
1.18 Baker R J Gaussian distribution
MDEV, MEL,
The inverseStatistical distributions MELD, MVAR
1.21 Baker R J identifier lists
Processing GLIM language and data manipulation
2.06 Baker R J GLIM language and data
Loops with macros: a cautionary tale manipulation
2.11 Baker R J Statistical analysis
INT, RUN, GLIM
Conversational probit modelling in START
3.06 Baker R J GLIM language? - a reply
INIT
A compiled GLIM language and data manipulation
3.12 Baker R J*, Pierce C B and Pierce J M Statistical controls
Wadleys problem withmodelling in GLIM
5.05 Baker R J GLIM-3 bugs GLIM 3 facilities and Information
5.06 Baker R J GLIM-4 et al GLIM 4 facilities and information
6.05 Baker R J GLIM 4
GLIM-4 and Prism facilities and information
9.03 Baker R J*, Green M and White R P GLIM 3.77 GLIM 3 facilities and Information
3.08 Bartlett N R Statistical modelling in GLIM
The complementary log-log transformation
21.05 Bassett E E Statistical modelling in GLIM, Macros DOL, EXC
AOVH, AOVIN,
GLIM macros for stepwise regression AOVOUT, and the ma
19.07 Becker M P Statistical modelling in GLIM
CONV, DEPR, FITQ, LINP, MADE, Q
Square contingency tables having ordered categories and G
21.04 Becker Mark P Analysis of designed experiments
for binary data from
Fitting marginal modelsFV, INIT, LIN, M1-M4two-period cross
4.04 Bennett Steve* and Whitehead John Survival analysis regression models to censored
IBER,
Fitting logistic and log-logistic ICER, ICTU, IFIN, LPLT, LRUB
5.021 Bennett Steve* and Whitehead John Fitting analysis
Corrigenda:Survivallogistic and log-logistic regression model
17.06 Bennett S Algorithms and computational methods
BRS1, BRS2, CHIS, OVER,OVRS, P
An extension of Williams method for overdispersion models
20.12 Bennett Steve Survival analysis model to POD2, data
odds
Fitting the proportional LOOP, POD1, survivalYCALin GLIM
14.05 Bergmann E and Busse H* GLIM language and data manipulation
Lack of double precision in GLIM
21.07 Bergmann Eckardt, Busse Horst* and Schafer Uwe GLIM language and data manipulation
Dependency of GLIM run times on input compression degre
19.05 Braga M and Duca P G* Algorithms and computational methods
KOMO, SENSI, TIES
A simple algorithm for the comparison of two observed cumu
20.05 Brown P J Book review Book reviews conference reports, etc
5.09 Burn R* and Thomson R calculatingCOVA, MUL1-MUL4 of and the of
the covariance matrix
A macro forStatistical modelling in GLIM, Macrosfunctionsma
8.09 Burn R Statistical data with classification errors
INIT, M1-M4
Fitting a logit model tomodelling in GLIM
12.06 Burn R orthogonal CCAL, COEF, DELE, ERR1 -ERR5,
polynomials in GLIM
Generating Statistical modelling in GLIM
14.05 Busse H* and Bergmann E GLIM language and data manipulation
Lack of double precision in GLIM
21.07 Busse Horst*, Bergmann Eckardt and Schafer Uwe GLIM language and data manipulation
Dependency of GLIM run times on input compression degre
4.02 Candy S G Book reviews conference reports, etc
Letter to the editor
7.07 Candy S Statistical modelling in GLIM
WEIB
Fitting a model with identity link and Weibull errors
11.14 Candy S G Statistical modelling in GLIM
LPD, M1-M4
Using factors in composite link function models
13.05 Candy S G Survival analysis
Fitting a parametric log-linear hazard function to grouped su
6.1 Chambers John M* and Schilling Judith M GLIM and other systems
S and GLIM : an experiment in interfacing statistical systems
Page 1
INDEX
5.13 Channon E GLIM in GLIM-3MULT
MP,
Matrix operations language and data manipulation
5.15 Channon E variance tables in GLIM-3
designed experiments
Analysis of Analysis of AOV, F, FU
1.06 Clarke M R B Graphics
GLIM plus graphics
1.08 Clarke M R B and Baker R J* GLIM - facilities 4
The next episode 4 Release and information
22.05 Clarke M, Francis B* and Green M GLIM 4 facilities and information
BOOT, WEIBULL
Model fitting applications in GLIM 4
5.12 Clayton D G and Jagger C* regressionCENSOR, KERNAL, NOTIES, PLOT
model
Fitting CoxsSurvival analysis to censored survival data
6.03 Clayton D G and Jagger C* - Fitting analysis
Corrigenda Survival Coxs regression model to censored surv
6.04 Clayton D G GLIM language and data manipulation
Letter to the editor
11.12 Coe R Statistical models fitted M1,
MW1-MW4, to Weibull,
Extending the class ofmodelling in GLIM SETW extreme valu
11.17 Coe R* and Seneviratne K Survival analysis MDERIV, MFIT, MVAR
MDEV,
Fitting generalised linear models to censored data
12.07 Collett D Statistical modelling inMESJ, PRWT, TCAL, TRA
ITERATE, GLIM
Transforming the y-variate in a regression analysis
16.06 Collett D* and Roger J H Statistical modelling in GLIM
ARES, BC1_, BC2_, BHAT, BIN_, CO
Computation of generalised linear model diagnostics
11.08 Comelli M XVAR
Independence in two-way contingency tables - a macro to co
3.1 Cormack R M Statistical modelling in GLIM
Model selection in captive-recapture experiments
1.23 Dann Richard GLIM 3 facilities and Information
Allocating workspace in GLIM 3 on ICL 1900s
13.04 bin Daud I and Pettitt A N* Survival analysis EXP2, ITER, STAG, STDK
EXPD,
Fitting student - t distributions to censored data
8.05 Dear K G B Statistical modelling in GLIM, Macros and the
and bivariate analysis COR3, DMY2
Macros for multivariateBIVAR, CORR, COR2,in GLIM-3 ma
8.06 Dear K G B Macros and the MAX, library
Macros for Tabulation INQ, macro PRNT, TAB1-TAB4
9.14 Dear K G B Macros for multivariate and bivariate analysis in GLIM-3 . Ma
3.14 Defize P R Statistical modelling in GLIM
The calculation of adjusted residuals for log-linear models in
4.02 Deichsel G GLIM language and data manipulation
Letter to the editor
6.07 Deichsel Guntram models of axplat iii an aid for model search in
designed
(log-) linear Analysis and lattices -experiments
5.11 Delcourt C*, Mathonet R and Mersch G Graphics
Log-linear models and lexical histograms
11.15 Dessens J*, Jansen W and Luijkx R Statistical modelling in GLIM
COL, HOM, models
Fitting log-multiplicative associationH1 - H2, INIT, IT1 - IT2,
8.07 Diamond I and McDonald John W* Statistical modelling with linear inequality param
Fitting generalised linear modelsin GLIM
19.05 Duca P G* and Braga M Algorithms and computational methods
KOMO, SENSI, TIES
A simple algorithm for the comparison of two observed cumu
23.05 Dyba T* and Hakulinen T intervals for Model-based cumulative relative M
MDE1,
ConfidenceSurvival analysis CON, CVAR, HVBI, HVB1,su
1.14 Edwards David Analysis of residuals in two-way contingency tables
1.22 Edwards David GLIM 3 facilities and
Univac implementation of GLIM Information
13.02 Ekholm A*, Green M and Palmgren J Statistical modelling in GLIM in GLIM M2,
DRK_, LP, models
Fitting exponential family nonlinearLPO, MEXT, M1, 3.77 NLI
18.07 Ekholm A* and Palmgren J models for an ordinal GLIM
RegressionStatistical modelling inresponse are best handled
23.03 Ekholm A* and Green M Statistical modelling in ETA, numerical derivativ
ADD, DER, using
Fitting nonlinear models in GLIM 4GLIM E1 - E3, FIRST, FIT
18.04 Evans S J W and Scallan A J* Statistical modelling in GLIM data FV, ITER,
DR, err1, ERR2,
Fitting truncated distributions to groupedFITM,using GLIMLO
19.06 de Falguerolles Antoine CAQI, CAQS, correspondance analy
Square contingency tables, GLIM andINDQ, PRP,QIND, QS
14.07 Farrington D P and Altham P M E* pairs problem with discrete data : a comparison o
A matched Statistical modelling in GLIM
16.04 Firth D* and Treat B R ADDSQUARE,
Square contingency tables and GLIM MHOM, QSYM, SETU
12.08 Forcina A Statistical modelling in GLIM
DR, ,FV, M0, error
Correlated observations with normal M1, M2, STAR, TREV,
17.07 Forcina A Statistical non-normal error
Correlated GLMs withmodelling in GLIMand the multinomial
2.09 Francis Brian and Aitkin Murray* Survival analysis, Macros and the macro library
WEIBULL,MODEL, RESPLOTS
A GLIM macro for the fitting of the extreme value distribution
3.13 Francis Brian and Aitkin Murray* Survival analysis, Macros and the macro library
A GLIM macro for the fitting of the extreme value distribution
9.1 Francis B J, Thompson S* and Whittaker J C, GLIM GLIM in an interactive environment
Teach - yourself -and teaching
11.04 Francis B GLIM and other systems
Statistical Computing Library
11.06 Francis B* and Whittaker J Book reviews conference 85 conference at Lanc
Generalised linear models : the GLIMreports, etc
15.04 Francis B J GLIM macro library - information for contributors
15.05 Francis B J Macros - submission guidelines
GLIM macro library and the macro library
22.05 Francis B*, Clarke M and Green M GLIM 4 facilities and information
BOOT, WEIBULL
Model fitting applications in GLIM 4
22.06 Francis B and Swan A V * GLIM 4 facilities and information, Statistical mod
GLIM 4
Medical applications in COVARS, COXMODEL, LOGLO
22.08 Francis B and Stasinopoulos M* GLIM 4 facilities in information,HELPGAM, INIT
FGAMOD, gamiter_, Statistical mod
Generalised additive modelsandGLIM 4
24.08 Friedl H* and Hatzinger R Statistical the factors for symmetry parameters in
A Note on generating modelling in GLIM
18.03 Galecki A Algorithms and computational methods
FIT1, FIT2
A note on GLIM and the weighted least squares algorithm
19.04 Gettinby G and Ripley B D* Book reviews Book reviews conference reports, etc
3.09 Gherardini P G Statistical modelling GLIM
Interactive ridge regression with in GLIM
Page 2
INDEX
1.07 Gilchrist R GLIM and teaching
GLIM and teaching: some experiences
4.06 Gilchrist R of residuals for all GLIM models
Calculation Statistical modelling in GLIM
5.022 Gilchrist R : Calculation of residuals for
Corrigenda Statistical modelling in GLIM all GLIM models
5.07 Gilchrist R Statistical a Poisson process
Estimating the rate of modelling in GLIM
6.16 Gilchrist R GLIM language and data
GLIM syntax for adjusted residuals manipulation
8.113 Gilchrist R Book reviews conference reports,
GLIM 82: proceedings of the 1982 conferenceetc
9.07 Gilchrist R Statistical modelling in GLIM
case control studies in GLIM
9.13 Gilchrist R Statistical composite link models
Adjusting residuals in modelling in GLIM
16.02 Gilchrist R Book reviews conference facilities for
Notes of a workshop on the statisticalreports, etc an intterac
20.04 Gilchrist R Book review Book reviews conference reports, etc
22.03 Gilchrist R * and Payne C GLIM of GLIM 4
The new facilities 4 facilities and information
22.1 Gilchrist R Graphics
More comments on fitting graphical models in GLIM
23.04 Gilchrist R and Portides G* Statistical modelling in GAMMA, INVGAU, MEST
4
resistant fitting in GLIMBINOMIAL,GLIM
1.02 Goddard M J, Jan P*, Kramar A Wartelle M GLIM language and data manipulation
Letter to the editor
1.19 Goddard M J, Jan P*, Kramar A Wartelle M Statistical statistic ANDM
ANDA,
The Anderson-Darlingmodelling in GLIM
13.03 Goddard M J Survival analysis
ADD
Exact failure time calculations using GLIM
2.14 Goldschmidt J GLIM 3 facilities and Information
Channel assignments for GLIM on the PDP-11
1.12 Goldstein Harvey Statistical modelling in GLIM
Specifying a multivariate logit model using GLIM
2.04 Goldstein Harvey Statistical modelling in GLIM
proffessor Goldstein replies
21.06 Gough K J Statistical modelling t-distribution
PT, PT1, PT3, T1, TDFP, and the ma
GLIM macros for evaluating the in GLIM, Macros TEV,TEVE
1.16 Green Mick Macros andDEV, INIT, ITER
the macro library
A GLIM macro to perform interactive proportional scaling
5.1 Green M Statistical modelling
Fitting models under constraintsin GLIM
9.03 Green M, Baker R J*, and White R P GLIM 3.77 GLIM 3 facilities and Information
13.02 Green M, Ekholm A* and Palmgren J Statistical modelling in models M1, M2, NLIN
drk_, LP, LPO, MEXT,
Fitting exponential family nonlinearGLIM in GLIM 3.77
14.06 Green M GLIM 3 facilities and
program
A GLIM demonstration DEMO Information
22.05 Green M, Francis B* and Clarke M GLIM 4 facilities and information
BOOT, WEIBULL
Model fitting applications in GLIM 4
23.03 Green M and Ekholm A* Statistical modelling in ETA, numerical derivativ
ADD, DER, using
Fitting nonlinear models in GLIM 4GLIM E1 - E3, FIRST, FIT
24.03 Green Mick TPROP
Converting Contingency Tables into Proportions
2.12 Griffiths P GLIM for ICL 1906s
Enhanced lookup 3 facilities and Information
23.05 Hakulinen T and Dyba T* intervals for Model-based cumulative relative M
MDE1,
ConfidenceSurvival analysis CON, CVAR, HVBI, HVB1,su
7.11 Hall S J GLIM 3 facilities and Information
Increasing the size of the data area in the Prime version of G
24.08 Hatzinger R and Friedl H* Statistical the factors for symmetry parameters in
A Note on generating modelling in GLIM
6.12 Healy M J R Survival estimation from censored
WOLY,
Maximum likelihood analysis WOL1, WOL2 normal data
3.05 Henstridge John D GLIM language?
A compiled GLIM language and data manipulation
9.05 Hutchison D Statistical modelling in GLIM M1-M4, WMU
CM, ETML, MEXT,
Ordinal variable regression using the McCullagh (Proportion
5.12 Jagger C* and Clayton D G regressionCENSOR, KERNAL, NOTIES, PLOT
model
Fitting CoxsSurvival analysis to censored survival data
6.03 Jagger C* and Clayton D G - Fitting analysis
Corrigenda Survival Coxs regression model to censored surv
1.02 Jan P*, Goddard M J, Kramar A and Wartelle M GLIM language and data manipulation
Letter to the editor
1.19 Jan P*, Goddard M J, Kramar A and Wartelle M Statistical statistic ANDM
ANDA,
The Anderson-Darlingmodelling in GLIM
11.15 Jansen W, Dessens J* and Luijkx R Statistical modelling in GLIM
COL, HOM, models
Fitting log-multiplicative associationH1 - H2, INIT, IT1 - IT2,
7.08 Jones F L* and Pittelkow Y E occupational mobility tables
Analysis of Statistical modelling in GLIMusing GLIM
2.1 Duncan-Jones Paul for binomial response models
efco
CoefficientsStatistical modelling in GLIM
22.11 Kateri M Statistical non-independence MSC, PSK, GLIM
KUTYL, in GLIM
Fitting asymmetry andmodellingMODELS, models with SKT
16.03 Kilpatrick S J tied ranks QUAD, REQL
Computing Statistical modelling in GLIM
24.07 Kilpatrick S J Statistical modelling of significance
X2POWER
The power of a Chi-squared testin GLIM
15.07 Kiiveri H T Statistical modelling in GLIM
EIGR, EIGT, M1-M4, SCH
Fitting some spatial correlation models with GLIM
1.02 Kramar A, Goddard M J, Jan P* and Wartelle M GLIM language and data manipulation
Letter to the editor
1.19 Kramar A, Goddard M J, Jan P* and Wartelle M Statistical statistic ANDM
ANDA,
The Anderson-Darlingmodelling in GLIM
19.08 Lawson A B Statistical distributions
INT, NWEI
The Von Mises distribution in GLIM
3.16 Leech J W GLIM and other systems
PDP-11 version of GLIM-3 under RSTS/E using FORTRAN
19.11 Leimer H G* and Rudas T GLIM and other systems
A note on the conversion between GLIM and BMDP-type log
21.03 Lindsey J K Statistical GLIM as log linear
Fitting distributions in modelling in GLIM models
Page 3
INDEX
11.15 Luijkx R, Dessens J* and Jansen W Statistical modelling in GLIM
COL, HOM, models
Fitting log-multiplicative associationH1 - H2, INIT, IT1 - IT2,
5.17 Macaskill P GLIM 3 size for and Information
Increasing the data facilities PDP-11 GLIM-3 under RT-11 VO
2.02 Macpherson I S Macros
Letter to the editor and the macro library
8.112 Maindonald J H GLIM GLIM-3
An introduction to 3 facilities and Information
5.11 Mathonet R, Delcourt C* and Mersch G Graphics
Log-linear models and lexical histograms
6.09 McCloud Philip I Statistical modelling in GLIM
MOJRA
Modified joint regression analysis for incomplete cariety x en
2.03 McCullagh Peter Some comments on professor Goldsteins article in the previ
2.08 McCullagh Peter Statistical modelling in GLIM
BOXCOX
A comparison of transformations of chimpanzee learning da
7.09 McCullagh P Statistical distributions
Approximating the hypergeometric likelihood
8.07 McDonald John W* and Diamond I Statistical modelling with linear inequality param
Fitting generalised linear modelsin GLIM
5.11 Mersch G, Delcourt C* and Mathonet R Graphics
Log-linear models and lexical histograms
18.05 Morgan B J T and Smith D M* Statistical modelling in GLIM
BPP, L1-L4, M3,
Extended models for Wadleys problem M4, M1X, M2X, NB1
9.11 Morris R W Survival analysis
On the application of Coxs proportional hazards model in GL
1.2 Nelder J A GLIM language and
Arithmetic with logical quantitiesdata manipulation
6.04 Nelder J A GLIM language and data manipulation
Letter to the editor
11.07 Nelder J A Macros and the macro macros
Notes on the construction of GLIM library
11.09 Nelder J A Macros andBRCK, LFIL, LGO, QI, QUAD, RFIL,
the macro library
GLIM 3.77 macros for univariate optimization of an arbitrary
18.02 Nelder J A Statistical modelling inGLMs
BFI_, and GLIM
Generalised additive models BFVF_, FIT, F3, GAMH, GETF
20.07 Nelder J A Statistical modelling in GLIM, Algorithms and com
by quadratic interpolation
Function maximization BRCK, GET1, GET2, QI, QIH, QUAD
20.1 Nelder J A Statistical modelling in GLIM GET2, GO, HELP
BRCK, FN, GET1,
Intervals based on profile likelihood for GLMs
12.05 OBrien C M the GLIM / IKBS initiative
One view ofGLIM and other systems (GLIMPSE)
13.06 OBrien C M method COL_, DRI, goodness-of-fit
A graphical Graphicsof checking theIMP_, IM_, PIT_of an inv
14.09 OBrien C M Statistical modelling concomitant EV9, EXP, MC
BTV, of in GLIM
Simultaneous transformationCV1-CV9, EV1 -variables in GL
14.1 OBrien C M Graphics egp
A note on the simultaneous plotting of vectors of unequal len
15.1 OBrien C M Statistical modelling in GLIM
Correlated observations with normal error : a rejoinder
22.12 OBrien C use in studies of tropical rainforest ecosystems
KLWN
A macro forStatistical modelling in GLIM, Macros and the ma
23.06 OBrien C Macros indices macro library
the of FIID, GRCD, quadrat counts
macros to calculateandCBQ_, dispersion forINCF, INCL, INM
13.02 Palmgren J, Ekholm A* and Green M Statistical modelling in models M1, M2, NLIN
drk_, LP, LPO, MEXT,
Fitting exponential family nonlinearGLIM in GLIM 3.77
18.07 Palmgren J and Ekholm A* models for an ordinal GLIM
RegressionStatistical modelling inresponse are best handled
15.08 de Paula Statistical GLIMMULT
MU,
Projected residuals in modelling in GLIM
1.13 Payne C Statistical modelling in GLIM
Estimation of the parameters in the log-linear model using th
1.15 Payne C calculating the macro degrees
the
A macro forMacros andCDFcorrect library of freedom for a
3.11 Payne C GLIM language and
Leading and lagging in GLIM data manipulation
22.03 Payne C and Gilchrist R * GLIM of GLIM 4
The new facilities 4 facilities and information
5.16 Payne R W comparison with prism aov facilities
Comment - Analysis of designed experiments
6.08 Peacock S D and Roger J H* Survival analysis
ML1-ML4, SETL
Fitting the scale as a GLIM parameter for Weibull, extreme v
7.05 Pettitt A N Survival analysis ICER, LFIT, models to cens
IBER,
Fitting generalised logistic and log-logistic LPLT, LRUN
13.04 Pettitt A N* and bin Daud I Survival analysis EXP2, ITER, STAG, STDK
EXPD,
Fitting student - t distributions to censored data using GLIM
3.12 Pierce C B, Baker R J* and Pierce J M Statistical controls
Wadleys problem withmodelling in GLIM
3.12 Pierce J M, Baker R J* and Pierce C B Statistical controls
Wadleys problem withmodelling in GLIM
19.09 de Pinho Sheila Zambello Analysis with missingexperiments - a solution u
designed LIN1, LIN2,
The split-plot design of ERA, ERB,observations RESUL, SPL
7.08 Pittelkow Y E and Jones F L* occupational mobility tables
Analysis of Statistical modelling in GLIMusing GLIM
11.16 Pittelkow Y E and Wilson S R* Statistical modelling indata analysis and the ma
ERROR, GLIM, Macros
Macros for stratified case-control INITIALISE, MINF, MODFIT
11.18 Porteous B T and Altham P M E* Statistical modelling in GLIM
Markov chains : reversibility, equilibrium and GLIM
23.04 Portides G* and Gilchrist R Statistical modelling in GAMMA, INVGAU, MEST
4
resistant fitting in GLIMBINOMIAL,GLIM
6.11 Pregibon Daryl An alternative covariance estimated for generalised linear m
1.02 Reese R A GLIM language and data manipulation
Letter to the editor
1.09 Rese R A Macros andBREAKDOWN, CODEBOOK, COND
at macro library
The GLIM macro library thethe University of Hull
2.02 Reese R A Book reviews conference reports, etc
Letter to the editor
2.07 Reese R A Macros andBREAKDOWN, CODEBOOK,Hull
the macro the University of COND
Status of the GLIM macro library atlibrary
2.13 Reese R A GLIM the implementation of GLIM
Considerations in 3 facilities and Information
3.02 Reese R A Macros
Letter to the editor and the macro library
Page 4
INDEX
3.04 Reese R A Macros and the macro the University of Hull
Status of the GLIM macro library atlibrary
3.07 Reese R A GLIM language
Matrix definition in GLIM-4 and data manipulation
5.14 Reese R A matrix operations in GLIM-3 and -4
Comment - GLIM language and data manipulation
8.04 Reese R A* and Richardson M G GLIM-3A GLIM 3 facilities and Information
8.1 Reese R GLIM 3 under SAUF77
GLIM on the Harris facilities and Information
9.04 Reese R A Macros andBREAKDOWN, CHIP, CODEBOOK,
the macro the University of Hull
Status of the GLIM macro library atlibrary
9.12 Reese R A Statistical modelling in GLIM
A logistic model for acne scarring and its interpretation
18.08 Reese A Book reviews conference reports, etc
A cumulative index for GLIM newsletters
1.04 Richardson M G GLIM 3 facilities and Information
GLIM-3 status report
2.05 Richardson M G GLIM 3 facilities and Information
GLIM-3 status report
2.15 Richardson M G GLIM 3 facilities and Information
Dumping on ICL 1906s
3.03 Richardson M G GLIM 3 facilities and Information
GLIM-3 status report
4.03 Richardson M G GLIM 3 facilities and Information
GLIM-3 status report
5.04 Richardson M G GLIM 3 facilities and Information
GLIM-3 status report
7.03 Richardson M G GLIM 3 facilities and Information
GLIM-3 status report
8.03 Richardson M G GLIM 3 facilities and Information
GLIM-3 status report
8.04 Richardson M G and Reese R A GLIM-3A GLIM 3 facilities and Information
12.1 Richardson M G Statistical
The birthday problem modelling in GLIM
20.06 Ridout M S Statistical modelling in GLIM
Non-convergence of Fishers method of scoring - a simple ex
20.09 Rigby Bob and Stasinopoulos Mikis* Statistical modelling in FITF, FITM, FIT0, FIT1,
ADD, polynomials with one break po
GLIM macros to fit piecewiseALLL,GLIM, Macros and the ma
24.06 Rigby Bob and Swan Tony* GLIM 4 facilities and information, Statistical mod
safety in space
Logistic regression andBDN_, BD1_, CHITST, GTX_, LEVI,
19.04 Ripley B D* and Gettinby G Book reviews Book reviews conference reports, etc
6.08 Roger J H* and Peacock S D Survival analysis
ML1-ML4, SETL
Fitting the scale as a GLIM parameter for Weibull, extreme v
7.04 Roger J H Statistical modelling in GLIM
ERR, linear M1-M5, N1-N3, N8, erro
Composite link functions withERR2, log link and Poisson STA
11.1 Roger J H Survival analysis
ML1-ML4, SETL
Using factors when fitting the scale parameter to Weibull and
16.06 Roger J H and Collett D* Statistical modelling in GLIM
ARES, BC1_, BC2_, BHAT, BIN_, CO
Computation of generalised linear model diagnostics
5.08 Royston P Statistical or power-normal PERR
LERR, M1-M4, errors
Fitting with lognormal modelling in GLIM
6.15 Royston Patrick Macros and the macro library
Documenting GLIM-3 macros
19.11 Rudas T and Leimer H G* GLIM and other systems
A note on the conversion between GLIM and BMDP-type log
7.1 Saunders D I versus model
InteractionsStatistical modelling in GLIM
15.06 Scallan A J Analysis of DR, measurements Statistical mo
designed experiments,
A GLIM model for repeated dyvar, FV, GVAL, ITER, MLOG,
18.04 Scallan A J* and Evans S J W Statistical modelling in GLIM data FV, ITER,
DR, err1, ERR2,
Fitting truncated distributions to groupedFITM,using GLIMLO
20.08 Scallan A J Macros andCPE1-CPE3, CPLOT, CPL1, SYM, X
the macro library, Graphics
GLIM macros for contour plots
22.07 Scallan A J Macros Information for contributors
GLIM macro library:and the macro library
24.02 Scallan A J Macros andCOVARS, FITM, FIT1, FIT2,FVDR, M
the macro library
GLIM Macro Library: Request for Contributions
24.05 Scallan A J Survival analysis
Fitting mixture models to survival data
9.06 Scallon C V Statistical processes in GLIM
Fitting autoregressive modelling in GLIM
14.08 Scallon T Graphics an example of the PASS facility
Contour plots in GLIM :RGO
21.07 Schafer Uwe, Bergmann Eckardt and Busse Horst* GLIM language and data manipulation
Dependency of GLIM run times on input compression degre
4.09 Schektman Y GLIM and other using the
Setting up a program librarysystems GLIM and LAPMUS s
6.1 Schilling Judith M and Chambers John M* GLIM and other systems
S and GLIM : an experiment in interfacing statistical systems
11.17 Seneviratne K and Coe R* and Survival analysis MDERIV, MFIT, MVAR
MDEV,
Fitting generalised linear models to censored data
11.05 Shea B L GLIM and other systems
The role of a subroutine library for statisticians
4.05 Slater M Statistical modelling in GLIM CONTINUE, G1-G
ALL, COMPLETE,
A GLIM program for stepwise analysis
18.05 Smith D M* and Morgan B J T Statistical modelling in GLIM
BPP, L1-L4, M3,
Extended models for Wadleys problem M4, M1X, M2X, NB1
17.08 Stasinopoulos M Statistical modelling in
BPDE, CPDE, FITC, FITU, MODE, X
Using GLIM to fit Split-line curves GLIM
19.1 Stasinopoulos M Statistical modelling in GLIM
fiducial
Relative potencies and FIDU limits in GLIM
20.03 Stasinopoulos Mikis Statistical modelling in models
Correction to Generalised additive GLIM and GLMs
20.09 Stasinopoulos Mikis* and Rigby Bob Statistical modelling in FITF, FITM, FIT0, FIT1,
ADD, polynomials with one break po
GLIM macros to fit piecewiseALLL,GLIM, Macros and the ma
22.08 Stasinopoulos M* and Francis B GLIM 4 facilities in information,HELPGAM, INIT
FGAMOD, gamiter_, Statistical mod
Generalised additive modelsandGLIM 4
24.04 Stasinopoulos Mikis and Tran Giao* Graphics CALC_PAR, FITGAM, gamplots, MIN
Plotting additive fits in generalised additive models
3.15 Stuart Michael Survival analysis, Macros and the macro library,
Macro BOXCOX, incorporating a graphical procedure for es
Page 5
INDEX
4.08 Swan A V Graphics SVL4
Plotting several survival curves
6.13 Swan A V Statistical modelling in
LINC,
Linear contrasts in GLIM-3 TRICGLIM
9.15 Swan A V GLIM and other systems
Data analysis with Minitab and GLIM
11.11 Swan A V Statistical modelling in GLIM
Fitting linear models by maximum likelihood methods to grou
16.05 Swan A V Survival analysis DEL_, LFTS, LR2, LT1_, LT2
MED_,
Lifetables and survival curves in GLIM
17.04 Swan A V statistics from grouped data
gmv2
Descriptive GLIM language and data manipulation
17.05 Swan A V Log-linear modelling for contingency tables
20.11 Swan A V* and Vadera P Graphics ELFT, ESC
Graphical comparison of the mortality in a sample of individu
22.04 Swan A V GLIM 4 facilities and information
The GLIM 4 manual
22.06 Swan A V * and Francis B GLIM 4 facilities and information, Statistical mod
GLIM 4
Medical applications in COVARS, COXMODEL, LOGLO
22.09 Swan A V random allocation AD_, ALLOC, ASQ_,
AD0_, schemes in GLIM
Generating GLIM language and data manipulation MAG1, M
23.02 Swan A V and Andrews N J * GLIM 4 facilities and information
GRAPHS, NNCA, GLIM 4
A sample size investigation system using TABLES
24.06 Swan Tony* and Rigby Bob GLIM 4 facilities and information, Statistical mod
safety in space
Logistic regression andBDN_, BD1_, CHITST, GTX_, LEVI,
9.1 Thompson S*, Francis B J and Whittaker J C GLIM GLIM in an interactive environment
Teach - yourself -and teaching
5.09 Thomson R and Burn R* calculatingCOVA, MUL1-MUL4 of and the of
the covariance matrix
A macro forStatistical modelling in GLIM, Macrosfunctionsma
24.04 Tran Giao* and Stasinopoulos Mikis Graphics CALC_PAR, FITGAM, gamplots, MIN
Plotting additive fits in generalised additive models
16.04 Treat B R and Firth D* ADDSQUARE,
Square contingency tables and GLIM MHOM, QSYM, SETU
20.11 Vadera P and Swan A V* Graphics ELFT, ESC
Graphical comparison of the mortality in a sample of individu
11.13 Vanderhoeft C Macros andCOVA, MUL5-MUL7, PADE
the macro library
Macros for calculating the covariancematrix of functions of p
1.02 Wartelle M, Goddard M J, Jan P* and Kramar A GLIM language and data manipulation
Letter to the editor
1.19 Wartelle M, Goddard M J, Jan P* and Kramar A Statistical statistic ANDM
ANDA,
The Anderson-Darlingmodelling in GLIM
7.12 Wasniewski J GLIM 3 facilities and Information
Univac 1100 GLIM-3 executive system level 37
9.02 Webb Janet GLIM 3 facilities and Information
GLIM 3 status report
4.07 Weber Neville Macros andDEV, TE1-TE3
the macro library
A GLIM macro to test for marginal homogeneity in contingen
9.03 White R P, Baker R J* and Green M GLIM 3.77 GLIM 3 facilities and Information
4.04 Whitehead John and Bennett Steve* Survival analysis regression models to censored
IBER,
Fitting logistic and log-logistic ICER, ICTU, IFIN, LPLT, LRUB
5.021 Whitehead John and Bennett Steve* Fitting analysis
Corrigenda:Survivallogistic and log-logistic regression model
8.08 Whitehead John Statistical modelling in GLIM
FITT, WCOND, WINF
Fitting stratified case-control models using GLIM-3
9.1 Whittaker J C, Francis B J and Thompson S* GLIM GLIM in an interactive environment
Teach - yourself -and teaching
11.06 Whittaker J and Francis B* Book reviews conference 85 conference at Lanc
Generalised linear models : the GLIMreports, etc
18.06 Williams D A tests for overdispersed generalised linear model
Hypothesis Statistical modelling in GLIM
11.16 Wilson S R* and Pittelkow Y E Statistical modelling indata analysis and the ma
ERROR, GLIM, Macros
Macros for stratified case-control INITIALISE, MINF, MODFIT
17.09 Winstanley D AUTY, AXES, CNTL, FNOR,
A GLIM 3.77 / NAG graphical supplement interface FREQ,
5.03 Wood John Statistical modelling in GLIM
Letter to the editor
15.09 Worsley K J Statistical distributions
Inverse distributions, censored data and GLIM
25.03 Lindsey J K comparing AIC, AICNB, AICNB, SPW_, BD_, PD
models in GLIM
The AIC forStatistical modellingin GLIM
25.04 de Falguerolles A and Francis B Statistical in GLIM
BILIN, GRCML, CAML, CAMLLINK, C
Fitting bilinear modelsmodelling in GLIM
25.04 Francis B and de Falguerolles A Statistical in GLIM
BILIN, GRCML, CAML, CAMLLINK, C
Fitting bilinear modelsmodelling in GLIM
25.05 Weixlbaumer E Statistical of freedom GLIM
UEP, CALC, LOOP1 through LOOP9
calcilation of Degrees modelling inin sparse contingency tabl
25.06 Dyba T and prediction intervals for disease incidence us
PRO, MDE1, MDER, MWE1, MWEI,
ConfidenceStatistical modelling in GLIM
25.07 Scallan A J Statistical distributions, macros and the INIT, lib
FITM, FVDR, distributions
Macros for fitting two-piece toleranceNORM, LOGI, macroNE
25.08 Aitkin Murray and Francis B Statistical modellingthrough models REMZ, REM
MASS1 in GLIM
Fitting overdispersed generalized linear MASS6, by nonparam
25.08 Francis B and Aitkin M Statistical modellingthrough models REMZ, REM
MASS1 in GLIM
Fitting overdispersed generalized linear MASS6, by nonparam
25.09 Lindsey J K GLIM and other systems
Generalized linear models in Lisp-Stat
26.02 Green M and Hinde J GLIM MACLIST, TRMAC, GENLTEXT, MA
Hints and Tips 4 Facilities and information
26.02 Hinde J AND Green M GLIM MACLIST, TRMAC, GENLTEXT, MA
Hints and Tips 4 Facilities and information
26.03 Scallan A J Macros andALL MACROS
update
The GLIM macro library macro library IN LIBRARY - EMAIL
26.04 Hinde J Macros andODBIN, library, Statistical modelling in
macro WILLIAMS
Macros for fitting overdispersion models
26.05 Wolfe R Macros andORDINAL
macro library, to an ordinal response
General purpose macros to fit models Statistical modelling in
26.06 Farrington C P Statistical modelling in Fits OLD, predictor bo
breakdown: NEW,
Models on the edge of CLOSEFIT,GLIMclose to PRED, LOG
26.07 Green M Macros andproportional scaling macro for log-line
macro MODEL, IPSFIT
An automated iterative INTER, library, Statistical modelling in
26.08 Vanderhoeft C GLIM and other systems
GLIM DECO: Fitting multi-linear regression models in GLIM
Page 6
INDEX
27.02 Green M with and tips
RegressionHintsmany covariates
27.02 Green M Hints
Recoding data and tips
27.02 Hinde J Hints and tips
On-line documentation
27.03 Scallan A J deletion diagnostics using GLIM
MASK, DISP_RES,
Calculating Statistical modelling in GLIM MULTBETA, MU
27.04 Lindsey J K Statistical modellingVTINE,
POINT, in GLIM
Macros for fitting counting processes COVAR, MRES, ITET
27.05 Demetrio C G B and Hinde J Graphics HNP_ARGS, HNP, ENVELOPE, OD_
Half-Normal plots and overdispersion
27.05 Hinde J and Demetrio C G B Graphics HNP_ARGS, HNP, ENVELOPE, OD_
Half-Normal plots and overdispersion
27.06 Dittrich R, Hatzinger R and Katzenbeisser W Statistical modelling in GLIM
PAIRCOMP, GLIM
Fitting paired comparison models in INIT, DEF_RESP, DEF_
27.06 Hatzinger R, Dittrich R and Katzenbeisser W Statistical modelling in GLIM
PAIRCOMP, GLIM
Fitting paired comparison models in INIT, DEF_RESP, DEF_
27.06 Katzenbeisser W, Dittrich R and Hatzinger R Statistical modelling in GLIM
PAIRCOMP, GLIM
Fitting paired comparison models in INIT, DEF_RESP, DEF_
10.03 Iyer Rajaram Statistical modelling in GLIM
Continuation-Odds Model in Ordinal Variable Regression
10.04 Whittaker Joe Statistical modelling in GLIM
Transforming to the Additive Elements of the Deviance
10.05 Breen Richard Statistical modelling FIT, DROP, LAST
START, in GLIM
Log-Multiplicative Models for Contingency Tables using GLIM
10.06 Smith D M Statistical modelling in andBCML,
TR1-3, MO1-4,
Single Parameter TransformationsGLIMGLIM NP, SP, CP, P
10.07 Wilson S R Statistical modelling in GLIM
Analysing Case-Control Data in GLIM
10.08 Gilchrist R or retrospective models for
ProspectiveStatistical modelling in GLIMretrospective case-c
10.09 Reese R A Average Ranks for Ties
Computing Statistical modelling in GLIM
10.1 McCullagh P Statistical modelling Conditional Cumulants of P
CUMPS
Macro to Calculate Approximatein GLIM
10.11 Gherardini P Graphics
Smoothing ScatterplotsSMOOTH, LWF, ITER, RBW, LFIT, B
10.12 Kikuchi D A of Your Iterations
Keep TrachStatistical modelling in GLIM
28.02 Scallan A J Statistical modelling in GLIM, Macros and macro
STEPBACK, STEPFRWD
GLIM macros for stepwise regression
28.03 Hinde J Hints and tips
WALD,
Matrix tricks and the Wald test INVERT
28.04 Dittrich R, Hatzinger R and Katzenbeisser W Statistical modelling in in GLIM (II): Subject and o
EXTEST, GLIM
Fitting paired comparison modelsPAIRCOMP, INIT, DEF_TA
28.04 Hatzinger R, Katzenbeisser W and Dittrich R Statistical modelling in in GLIM (II): Subject and o
EXTEST, GLIM
Fitting paired comparison modelsPAIRCOMP, INIT, DEF_TA
28.04 Katzenbeisser W, Dittrich R and Hatzinger R Statistical modelling in in GLIM (II): Subject and o
EXTEST, GLIM
Fitting paired comparison modelsPAIRCOMP, INIT, DEF_TA
Page 7
INDEX
utational methods
, MODEL, RESPLOTS
cros and the macro library
ence reports, etc
, NNCA, TABLES
EL, MELD, MVAR
data manipulation
data manipulation
OVIN, AOVOUT, DOL, EXCL, IN2, NULLFIT, OUT2, REFT, SETO, SETW, TERMS, TRM2, TSTAT, TSTB, TSTH
EPR, FITQ, LINP, MADE, QSOD, SCALE, UP, WARN
ER, ICTU, IFIN, LPLT, LRUB, LTPLT
RS2, CHIS, OVER,OVRS, PLGD
OD1, POD2, YCAL
data manipulation
data manipulation
ence reports, etc
OEF, DELE, ERR1 -ERR5, OPWT, ORTH, PERR, POL, PTR1, PTRN
data manipulation
data manipulation
ence reports, etc
Page 8
INDEX
, KERNAL, NOTIES, PLOT, REARRANGE, SORT, TETS, TIES
data manipulation
W4, M1, SETW
DERIV, MFIT, MVAR
, MESJ, PRWT, TCAL, TRANS
C1_, BC2_, BHAT, BIN_, COOK, DRES, END_, ERR_, IC_, INFL, LEV, PWT_, SD_, SRES, VF_
XP2, ITER, STAG, STDK
ORR, COR2, COR3, DMY2. DMY3, HEI, HELP, LK, LK3, NINP, SSP, SSP4, UIT
X, PRNT, TAB1-TAB4
iate analysis in GLIM-3 . Macros for tabulation.
data manipulation
M, H1 - H2, INIT, IT1 - IT2, ROW, ST1, ST2
ON, CVAR, HVBI, HVB1, MDER, MDE1, MWEI, MWE1, VBIN, VBI1, VBI2, VDER, VDE1
contingency tables
, LPO, MEXT, M1, M2, NLIN
R, ETA, E1 - E3, FIRST, FIT, LP, M1 - M4, NDER, NI2, NLIN, PROB, VAR, WRITE
ERR2, FITM, FV, ITER, LOGI, MEXT, MULT, NGAM, NORM, TIDY, VA, WVAR
QS, INDQ, PRP,QIND, QSYM, RPR, SET1, SET2, SYMQ
ARE, MHOM, QSYM, SETUP, SYMM, QSYM
M0, M1, M2, STAR, TREV, VA
,MODEL, RESPLOTS
cros and the macro library
ence reports, etc
for contributors
, COXMODEL, LOGLO
, gamiter_, HELPGAM, INIT, METH1_, METH2_, RESET, SMOOTH, star_smo
ence reports, etc
Page 9
INDEX
data manipulation
ence reports, etc
ence reports, etc
ence reports, etc
L, GAMMA, INVGAU, MEST, NORMAL, NORMCONT, NULL, ORDER, PHI, POISSON,, RESIST, WTS
data manipulation
PT3, T1, TDFP, TEV,TEVE, TOD, TODD, TP, TPDF, TPP
LPO, MEXT, M1, M2, NLIN
R, ETA, E1 - E3, FIRST, FIT, LP, M1 - M4, NDER, NI2, NLIN, PROB, VAR, WRITE
ON, CVAR, HVBI, HVB1, MDER, MDE1, MWEI, MWE1, VBIN, VBI1, VBI2, VDER, VDE1
data manipulation
L, MEXT, M1-M4, WMU
, KERNAL, NOTIES, PLOT, REARRANGE, SORT, TETS, TIES
data manipulation
M, H1 - H2, INIT, IT1 - IT2, ROW, ST1, ST2
MODELS, MSC, PSK, SKT
GT, M1-M4, SCH
data manipulation
Page 10
INDEX
M, H1 - H2, INIT, IT1 - IT2, ROW, ST1, ST2
oldsteins article in the previous issue
L4, M3, M4, M1X, M2X, NB1, NEXT
data manipulation
data manipulation
FIL, LGO, QI, QUAD, RFIL, SUQI
VF_, FIT, F3, GAMH, GETF, GVF_, INCF, LPO, NFV_, PLC_, PLN_, PL10_, PL1_, PL_, PRS_, PVF_, SU
ET1, GET2, QI, QIH, QUAD, RBRCK, RGO
N, GET1, GET2, GO, HELP, PRIN, QI, QUAD, RBRCK, RGO, SUQI
RI, IMP_, IM_, PIT_
-CV9, EV1 - EV9, EXP, MCA, PLM
D, GRCD, INCF, INCL, INMC, INPA, MMOI
LPO, MEXT, M1, M2, NLIN
data manipulation
ER, LFIT, LPLT, LRUN
XP2, ITER, STAG, STDK
B, LIN1, LIN2, RESUL, SPLIT, TEX1-TEX4, TREA, TREB, TXBLOCK, TXINTER
INITIALISE, MINF, MODFIT
L, GAMMA, INVGAU, MEST, NORMAL, NORMCONT, NULL, ORDER, PHI, POISSON,, RESIST, WTS
ted for generalised linear models
data manipulation
OWN, CODEBOOK, CONDESCRIPTIVE, CROSSTABS, RECODE
ence reports, etc
OWN, CODEBOOK, CONDESCRIPTIVE, CROSSTABS, HISTOGRAM, RECODE, TNORMAL, TUNIFORM
Page 11
INDEX
data manipulation
data manipulation
OWN, CHIP, CODEBOOK, CONDESCRIPTIVE, CROSSTABS, HISTOGRAM, RECODE, SMOOTH, STEM, TNORMAL, VBIN, VBI1, VBI2,
ence reports, etc
L, FITF, FITM, FIT0, FIT1, FIT3, ITE1 - ITE3, MODE, PRI1 - PRI3, XVAR
D1_, CHITST, GTX_, LEVI, LEVX, LEVY, LEV_HELP
ence reports, etc
R2, M1-M5, N1-N3, N8, STAR
C1_, BC2_, BHAT, BIN_, COOK, DRES, END_, ERR_, IC_, INFL, LEV, PWT_, SD_, SRES, VF_
, FV, GVAL, ITER, MLOG, NOFF, NPHI, PROBS, TIDY, VA
ERR2, FITM, FV, ITER, LOGI, MEXT, MULT, NGAM, NORM, TIDY, VA, WVAR
E3, CPLOT, CPL1, SYM, XDIR, XDI1, YDIR
, FITM, FIT1, FIT2,FVDR, MEXT, VADI
data manipulation
DERIV, MFIT, MVAR
MPLETE, CONTINUE, G1-G10, INITIALISE, MAX, START, UPDATE, V1-V10
L4, M3, M4, M1X, M2X, NB1, NEXT
PDE, FITC, FITU, MODE, XVAR
L, FITF, FITM, FIT0, FIT1, FIT3, ITE1 - ITE3, MODE, PRI1 - PRI3, XVAR
, gamiter_, HELPGAM, INIT, METH1_, METH2_, RESET, SMOOTH, star_smo
AR, FITGAM, gamplots, MIN_MAX, MULTIP_GRAPH, plot_gra, plot_gr1, prompt_p, save_pos, single_graph, which_res
cros and the macro library, Graphics
Page 12
INDEX
EL_, LFTS, LR2, LT1_, LT2_, NMND
, COXMODEL, LOGLO
_, ALLOC, ASQ_, MAG1, MSG2
, NNCA, TABLES
D1_, CHITST, GTX_, LEVI, LEVX, LEVY, LEV_HELP
AR, FITGAM, gamplots, MIN_MAX, MULTIP_GRAPH, plot_gra, plot_gr1, prompt_p, save_pos, single_graph, which_res
ARE, MHOM, QSYM, SETUP, SYMM, QSYM
UL5-MUL7, PADE
data manipulation
ER, ICTU, IFIN, LPLT, LRUB, LTPLT
ence reports, etc
INITIALISE, MINF, MODFIT
XES, CNTL, FNOR, FREQ, FRQS, FRQ1, GMESS, GREX, GROUT, HEADER, HELP, HIST, HLP2, LENG, PIE, SCAT, START, TEXT, TITL
NB, AICNB, SPW_, BD_, PD_, CD_, LD_, ND_, GD_, ID_
RCML, CAML, CAMLLINK, CAINIT
RCML, CAML, CAMLLINK, CAINIT
LC, LOOP1 through LOOP9, PRTPARAM
E1, MDER, MWE1, MWEI, CVAR, VDER, PRI
DR, NORM, LOGI, INIT, NEXT, FVDR1
hrough MASS6, REMZ, REMNP, REFITZ, REFITNP, SRARTUP, SETUP, INITIAL, WEI, DEV, EMZ, EMNP, SDMAC, BINOTIDYUP
hrough MASS6, REMZ, REMNP, REFITZ, REFITNP, SRARTUP, SETUP, INITIAL, WEI, DEV, EMZ, EMNP, SDMAC, BINOTIDYUP
, TRMAC, GENLTEXT, MAKEGINT, INTERACT, EDCOM
, TRMAC, GENLTEXT, MAKEGINT, INTERACT, EDCOM
ROS IN LIBRARY - EMAIL SUPPORT@NAG.CO.UK FOR MORE DETAIL
T, NEW, OLD, PRED, LOGBIN, IDBIN, IDPOIS
ODEL, IPSFIT
Page 13
INDEX
SP_RES, MULTBETA, MULT_2, GETH, MULT_ HIR_, IJ_VAL
TINE, COVAR, MRES, ITET_, ITEC
GS, HNP, ENVELOPE, OD_F, OD_S
GS, HNP, ENVELOPE, OD_F, OD_S
MP, INIT, DEF_RESP, DEF_LP, FIT_BASIC
MP, INIT, DEF_RESP, DEF_LP, FIT_BASIC
MP, INIT, DEF_RESP, DEF_LP, FIT_BASIC
IT, DROP, LAST
O1-4, BCML, NP, SP, CP, PFIT, PLOT, SET, MC1-4, ZP, FIT
, LWF, ITER, RBW, LFIT, BWF, CWF, UWF, RBP
CK, STEPFRWD
PAIRCOMP, INIT, DEF_TABL, DEF_RESP, DEF_LP, FIT_BASIC
PAIRCOMP, INIT, DEF_TABL, DEF_RESP, DEF_LP, FIT_BASIC
PAIRCOMP, INIT, DEF_TABL, DEF_RESP, DEF_LP, FIT_BASIC
Page 14
INDEX
Page 15
INDEX
Page 16
INDEX
Page 17
INDEX
Page 18
INDEX
TEM, TNORMAL, VBIN, VBI1, VBI2, VDER, VDE1, TUNIFORM
raph, which_res
Page 19
INDEX
raph, which_res
NG, PIE, SCAT, START, TEXT, TITLE, TRAO, YAX
NP, SDMAC, BINOTIDYUP
NP, SDMAC, BINOTIDYUP
Page 20