NICE LIST OF LECTURES ON
REGRESSION AND ANOVA BY Dr Pia Veldt Larsen, Uni of Southern Denmark, Odense
- including regression model selection, residual analysis, factor analysis, logistic regression, and topics like transformation of variables etc.
pondelok 18. augusta 2008
streda 6. augusta 2008
statistics lectures, python, spss, R
a FANTASTIC COLLECTION of lectures on statistics - from basic concepts to advanced topics such as nonlinear regression. with examples.
- refers mainly to SPSS software, but this is marginal. MAINLY the mechanics behind the different tests is explained. VERY INSIGHTFUL.
dr FRIEL
******
seems like INTRO / TUTORIAL on the following topics.
ON PYTHON AND SPSS
on R - the open source statistical program "R"
- refers mainly to SPSS software, but this is marginal. MAINLY the mechanics behind the different tests is explained. VERY INSIGHTFUL.
dr FRIEL
******
seems like INTRO / TUTORIAL on the following topics.
ON PYTHON AND SPSS
on R - the open source statistical program "R"
utorok 29. apríla 2008
weighted least square regression
A technique for correcting the problem of heteroskedasticity by log-likelihood estimation of a weight that adjusts the errors of prediction
if some assumptions of ordinarly least square OLS regression do not hold.
.... in case of heteroscedasticity
.....unequal precision/reliability of datapoints
1. san Houston Uni - Weighted least squares regression XXX
http://www.shsu.edu/~icc_cmf/cj_789/weightedLeastSquares2.doc
- very good and detailed review of the technique, from the description of situations when to use WLS /ie if violation of homoscedasticity/ to detailed and very clear examples how to perform it /mainly applies to SPSS, the output is provided/.
- discusses both approaches on calculating weights 1/residualising the response variable 2/log-likelihood estimation of weights
- includes some basic mathematics, mostly well comprehensible college-level, formulas perfectly support the statements in text
/dr Charles M. Friel - author of this text provides lecture notes to a wide selection of statistical topics, comparable to Garson's Statnotes, in some topic I found dr Friel's explanations more poignant.
http://www.shsu.edu/~icc_cmf/directory.htm
2. statnotes - garson -including exemplification on SPSS ooutput - insightful, but not much regarding the mechanics of weight estimation / but maybe not so important/.
3. NIST Handbook - nice figures, comparison of WLS with alternative of nonlinear transformation of response and predictor variables.
if some assumptions of ordinarly least square OLS regression do not hold.
.... in case of heteroscedasticity
.....unequal precision/reliability of datapoints
1. san Houston Uni - Weighted least squares regression XXX
http://www.shsu.edu/~icc_cmf/cj_789/weightedLeastSquares2.doc
- very good and detailed review of the technique, from the description of situations when to use WLS /ie if violation of homoscedasticity/ to detailed and very clear examples how to perform it /mainly applies to SPSS, the output is provided/.
- discusses both approaches on calculating weights 1/residualising the response variable 2/log-likelihood estimation of weights
- includes some basic mathematics, mostly well comprehensible college-level, formulas perfectly support the statements in text
/dr Charles M. Friel - author of this text provides lecture notes to a wide selection of statistical topics, comparable to Garson's Statnotes, in some topic I found dr Friel's explanations more poignant.
http://www.shsu.edu/~icc_cmf/directory.htm
2. statnotes - garson -including exemplification on SPSS ooutput - insightful, but not much regarding the mechanics of weight estimation / but maybe not so important/.
3. NIST Handbook - nice figures, comparison of WLS with alternative of nonlinear transformation of response and predictor variables.
piatok 11. apríla 2008
relationship between
median, mean, mode
in skewed distributions
violations in discrete distributions and multimodal continuous ADVANCED, but accessible
by Hippel 2005 , journal of statistical education
transformations
Transformations
Statnotes - transformations - as part of testing assumptions, by Garson
- very nice, some pictures
Dallal's example of transformation in linear REGRESSION
RULE of THUMB: first transform the response variable y to correct heteroscedasticity (heterogeneity of variance) than apply transformation to predictiors to achieve linear relationship (bivariate scatterplot)
similar recommendations by NIST
Transformations:
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
NIST handbook of statistics
http://www.itl.nist.gov/div898/handbook/index.htm
from STATNOTES: To correct left (negative) skew, first subtract all values from the highest value plus 1, then apply square root, inverse, or logarithmic transforms.
Journal
chass
journal of statistical teaching and education ???
Statnotes - transformations - as part of testing assumptions, by Garson
- very nice, some pictures
Dallal's example of transformation in linear REGRESSION
RULE of THUMB: first transform the response variable y to correct heteroscedasticity (heterogeneity of variance) than apply transformation to predictiors to achieve linear relationship (bivariate scatterplot)
similar recommendations by NIST
Transformations:
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
NIST handbook of statistics
http://www.itl.nist.gov/div898/handbook/index.htm
from STATNOTES: To correct left (negative) skew, first subtract all values from the highest value plus 1, then apply square root, inverse, or logarithmic transforms.
Journal
chass
journal of statistical teaching and education ???
sobota 29. marca 2008
2 factor anova
from Dallals page Little Handbook of Statistical Practice
dairy farmer wished to determine which type of feed will produce the greatest yield of milk. From the research literature she is able to determine the mean milk output for each of the breeds she owns for each type of feed she is considering. As a practical matter, she can use only one type of feed for her herd.
Since she can use only one type of feed, she wants the one that will produce the greatest yield from her herd. She wants the feed type that produces the greatest yield when averaged over all breeds, even if it means using a feed that is not optimal for a particular breed. (In fact, it is easy to construct examples where the feed-type that is best on average is not the best for any breed!) The dairy farmer is interested in what the main effects have to say even in the presence of the interaction. She wants to compare
where the means are obtained by averaging over breed.
For the sake of rigor, it is worth remarking that this assumes the herd is composed of equal numbers of each breed. Otherwise, the feed-types would be compared through weighted averages with weights determined by the composition of the herd. For example, suppose feed A is splendid for Jerseys but mundane for Holsteins while feed B is splendid for Holsteins but mundane for Jerseys. Finally, let feed C be pretty good for both. In a mixed herd, feed C would be the feed of choice. If the composition of the herd were to become predominantly Jerseys, A might be the feed of choice with the gains in the Jerseys more than offsetting the losses in the Holsteins. A similar argument applies to feed B and a herd that is predominantly Holsteins.
dairy farmer wished to determine which type of feed will produce the greatest yield of milk. From the research literature she is able to determine the mean milk output for each of the breeds she owns for each type of feed she is considering. As a practical matter, she can use only one type of feed for her herd.
Since she can use only one type of feed, she wants the one that will produce the greatest yield from her herd. She wants the feed type that produces the greatest yield when averaged over all breeds, even if it means using a feed that is not optimal for a particular breed. (In fact, it is easy to construct examples where the feed-type that is best on average is not the best for any breed!) The dairy farmer is interested in what the main effects have to say even in the presence of the interaction. She wants to compare
where the means are obtained by averaging over breed.
For the sake of rigor, it is worth remarking that this assumes the herd is composed of equal numbers of each breed. Otherwise, the feed-types would be compared through weighted averages with weights determined by the composition of the herd. For example, suppose feed A is splendid for Jerseys but mundane for Holsteins while feed B is splendid for Holsteins but mundane for Jerseys. Finally, let feed C be pretty good for both. In a mixed herd, feed C would be the feed of choice. If the composition of the herd were to become predominantly Jerseys, A might be the feed of choice with the gains in the Jerseys more than offsetting the losses in the Holsteins. A similar argument applies to feed B and a herd that is predominantly Holsteins.
utorok 4. marca 2008
Linar mixed models - resources
Linar mixed models - resources
West, Galecki: Practical Approach
book-accompanying site with datasets, code, errata etc
http://www-personal.umich.edu/~bwest/almmussp.html
books google: Designing Experiments and Analyzing Data....
http://books.google.com/books?id=h-bMhmQMifsC&printsec=frontcover&dq=designing+experiments+and+analyzing+data&ei=pxfQR6mWHJPAzATw04iwBQ&sig=DlC1utBSuL1magJSjkdDxa-_-xw#PRA2-PA145,M1
- provides explanation of both ANOVA based mixed models with univariate and multivariate approach, as well as linear mixed models in the frame of multilevels modeling (chapters ,,,,)
- uses some mathematics, but in aN understandable form.
LIST of from site dedicated to HIERARCHICAL modeling
http://www.hlm-online.com/books/
multilevel data analysis - from basics - WEBCAST by Leroux
uwtv.org Multilevel data analysis
www.geocities.com/joophox/publist/whenwhy.pdf
Gelman
Eugen Demidenko LMM: Theory and Application
MULTILEVEL MODELING - SOFTWARE
http://www.cmm.bristol.ac.uk/learning-training/multilevel-m-software/index.shtml
MM and R
http://www.cmm.bristol.ac.uk/learning-training/multilevel-m-software/r.shtml
package lme (Pinheiro, Bates 2000)
more recent version - lmer, lme4 (2005)
www.r-project.org/doc/Rnews/Rnews_2005-1.pdf
for Splus, R by Fox (2002)
cran.r-project.org/doc/contrib/Fox-Companion/appendix-mixed-models.pdf
LINEAR MIXED MODELS
http://www2.chass.ncsu.edu/garson/pa765/multilevel.htm
no mathematical formulas
mainly refers to SPSS, but can can be useful as introduction for any platform
Hubbard Alan longitudinal data analysis
marginal models, generalized estimating equations GEE, with different link functions - Poisson regression etc.
provides lecture notes, and chapters from his textbook on the analysis of longitudinal data (quite readable, but at moments requires to think about vectors, matrix albebra etc., last chapter on Mixed models is not (yet?) available online), includes examples of code for STATA, SAS.
www.nyu.edu/its/socsci/Docs/SPSSMixed.ppt
comparison of mixed models with GLM - some formulas, many PRACTICAL points
www.spss.ch/upload/1107355943_LinearMixedEffectsModelling.pdf
www.spss.ch/upload/1126184451_Linear%20Mixed%20Effects%20Modeling%20in%20SPSS.pdf
gsic.syr.edu/manuals/spss/SPSS%20Advanced%20Models%2015.0.pdf
longitudinal data analysis - mixed models,
COURSES
ONLINE -
statistics.com - Galecki see above - also author of a textbook
www.statistics.com/ourcourses/mixedmodels/
also check - http://www.hlm-online.com/books/
http://www.csc.fi/english/csc/courses/archive/GLM2008
september 2008 - helsinki - Mixed models in R
MAXIMUM LIKELIHOOD ESTIMATION
explained - BGIM - Purcell
http://statgen.iop.kcl.ac.uk/bgim/mle/sslike_1.html
http://tigger.uic.edu/~hedeker/ml.html
SAS, SPSS
PPT
blog.case.edu/jjw17/2006/04/27/ZyzanskiSeminar4bPowerpoint.ppt
SURVIVAL mixed models for survival analysis
doi.wiley.com/10.1002/bimj.4710390102
jas.fass.org/cgi/reprint/77/E-Suppl_2/147.pdf
http://www.ingentaconnect.com/content/bpl/biom/2001/00000057/00000001/art00012
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4KFV26C-1&_user=949847&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000049130&_version=1&_urlVersion=0&_userid=949847&md5=8f89a4cc66b9fe62715703c0323305f0
http://www.wiwi.uni-bielefeld.de/~kauermann/survival/kauermann.pdf
http://links.jstor.org/sici?sici=0006-341X(200103)57%3A1%3C96%3AMMFSAW%3E2.0.CO%3B2-M
generalized linear model
online courses
XXXXXXX generalized - for quantitative, categorical, binary, count data XXXXXXXXXXXXX
http://genetics.agrsci.dk/statistics/courses/phd07/
in R
West, Galecki: Practical Approach
Linear Mixed Models: A Practical Guide Using Statistical Software
http://books.google.com/books?id=LSJ__7lDSdgC&printsec=frontcover&dq=west+galecki&ei=nPzOR7f0OJy8zATGpqmwBQ&sig=rI8DjvFAgy8W-5lcgbZX0WZTiXYbook-accompanying site with datasets, code, errata etc
http://www-personal.umich.edu/~bwest/almmussp.html
books google: Designing Experiments and Analyzing Data....
http://books.google.com/books?id=h-bMhmQMifsC&printsec=frontcover&dq=designing+experiments+and+analyzing+data&ei=pxfQR6mWHJPAzATw04iwBQ&sig=DlC1utBSuL1magJSjkdDxa-_-xw#PRA2-PA145,M1
- provides explanation of both ANOVA based mixed models with univariate and multivariate approach, as well as linear mixed models in the frame of multilevels modeling (chapters ,,,,)
- uses some mathematics, but in aN understandable form.
LIST of from site dedicated to HIERARCHICAL modeling
http://www.hlm-online.com/books/
multilevel data analysis - from basics - WEBCAST by Leroux
uwtv.org Multilevel data analysis
www.geocities.com/joophox/publist/whenwhy.pdf
Gelman
Data Analysis Using Regression and Multilevel/Hierarchical Models
Eugen Demidenko LMM: Theory and Application
MULTILEVEL MODELING - SOFTWARE
http://www.cmm.bristol.ac.uk/learning-training/multilevel-m-software/index.shtml
MM and R
http://www.cmm.bristol.ac.uk/learning-training/multilevel-m-software/r.shtml
package lme (Pinheiro, Bates 2000)
more recent version - lmer, lme4 (2005)
www.r-project.org/doc/Rnews/Rnews_2005-1.pdf
for Splus, R by Fox (2002)
cran.r-project.org/doc/contrib/
LINEAR MIXED MODELS
http://www2.chass.ncsu.edu/garson/pa765/multilevel.htm
no mathematical formulas
mainly refers to SPSS, but can can be useful as introduction for any platform
Hubbard Alan longitudinal data analysis
marginal models, generalized estimating equations GEE, with different link functions - Poisson regression etc.
provides lecture notes, and chapters from his textbook on the analysis of longitudinal data (quite readable, but at moments requires to think about vectors, matrix albebra etc., last chapter on Mixed models is not (yet?) available online), includes examples of code for STATA, SAS.
www.nyu.edu/its/socsci/Docs/SPSSMixed.ppt
comparison of mixed models with GLM - some formulas, many PRACTICAL points
www.spss.ch/upload/1107355943_LinearMixedEffectsModelling.pdf
www.spss.ch/upload/1126184451_Linear%20Mixed%20Effects%20Modeling%20in%20SPSS.pdf
gsic.syr.edu/manuals/spss/SPSS%20Advanced%20Models%2015.0.pdf
longitudinal data analysis - mixed models,
COURSES
ONLINE -
statistics.com - Galecki see above - also author of a textbook
www.statistics.com/ourcourses/mixedmodels/
also check - http://www.hlm-online.com/books/
http://www.csc.fi/english/csc/courses/archive/GLM2008
september 2008 - helsinki - Mixed models in R
MAXIMUM LIKELIHOOD ESTIMATION
explained - BGIM - Purcell
http://statgen.iop.kcl.ac.uk/bgim/mle/sslike_1.html
http://tigger.uic.edu/~hedeker/ml.html
SAS, SPSS
PPT
blog.case.edu/jjw17/2006/
SURVIVAL mixed models for survival analysis
jas.fass.org/cgi/reprint/77/E-Suppl_2/147.pdf
http://www.ingentaconnect.com/content/bpl/biom/2001/00000057/00000001/art00012
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4KFV26C-1&_user=949847&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000049130&_version=1&_urlVersion=0&_userid=949847&md5=8f89a4cc66b9fe62715703c0323305f0
http://www.wiwi.uni-bielefeld.de/~kauermann/survival/kauermann.pdf
http://links.jstor.org/sici?sici=0006-341X(200103)57%3A1%3C96%3AMMFSAW%3E2.0.CO%3B2-M
generalized linear model
online courses
XXXXXXX generalized - for quantitative, categorical, binary, count data XXXXXXXXXXXXX
http://genetics.agrsci.dk/statistics/courses/phd07/
in R
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