started lecture
This commit is contained in:
Fabian Sinz
parent
001a2aab55
commit
f210d00ca5
@ -3,12 +3,9 @@ DOTSOURCES = $(wildcard figs/*.dot)
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all: $(DOTSOURCES:dot=pdf)
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python figs/generate.py
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python figs/generate03.py
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python figs/generateTPlots.py
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pdflatex talk*.tex
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pdflatex talk*.tex
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pdflatex talk*.tex
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pdflatex lecture_statistics*.tex
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pdflatex lecture_statistics*.tex
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pdflatex lecture_statistics*.tex
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figs/prob%.pdf : figs/prob%.dot
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@ -1,218 +0,0 @@
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#LyX 2.0 created this file. For more info see http://www.lyx.org/
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\lyxformat 413
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%\unserzeichen % prints "our ref" instead of "my ref"
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\use_default_options false
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status open
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Note also the document preamble settings.
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\end_layout
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\begin_layout NameRowA
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Dr.
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rer.
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nat.
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Fabian Sinz
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\end_layout
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\begin_layout NameRowB
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\end_layout
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\begin_layout NameRowC
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\end_layout
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\begin_layout NameRowD
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\end_layout
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\end_layout
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\begin_layout NameRowG
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\end_layout
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\begin_layout AddressRowA
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University Tübingen
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\end_layout
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\begin_layout AddressRowB
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\end_layout
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\begin_layout AddressRowC
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Auf der Morgenstelle 28
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\end_layout
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\begin_layout AddressRowD
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72076 Tübingen
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\end_layout
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\begin_layout InternetRowA
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http:/
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\begin_inset Formula $\!$
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\end_inset
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/www.epagoge.de
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\end_layout
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\begin_layout InternetRowB
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fabian.sinz@epagoge.de
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\end_layout
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\begin_layout ReturnAddress
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University Tübingen
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\begin_inset Formula $\cdot$
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\end_inset
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Auf der Morgenstelle 28
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\begin_inset Formula $\cdot$
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\end_inset
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72076 Tübingen
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\end_layout
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\begin_layout Date
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status collapsed
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\begin_layout Plain Layout
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\backslash
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today
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\end_layout
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\end_inset
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\end_layout
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\begin_layout Reference
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\end_layout
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\begin_layout Opening
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To whom it may concern,
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\end_layout
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\begin_layout Closing
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Best regards
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\end_layout
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\begin_layout Signature
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Dr.
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Fabian Sinz
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\end_layout
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\begin_layout Encl.
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\end_layout
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\begin_layout Letter
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this letter certifies that
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\emph on
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Lakshmi Channappa
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\emph default
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attended the course
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\emph on
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Statistics in a Nutshell
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\emph default
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held at the Neurochip research group at the
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\emph on
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Naturwissenschaftliches und Medizinisches Institut Reutlingen
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\emph default
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in 2013
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\emph on
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.
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\emph default
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The course was organized in two lectures of four hours each and covered
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topics such as basics of probability theory, errorbars and confidence intervals
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, statistical tests, p-values, multiple hypothesis testing, basics of study
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design, and basics of ANOVA.
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Small calculation and programming exercises were used to clarify selected
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material.
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\end_layout
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\end_body
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\end_document
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statistics/figs/feeding.jpg
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statistics/figs/feeding.jpg
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@ -1,15 +0,0 @@
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trulynotnormal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
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twosampNN->signrank[label="paired"];
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data->nd_test_type[label="nominal/discrete",color="red"];
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twosampNN->signtest[label="paired"];
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twosampOrd->signtest[label="paired"];
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trulynotnormal[label="1, 2, or >2 groups?"];
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onesamp[label="one-sample\nt-test"];
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signrank[label="Wilcoxon signed\nrank test"];
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trulynotnormal->signrank[label="1 group\n(fix other\ngroup to\n one value)"];
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}
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trulynotnormal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
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twosampNN->signrank[label="paired"];
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twosampNN->indepTwosampNN[label="independent"];
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data->ordinal[label="ordinal"];
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subgraph cluster_O {
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twosampOrd[label="paired or\nindependent?"];
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ordinal->twosampOrd[label="2 groups"];
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twosampOrd->indepTwosampNN[label="independent"];
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signtest[label="sign test"];
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ordinal[label="1, 2, or >2 groups?"];
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ordinal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
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twosampNN->signtest[label="paired"];
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twosampOrd->signtest[label="paired"];
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}
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@ -1,98 +0,0 @@
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||||
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||||
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||||
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||||
edge [penwidth=2, fontsize=10 ];
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subgraph cluster_IR {
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label = "interval/ ratio";
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bgcolor=lightblue;
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IR[label="data normal distributed\nor n large?"];
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normal[label="1, 2, or >2 groups?"];
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notnormal[label="transform into\nnormal?"];
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trulynotnormal[label="1, 2, or >2 groups?"];
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IR->normal[label="normal"];
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IR->notnormal[label="not normal"];
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notnormal->trulynotnormal[label="no"];
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onesamp[label="one-sample\nt-test"];
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normal->onesamp[label="1 group"];
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ttest[label="t-test"];
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twosamp->ttest[label="independent"];
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twosamp[label="paired or\nindependent?"];
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pairedttest[label="paired\nt-test"];
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normal->twosamp[label="2 groups"];
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twosamp->pairedttest[label="paired"];
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twosampNN[label="paired or\nindependent?"];
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trulynotnormal->twosampNN[label="2 groups"];
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signrank[label="Wilcoxon signed\nrank test"];
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trulynotnormal->signrank[label="1 group\n(fix other\ngroup to\n one value)"];
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}
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trulynotnormal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
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twosampNN->signrank[label="paired"];
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twosampNN->indepTwosampNN[label="independent"];
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data->ordinal[label="ordinal"];
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subgraph cluster_O {
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label = "ordinal";
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bgcolor=lightblue;
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indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
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twosampOrd[label="paired or\nindependent?"];
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ordinal->twosampOrd[label="2 groups"];
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twosampOrd->indepTwosampNN[label="independent"];
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signtest[label="sign test"];
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ordinal[label="1, 2, or >2 groups?"];
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ordinal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
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}
|
||||
|
||||
|
||||
subgraph cluster_ND {
|
||||
label = "nominal/discrete";
|
||||
bgcolor=lightblue;
|
||||
nd_test_type[label="1, 2, or >2 variables",color="green"];
|
||||
onesampND[label="chi square for\ngoodness of fit"];
|
||||
twosampND[label="chi square for\nindependence"];
|
||||
|
||||
nd_test_type->onesampND[label="1 variable"];
|
||||
nd_test_type->twosampND[label="2 variables"];
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nd_test_type[label="nominal/discrete",color="red"];
|
||||
|
||||
|
||||
|
||||
twosampNN->signtest[label="paired"];
|
||||
twosampOrd->signtest[label="paired"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,99 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.95,ranksep=0.95];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
subgraph cluster_IR {
|
||||
label = "interval/ ratio";
|
||||
bgcolor=lightblue;
|
||||
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
ttest[label="t-test"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
|
||||
signrank[label="Wilcoxon signed\nrank test"];
|
||||
|
||||
trulynotnormal->signrank[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
trulynotnormal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
|
||||
twosampNN->signrank[label="paired"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
subgraph cluster_O {
|
||||
label = "ordinal";
|
||||
bgcolor=lightblue;
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="independent"];
|
||||
|
||||
signtest[label="sign test"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
ordinal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
}
|
||||
|
||||
|
||||
subgraph cluster_ND {
|
||||
label = "nominal/discrete";
|
||||
bgcolor=lightblue;
|
||||
nd_test_type[label="1, 2, or >2 variables",color="green"];
|
||||
onesampND[label="chi square for\ngoodness of fit"];
|
||||
twosampND[label="chi square for\nindependence"];
|
||||
|
||||
nd_test_type->onesampND[label="1 variable"];
|
||||
nd_test_type->onesampND[label="n variables",color="red"];
|
||||
nd_test_type->twosampND[label="2 variables"];
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nd_test_type[label="nominal/discrete"];
|
||||
|
||||
|
||||
|
||||
twosampNN->signtest[label="paired"];
|
||||
twosampOrd->signtest[label="paired"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,99 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.95,ranksep=0.95];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
subgraph cluster_IR {
|
||||
label = "interval/ ratio";
|
||||
bgcolor=lightblue;
|
||||
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?",color="green"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
ttest[label="t-test"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
|
||||
signrank[label="Wilcoxon signed\nrank test"];
|
||||
|
||||
trulynotnormal->signrank[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
trulynotnormal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
|
||||
twosampNN->signrank[label="paired"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
subgraph cluster_O {
|
||||
label = "ordinal";
|
||||
bgcolor=lightblue;
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="independent"];
|
||||
|
||||
signtest[label="sign test"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
ordinal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
}
|
||||
|
||||
|
||||
subgraph cluster_ND {
|
||||
label = "nominal/discrete";
|
||||
bgcolor=lightblue;
|
||||
nd_test_type[label="1, 2, or >2 variables"];
|
||||
onesampND[label="chi square for\ngoodness of fit"];
|
||||
twosampND[label="chi square for\nindependence"];
|
||||
|
||||
nd_test_type->onesampND[label="1 variable"];
|
||||
nd_test_type->onesampND[label="n variables"];
|
||||
nd_test_type->twosampND[label="2 variables"];
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nd_test_type[label="nominal/discrete"];
|
||||
|
||||
|
||||
|
||||
twosampNN->signtest[label="paired"];
|
||||
twosampOrd->signtest[label="paired"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,101 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.95,ranksep=0.95];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
subgraph cluster_IR {
|
||||
label = "interval/ ratio";
|
||||
bgcolor=lightblue;
|
||||
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?",color="green"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
ttest[label="t-test"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
|
||||
signrank[label="Wilcoxon signed\nrank test"];
|
||||
|
||||
trulynotnormal->signrank[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
|
||||
ANOVA;
|
||||
normal->ANOVA[color="red"];
|
||||
|
||||
}
|
||||
|
||||
trulynotnormal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
|
||||
twosampNN->signrank[label="paired"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
subgraph cluster_O {
|
||||
label = "ordinal";
|
||||
bgcolor=lightblue;
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="independent"];
|
||||
|
||||
signtest[label="sign test"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
ordinal->signtest[label="1 group\n(fix other\ngroup to\n one value)"];
|
||||
}
|
||||
|
||||
|
||||
subgraph cluster_ND {
|
||||
label = "nominal/discrete";
|
||||
bgcolor=lightblue;
|
||||
nd_test_type[label="1, 2, or >2 variables"];
|
||||
onesampND[label="chi square for\ngoodness of fit"];
|
||||
twosampND[label="chi square for\nindependence"];
|
||||
|
||||
nd_test_type->onesampND[label="1 variable"];
|
||||
nd_test_type->onesampND[label="n variables"];
|
||||
nd_test_type->twosampND[label="2 variables"];
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nd_test_type[label="nominal/discrete"];
|
||||
|
||||
|
||||
|
||||
twosampNN->signtest[label="paired"];
|
||||
twosampOrd->signtest[label="paired"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,22 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
|
||||
|
||||
}
|
||||
Binary file not shown.
@ -1,58 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.95,ranksep=0.95];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="independent"];
|
||||
|
||||
|
||||
ttest[label="t-test"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
signtest[label="sign test"];
|
||||
signrank[label="Wilcoxon signed\nrank test",color="lightblue"];
|
||||
twosampNN->signtest[label="paired"];
|
||||
twosampOrd->signtest[label="paired"];
|
||||
|
||||
twosampNN->signrank[label="paired"];
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,81 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.95,ranksep=0.95];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
subgraph cluster_IR {
|
||||
label = "interval/ ratio";
|
||||
bgcolor=lightblue;
|
||||
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
ttest[label="t-test"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
|
||||
signrank[label="Wilcoxon signed\nrank test"];
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
twosampNN->signrank[label="paired"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
subgraph cluster_O {
|
||||
label = "ordinal";
|
||||
bgcolor=lightblue;
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="independent"];
|
||||
|
||||
signtest[label="sign test"];
|
||||
|
||||
}
|
||||
|
||||
data[label="type of data?"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
|
||||
|
||||
|
||||
twosampNN->signtest[label="paired"];
|
||||
twosampOrd->signtest[label="paired"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,83 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.95,ranksep=0.95];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
subgraph cluster_IR {
|
||||
label = "interval/ ratio";
|
||||
bgcolor=lightblue;
|
||||
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
ttest[label="t-test"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
|
||||
signrank[label="Wilcoxon signed\nrank test"];
|
||||
|
||||
trulynotnormal->signrank[label="1 group\n(fix other\ngroup to\n one value)",color="red"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
trulynotnormal->signtest[label="1 group\n(fix other\ngroup to\n one value)",color="red"];
|
||||
|
||||
twosampNN->signrank[label="paired"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
subgraph cluster_O {
|
||||
label = "ordinal";
|
||||
bgcolor=lightblue;
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="independent"];
|
||||
|
||||
signtest[label="sign test"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
ordinal->signtest[label="1 group\n(fix other\ngroup to\n one value)",color="red"];
|
||||
}
|
||||
|
||||
data[label="type of data?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
|
||||
|
||||
|
||||
twosampNN->signtest[label="paired"];
|
||||
twosampOrd->signtest[label="paired"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,28 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="?",color="lightblue"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
Binary file not shown.
@ -1,28 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.75,ranksep=0.75];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
Binary file not shown.
@ -1,30 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.75,ranksep=0.75];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
}
|
||||
|
||||
Binary file not shown.
@ -1,38 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.75,ranksep=0.75];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nnot paired?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,49 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.75,ranksep=0.75];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="1, 2, or >2 groups?",color="lightblue"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?",color="lightblue"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?",color="lightblue"];
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest",color="lightblue"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
twosampOrd[label="paired or\nindependent?",color="lightblue"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="independent"];
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,52 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.75,ranksep=0.75];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="indepdendent"];
|
||||
|
||||
|
||||
ttest[label="?",color="lightblue"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,54 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.75,ranksep=0.75];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="indepdendent"];
|
||||
|
||||
|
||||
ttest[label="t-test",color="lightblue"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
pairedTwosampNN[label="?",color="lightblue"];
|
||||
twosampNN->pairedTwosampNN[label="paired"];
|
||||
twosampOrd->pairedTwosampNN[label="paired"];
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,54 +0,0 @@
|
||||
digraph G {
|
||||
rankdir=TB;
|
||||
ranksep=0.2;
|
||||
node [fontsize=12, shape=rectangle, style=filled, nodesep=0.75,ranksep=0.95];
|
||||
edge [penwidth=2, fontsize=10 ];
|
||||
|
||||
|
||||
data[label="type of data?"];
|
||||
IR[label="data normal distributed\nor n large?"];
|
||||
ordinal[label="1, 2, or >2 groups?"];
|
||||
nominal[label="?"];
|
||||
|
||||
data->IR[label="interval/ratio"];
|
||||
data->nominal[label="nominal/discrete"];
|
||||
data->ordinal[label="ordinal"];
|
||||
|
||||
normal[label="1, 2, or >2 groups?"];
|
||||
notnormal[label="transform into\nnormal?"];
|
||||
trulynotnormal[label="1, 2, or >2 groups?"];
|
||||
|
||||
IR->normal[label="normal"];
|
||||
IR->notnormal[label="not normal"];
|
||||
notnormal->normal[label="yes"];
|
||||
notnormal->trulynotnormal[label="no"];
|
||||
|
||||
onesamp[label="one-sample\nt-test"];
|
||||
normal->onesamp[label="1 group"];
|
||||
|
||||
|
||||
twosamp[label="paired or\nindependent?"];
|
||||
pairedttest[label="paired\nt-test"];
|
||||
normal->twosamp[label="2 groups"];
|
||||
twosamp->pairedttest[label="paired"];
|
||||
|
||||
twosampNN[label="paired or\nindependent?"];
|
||||
indepTwosampNN[label="Wilcoxon-Mann-Whitney\ntest"];
|
||||
|
||||
trulynotnormal->twosampNN[label="2 groups"];
|
||||
twosampNN->indepTwosampNN[label="independent"];
|
||||
|
||||
twosampOrd[label="paired or\nindependent?"];
|
||||
ordinal->twosampOrd[label="2 groups"];
|
||||
twosampOrd->indepTwosampNN[label="independent"];
|
||||
|
||||
|
||||
ttest[label="t-test"];
|
||||
twosamp->ttest[label="independent"];
|
||||
|
||||
signtest[label="sign test"];
|
||||
twosampNN->signtest[label="paired"];
|
||||
twosampOrd->signtest[label="paired"];
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 49 KiB |
@ -1,106 +0,0 @@
|
||||
from __future__ import division
|
||||
import seaborn as sns
|
||||
import sys
|
||||
sys.path.append('/home/fabee/code/')
|
||||
from matplotlib.pyplot import *
|
||||
from fabee.Plotting import *
|
||||
from scipy import stats
|
||||
from numpy import *
|
||||
|
||||
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
fig, ax = subplots()
|
||||
fig.subplots_adjust(bottom=.3, left=.3)
|
||||
n = 50
|
||||
x = loadtxt('scripts/thymusglandweights.dat')[:n]
|
||||
ax.bar([0,1],[mean(x),mean(x)],yerr = [std(x,ddof=1), std(x,ddof=1)/sqrt(n)],
|
||||
facecolor='dodgerblue', alpha=.8,width=.7, align='center',
|
||||
error_kw={'color':'k','lw':2}, capsize=10, ecolor='k')
|
||||
ax.set_title('standard deviation or standard error?',fontsize=14, fontweight='bold')
|
||||
|
||||
ax.set_xlim([-.5,1.5])
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xticks([0,1])
|
||||
ax.set_xticklabels([r'$\hat\sigma$', r'$\frac{\hat\sigma}{\sqrt{n}}$'], fontsize=30)
|
||||
|
||||
ax.set_ylabel(r'$\frac{1}{n}\sum_{i=1}^n x_i$',fontsize=30, fontweight='bold')
|
||||
|
||||
fig.savefig('figs/StandardErrorOrStandardDeviation.pdf')
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
fig, ax = subplots()
|
||||
|
||||
t = linspace(-5,5,1000)
|
||||
t2 = linspace(stats.laplace.ppf(0.025),stats.laplace.ppf(1-0.025),1000)
|
||||
|
||||
ax.fill_between(t,stats.laplace.pdf(t),color='dodgerblue')
|
||||
ax.set_xticks([])
|
||||
ax.text(5,-0.05, r'$\hat m$',fontsize=30)
|
||||
ax.text(0,0.7, r'$m$',fontsize=30)
|
||||
ax.set_yticks([])
|
||||
#disjoint_axes(ax)
|
||||
box_off(ax)
|
||||
|
||||
ax.set_title('putative sampling distribution of the median',fontsize=14, fontweight='bold')
|
||||
ax.axis([-5,5,0,.8])
|
||||
ax.plot([0,0],[0,.7],'--k',lw=2)
|
||||
|
||||
fig.savefig('figs/samplingDistributionMedian00.pdf')
|
||||
|
||||
ax.fill_between(t2,stats.laplace.pdf(t2),color='crimson')
|
||||
|
||||
fig.savefig('figs/samplingDistributionMedian01.pdf')
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
fig, ax = subplots()
|
||||
k = 7
|
||||
N = 21
|
||||
F = stats.f
|
||||
t = linspace(1e-6,8,1000)
|
||||
t2= linspace(F.ppf(0.95,k-1,N-k),8,1000)
|
||||
|
||||
ax.fill_between(t,F.pdf(t,k-1,N-k),color='dodgerblue')
|
||||
ax.fill_between(t2,F.pdf(t2,k-1,N-k),color='crimson')
|
||||
ax.set_xlabel('group MS/ error MS')
|
||||
ax.set_ylabel(r'p(group MS/ error MS| $H_0$)')
|
||||
ax.set_title('F-distribution',fontsize=14, fontweight='bold')
|
||||
ax.set_ylim((0,0.8))
|
||||
box_off(ax)
|
||||
fig.savefig('figs/Fdistribution00.pdf')
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
fig, ax = subplots()
|
||||
n = 5
|
||||
p = stats.t.pdf
|
||||
t = linspace(-5,8,1000)
|
||||
t0 = 1.5
|
||||
t00 = 1.
|
||||
|
||||
mu0 = 3
|
||||
t1 = linspace(-5,t00,1000)
|
||||
t2 = linspace(t0,8,1000)
|
||||
t3 = linspace(-5,-t0,1000)
|
||||
ax.fill_between(t,p(t,n-1),color='dodgerblue',alpha=1)
|
||||
ax.fill_between(t2,p(t2,n-1),color='indigo',alpha=1)
|
||||
ax.fill_between(t3,p(t3,n-1),color='indigo',alpha=1)
|
||||
ax.set_xlabel('t')
|
||||
ax.set_ylabel(r'sampling distribution')
|
||||
ax.set_ylim((0,0.8))
|
||||
box_off(ax)
|
||||
fig.savefig('figs/experimentalDesign00.pdf')
|
||||
|
||||
|
||||
ax.fill_between(t,p(t,n-1,loc=mu0),color='lime',alpha=.5)
|
||||
ax.fill_between(t1,p(t1,n-1,loc=mu0),color='magenta',alpha=1)
|
||||
ax.arrow(0,.4,mu0,0,head_width=0.05)
|
||||
ax.arrow(mu0,.4,-mu0,0,head_width=0.05)
|
||||
ax.text(mu0/2,.45,r'$\delta$',fontsize=20)
|
||||
ax.set_xlabel('t')
|
||||
ax.set_ylabel(r'sampling distribution')
|
||||
ax.set_ylim((0,0.8))
|
||||
box_off(ax)
|
||||
fig.savefig('figs/experimentalDesign01.pdf')
|
||||
|
||||
|
||||
@ -1,265 +0,0 @@
|
||||
import sys
|
||||
import seaborn as sns
|
||||
sys.path.append('/home/fabee/code/')
|
||||
from matplotlib.pyplot import *
|
||||
from fabee.Plotting import *
|
||||
from scipy import stats
|
||||
from numpy import *
|
||||
|
||||
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
|
||||
|
||||
def hinton(matrix, max_weight=None, ax=None):
|
||||
"""Draw Hinton diagram for visualizing a weight matrix."""
|
||||
ax = ax if ax is not None else gca()
|
||||
|
||||
if not max_weight:
|
||||
max_weight = 2**np.ceil(np.log(np.abs(matrix).max())/np.log(2))
|
||||
|
||||
ax.patch.set_facecolor('gray')
|
||||
ax.set_aspect('equal', 'box')
|
||||
ax.xaxis.set_major_locator(NullLocator())
|
||||
ax.yaxis.set_major_locator(NullLocator())
|
||||
|
||||
for (x,y),w in np.ndenumerate(matrix):
|
||||
color = 'white' if w > 0 else 'black'
|
||||
size = np.sqrt(np.abs(w))
|
||||
rect = Rectangle([x - size / 2, y - size / 2], size, size,
|
||||
facecolor=color, edgecolor=color)
|
||||
ax.add_patch(rect)
|
||||
|
||||
ax.autoscale_view()
|
||||
ax.invert_yaxis()
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
fig, ax = subplots()
|
||||
fig.subplots_adjust(bottom=.2)
|
||||
|
||||
ax.bar([0,1],[.2,.8],facecolor='dodgerblue', alpha=.8,width=.7, align='center')
|
||||
ax.set_title('Bernoulli distribution',fontsize=16, fontweight='bold')
|
||||
|
||||
ax.set_xlim([-.5,1.5])
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xlabel('outcomes',fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('P(outcome)',fontsize=14, fontweight='bold')
|
||||
ax.set_xticks([0,1])
|
||||
ax.set_xticklabels([0,1])
|
||||
ax.set_ylim((0,1))
|
||||
|
||||
fig.savefig('figs/Bernoulli.pdf')
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
fig, ax = subplots()
|
||||
fig.subplots_adjust(bottom=.2)
|
||||
n = 5
|
||||
k = arange(0,n)
|
||||
ax.bar(k,0*k+1./n,facecolor='dodgerblue', alpha=.8,width=.7, align='center')
|
||||
ax.set_title('uniform distribution',fontsize=16, fontweight='bold')
|
||||
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xlabel('k',fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('P(X=k)',fontsize=14, fontweight='bold')
|
||||
ax.set_xticks(k)
|
||||
ax.set_xticklabels(k+1)
|
||||
ax.set_ylim((0,1))
|
||||
fig.savefig('figs/Uniform.pdf')
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
for i,(n,p) in enumerate(zip([10,20],[.5,.8])):
|
||||
fig, ax = subplots()
|
||||
|
||||
fig.subplots_adjust(bottom=.2)
|
||||
k = arange(n+1)
|
||||
|
||||
ax.bar(k,stats.binom.pmf(k,n,p),facecolor='dodgerblue', alpha=.8,width=.7, align='center')
|
||||
ax.set_title(r'binomial distribution $B\left(%.2f, %i\right)$' % (p,n),fontsize=16, fontweight='bold')
|
||||
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xlabel('k',fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('P(k)',fontsize=14, fontweight='bold')
|
||||
ax.set_xticks(k)
|
||||
ax.set_xticklabels(k)
|
||||
ax.set_xlim((-1,n+1))
|
||||
ax.set_ylim((0,1))
|
||||
fig.savefig('figs/Binomial%02i.pdf' % (i,))
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
n = 20
|
||||
for i, lam in enumerate([5, 0.05]):
|
||||
fig, ax = subplots()
|
||||
|
||||
fig.subplots_adjust(bottom=.2)
|
||||
k = arange(n+1)
|
||||
|
||||
ax.bar(k,stats.poisson.pmf(k,lam),facecolor='dodgerblue', alpha=.8,width=.7, align='center')
|
||||
ax.set_title(r'Poisson distribution $\lambda=%.2f$' % (lam,),fontsize=16, fontweight='bold')
|
||||
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xlabel('k',fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('P(k)',fontsize=14, fontweight='bold')
|
||||
ax.set_xticks(k)
|
||||
ax.set_xticklabels(k)
|
||||
ax.set_xlim((-1,n+1))
|
||||
ax.set_ylim((0,1))
|
||||
fig.savefig('figs/Poisson%02i.pdf' % (i,))
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
fig, ax = subplots()
|
||||
|
||||
fig.subplots_adjust(bottom=.2)
|
||||
t = linspace(-3,3,200)
|
||||
ax.fill_between(t,stats.norm.pdf(t),facecolor='dodgerblue', alpha=.8)
|
||||
ax.set_title(r'Gaussian/Normal distribution $N(\mu,\sigma)$',fontsize=16, fontweight='bold')
|
||||
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xlabel('x',fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('p(x)',fontsize=14, fontweight='bold')
|
||||
fig.savefig('figs/Gaussian00.pdf')
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
fig, ax = subplots()
|
||||
n = 10
|
||||
kk = 5
|
||||
p = .5
|
||||
fig.subplots_adjust(bottom=.2)
|
||||
k = arange(n+1)
|
||||
|
||||
ax.bar(k,stats.binom.pmf(k,n,p),facecolor='dodgerblue', alpha=.8,width=.7, align='center')
|
||||
ax.bar(k[:kk+1],stats.binom.pmf(k[:kk+1],n,p),facecolor='crimson', alpha=.5,width=.7, align='center')
|
||||
ax.set_title(r'binomial distribution $B\left(\frac{1}{2}, %i\right)$' % (n,), fontsize=16, fontweight='bold')
|
||||
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xlabel('k',fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('P(k)',fontsize=14, fontweight='bold')
|
||||
ax.set_xticks(k)
|
||||
ax.set_xticklabels(k)
|
||||
ax.set_xlim((-1,n+1))
|
||||
ax.set_ylim((0,1))
|
||||
fig.savefig('figs/BinomialCdf00.pdf' )
|
||||
|
||||
|
||||
fig, ax = subplots()
|
||||
n = 10
|
||||
kk = 5
|
||||
p = .5
|
||||
fig.subplots_adjust(bottom=.2)
|
||||
k = arange(n+1)
|
||||
|
||||
ax.bar(k,stats.binom.pmf(k,n,p),facecolor='dodgerblue', alpha=.8,width=.7, align='center',label='p.m.f.')
|
||||
ax.bar(k[:kk+1],stats.binom.pmf(k[:kk+1],n,p),facecolor='crimson', alpha=.5,width=.7, align='center')
|
||||
ax.plot(k,stats.binom.cdf(k,n,p),'ok',mfc='crimson', alpha=1.,label='c.d.f.', ms=15)
|
||||
|
||||
ax.set_title(r'binomial distribution $B\left(\frac{1}{2}, %i\right)$' % (n,), fontsize=16, fontweight='bold')
|
||||
ax.legend(frameon=False, loc='best')
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xlabel('k',fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('P(k)',fontsize=14, fontweight='bold')
|
||||
ax.set_xticks(k)
|
||||
ax.set_xticklabels(k)
|
||||
ax.set_xlim((-1,n+1))
|
||||
ax.set_ylim((0,1.1))
|
||||
fig.savefig('figs/BinomialCdf01.pdf' )
|
||||
|
||||
fig, ax = subplots()
|
||||
n = 10
|
||||
kk = 2
|
||||
p = .5
|
||||
fig.subplots_adjust(bottom=.2)
|
||||
k = arange(n+1)
|
||||
|
||||
ax.bar(k,stats.binom.pmf(k,n,p),facecolor='dodgerblue', alpha=.8,width=.7, align='center',label='p.m.f.')
|
||||
ax.bar(k[:kk+1],stats.binom.pmf(k[:kk+1],n,p),facecolor='crimson', alpha=.5,width=.7, align='center')
|
||||
ax.bar(k[-kk-1:],stats.binom.pmf(k[-kk-1:],n,p),facecolor='crimson', alpha=.5,width=.7, align='center')
|
||||
|
||||
ax.set_title(r'binomial distribution $B\left(\frac{1}{2}, %i\right)$' % (n,), fontsize=16, fontweight='bold')
|
||||
ax.legend(frameon=False, loc='best')
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xlabel('k',fontsize=14, fontweight='bold')
|
||||
ax.set_ylabel('P(k)',fontsize=14, fontweight='bold')
|
||||
ax.set_xticks(k)
|
||||
ax.set_xticklabels(k)
|
||||
ax.set_xlim((-1,n+1))
|
||||
ax.set_ylim((0,1.1))
|
||||
fig.savefig('figs/BinomialExample00.pdf' )
|
||||
|
||||
|
||||
#------------------------------------------------------
|
||||
fig = figure(figsize=(10,3.5))
|
||||
ax = fig.add_axes([.1,.13,.6,.3])
|
||||
|
||||
n = 10
|
||||
p = [.5,.8]
|
||||
q = [.7, .3]
|
||||
fig.subplots_adjust(bottom=0.2)
|
||||
k = arange(n+1)
|
||||
P = vstack((stats.binom.pmf(k,n,p[0])*q[0], stats.binom.pmf(k,n,p[1])*q[1])).T
|
||||
|
||||
hinton(P, ax = None)
|
||||
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xticks(k)
|
||||
ax.set_xticklabels(k)
|
||||
ax.set_yticks([0,1])
|
||||
ax.set_ylim((-.5,1.5))
|
||||
ax.set_xlim((-.5,n+.5))
|
||||
ax.set_yticklabels(['subject #1', 'subject #2'])
|
||||
fig.savefig('figs/Joint00.pdf' )
|
||||
|
||||
ax = fig.add_axes([.75,.13,.2,.3])
|
||||
ax.barh([0,1],q, facecolor='dodgerblue',alpha=.8, align='center')
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xticks([0,.5,1.])
|
||||
ax.set_yticks([])
|
||||
ax.set_ylim((-.5,1.5))
|
||||
fig.savefig('figs/Joint01.pdf' )
|
||||
|
||||
ax = fig.add_axes([.1,.6,.6,.2])
|
||||
ax.bar(k,sum(P,axis=1), facecolor='dodgerblue',alpha=.8, align='center')
|
||||
a = .7
|
||||
ax.axis([-a,n-a+1.5,0,1])
|
||||
box_off(ax)
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xticks([])
|
||||
ax.set_yticks([0,.3])
|
||||
ax.set_ylim((0,.3))
|
||||
fig.savefig('figs/Joint02.pdf' )
|
||||
|
||||
|
||||
#------------------------------------------------------
|
||||
n = 10
|
||||
k = arange(n+1)
|
||||
p = [.5,.8]
|
||||
q = [.7, .3]
|
||||
P = vstack((stats.binom.pmf(k,n,p[0])*q[0], stats.binom.pmf(k,n,p[1])*q[1]))
|
||||
Pk = sum(P,axis=0)
|
||||
|
||||
fig = figure()
|
||||
for i,kk in enumerate(k):
|
||||
ax = fig.add_subplot(3,4,i+1)
|
||||
fig.subplots_adjust(bottom=0.2)
|
||||
|
||||
ax.bar([0,1],P[:,i]/Pk[i], facecolor='dodgerblue',alpha=.8, align='center')
|
||||
|
||||
|
||||
#disjoint_axes(ax)
|
||||
ax.set_xticks([0,1])
|
||||
ax.set_xticklabels(['#1','#2'], fontsize=8)
|
||||
ax.set_yticks([0,.5,1])
|
||||
ax.set_yticklabels([0,.5,1],fontsize=8)
|
||||
ax.set_xlim((-.5,1.5))
|
||||
ax.set_ylim((0,1))
|
||||
ax.set_title('P({#1,#2}| %i successes)' % (i,), fontsize=8)
|
||||
|
||||
fig.subplots_adjust(wspace=.8, hspace=.8)
|
||||
fig.savefig('figs/Posterior00.pdf')
|
||||
@ -1,216 +0,0 @@
|
||||
import sys
|
||||
sys.path.append('/home/fabee/code/')
|
||||
import seaborn as sns
|
||||
from matplotlib.pyplot import *
|
||||
from scipy import stats
|
||||
from numpy import *
|
||||
|
||||
from matplotlib.ticker import NullFormatter
|
||||
|
||||
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
|
||||
|
||||
# --------------- PLOT 1 -------------------------
|
||||
# the random data
|
||||
distr = stats.uniform
|
||||
col = '+*0<>v'
|
||||
|
||||
for k,distr in enumerate([stats.laplace, stats.norm, stats.expon,stats.uniform]):
|
||||
col = [col[i] for i in random.permutation(6)]
|
||||
x = random.randn(5000)
|
||||
|
||||
nullfmt = NullFormatter() # no labels
|
||||
|
||||
# definitions for the axes
|
||||
left, width = 0.1, 0.65
|
||||
bottom, height = 0.1, 0.65
|
||||
bottom_h = left_h = left+width+0.02
|
||||
|
||||
rect_scatter = [left + 0.22, bottom + 0.22 , width, height]
|
||||
rect_histx = [left + 0.22, bottom, width, 0.2]
|
||||
rect_histy = [left, bottom + 0.22 , 0.2, height]
|
||||
|
||||
# start with a rectangular Figure
|
||||
fig = figure(figsize=(8,8))
|
||||
|
||||
axQQ = axes(rect_scatter)
|
||||
axHistx = axes(rect_histx)
|
||||
axHisty = axes(rect_histy)
|
||||
|
||||
# no labels
|
||||
axHistx.yaxis.set_major_formatter(nullfmt)
|
||||
axHisty.xaxis.set_major_formatter(nullfmt)
|
||||
axQQ.xaxis.set_major_formatter(nullfmt)
|
||||
axQQ.yaxis.set_major_formatter(nullfmt)
|
||||
|
||||
|
||||
|
||||
# the scatter plot:
|
||||
z = distr.ppf(stats.norm.cdf(x))
|
||||
y = linspace(amin(z),amax(z),1000)
|
||||
|
||||
|
||||
z = distr.ppf(stats.norm.cdf(x))
|
||||
if distr != stats.norm:
|
||||
if distr == stats.uniform:
|
||||
axQQ.plot(x, z,'ok',marker=col[0],ms=5,label='c.d.f.')
|
||||
else:
|
||||
axQQ.plot(x, z,'ok',marker=col[0],ms=5,label='correct')
|
||||
|
||||
if distr != stats.expon:
|
||||
axQQ.plot((z-amin(z))/(amax(z)-amin(z))*(amax(x)-amin(x)) + amin(x),\
|
||||
(x-amin(x))/(amax(x)-amin(x))*(amax(z)-amin(z)) + amin(z),'ok',marker=col[1],ms=5)
|
||||
axQQ.plot(x, (x-amin(x))/(amax(x)-amin(x))*(amax(z)-amin(z)) + amin(z),'ok',marker=col[2],ms=5)
|
||||
|
||||
|
||||
# now determine nice limits by hand:
|
||||
axHistx.hist(x, bins=100,normed=True)
|
||||
if distr != stats.expon:
|
||||
axHisty.plot(distr.pdf(y),y)
|
||||
z2 = distr.pdf(y)
|
||||
y = hstack((y[0],y,y[-1]))
|
||||
z2 = hstack((0,z2,0))
|
||||
axHisty.fill(z2,y,color=(.0,.0,1.))
|
||||
|
||||
axQQ.set_xlim(axHistx.get_xlim())
|
||||
axQQ.set_ylim(axHisty.get_ylim())
|
||||
|
||||
if distr == stats.uniform:
|
||||
axQQ.set_ylim((-.1,1.1))
|
||||
axHisty.set_ylim((-.1,1.1))
|
||||
axHisty.set_xlim((.0,1.1))
|
||||
|
||||
axHistx.set_xlabel('x',fontsize=16)
|
||||
axHistx.set_ylabel('p(x)',fontsize=16)
|
||||
axHisty.set_ylabel('y',fontsize=16)
|
||||
axHisty.set_xlabel('p(y)',fontsize=16)
|
||||
|
||||
fig.savefig('figs/HE%i.png' % (k,))
|
||||
if distr == stats.norm:
|
||||
axQQ.plot(x, z,'ok',marker=col[0],ms=5)
|
||||
elif distr == stats.expon:
|
||||
axHisty.plot(distr.pdf(y),y)
|
||||
z2 = distr.pdf(y)
|
||||
y = hstack((y[0],y,y[-1]))
|
||||
z2 = hstack((0,z2,0))
|
||||
axHisty.fill(z2,y,color=(.0,.0,1.))
|
||||
|
||||
else:
|
||||
axQQ.legend(loc=2)
|
||||
fig.savefig('figs/HE%iSolution.png' % (k,))
|
||||
|
||||
# ####################################################3
|
||||
fig = figure()
|
||||
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
xx = linspace(-3.,stats.norm.ppf(1-0.2),1000)
|
||||
|
||||
x = linspace(-3.,3.,1000)
|
||||
y = stats.norm.pdf(x,scale=1)
|
||||
yy = stats.norm.pdf(xx,scale=1)
|
||||
yy[0] = 0
|
||||
yy[-1] = 0
|
||||
|
||||
ax.plot(x,y,'k-',lw=2)
|
||||
ax.plot(x,stats.norm.pdf(x),'k-',lw=1)
|
||||
ax.set_xlabel('x',fontsize=16)
|
||||
ax.set_ylabel('pdf',fontsize=16)
|
||||
ax.fill(xx,yy,'b')
|
||||
|
||||
ax.set_xlim(-3.,3.)
|
||||
|
||||
ax.text(xx[-1],-.1,'b');
|
||||
|
||||
ax.text(xx[-1],.4,'p(x)',color='k');
|
||||
ax.text(xx[0],.3,'F(b) = P(x <= b)',color='b');
|
||||
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
|
||||
fig.savefig('figs/cdf.png')
|
||||
|
||||
#-----------------------------
|
||||
fig = figure()
|
||||
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
xx = linspace(-3.,stats.norm.ppf(1-0.2),1000)
|
||||
|
||||
x = linspace(-3.,3.,1000)
|
||||
y = stats.norm.pdf(x,scale=1)
|
||||
yy = stats.norm.pdf(xx,scale=1)
|
||||
yy[0] = 0
|
||||
yy[-1] = 0
|
||||
|
||||
ax.plot(x,y,'k-',lw=2)
|
||||
ax.plot(x,stats.norm.cdf(x),'b-',lw=1)
|
||||
ax.set_xlabel('x/b',fontsize=16)
|
||||
ax.set_ylabel('pdf/cdf',fontsize=16)
|
||||
|
||||
ax.set_xlim(-3.,3.)
|
||||
|
||||
ax.text(xx[-1],.4,'p(x)',color='k');
|
||||
ax.text(xx[0],.3,'F(b) = P(x <= b)',color='b');
|
||||
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
|
||||
fig.savefig('figs/cdf2.png')
|
||||
|
||||
# ####################################################3
|
||||
fig = figure()
|
||||
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
x = hstack((linspace(-3.,stats.norm.ppf(0.13),1000),\
|
||||
linspace(stats.norm.ppf(1-0.13),3.,1000)))
|
||||
|
||||
xx = hstack((linspace(-3.,stats.norm.ppf(0.2),1000),\
|
||||
linspace(stats.norm.ppf(1-0.2),3.,1000)))
|
||||
|
||||
y = stats.norm.pdf(x,scale=1)
|
||||
yy = stats.norm.pdf(xx,scale=1)
|
||||
|
||||
y[[0,999,1000,-1]] = 0
|
||||
yy[[0,999,1000,-1]] = 0
|
||||
|
||||
t = linspace(-3.,3.,1000)
|
||||
ax.plot(t,stats.norm.pdf(t),'k-',lw=2)
|
||||
|
||||
ax.fill(xx[:1000],yy[:1000],'b')
|
||||
ax.fill(xx[1000:],yy[1000:],'b')
|
||||
ax.text(xx[1000],-.1,'b')
|
||||
ax.text(xx[999],-.1,'-b')
|
||||
ax.text(.2,.7,'P(|x|>b) =$\\alpha$',color='b');
|
||||
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
|
||||
fig.savefig('figs/pval0.png')
|
||||
|
||||
#---------------------------------------------------
|
||||
fig = figure()
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
t = linspace(-3.,3.,1000)
|
||||
ax.plot(t,stats.norm.pdf(t),'k-',lw=2)
|
||||
|
||||
|
||||
ax.fill(x[:1000],y[:1000],'r')
|
||||
ax.fill(x[1000:],y[1000:],'r')
|
||||
|
||||
|
||||
ax.text(x[1000],-.1,'t')
|
||||
ax.text(x[999],-.1,'-t')
|
||||
|
||||
ax.text(.2,.5,'P(|x| > t) = p-value',color='r');
|
||||
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
|
||||
fig.savefig('figs/ pval1.png')
|
||||
|
||||
|
||||
|
||||
# show()
|
||||
|
||||
|
||||
#-----------------------------
|
||||
@ -1,184 +0,0 @@
|
||||
import sys
|
||||
import seaborn as sns
|
||||
sys.path.append('/home/fabee/code')
|
||||
from matplotlib.pyplot import *
|
||||
from scipy import stats
|
||||
from numpy import *
|
||||
|
||||
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})
|
||||
|
||||
# define the curves
|
||||
x = np.linspace(2, 20, 200)
|
||||
n = 16.
|
||||
|
||||
|
||||
X =random.randn(n)*4.+12.5
|
||||
fig = figure()
|
||||
ax = fig.add_subplot(111)
|
||||
ax.set_xlim(5, 18)
|
||||
#ax.set_ylim(0, .5)
|
||||
ax.plot([10,10],[-.2,.2],'k-',lw=2)
|
||||
ax.text(10,.3,r'stimulus position',rotation=-30);
|
||||
ax.plot([12.5,12.5],[-.2,.2],'b-',lw=2)
|
||||
ax.text(12.5,.3,r'$\hat\mu$',rotation=-45);
|
||||
|
||||
ax.set_xlabel('x eye position')
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
ax.plot(X,0*X,'ob',label='fixations',mfc='orange',ms=10)
|
||||
fig.savefig('figs/repetition0.png')
|
||||
|
||||
# ####################################################3
|
||||
fig = figure()
|
||||
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
ax.plot(x,-stats.norm.pdf(x,loc=10,scale=4),'orange',label=r'Null distribution of x')
|
||||
|
||||
|
||||
ax.set_xlim(5, 18)
|
||||
# ax.set_ylim(0, .5)
|
||||
|
||||
ax.plot([10,10],[-.2,.2],'k-',lw=2)
|
||||
ax.text(10,.3,r'stimulus position',rotation=-30);
|
||||
ax.plot([12.5,12.5],[-.2,.2],'b-',lw=2)
|
||||
ax.text(12.5,.3,r'$\hat\mu$',rotation=-45);
|
||||
ax.legend()
|
||||
|
||||
ax.set_xlabel('x eye position')
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
ax.plot(X,0*X,'ob',label='fixations',mfc='orange',ms=10)
|
||||
|
||||
fig.savefig('figs/repetition1.png')
|
||||
|
||||
|
||||
# ####################################################3
|
||||
fig = figure()
|
||||
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
ax.plot(x,-stats.norm.pdf(x,loc=10,scale=4),'orange',label=r'Null distribution of x')
|
||||
ax.plot(x,-stats.t.pdf(x,n-1,loc=10,scale=1),'b',label=r'Null distribution of $t$')
|
||||
|
||||
ax.set_xlim(5, 18)
|
||||
# ax.set_ylim(0, .5)
|
||||
|
||||
ax.plot([10,10],[-.2,.2],'k-',lw=2)
|
||||
ax.text(10,.3,r'stimulus position',rotation=-30);
|
||||
ax.plot([12.5,12.5],[-.2,.2],'b-',lw=2)
|
||||
ax.text(12.5,.3,r'$\hat\mu$',rotation=-45);
|
||||
ax.legend()
|
||||
|
||||
ax.set_xlabel('x eye position')
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
ax.plot(X,0*X,'ob',label='fixations',mfc='orange',ms=10)
|
||||
|
||||
fig.savefig('figs/repetition2.png')
|
||||
|
||||
# ####################################################3
|
||||
fig = figure()
|
||||
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
xx = linspace(stats.norm.ppf(0.05),stats.norm.ppf(1-0.05),100)
|
||||
xx += 10.
|
||||
|
||||
yy = -stats.norm.pdf(xx,loc=10.,scale=1)
|
||||
xx = hstack((xx[0],xx,xx[-1]))
|
||||
yy = hstack((0,yy,0))
|
||||
|
||||
ax.plot(x,-stats.norm.pdf(x,loc=10,scale=4),'orange',label=r'Null distribution of x')
|
||||
ax.plot(x,-stats.t.pdf(x,n-1,loc=10,scale=1),'b',label=r'Null distribution of $t$')
|
||||
|
||||
ax.fill(xx,yy,'c')
|
||||
|
||||
ax.set_xlim(5, 18)
|
||||
# ax.set_ylim(0, .5)
|
||||
|
||||
ax.plot([10,10],[-.2,.2],'k-',lw=2)
|
||||
ax.text(10,.3,r'stimulus position',rotation=-30);
|
||||
ax.plot([12.5,12.5],[-.2,.2],'b-',lw=2)
|
||||
ax.text(12.5,.3,r'$\hat\mu$',rotation=-45);
|
||||
ax.legend()
|
||||
|
||||
ax.set_xlabel('x eye position')
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
ax.plot(X,0*X,'ob',label='fixations',mfc='orange',ms=10)
|
||||
|
||||
fig.savefig('figs/repetition3.png')
|
||||
|
||||
|
||||
# ####################################################3
|
||||
fig = figure()
|
||||
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
xx = linspace(stats.norm.ppf(0.05),stats.norm.ppf(1-0.05),100)
|
||||
xx += 10.
|
||||
|
||||
yy = -stats.norm.pdf(xx,loc=10.,scale=1)
|
||||
xx = hstack((xx[0],xx,xx[-1]))
|
||||
yy = hstack((0,yy,0))
|
||||
|
||||
ax.plot(x,-stats.norm.pdf(x,loc=10,scale=4),'orange',label=r'Null distribution of x')
|
||||
ax.plot(x,-stats.t.pdf(x,n-1,loc=10,scale=1),'b',label=r'Null distribution of $t$')
|
||||
|
||||
ax.fill(xx,yy,'c')
|
||||
|
||||
ax.set_xlim(5, 18)
|
||||
# ax.set_ylim(0, .5)
|
||||
|
||||
ax.plot([10,10],[-.2,.2],'k-',lw=2)
|
||||
ax.text(10,.3,r'stimulus position',rotation=-30);
|
||||
ax.plot([12.5,12.5],[-.2,.2],'b-',lw=2)
|
||||
ax.text(12.5,.3,r'$\hat\mu$',rotation=-45)
|
||||
|
||||
ax.plot([xx[0],xx[-1]],[0,0],'-g',label=r'$H_0$',lw=4)
|
||||
ax.plot([0,xx[0]],[0,0],'-r',label=r'$H_1$',lw=4)
|
||||
ax.plot([xx[-1],20],[0,0],'-r',lw=4)
|
||||
|
||||
ax.legend()
|
||||
|
||||
ax.set_xlabel('x eye position')
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
ax.plot(X,0*X,'ob',label='fixations',mfc='orange',ms=10)
|
||||
|
||||
fig.savefig('figs/repetition4.png')
|
||||
|
||||
|
||||
# ####################################################3
|
||||
fig = figure()
|
||||
|
||||
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
ax.plot(x,-stats.norm.pdf(x,loc=10,scale=4),'orange',label=r'Null distribution of x')
|
||||
ax.plot(x,-stats.t.pdf(x,n-1,loc=10,scale=1),'b',label=r'Null distribution of $t$')
|
||||
|
||||
xx = linspace(0,stats.norm.ppf(0.05)+10.,100)
|
||||
yy = -stats.norm.pdf(xx,loc=10.,scale=1)
|
||||
xx = hstack((xx[0],xx,xx[-1]))
|
||||
yy = hstack((0,yy,0))
|
||||
ax.fill(xx,yy,'magenta')
|
||||
|
||||
xx = linspace(stats.norm.ppf(1-0.05)+10.,20,100)
|
||||
yy = -stats.norm.pdf(xx,loc=10.,scale=1)
|
||||
xx = hstack((xx[0],xx,xx[-1]))
|
||||
yy = hstack((0,yy,0))
|
||||
ax.fill(xx,yy,'magenta')
|
||||
|
||||
ax.set_xlim(5, 18)
|
||||
# ax.set_ylim(0, .5)
|
||||
|
||||
ax.plot([10,10],[-.2,.2],'k-',lw=2)
|
||||
ax.text(10,.3,r'stimulus position',rotation=-30);
|
||||
ax.plot([12.5,12.5],[-.2,.2],'b-',lw=2)
|
||||
ax.text(12.5,.3,r'$\hat\mu$',rotation=-45);
|
||||
ax.legend()
|
||||
|
||||
ax.set_xlabel('x eye position')
|
||||
#XKCDify(ax, expand_axes=True,yaxis_loc=0,xaxis_loc=0)
|
||||
ax.plot(X,0*X,'ob',label='fixations',mfc='orange',ms=10)
|
||||
|
||||
fig.savefig('figs/repetition5.png')
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 39 KiB |
Binary file not shown.
Binary file not shown.
@ -1,72 +0,0 @@
|
||||
from __future__ import division
|
||||
from numpy import *
|
||||
from scipy import stats
|
||||
from matplotlib.pyplot import *
|
||||
|
||||
N = random.randn
|
||||
|
||||
m = 2000
|
||||
n = 20
|
||||
|
||||
T = zeros((m,))
|
||||
R = zeros((m,))
|
||||
pT = zeros((m,))
|
||||
pR = zeros((m,))
|
||||
|
||||
for k in xrange(m):
|
||||
x = N(n)
|
||||
y = N(n)
|
||||
|
||||
T[k], pT[k] = stats.ttest_ind(x,y)
|
||||
R[k], pR[k] = stats.ranksums(x,y)
|
||||
|
||||
a = stats.t.ppf([0.025,1.-0.025], n-1)
|
||||
b = stats.norm.ppf([0.025,1.-0.025])
|
||||
|
||||
|
||||
fig = figure(figsize=(8,8),dpi=100)
|
||||
ax = fig.add_axes([.3,.3,.6,.6])
|
||||
axb = fig.add_axes([.3,.1,.6,.2])
|
||||
axl = fig.add_axes([.1,.3,.2,.6])
|
||||
|
||||
ax.plot(T,R,'ok',mfc=(.7,.7,.7))
|
||||
axb.hist(T,bins=50,facecolor=(1.,.7,.7),normed=True)
|
||||
axl.hist(R,bins=50,facecolor=(.7,.7,1.),normed=True,orientation='horizontal')
|
||||
axl.axis([0,1,-5,5])
|
||||
axb.plot([a[0],a[0]],[0,1],'k--',lw=2)
|
||||
axb.plot([a[1],a[1]],[0,1],'k--',lw=2)
|
||||
|
||||
axl.plot([0,1],[b[0],b[0]],'k--',lw=2)
|
||||
axl.plot([0,1],[b[1],b[1]],'k--',lw=2)
|
||||
axl.set_ylabel('standardized U statistic', fontsize=16)
|
||||
axb.set_xlabel('t statistic', fontsize=16)
|
||||
|
||||
# print sum(1.*(T < a[0] ))/m + sum(1.*(T > a[1]))/m
|
||||
# print sum(1.*(R < b[0] ))/m + sum(1.*(R > b[1]))/m
|
||||
|
||||
ax.fill([-5,a[0],a[0],-5],[-5,-5,5,5],color=(1.,.7,.7),alpha=.5)
|
||||
ax.fill([a[1],5,5,a[1]],[-5,-5,5,5],color=(1.,.7,.7),alpha=.5)
|
||||
axb.fill([-5,a[0],a[0],-5],[0,0,1,1],color=(1.,.7,.7),alpha=.5)
|
||||
axb.fill([a[1],5,5,a[1]],[0,0,1,1],color=(1.,.7,.7),alpha=.5)
|
||||
|
||||
|
||||
ax.fill([-5,-5,5,5],[-5,b[0],b[0],-5],color=(.7,.7,1.),alpha=.5)
|
||||
ax.fill([-5,-5,5,5],[b[1],5,5,b[1]],color=(.7,.7,1.),alpha=.5)
|
||||
axl.fill([0,0,1,1],[-5,b[0],b[0],-5],color=(.7,.7,1.),alpha=.5)
|
||||
axl.fill([0,0,1,1],[b[1],5,5,b[1]],color=(.7,.7,1.),alpha=.5)
|
||||
|
||||
|
||||
|
||||
axb.axis([-5,5,0,1])
|
||||
ax.axis([-5,5,-5,5])
|
||||
|
||||
axl.set_xticks([])
|
||||
axb.set_yticks([])
|
||||
axl = axl.twiny()
|
||||
axb = axb.twinx()
|
||||
axl.set_xticks([0,.5,1.])
|
||||
axb.set_yticks([0,.5,1.])
|
||||
|
||||
|
||||
|
||||
fig.savefig('multipletesting.pdf')
|
||||
BIN
statistics/figs/nacho-trainer.jpg
Normal file
BIN
statistics/figs/nacho-trainer.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 132 KiB |
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Binary file not shown.
|
Before Width: | Height: | Size: 28 KiB |
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user