APA2: APA Style HTML-Tabellen-Ausgabe
In stp4/stp25stat: Functions for statistical computations

Description Usage Arguments Value Examples

APA2.matchit print a short summary

Corr1() wird in in APA2 beim Einzelvergeich verwendet.

Die Interne Funktion Corr2() wird in APA2 verwendete um Korrelation zu berechnen.

APA2 erstellt fertigen HTML-Tabellen Output.

Die Funktion APA2.formula estellt die Standard-Tabellen (analog wie die Hmisc:summary). Links stehen die Zielvariablen rechts die Gruppen.

Fie Formel a1 + a2[4] +a3 ~ group1 + group2 ergibt zwei Auswertungen. Die Zahle in eckiger Klammer sind die Nachkommastellen. Achtung die Formeln sind auf 500 zeichen begrenzt (Limitation von der Funktion deparse()) Einstellungen werden global erstellt:

set_my_options(prozent=list(digits=c(1,0), style=2))

get_my_options()$apa.style$prozent

Likert type : c(1, 2), oder c("Freq", "Precent")

Errate korekte Auswertung und Extrahieren der Variablen aus Formula.

Kano: von Kano-Fragebogen.

APA-Methode fuer pwr::pwr.f2.test

APA-Methode fuer meta

Ausgabe von Regressions Tabelle nach der APA-Style vorgabe. Die Funktion ist eine Kopie von texreg aggregate.matrix.

Ordered Logistic or Probit Regression https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/

MANOVA: APA2(x, , test="Wilks") test : "Wilks", "Pillai"

Canonical Discriminant Analysis (MANOVA)

Anova- Methode (MANOVA)

LDA (linear discriminants analysis) Erweiterung der MANOVA

APA2.glht multcomp::glht

anova: APA2.aov(x, include.eta = TRUE)

## S3 method for class 'htest'
APA2(x, caption = "", ...)

## S3 method for class 'pairwise.htest'
APA2(x, caption = "", ...)

## S3 method for class 'ICC'
APA2(x, caption = "ICC", note = NULL, type = c(1, 4), output = which_output())

## S3 method for class 'matchit'
APA2(x, ...)

## S3 method for class 'summary.matchit'
APA2(x, ..., digits = 2)

## S3 method for class 'bland_altman'
APA2(
  x,
  caption = paste0("Difference (", x$name.diff, "), Mean (", x$name, ")"),
  note = "",
  ...
)

## S3 method for class 'bland_altman'
print(x)

Corr1(
  y,
  n = nrow(y),
  type = "pearson",
  include.p = FALSE,
  include.stars = TRUE,
  cor_diagonale_up = TRUE,
  dimnames = FALSE
)

Corr2(x, y = NULL, type = "pearson", stars, ...)

APA2(x, ...)

## S3 method for class ''NULL''
APA2(x, ...)

## S3 method for class 'psychobject'
APA2(
  x,
  caption = "",
  note = NULL,
  include.ci = FALSE,
  include.effect = FALSE,
  output = stp25output::which_output(),
  ...
)

## Default S3 method:
APA2(x, ...)

## S3 method for class 'formula'
APA2(
  x,
  data = NULL,
  caption = "",
  fun = NULL,
  type = c("auto", "freq", "mean", "median", "ci", "multiresponse", "cohen.d",
    "effsize", "freq.ci", "describe"),
  note = "",
  na.action = na.pass,
  test = FALSE,
  corr_test = "pearson",
  cor_diagonale_up = TRUE,
  direction = "long",
  order = FALSE,
  decreasing = TRUE,
  use.level = 1,
  include.n = TRUE,
  include.all.n = NULL,
  include.header.n = TRUE,
  include.total = FALSE,
  include.test = test,
  include.p = FALSE,
  include.stars = TRUE,
  include.names = FALSE,
  include.labels = TRUE,
  digits = NULL,
  digits.mean = if (!is.null(digits)) c(digits, digits) else NULL,
  digits.percent = if (is.null(digits)) options()$stp25$apa.style$prozent$digits else
    c(digits, 0),
  output = which_output(),
  ...
)

Recast2_fun(
  x,
  data,
  caption = "",
  fun,
  direction = "long",
  note = "",
  include.n = FALSE,
  ...
)

## S3 method for class 'likert'
APA2(
  x,
  caption = "",
  note = "",
  ReferenceZero = NULL,
  type = "percent",
  include.mean = TRUE,
  na.exclude = FALSE,
  labels = c("low", "neutral", "high"),
  order = FALSE,
  output = which_output(),
  ...
)

errate_statistik2(
  Formula,
  data,
  caption = "caption",
  note = "note",
  type = NULL,
  na.action = na.pass,
  exclude = NA,
  include.n = TRUE,
  include.all.n = NULL,
  include.header.n = TRUE,
  include.total = FALSE,
  include.test = FALSE,
  include.p = TRUE,
  include.stars = FALSE,
  corr_test = "pearson",
  cor_diagonale_up = TRUE,
  max_factor_length = 35,
  order = FALSE,
  decreasing = FALSE,
  useconTest = FALSE,
  normality.test = FALSE,
  digits.mean = options()$stp25$apa.style$m$digits,
  digits.percent = options()$stp25$apa.style$prozent$digits[1],
  test_name = "Hmisc",
  ...
)

## S3 method for class 'fa'
APA2(...)

## S3 method for class 'principal'
APA2(
  x,
  caption = "",
  note = "",
  digits = 2,
  all = FALSE,
  cut = 0.3,
  sort = TRUE,
  suppress.warnings = TRUE,
  ...
)

## S3 method for class 'stp25_reliability'
APA2(x, caption = "", note = "", output = which_output(), ...)

## S3 method for class 'Kano'
APA2(x, caption = "", note = NULL, output = which_output(), ...)

## S3 method for class 'Kano'
print(x, ...)

## S3 method for class 'power.htest'
APA(x, ..., output = which_output())

## S3 method for class 'meta'
APA2(
  x,
  caption = "Meta-analysis",
  note = paste("estimate:", x$sm, x$method.smd),
  include.table = TRUE,
  include.fixed = FALSE,
  include.random = TRUE,
  include.sub = TRUE,
  include.total = TRUE,
  include.heterogeneity = TRUE,
  digits = 2,
  output = which_output(),
  ...
)

## S3 method for class 'list'
APA2(...)

## S3 method for class 'lm'
APA2(
  x,
  caption = NULL,
  note = NULL,
  output = stp25output::which_output(),
  col_names = NULL,
  include.b = TRUE,
  include.se = TRUE,
  include.beta = FALSE,
  include.ci = FALSE,
  include.r = TRUE,
  include.test = FALSE,
  include.eta = TRUE,
  include.sumsq = TRUE,
  include.meansq = FALSE,
  digits.test = 2,
  ci.level = 0.95,
  test.my.fun = FALSE,
  conf.style.1 = TRUE,
  ...
)

## S3 method for class 'glm'
APA2(
  x,
  caption = NULL,
  note = NULL,
  output = stp25output::which_output(),
  col_names = NULL,
  include.b = TRUE,
  include.se = TRUE,
  include.ci = FALSE,
  include.odds = TRUE,
  include.odds.ci = include.ci,
  include.statistic = TRUE,
  include.p = TRUE,
  include.stars = FALSE,
  include.r = TRUE,
  include.pseudo = include.r,
  include.test = FALSE,
  include.rr = FALSE,
  include.rr.ci = include.ci,
  ci.level = 0.95,
  conf.method = "Wald",
  test.my.fun = FALSE,
  conf.style.1 = TRUE,
  digits = 2,
  ...
)

## S3 method for class 'lme'
APA2(...)

## S3 method for class 'lmerMod'
APA2(
  x,
  caption = NULL,
  note = NULL,
  output = stp25output::which_output(),
  col_names = NULL,
  include.b = TRUE,
  include.se = TRUE,
  include.ci = FALSE,
  include.odds = FALSE,
  include.odds.ci = include.ci,
  include.statistic = TRUE,
  include.p = TRUE,
  include.stars = FALSE,
  include.r = TRUE,
  include.pseudo = include.r,
  include.test = FALSE,
  ci.level = 0.95,
  conf.method = "Wald",
  test.my.fun = FALSE,
  conf.style.1 = TRUE,
  digits = 2,
  ...
)

## S3 method for class 'polr'
APA2(
  x,
  caption = NULL,
  note = NULL,
  include.b = TRUE,
  include.se = TRUE,
  include.ci = FALSE,
  include.odds = TRUE,
  ...
)

## S3 method for class 'step'
APA2(
  x,
 
    caption = "Backward elimination of non-significant effects of linear mixed effects model",
  note = "",
  include.se = FALSE,
  include.df = FALSE,
  ...
)

## S3 method for class 'manova'
APA2(
  x,
  test = "Wilks",
  caption = "MANOVA",
  note = "",
  type = c("anova", "manova"),
  include.eta = TRUE,
  output = which_output()
)

## S3 method for class 'candisc'
APA2(x, caption = NA, note = "", output = which_output(), LRtests = TRUE, ...)

## S3 method for class 'Anova.mlm'
APA2(x, caption = NA, note = "", output = which_output(), ...)

## S3 method for class 'lda'
APA2(
  x,
  fit_predict = MASS:::predict.lda(x),
  newdata = model.frame(x),
  caption = "",
  note = "",
  output = which_output(),
  ...
)

## S3 method for class 'TukeyHSD'
APA2(
  x,
  caption = "TukeyHSD",
  note = "",
  output = stp25output::which_output(),
  ...
)

## S3 method for class 'glht'
APA2(
  x,
  caption = "Multiple Comparisons of Means",
  note = "",
  output = stp25output::which_output(),
  include.ci = TRUE,
  level = 0.95,
  ...
)

## S3 method for class 'multicomp'
APA2(x, ...)

## S3 method for class 'anova'
APA2(
  x,
  caption = gsub("\\n", "", paste(attr(x, "heading"), collapse = ", ")),
  note = paste("contrasts: ", paste(options()$contrasts, collapse = ", ")),
  output = stp25output::which_output(),
  include.eta = FALSE,
  include.sumsq = TRUE,
  include.meansq = FALSE,
  ...
)

## S3 method for class 'aov'
APA2(
  x,
  caption = "ANOVA",
  note = paste("contrasts: ", paste(options()$contrasts, collapse = ", ")),
  output = stp25output::which_output(),
  col_names = NULL,
  ...
)

## S3 method for class 'summary.aov'
APA2(
  x,
  caption = "ANOVA",
  note = "",
  output = stp25output::which_output(),
  col_names = NULL,
  ...
)

## S3 method for class 'aovlist'
APA2(x, output = stp25output::which_output(), col_names = NULL, ...)

## S3 method for class 'epi.tests'
APA2(x, ...)

## S3 method for class 'loglm'
APA2(
  x,
  caption = "Likelihood",
  note = "",
  output = stp25output::which_output(),
  col_names = NULL,
  ...
)

## S3 method for class 'summary.table'
APA2(x, ...)

## S3 method for class 'table'
APA2(...)

## S3 method for class 'xtabs'
APA2(
  x,
  caption = "",
  note = "",
  output = stp25output::which_output(),
  digits = NULL,
  include.total = FALSE,
  include.total.columns = FALSE,
  include.total.sub = FALSE,
  include.total.rows = FALSE,
  include.percent = TRUE,
  include.count = TRUE,
  include.margins = TRUE,
  margin = NA,
  add.margins = NA,
  include.correlation = FALSE,
  include.test = FALSE,
  include.sensitivity = FALSE,
  prevalence = NULL,
  ...
)

`x`	epi.tests Objekt
`caption, note`	Ueberschrift an Output
`...`	an Output
`type`	formula: `c("auto", "freq", "mean", "median", "ci", "freq.ci")` xtabs: type = c("fischer", "odds","sensitivity", "chisquare","correlation", "r")
`output`	Ausgabe von Ergebiss ueber Output
`digits`	Nachkommastellen
`cor_diagonale_up`	bei Correlation art der Formatierung
`include.ci`	APA2.glht: Cis
`data`	data.frame wenn x eine Formel ist
`fun, na.action, direction`	eigene Funktion na.action=na.pass
`test, include.test, corr_test, include.p, include.stars`	Sig test bei `type = auto` moegliche Parameter sind test=TRUE, test="conTest" oder "sapiro.test" fuer den Test auf Normalverteilung, fuer SPSS-like `test=="wilcox.test"` oder `test=="kruskal.test"` corr_test-ddefault ist "pearson" c("pearson","spearman")
`direction`	long or wide
`order, decreasing`	Sortieren Reihenfolge der Sortierung
`use.level`	Benutzter level in Multi zB ja/nein
`include.n, include.all.n, include.header.n, include.total`	N mit ausgeben
`include.total`	Zusammenfassung als Gesamtwert
`include.names, include.labels`	Beschriftung der zeilen
`digits.mean, digits.percent`	Nachkommastellen
`ReferenceZero, labels, na.exclude, labels, include.mean`	Likert: ReferenceZero=2 Neutrales Element in Kombination mit labels = c("low", "neutral", "high") Mittelwerte T/F
`max_factor_length, useconTest, normality.test, test_name, exclude, stars, Formula`	interne Parameter in erate_statistik
`all`	APA2.principal if all=TRUE, then the object is declassed and all output from the function is printed
`suppress.warnings`	APA2.principal Fehlermeldung unterdruecken
`include.fixed, include.random`	Fixed effect and random effects model:
`include.sub`	Subgruppen-Analyse
`include.se, include.ci, include.odds`	SE, 95-Ci, OR noch nicht fertig
`include.r, include.pseudo`	pseudo R
`include.eta`	die Manova wird ueber heplots::etasq berechnet und die anova mit den SS eta2=SS/SS_total
`ci.level`	Ci default 95 Prozent
`include.df`	df mit ausgeben
`LRtests`	an candisc::Wilks Wilks Lambda Tests for Canonical Correlations
`fit_predict`	lda: MASS predict.lda
`newdata`	lda:model.frame
`include.percent, include.count`	ausgabe
`include.margins`	sollen überhaupt Summen (margins) ausgegeben werden die Prozent werden aber wie mit den Margins gerechnet
`margin, add.margins`	alternative zu include.total
`custom.model.names`	Namen ner Modelle
`include.custom`	liste mit Statistiken für Gofs also zB F-Tests
`include.ftest, include.loglik`	noch nicht fertig
`include.aic, include.bic`	geht nur zusammen
`rgroup`	Zwischen Beschriftung
`anova_type`	bei lme: "F" F-werte (wie SPSS) oder Chi (car::Anova)
`caption, note, output, col_names, print_col, labels`	an Output
`test, type`	fischer chi usw
`include.total, include.total.columns, include.total.sub, include.total.rows`	Zeilen Prozenz usw

html-String ueber cat sowi einen data.frame

liste mit data.frames

invisible data.frame und Output mit html/knit oder Text.

dataframe mit p.werte

data.frame

list(xtab, test)

 
require(MatchIt)
require(wakefield)
require(stpvers)
#Projekt("html")


set.seed(0815)

nonMetra <- 702
Metra <- 59

DF3 <- r_data_frame(
  n = nonMetra + Metra,
  d.age = age,
  d.sex = sex,
  r.hcv = rnorm,
  et.dri  = rnorm,
  d.dcd = rnorm,
  d.steatose = answer
)


#+ results='asis'
DF3$metra <- c(rep(0, nonMetra),  rep(1, Metra))
DF3$Metra<- factor(DF3$metra, 0:1,  c("non-Metra", "Metra"))
fm <-  metra ~  d.age + d.sex + r.hcv + et.dri + d.dcd + d.steatose


# 
# m.out <- matchit(fm, data = DF3,   method = "nearest")
# APA2(m.out,caption = "Exact Matching ")
# df.match <- match.data(m.out)
# summary(df.match)





m.out0 <- matchit(fm, data = DF3,   method = "full")
APA2(m.out0,caption = "Matching  method = full")

# m.out1 <- matchit(fm, data = DF3, method = "genetic")
# APA2(m.out1,caption = "Matching method = genetic")

m.out2 <- matchit(fm, data = DF3, method = "optimal", ratio = 1)
APA2(m.out2, caption = "Matching  method = optimal, ratio = 1")

#APA2(summary(m.out0, standardize = TRUE))
# plot(m.out0, type = 'jitter', interactive = FALSE)


df.match0 <- match.data(m.out0)
summary(df.match0)
df.match0 %>% Tabelle2(
  d.age, d.sex, r.hcv, et.dri, d.dcd, d.steatos,
  distance,
  weights,
  subclass,
  by =  ~ Metra,
  APA = TRUE
)

# df.match1 <- match.data(m.out1)
# summary(df.match1)
# df.match1 %>% Tabelle2(
#   d.age, d.sex, r.hcv, et.dri, d.dcd, d.steatos,
#   distance,
#   weights,
#   by =  ~ Metra,
#   APA = TRUE
# )

df.match2 <- match.data(m.out2)
summary(df.match2)
df.match2 %>% Tabelle2(
  d.age, d.sex, r.hcv, et.dri, d.dcd, d.steatos,
  distance,
  weights,
  subclass,
  by =  ~ Metra,
  APA = TRUE
)

#df.match0$subclass 
# End()

#-- Corr1
# APA2(~a+b+cd, data)
#stp25stat:::Corr1(data[1:3], dimnames=TRUE)
#stp25stat:::Corr1(data[1:3], dimnames=TRUE, include.p=TRUE)

#-- Corr2
# APA2(a+b+c~d, data )
#stp25APA2:::Corr2(data[1:3], data[4], "pearson", TRUE)


#-- APA2.formula --
require(stp25data)
APA2(chol0+chol1 ~ g, hyper, print.n=FALSE)
APA2(~ g, hyper, caption="Einfache Tabelle")
APA2(chol0+chol1 ~ g, hyper, caption="Spalte mit Characteristik loeschen", print_col=-2)
APA2(gew + rrs0 ~ g, hyper, print.n=FALSE, test=TRUE)
APA2(~chol0+chol1~chol6+chol12, hyper, caption="Korrelation", test=TRUE)
APA2(~chol0+chol1+chol6+chol12, hyper, caption="Korrelation", test=TRUE, stars=FALSE)




#varana <- varana %>% Label(m1="Mesung1", m2="BMI")
x<-APA2( ~m1,varana)
x<-APA2( ~m1+m2,varana)

x<-APA2( m1~geschl,varana)
x<-APA2( m1+m2~alter,varana)
x<-APA2( m1+m2+geschl~alter,varana, include.test = TRUE)
x<-APA2( ~m1+m2+m3+m4,varana, test=TRUE)


library(lmerTest)

fit1 <- lm(chol0 ~  ak + rrs0 + med + g, hyper)
fit2 <- glm(chol0 ~ med +   ak +   g + rrs0 , hyper, family = poisson())
fit3 <- lmerTest::lmer(chol0 ~ rrs0 + med +  ak  +  (1|g) , hyper )
fits <- list(fit1, fit2, fit3)

APA2(fits,
          custom.model.names=c("lm", "glm", "lmer"),
          digits= list(c(1,2,3,4,5,6,7),
                       c(1,2,3,4,5,6,7),
                       c(1,2,3,4,5,6)),
          include.custom=list(
                       Wald=c("F(1)=245", "F(2)=245","F(3)=245"),
                       Chi=c("X(4)=2.45", "X(5)=24.5","X(6)=24.5")))



 #--- Ordered Logistic or Probit Regression
 require(MASS)
 # options(contrasts = c("contr.treatment", "contr.poly"))
  house.plr <- MASS::polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
  APA2(house.plr, note= APA(house.plr))



summary(lm1 <- lm(Fertility ~ ., data = swiss))
slm1 <- stats::step(lm1)
APA2(slm1)
# require(lmerTest)
 #m <- lmerTest::lmer(
 # Informed.liking ~ Product * Information * Gender +
#  (1 | Consumer) + (1 | Product:Consumer),
#  data = ham
# )
# elimination of non-significant effects
# s <- lmerTest::step(m)

# APA2(s)


#- manova ---------------------------------------------

## Set orthogonal contrasts.
op <- options(contrasts = c("contr.helmert", "contr.poly"))
## Fake a 2nd response variable
npk2 <- within(npk, foo <- rnorm(24))
npk2 <- within(npk2, foo2 <- rnorm(24))
npk2.aov <- manova(cbind(yield, foo, foo2) ~ block + N * P * K, npk2)

APA2(npk2.aov) #wilks
APA2(npk2.aov, "Pillai")

#npk2.aovE <- manova(cbind(yield, foo) ~  N*P*K + Error(block), npk2)
#APA2(npk2.aovE)



 DF<-stp25aggregate::GetData(
"C:/Users/wpete/Dropbox/3_Forschung/R-Project/stp25data/extdata/manova.sav"
)

#information from
DF$GROUP<- factor(DF$GROUP, 1:3, Cs("website", "nurse ", "video tape" ))
#DF %>% Tabelle2(USEFUL, DIFFICULTY, IMPORTANCE, by=~GROUP )

z<- as.matrix(DF[,-1])
fit1<- manova(z ~ DF$GROUP)
APA2(fit1)
summary(fit1)$Eigenvalues

# SPSS
# Multivariate Tests of Significance (S = 2, M = 0, N = 13 )
#
# Test Name       Value  Approx. F Hypoth. DF   Error DF  Sig. of F
#
# Pillais          .48    3.02       6.00      58.00       .012
# Hotellings       .90    4.03       6.00      54.00       .002
# Wilks            .53    3.53       6.00      56.00       .005
# Roys             .47
# Note.. F statistic for WILKS' Lambda is exact.
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Univariate F-tests with (2,30) D. F.
#
# Variable   Hypoth. SS   Error SS Hypoth. MS   Error MS          F  Sig. of F
#
# USEFUL       52.92424  293.96544   26.46212    9.79885    2.70053       .083
# DIFFICUL      3.97515  126.28728    1.98758    4.20958     .47216       .628
# IMPORTAN     81.82969  426.37090   40.91485   14.21236    2.87882       .072
#
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#   Eigenvalues and Canonical Correlations
#
# Root No.    Eigenvalue        Pct.   Cum. Pct.  Canon Cor.
#
# 1          .892      99.416      99.416        .687
# 2          .005        .584     100.000        .072
#
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -





#-- LDA -------------------------

library(MASS)

require(stp25output)


DF2 <- stp25aggregate::GetData(
"C:/Users/wpete/Dropbox/3_Forschung/R-Project/stp25data/extdata/discrim.sav")
#--https://stats.idre.ucla.edu/spss/dae/discriminant-function-analysis/
DF2$Job <- factor(DF2$JOB, 1:3, Cs("customer service", "mechanic","dispatcher"))
DF2$Job2 <- factor(DF2$JOB, c(2,3,1), Cs( "mechanic","dispatcher","customer service"))

#APA2(.~JOB ,DF2)
#DF2 %>%  APA_Correlation(OUTDOOR,SOCIAL,CONSERVATIVE )

fit2 <- lda(Job ~ OUTDOOR+SOCIAL+CONSERVATIVE, data=DF2)
fit3 <- lda(Job2 ~ OUTDOOR+SOCIAL+CONSERVATIVE, data=DF2)

APA2(fit2)
APA2(fit3)

#' #fit2 <- lda(GROUP ~ ., data=DF )
#APA2(fit2)
#plot(fit2)
#----------------------------------------------------------------
# multcomp
#----------------------------------------------------------------
#require(graphics)
#
#-- breaks ~ wool + tension ----------------------
#warpbreaks %>% Tabelle2(breaks, by= ~ wool + tension)
summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks))

#  ANOVA
APA2(fm1, caption="ANOVA")

# TukeyHSD
TukeyHSD(fm1, "tension", ordered = TRUE) %>%
  APA_Table(caption="TukeyHSD" )

#plot(TukeyHSD(fm1, "tension"))
#levels(warpbreaks$tension)

# Lm Split
fm1_split <-  summary(fm1,
                      split=list(tension=list( M=1,  H=3, L=2)),
                      expand.split=FALSE)
APA2(fm1_split)


# Multcomp
require(multcomp)

fit_Tukey <-glht(fm1,
                 linfct=mcp(tension="Tukey"),
                 alternative = "less"
)

APA_Table(fit_Tukey, caption="APA_Table: multcomp mcp Tukey")

APA2(fit_Tukey, caption="APA2: multcomp mcp Tukey")



### contrasts for `tension'
K <- rbind("L - M" = c( 1, -1,  0),
           "M - L" = c(-1,  1,  0),
           "L - H" = c( 1,  0, -1),
           "M - H" = c( 0,  1, -1))

warpbreaks.mc <- glht(fm1,
                      linfct = mcp(tension = K),
                      alternative = "less")
APA2(warpbreaks.mc, caption="APA2: multcomp mcp mit Contrasten")
### correlation of first two tests is -1
cov2cor(vcov(fm1))

### use smallest of the two one-sided
### p-value as two-sided p-value -> 0.0232
summary(fm1)


# -- Interaction ------------------------
summary(fm2 <- aov(breaks ~ wool * tension, data = warpbreaks))
APA_Table(fm2)
x <- TukeyHSD(fm2, "tension",
              ordered = TRUE)
APA2(x, caption="Interaction: TukeyHSD" )



warpbreaks$WW<-interaction(warpbreaks$wool,warpbreaks$tension )
mod2<-aov(breaks~WW, warpbreaks)
APA2(mod2, caption="ANOVA interaction haendich zu den Daten hinzugefuehgt")



library(multcomp)
 ### multiple linear model, swiss data
 lmod <- lm(Fertility ~ ., data = swiss)

 ### test of H_0: all regression coefficients are zero
 ### (ignore intercept)

### define coefficients of linear function directly
K <- diag(length(coef(lmod)))[-1,]
rownames(K) <- names(coef(lmod))[-1]
K
### set up general linear hypothesis
APA2(glht(lmod, linfct = K))


#- ANOVA ---------
# op <- options(contrasts = c("contr.helmert", "contr.poly"))
# npk.aov <- aov(yield ~ block + N*P*K, npk)
#summary(npk.aov)
#coefficients(npk.aov)

#APA2(npk.aov, include.eta = FALSE)


#- One way repeated Measures ---------------------

#datafilename="http://personality-project.org/r/datasets/R.appendix3.data"
#data.ex3=read.table(datafilename,header=T)   #read the data into a table
# #data.ex3                                      #show the data

#aov.ex3 = aov(Recall~Valence+Error(Subject/Valence),data.ex3)
#
#APA2(aov.ex3)


#' library(caret)
require(epiR)
DF<- GetData("
GoldStandart RT.qPCR Anzahl
positiv positiv 111
positiv negativ 12
negativ positiv 1
negativ negativ 62 ", Tabel_Expand =TRUE, id.vars=1:2, output=FALSE)
DF$GoldStandart<- factor( DF$GoldStandart, rev(levels( DF$GoldStandart)))
DF$RT.qPCR<- factor( DF$RT.qPCR, rev(levels( DF$RT.qPCR)))

 


dat <- as.table(matrix(c(111, 12, 1, 62), nrow = 2, byrow = TRUE))
colnames(dat) <- c("RT.qPCR +", "RT.qPCR -")
rownames(dat) <- c("Gold Standart +", "GoldStandart -")
rval <- epi.tests(dat, conf.level = 0.95)
APA2(rval)



# Fit Log-Linear Models by Iterative Proportional Scaling
library(MASS)

fit<-loglm(~ Type + Origin, xtabs(~ Type + Origin, Cars93))
APA2(fit)
#-- APA2.summary.table

a <- letters[1:3]
APA2(summary(table(a, sample(a))))