APA2: APA Style HTML-Tabellen-Ausgabe
In stp4/stp25APA2: Resultate Formatiern
Description
Usage
Arguments
Value
Examples
Description
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
Errate korekte Auswertung und Extrahieren der Variablen aus Formula.
Ordered Logistic or Probit Regression
https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/
Methode f<c3><bc>r car::durbinWatsonTest Kopie von car:::print.durbinWatsonTest
Ausgabe von Regressions Tabelle nach der APA-Style vorgabe. Die Funktion
ist eine Kopie von texreg:::aggregate.matrix.
anova: APA2.aov(x, include.eta = TRUE)
Corr1() wird in in APA2 beim Einzelvergeich verwendet.
Die Interne Funktion Corr2() wird in APA2 verwendete um Korrelation zu berechnen.
MANOVA: APA2(x, , test="Wilks") test : "Wilks", "Pillai"
LDA (linear discriminants analysis) Erweiterung der MANOVA
APA2.glht multcomp::glht
Usage
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APA2(x, ...)
## S3 method for class 'NULL'
APA2(x, ...)
## Default S3 method:
APA2(x, ..., caption = "", output = TRUE)
## 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 = TRUE, ...)
## S3 method for class 'likert'
APA2(x, caption = "", note = "", col_names = NULL,
print_col = NULL, ReferenceZero = NULL, type = "percent",
include.mean = TRUE, na.exclude = FALSE, labels = c("low",
"neutral", "high"), order = FALSE, output = TRUE, ...)
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 'ICC'
APA2(x, caption = "ICC", type = c(1, 4))
## 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 = "")
## S3 method for class 'Kano'
APA2(x, caption = "", note = NULL, ...)
## S3 method for class 'Kano'
print(x, ...)
## S3 method for class 'bland_altman'
APA(x, ...)
## S3 method for class 'bland_altman'
APA2(x, caption = paste0("Difference (",
x$name.diff, "), Mean (", x$name, ")"), note = "", ...)
## S3 method for class 'eff'
APA2(x, ...)
## S3 method for class 'efflist'
APA2(x, caption = "Effekte: ", type = NULL,
note = "", output = stp25output::which_output(), digits = 2,
include.fit = TRUE, include.n = FALSE, include.ci = TRUE,
include.se = FALSE, ...)
## 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 'durbinWatsonTest'
APA2(x,
caption = "Durbin-Watson Test for Autocorrelated Errors",
note = NULL, ...)
## S3 method for class 'list'
APA2(x, caption = "", note = "",
output = stp25output::which_output(), digits = 2,
custom.model.names = NULL, include.custom = NULL, include.b = TRUE,
include.ci = FALSE, include.odds = FALSE, include.se = if
(include.ci) FALSE else TRUE, include.t = FALSE, include.p = FALSE,
include.stars = if (include.p) FALSE else TRUE,
include.ftest = FALSE, include.loglik = FALSE,
include.pseudo = TRUE, include.r = TRUE, include.aic = TRUE,
include.bic = include.aic, include.sigma = FALSE,
include.rmse = TRUE, include.gof = TRUE, include.param = TRUE,
ci.level = 0.95, rgroup = c("Parameter", "Goodness of fit"),
test.my.fun = FALSE, ...)
## 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,
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 '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, ...)
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, ...)
## S3 method for class 'manova'
APA2(x, test = "Wilks", caption = "MANOVA",
note = "", output = c("anova", "manova"))
## S3 method for class 'lda'
APA2(x, fit_predict = MASS:::predict.lda(x),
newdata = model.frame(x), ...)
## 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 'summary.survfit'
APA2(fit, digits = NULL, percent = FALSE,
include = c(time = "time", n.risk = "n.risk", n.event = "n.event", surv
= "survival", std.err = "std.err", lower = "lower 95% CI", upper =
"upper 95% CI"), ...)
## S3 method for class 'survfit'
APA2(fit, caption = "NULL", note = "", type = 1,
digits = 2, ...)
## S3 method for class 'survdiff'
APA2(fit, caption = "Test Survival Curve Differences",
note = "")
## S3 method for class 'coxph'
APA2(fit, caption = "", note = "", ...)
## S3 method for class 'htest'
APA2(x, caption = "", ...)
## S3 method for class 'pairwise.htest'
APA2(x, caption = "", ...)
## 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(), col_names = NULL,
print_col = NULL, digits = NULL, test = FALSE, type = c("0",
"fischer", "odds", "sensitivity", "chisquare", "correlation", "r"),
include.total = FALSE, include.total.columns = include.total,
include.total.sub = include.total,
include.total.rows = include.total, include.percent = TRUE,
include.count = TRUE, include.margins = TRUE, margin = NA,
add.margins = NA, labels = NULL, ...)
Arguments
x
Ein R Objekt oder eine Formel oder ein data.frame
APA2.list: Liste mit Objekten (fits)
...
weitere Argumente
caption, note
Ueberschrift an Output
output
Ausgabe von Ergebiss ueber Output
data
data.frame wenn x eine Formel ist
fun, na.action, direction
eigene Funktion na.action=na.pass
(Auswertung ueeber die Funktionen melt, cast.)
type
formula: c("auto", "freq", "mean", "median", "ci", "freq.ci")
xtabs: type = c("fischer", "odds","sensitivity", "chisquare","correlation", "r")
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")
cor_diagonale_up
bei Correlation art der Formatierung
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, include.total.columns, include.total.sub, include.total.rows
Zeilen Prozenz usw
include.names, include.labels
Beschriftung der zeilen
digits
Nachkommastellen
APA2.list: Kommastellen bei uebergabe einer liste muss exakt die Reighenfolge eingehalten werden.
digits.mean, digits.percent
Nachkommastellen
ReferenceZero, labels
Likert: ReferenceZero=2 Neutrales Element in Kombination mit
labels = c("low", "neutral", "high")
include.mean
Likert: Mittelwerte T/F
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.ci
APA2.glht: Cis
include.se, include.ci, include.odds
SE, 95-Ci, OR noch nicht fertig
include.df
df mit ausgeben
custom.model.names
Namen ner Modelle
include.custom
liste mit Statistiken f<c3><bc>r Gofs also zB F-Tests
include.ftest, include.loglik
noch nicht fertig
include.r, include.pseudo
pseudo R
include.aic, include.bic
geht nur zusammen
ci.level
Ci default 95 Prozent
rgroup
Zwischen Beschriftung
fit_predict
lda: MASS predict.lda
newdata
lda:model.frame
include.percent, include.count
ausgabe
include.margins
sollen <c3><bc>berhaupt Summen (margins) ausgegeben werden die Prozent werden aber wie mit den Margins gerechnet
margin, add.margins
alternative zu include.total
anova_type
bei lme: "F" F-werte (wie SPSS) oder Chi (car::Anova)
caption, note, output, col_names, print_col, labels
an Output
digits
Nachkommastellen
test, type
fischer chi usw
Value
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
list(xtab, test)
Examples
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#-- APA2.formula --
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)
#End()
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)
require(stpvers)
res <- Reliability(~F3+F2+F10+F11, fkv, check.keys =TRUE)
APA2(res)
#----------------------------------------------------------
# Effekte / Mittelwerte
Tabelle2(fit1, digits=2) # mean SD
require(effects)
fit1 <- lm(chol0 ~ ak + rrs0 + med + g, hyper)
eff<-allEffects(fit1)
APA2(eff)
#--- Ordered Logistic or Probit Regression
options(contrasts = c("contr.treatment", "contr.poly"))
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
# house.plr
APA2(house.plr, note= APA(house.plr))
# require(stpvers)
require(lmerTest)
summary(lm1 <- lm(Fertility ~ ., data = swiss))
slm1 <- stats::step(lm1)
APA2(slm1)
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)
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.list(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")))
#- 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)
#-- Corr1
# APA2(~a+b+cd, data)
#stp25APA2:::Corr1(data[1:3], dimnames=TRUE)
#stp25APA2:::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)
#- 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)
#x<-summary(npk2.aov)
APA2(npk2.aov) #wilks
APA2(npk2.aov, "Pillai")
#npk2.aovE <- manova(cbind(yield, foo) ~ N*P*K + Error(block), npk2)
#APA2(npk2.aovE)
DF<-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)
#fit2 <- lda(GROUP ~ ., data=DF )
#APA2(fit2)
#plot(fit2)
DF2<- 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)
#----------------------------------------------------------------
# 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))
# 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))))
stp4/stp25APA2 documentation built on May 24, 2019, 9:59 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
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
Errate korekte Auswertung und Extrahieren der Variablen aus Formula.
Ordered Logistic or Probit Regression https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/
Methode f<c3><bc>r car::durbinWatsonTest Kopie von car:::print.durbinWatsonTest
Ausgabe von Regressions Tabelle nach der APA-Style vorgabe. Die Funktion ist eine Kopie von texreg:::aggregate.matrix.
anova: APA2.aov(x, include.eta = TRUE)
Corr1() wird in in APA2 beim Einzelvergeich verwendet.
Die Interne Funktion Corr2() wird in APA2 verwendete um Korrelation zu berechnen.
MANOVA: APA2(x, , test="Wilks") test : "Wilks", "Pillai"
LDA (linear discriminants analysis) Erweiterung der MANOVA
APA2.glht multcomp::glht
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | APA2(x, ...)
## S3 method for class 'NULL'
APA2(x, ...)
## Default S3 method:
APA2(x, ..., caption = "", output = TRUE)
## 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 = TRUE, ...)
## S3 method for class 'likert'
APA2(x, caption = "", note = "", col_names = NULL,
print_col = NULL, ReferenceZero = NULL, type = "percent",
include.mean = TRUE, na.exclude = FALSE, labels = c("low",
"neutral", "high"), order = FALSE, output = TRUE, ...)
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 'ICC'
APA2(x, caption = "ICC", type = c(1, 4))
## 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 = "")
## S3 method for class 'Kano'
APA2(x, caption = "", note = NULL, ...)
## S3 method for class 'Kano'
print(x, ...)
## S3 method for class 'bland_altman'
APA(x, ...)
## S3 method for class 'bland_altman'
APA2(x, caption = paste0("Difference (",
x$name.diff, "), Mean (", x$name, ")"), note = "", ...)
## S3 method for class 'eff'
APA2(x, ...)
## S3 method for class 'efflist'
APA2(x, caption = "Effekte: ", type = NULL,
note = "", output = stp25output::which_output(), digits = 2,
include.fit = TRUE, include.n = FALSE, include.ci = TRUE,
include.se = FALSE, ...)
## 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 'durbinWatsonTest'
APA2(x,
caption = "Durbin-Watson Test for Autocorrelated Errors",
note = NULL, ...)
## S3 method for class 'list'
APA2(x, caption = "", note = "",
output = stp25output::which_output(), digits = 2,
custom.model.names = NULL, include.custom = NULL, include.b = TRUE,
include.ci = FALSE, include.odds = FALSE, include.se = if
(include.ci) FALSE else TRUE, include.t = FALSE, include.p = FALSE,
include.stars = if (include.p) FALSE else TRUE,
include.ftest = FALSE, include.loglik = FALSE,
include.pseudo = TRUE, include.r = TRUE, include.aic = TRUE,
include.bic = include.aic, include.sigma = FALSE,
include.rmse = TRUE, include.gof = TRUE, include.param = TRUE,
ci.level = 0.95, rgroup = c("Parameter", "Goodness of fit"),
test.my.fun = FALSE, ...)
## 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,
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 '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, ...)
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, ...)
## S3 method for class 'manova'
APA2(x, test = "Wilks", caption = "MANOVA",
note = "", output = c("anova", "manova"))
## S3 method for class 'lda'
APA2(x, fit_predict = MASS:::predict.lda(x),
newdata = model.frame(x), ...)
## 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 'summary.survfit'
APA2(fit, digits = NULL, percent = FALSE,
include = c(time = "time", n.risk = "n.risk", n.event = "n.event", surv
= "survival", std.err = "std.err", lower = "lower 95% CI", upper =
"upper 95% CI"), ...)
## S3 method for class 'survfit'
APA2(fit, caption = "NULL", note = "", type = 1,
digits = 2, ...)
## S3 method for class 'survdiff'
APA2(fit, caption = "Test Survival Curve Differences",
note = "")
## S3 method for class 'coxph'
APA2(fit, caption = "", note = "", ...)
## S3 method for class 'htest'
APA2(x, caption = "", ...)
## S3 method for class 'pairwise.htest'
APA2(x, caption = "", ...)
## 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(), col_names = NULL,
print_col = NULL, digits = NULL, test = FALSE, type = c("0",
"fischer", "odds", "sensitivity", "chisquare", "correlation", "r"),
include.total = FALSE, include.total.columns = include.total,
include.total.sub = include.total,
include.total.rows = include.total, include.percent = TRUE,
include.count = TRUE, include.margins = TRUE, margin = NA,
add.margins = NA, labels = NULL, ...)
|
Arguments
x |
Ein R Objekt oder eine Formel oder ein data.frame APA2.list: Liste mit Objekten (fits) |
... |
weitere Argumente |
caption, note |
Ueberschrift an Output |
output |
Ausgabe von Ergebiss ueber Output |
data |
data.frame wenn x eine Formel ist |
fun, na.action, direction |
eigene Funktion na.action=na.pass
(Auswertung ueeber die Funktionen |
type |
formula: |
test, include.test, corr_test, include.p, include.stars |
Sig test bei |
cor_diagonale_up |
bei Correlation art der Formatierung |
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, include.total.columns, include.total.sub, include.total.rows |
Zeilen Prozenz usw |
include.names, include.labels |
Beschriftung der zeilen |
digits |
Nachkommastellen APA2.list: Kommastellen bei uebergabe einer liste muss exakt die Reighenfolge eingehalten werden. |
digits.mean, digits.percent |
Nachkommastellen |
ReferenceZero, labels |
Likert: ReferenceZero=2 Neutrales Element in Kombination mit labels = c("low", "neutral", "high") |
include.mean |
Likert: Mittelwerte T/F |
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.ci |
APA2.glht: Cis |
include.se, include.ci, include.odds |
SE, 95-Ci, OR noch nicht fertig |
include.df |
df mit ausgeben |
custom.model.names |
Namen ner Modelle |
include.custom |
liste mit Statistiken f<c3><bc>r Gofs also zB F-Tests |
include.ftest, include.loglik |
noch nicht fertig |
include.r, include.pseudo |
pseudo R |
include.aic, include.bic |
geht nur zusammen |
ci.level |
Ci default 95 Prozent |
rgroup |
Zwischen Beschriftung |
fit_predict |
lda: MASS predict.lda |
newdata |
lda:model.frame |
include.percent, include.count |
ausgabe |
include.margins |
sollen <c3><bc>berhaupt Summen (margins) ausgegeben werden die Prozent werden aber wie mit den Margins gerechnet |
margin, add.margins |
alternative zu include.total |
anova_type |
bei lme: "F" F-werte (wie SPSS) oder Chi (car::Anova) |
caption, note, output, col_names, print_col, labels |
an Output |
digits |
Nachkommastellen |
test, type |
fischer chi usw |
Value
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
list(xtab, test)
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 | #-- APA2.formula --
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)
#End()
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)
require(stpvers)
res <- Reliability(~F3+F2+F10+F11, fkv, check.keys =TRUE)
APA2(res)
#----------------------------------------------------------
# Effekte / Mittelwerte
Tabelle2(fit1, digits=2) # mean SD
require(effects)
fit1 <- lm(chol0 ~ ak + rrs0 + med + g, hyper)
eff<-allEffects(fit1)
APA2(eff)
#--- Ordered Logistic or Probit Regression
options(contrasts = c("contr.treatment", "contr.poly"))
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
# house.plr
APA2(house.plr, note= APA(house.plr))
# require(stpvers)
require(lmerTest)
summary(lm1 <- lm(Fertility ~ ., data = swiss))
slm1 <- stats::step(lm1)
APA2(slm1)
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)
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.list(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")))
#- 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)
#-- Corr1
# APA2(~a+b+cd, data)
#stp25APA2:::Corr1(data[1:3], dimnames=TRUE)
#stp25APA2:::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)
#- 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)
#x<-summary(npk2.aov)
APA2(npk2.aov) #wilks
APA2(npk2.aov, "Pillai")
#npk2.aovE <- manova(cbind(yield, foo) ~ N*P*K + Error(block), npk2)
#APA2(npk2.aovE)
DF<-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)
#fit2 <- lda(GROUP ~ ., data=DF )
#APA2(fit2)
#plot(fit2)
DF2<- 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)
#----------------------------------------------------------------
# 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))
# 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))))
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