APA_: APA Syle Table
In stp4/stp25APA2: Resultate Formatiern
Description
Usage
Arguments
Value
Examples
Description
All functions in stringr start with APA_ and take a Object (formula, data.frame, lm, ...)
as the first argument.
APA_ICC Intra-Klassen-Korrelation ( mit psych ).
APA_PCA: Faktoranalyse wie SPSS PCA
APA_Validation
Validation of Linear Models Assumptions
(Testing Linear Regression Models)
loglik Likelihood Ratio Test The log-likelihood from the intercept-only restricted model.
-2LL: The LL (log-likelihood from the fitted model)
llhNull (The log-likelihood from the intercept-only restricted model),
G2 (Minus two times the difference in the log-likelihoods)
Autocorrelation Durbin-Watson test for autocorrelation of disturbances. Ist nur bei Zeitreihendaten sinnvoll.
Homogeneity of Variances Levene Computes Levene's test for homogeneity of variance across groups.
Bartlett Test of Homogeneity of Variances
Heteroskedasticity Breusch-Pagan test against heteroskedasticity.
VIF variance inflation factor. VIF values over 5 are troubling, should probably investigate anything over 2.5.
Residual RMSE values should be low (<0.5 and <0.3, respectively).
SigmaResidual standard error When the residual standard error is exactly 0 then the model fits the data perfectly (likely due to overfitting)
R-Quadrats Cox und Snell R2: [ 0.2 = akzeptabel, 0.4 = gut ]
Nagelkerke R2: [ 0.2 = akzeptabel, 0.4 = gut, 0.5 = sehr gut]
McFaddens R2: [ 0.2 = akzeptabel, 0.4 = gut ] (see pR2)
APA_R2: R2-Tabelle (noch nicht fretig)
APA_Durbin_Watson(fit, max.lag=1, simulate=TRUE, reps=1000,
method=c("resample","normal"),
alternative=c("two.sided", "positive", "negative")): Durbin-Watson Test for Autocorrelated Errors.
Kopie der Funktion car::durbinWatsonTest
APA_vif: variance inflation factor, aka VIF
APA_Correlation: Korrelationstabelle (Interkorrelationen mit der Hilfe
der Funktion Hmisc::rcorr. Erlaubt ist die getrennte Auswertung ueber by=~b
APA_Ttest:
Berechnung von Mittelwerten und Mittelwertdifferenzen und des T-Test.
Kreuztabellen APA_Xtabs, APA2.xtabs :
Die Funktion Formatiert xtabs() mit NxM und NxMxO Tabellen.
"Haufigkeit/Prozent 1"
Die Prozent werden ueber include.percent mit margin erstellt. Der Parameter
add.margins wird automatisch vergeben.
include.total, include.total.columns, include.total.sub, include.total.rows, include.percent,
include.count.
Feineinstellungen erfolgt ueber margin = 2).
"Sensitivitaets Analyse"Nur bei 2x" Tabellen ueber test=TRUE
"Sig.-Tests"Bei 2x" Tabellen Fischer sonst Chi-test.
Die Berechnung erfolgt hier mit assocstats.
Weiter Einstellungen sind Correlationen, Pearson, Kontingentkoeffizient
berechnet alternativ steht auch der Phi-Coefficient
Usage
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APA_NULL(x, ...)
APA_Likert(...)
APA2_multiresponse(Formula, data, caption = "", note = "",
test = FALSE, order = FALSE, decreasing = TRUE,
sig_test = "fischer.test", na.action = na.pass, use.level = 1, ...)
APA_ICC(x, ..., caption = "ICC", type = c(1, 4))
ICC2(x, ..., caption = "ICC", type = c(1, 4))
APA_PCA(data, ..., include.pca = TRUE, include.plot = FALSE,
include.kmo = TRUE, w = 5, h = 5,
caption = "Standardized loadings (pattern matrix) based upon correlation matrix",
note = "", main = "")
APA_Reliability(..., caption = "", note = "")
APA_Validation(..., include.ftest = TRUE, include.loglik = FALSE,
include.minus.LL = include.loglik, include.pseudo = TRUE,
include.r = include.pseudo, include.heteroskedasticity = TRUE,
include.durbin = TRUE, include.levene = FALSE,
include.bartlett = FALSE, include.vif = FALSE,
include.sigma = FALSE, include.rmse = FALSE, include.aic = TRUE,
include.bic = include.aic, include.residual = TRUE,
include.normality = TRUE, include.multicollin = include.vif,
include.deviance = TRUE, caption = "Testing Regression Models",
note = "", names = NULL)
APA_R2(..., caption, note)
APA_Durbin_Watson(x,
caption = "Durbin-Watson Test for Autocorrelated Errors",
note = NULL, ...)
APA_vif(..., caption = "VIF", notes = "")
APA_Correlation(..., caption = "Korrelation", note = NULL,
output = stp25output::which_output(), col_names = NULL,
cor_diagonale_up = TRUE, type = c("pearson", "spearman"),
include.mean = FALSE, include.n = TRUE, stars = TRUE,
p.value = FALSE, include.stars = stars, include.p = p.value)
Hmisc_rcorr(..., cor_diagonale_up = TRUE, include.stars = TRUE,
include.p = FALSE, include.mean = FALSE, include.n = TRUE,
type = c("pearson", "spearman"))
APA_Ttest(x, data, caption = "T Test", note = alternative,
output = stp25output::which_output(), var.equal = FALSE,
paired = FALSE, alternative = "two.sided", include.mean = TRUE,
include.d = TRUE, include.mean.diff = TRUE, include.se = FALSE,
type = "t.test", digits = 2, ...)
APA_Xtabs(x, ...)
## S3 method for class 'glm'
APA_Xtabs(x, caption = "",
output = stp25output::which_output(), thresh = 0.5, ...)
## S3 method for class 'formula'
APA_Xtabs(x, data = NULL, caption = "",
output = stp25output::which_output(), labels = FALSE,
addNA = FALSE, exclude = if (!addNA) c(NA, NaN),
drop.unused.levels = FALSE, include.prop.chisq = TRUE,
include.chisq = FALSE, include.correlation = FALSE,
include.fisher = FALSE, include.mcnemar = FALSE,
include.resid = FALSE, include.sresid = FALSE,
include.sensitivity = FALSE, test = include.prop.chisq |
include.chisq | include.fisher | include.mcnemar, ...)
## S3 method for class 'data.frame'
APA_Xtabs(data = NULL, formula, caption = "",
output = stp25output::which_output(), labels = FALSE, ...)
## Default S3 method:
APA_Xtabs(x, ...)
Arguments
x
An object to be converted into a tidy data.frame or Formula
...
extra arguments
data
data.frame wenn x eine Formel ist
caption, note
Ueberschrift an Output
type
enweder "pearson" oder "spearman"
include.pca, include.plot, include.kmo
type=c("pca", "plot", "kmo")
w, h, main
Grafik Fenster w=5 h=5 Ueberschrift
include.ftest, include.loglik
F-sratistik
include.r, include.pseudo
R-Quadrat
include.heteroskedasticity
Breusch-Pagan test
include.durbin
autocorrelation
include.levene
homogeneity of variance across groupssiehe T-Test
include.bartlett
Homogeneity of Variances siehe T-Test
include.vif
noch nicht Implementiert
include.sigma, include.rmse
RMSE Extract Residual Standard Deviation Sigma
output
Html oder Text
col_names
nicht benutzt
cor_diagonale_up
Diagonale oben oder unter
include.mean
Mittelwerte
include.n
Anzahl an gueltigen Werten
include.stars
Sternchen als p-Werte
include.p
Explizite p-Werte
var.equal, paired, alternative
an t.Test var.equal = FALSE "two.sided"
include.d
Cohens d
include.mean.diff
Mittlere Differenzen
include.se
Standardfehler noch nicht Fertig
digits
Nachkommastellen
addNA, exclude, drop.unused.levels
An xtabs() default = FALSE
x
model- fit
...
Formel mit Daten wird von prepare_data2()aufgedroeselt
caption, note
Ueberschrift
include.mean
Mittelwerte mit SD
Value
a data.frame or list with data.frame
Examples
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APA_Reliability(~F3+F2+F10+F11, fkv, check.keys =TRUE)
hkarz$Lai <- factor(hkarz$lai, 0:1, Cs(.neg, .pos))
fit2 <- glm(gruppe ~ tzell + Lai, hkarz, family = binomial)
fit1<-lm(score ~ grade + treatment + stdTest, schools)
APA_Validation(x)
APA_Validation(fit1,fit2, include.pseudo = TRUE, include.r = TRUE, include.loglik = TRUE,
include.rmse = TRUE)
fit1<-lm(score ~ grade + treatment, schools)
fit2<-lm(score ~ grade + treatment + stdTest, schools)
APA_R2(fit1, fit2)
x<-lm(score ~ grade + treatment + stdTest, schools)
APA2(car::durbinWatsonTest(x))
DW_Test2(x)
lmtest::dwtest(x)
car::durbinWatsonTest(x)
n <- 2 * 20
e <- rnorm(n)
dat <- Label(
data.frame(
a = rnorm(n) + e / 2,
b = rnorm(n) + e,
c = rnorm(n),
d = rnorm(n) + e * 10,
g = gl(2, 20, labels = c("Control", "Treat"))
),
a = "Alpha",
b = "Beta",
c = "Gamma"
)
APA_Correlation( ~ a + b + c, dat)
APA_Correlation(a ~ c, dat)
APA_Correlation(a + b + c ~ d, dat)
APA_Correlation(a + b ~ c + d, dat)
APA_Correlation(a + b + c ~ d, dat, groups = ~ g)
APA_Correlation( ~ a + b + c, dat)
APA_Correlation( ~ a + b + c, dat, include.p = TRUE)
APA_Correlation( ~ a + b + c, dat, include.p = TRUE, include.stars = FALSE)
# library(arm)
# windows(7,7)
# corrplot(data[Cs(a,b,c,d)], abs=TRUE, n.col.legend=7)
# install.packages("PerformanceAnalytics")
#library("PerformanceAnalytics")
#?chart.Correlation#(decathlon2.active[, 1:6], histogram=TRUE, pch=19)
#data(managers)
#chart.Correlation(managers[,1:8], histogram=TRUE, pch="+")
#require(stpvers)
#Projekt("html")
APA_Ttest(m1+m2 ~ geschl, varana)
varanax<-Melt2(m1+m2~nr,varana , key="time", value="m")
# broom::tidy(with(varana, t.test(m1,m2 ) ))
# broom::tidy( t.test(m~time, varanax, var.equal=FALSE)) )
APA_Ttest(m~time, varanax, paired = TRUE)
APA_Ttest(m~time, varanax, var.equal=TRUE)
APA_Ttest(m~time, varanax, var.equal=FALSE)
#broom::tidy(t.test(m~time, varanax, var.equal=TRUE))
#broom::tidy(t.test(m~time, varanax, var.equal=FALSE))
#End()
# APA_Xtabs ################################
# Projekt("html")
dat<-GetData("
sex treatment neg pos
f KG 10 9
f UG 14 5
m KG 23 7
m UG 18 14",Tabel_Expand = TRUE, id.vars = 1:2, value="befund")
x1<-xtabs( ~ sex + treatment , dat)
x2<-xtabs( ~ sex + befund + treatment , dat)
APA2(x1)
APA2(x1, include.total=TRUE, percent=F)
APA2(x2, include.total=TRUE)
APA2(x1, include.total.columns=T)
APA2(x2, include.total.columns=T)
APA2(x1, include.total.rows=T)
APA2(x2,include.total.rows=T)
APA2(x2,include.total.sub=T)
APA2(x2,include.total.sub=T, include.total.rows=T)
APA2(x2,
include.total.columns = T,
include.total.rows = T)
hkarz$LAI<- factor(hkarz$lai, 0:1, c("pos", "neg"))
hkarz$Tzell<- cut(hkarz$tzell, 3, c("low", "med", "hig"))
xtab <- xtabs(~ gruppe+LAI, hkarz)
APA2(xtab,
caption="Harnblasenkarzinom", test=FALSE)
APA2(xtab, type="sens",
test=TRUE, caption = "type=sens")
APA2(xtab, type="sens",
caption = "geht nur mit teat=TRUE + type=sens")
APA2(xtabs(~ gruppe+Tzell, hkarz),
caption="APA_Xtabs: 2x3 Tabelle", test=FALSE)
APA2(xtabs(~ gruppe+LAI+Tzell, hkarz),
caption="APA_Xtabs: 2x2x3 Tabelle", test=FALSE)
APA2(xtab,
include.total.columns=TRUE, caption = "include.total.columns")
APA2(xtab,
include.total.sub=TRUE, caption = "include.total.sub")
xtab <- xtabs(~ gruppe+Tzell, hkarz)
APA2(xtab, test=FALSE, caption="APA2: 2x3 Tabelle")
#################################
stp4/stp25APA2 documentation built on May 24, 2019, 9:59 p.m.
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Description Usage Arguments Value Examples
Description
All functions in stringr start with APA_ and take a Object (formula, data.frame, lm, ...) as the first argument.
APA_ICC Intra-Klassen-Korrelation ( mit psych ).
APA_PCA: Faktoranalyse wie SPSS PCA
APA_Validation Validation of Linear Models Assumptions (Testing Linear Regression Models)
loglik Likelihood Ratio Test The log-likelihood from the intercept-only restricted model. -2LL: The LL (log-likelihood from the fitted model) llhNull (The log-likelihood from the intercept-only restricted model), G2 (Minus two times the difference in the log-likelihoods)
Autocorrelation Durbin-Watson test for autocorrelation of disturbances. Ist nur bei Zeitreihendaten sinnvoll.
Homogeneity of Variances Levene Computes Levene's test for homogeneity of variance across groups. Bartlett Test of Homogeneity of Variances
Heteroskedasticity Breusch-Pagan test against heteroskedasticity.
VIF variance inflation factor. VIF values over 5 are troubling, should probably investigate anything over 2.5.
Residual RMSE values should be low (<0.5 and <0.3, respectively). SigmaResidual standard error When the residual standard error is exactly 0 then the model fits the data perfectly (likely due to overfitting)
R-Quadrats Cox und Snell R2: [ 0.2 = akzeptabel, 0.4 = gut ] Nagelkerke R2: [ 0.2 = akzeptabel, 0.4 = gut, 0.5 = sehr gut] McFaddens R2: [ 0.2 = akzeptabel, 0.4 = gut ] (see pR2)
APA_R2: R2-Tabelle (noch nicht fretig)
APA_Durbin_Watson(fit, max.lag=1, simulate=TRUE, reps=1000, method=c("resample","normal"), alternative=c("two.sided", "positive", "negative")): Durbin-Watson Test for Autocorrelated Errors. Kopie der Funktion car::durbinWatsonTest
APA_vif: variance inflation factor, aka VIF
APA_Correlation: Korrelationstabelle (Interkorrelationen mit der Hilfe der Funktion Hmisc::rcorr. Erlaubt ist die getrennte Auswertung ueber by=~b
APA_Ttest: Berechnung von Mittelwerten und Mittelwertdifferenzen und des T-Test.
Kreuztabellen APA_Xtabs, APA2.xtabs : Die Funktion Formatiert xtabs() mit NxM und NxMxO Tabellen.
"Haufigkeit/Prozent 1" Die Prozent werden ueber include.percent mit margin erstellt. Der Parameter add.margins wird automatisch vergeben. include.total, include.total.columns, include.total.sub, include.total.rows, include.percent, include.count. Feineinstellungen erfolgt ueber
margin = 2)."Sensitivitaets Analyse"Nur bei 2x" Tabellen ueber test=TRUE
"Sig.-Tests"Bei 2x" Tabellen Fischer sonst Chi-test. Die Berechnung erfolgt hier mit assocstats. Weiter Einstellungen sind Correlationen, Pearson, Kontingentkoeffizient berechnet alternativ steht auch der Phi-Coefficient
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 | APA_NULL(x, ...)
APA_Likert(...)
APA2_multiresponse(Formula, data, caption = "", note = "",
test = FALSE, order = FALSE, decreasing = TRUE,
sig_test = "fischer.test", na.action = na.pass, use.level = 1, ...)
APA_ICC(x, ..., caption = "ICC", type = c(1, 4))
ICC2(x, ..., caption = "ICC", type = c(1, 4))
APA_PCA(data, ..., include.pca = TRUE, include.plot = FALSE,
include.kmo = TRUE, w = 5, h = 5,
caption = "Standardized loadings (pattern matrix) based upon correlation matrix",
note = "", main = "")
APA_Reliability(..., caption = "", note = "")
APA_Validation(..., include.ftest = TRUE, include.loglik = FALSE,
include.minus.LL = include.loglik, include.pseudo = TRUE,
include.r = include.pseudo, include.heteroskedasticity = TRUE,
include.durbin = TRUE, include.levene = FALSE,
include.bartlett = FALSE, include.vif = FALSE,
include.sigma = FALSE, include.rmse = FALSE, include.aic = TRUE,
include.bic = include.aic, include.residual = TRUE,
include.normality = TRUE, include.multicollin = include.vif,
include.deviance = TRUE, caption = "Testing Regression Models",
note = "", names = NULL)
APA_R2(..., caption, note)
APA_Durbin_Watson(x,
caption = "Durbin-Watson Test for Autocorrelated Errors",
note = NULL, ...)
APA_vif(..., caption = "VIF", notes = "")
APA_Correlation(..., caption = "Korrelation", note = NULL,
output = stp25output::which_output(), col_names = NULL,
cor_diagonale_up = TRUE, type = c("pearson", "spearman"),
include.mean = FALSE, include.n = TRUE, stars = TRUE,
p.value = FALSE, include.stars = stars, include.p = p.value)
Hmisc_rcorr(..., cor_diagonale_up = TRUE, include.stars = TRUE,
include.p = FALSE, include.mean = FALSE, include.n = TRUE,
type = c("pearson", "spearman"))
APA_Ttest(x, data, caption = "T Test", note = alternative,
output = stp25output::which_output(), var.equal = FALSE,
paired = FALSE, alternative = "two.sided", include.mean = TRUE,
include.d = TRUE, include.mean.diff = TRUE, include.se = FALSE,
type = "t.test", digits = 2, ...)
APA_Xtabs(x, ...)
## S3 method for class 'glm'
APA_Xtabs(x, caption = "",
output = stp25output::which_output(), thresh = 0.5, ...)
## S3 method for class 'formula'
APA_Xtabs(x, data = NULL, caption = "",
output = stp25output::which_output(), labels = FALSE,
addNA = FALSE, exclude = if (!addNA) c(NA, NaN),
drop.unused.levels = FALSE, include.prop.chisq = TRUE,
include.chisq = FALSE, include.correlation = FALSE,
include.fisher = FALSE, include.mcnemar = FALSE,
include.resid = FALSE, include.sresid = FALSE,
include.sensitivity = FALSE, test = include.prop.chisq |
include.chisq | include.fisher | include.mcnemar, ...)
## S3 method for class 'data.frame'
APA_Xtabs(data = NULL, formula, caption = "",
output = stp25output::which_output(), labels = FALSE, ...)
## Default S3 method:
APA_Xtabs(x, ...)
|
Arguments
x |
An object to be converted into a tidy data.frame or Formula |
... |
extra arguments |
data |
data.frame wenn x eine Formel ist |
caption, note |
Ueberschrift an Output |
type |
enweder "pearson" oder "spearman" |
include.pca, include.plot, include.kmo |
type=c("pca", "plot", "kmo") |
w, h, main |
Grafik Fenster w=5 h=5 Ueberschrift |
include.ftest, include.loglik |
F-sratistik |
include.r, include.pseudo |
R-Quadrat |
include.heteroskedasticity |
Breusch-Pagan test |
include.durbin |
autocorrelation |
include.levene |
homogeneity of variance across groupssiehe T-Test |
include.bartlett |
Homogeneity of Variances siehe T-Test |
include.vif |
noch nicht Implementiert |
include.sigma, include.rmse |
RMSE Extract Residual Standard Deviation Sigma |
output |
Html oder Text |
col_names |
nicht benutzt |
cor_diagonale_up |
Diagonale oben oder unter |
include.mean |
Mittelwerte |
include.n |
Anzahl an gueltigen Werten |
include.stars |
Sternchen als p-Werte |
include.p |
Explizite p-Werte |
var.equal, paired, alternative |
an t.Test var.equal = FALSE "two.sided" |
include.d |
Cohens d |
include.mean.diff |
Mittlere Differenzen |
include.se |
Standardfehler noch nicht Fertig |
digits |
Nachkommastellen |
addNA, exclude, drop.unused.levels |
An xtabs() default = FALSE |
x |
model- fit |
... |
Formel mit Daten wird von prepare_data2()aufgedroeselt |
caption, note |
Ueberschrift |
include.mean |
Mittelwerte mit SD |
Value
a data.frame or list with data.frame
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 | APA_Reliability(~F3+F2+F10+F11, fkv, check.keys =TRUE)
hkarz$Lai <- factor(hkarz$lai, 0:1, Cs(.neg, .pos))
fit2 <- glm(gruppe ~ tzell + Lai, hkarz, family = binomial)
fit1<-lm(score ~ grade + treatment + stdTest, schools)
APA_Validation(x)
APA_Validation(fit1,fit2, include.pseudo = TRUE, include.r = TRUE, include.loglik = TRUE,
include.rmse = TRUE)
fit1<-lm(score ~ grade + treatment, schools)
fit2<-lm(score ~ grade + treatment + stdTest, schools)
APA_R2(fit1, fit2)
x<-lm(score ~ grade + treatment + stdTest, schools)
APA2(car::durbinWatsonTest(x))
DW_Test2(x)
lmtest::dwtest(x)
car::durbinWatsonTest(x)
n <- 2 * 20
e <- rnorm(n)
dat <- Label(
data.frame(
a = rnorm(n) + e / 2,
b = rnorm(n) + e,
c = rnorm(n),
d = rnorm(n) + e * 10,
g = gl(2, 20, labels = c("Control", "Treat"))
),
a = "Alpha",
b = "Beta",
c = "Gamma"
)
APA_Correlation( ~ a + b + c, dat)
APA_Correlation(a ~ c, dat)
APA_Correlation(a + b + c ~ d, dat)
APA_Correlation(a + b ~ c + d, dat)
APA_Correlation(a + b + c ~ d, dat, groups = ~ g)
APA_Correlation( ~ a + b + c, dat)
APA_Correlation( ~ a + b + c, dat, include.p = TRUE)
APA_Correlation( ~ a + b + c, dat, include.p = TRUE, include.stars = FALSE)
# library(arm)
# windows(7,7)
# corrplot(data[Cs(a,b,c,d)], abs=TRUE, n.col.legend=7)
# install.packages("PerformanceAnalytics")
#library("PerformanceAnalytics")
#?chart.Correlation#(decathlon2.active[, 1:6], histogram=TRUE, pch=19)
#data(managers)
#chart.Correlation(managers[,1:8], histogram=TRUE, pch="+")
#require(stpvers)
#Projekt("html")
APA_Ttest(m1+m2 ~ geschl, varana)
varanax<-Melt2(m1+m2~nr,varana , key="time", value="m")
# broom::tidy(with(varana, t.test(m1,m2 ) ))
# broom::tidy( t.test(m~time, varanax, var.equal=FALSE)) )
APA_Ttest(m~time, varanax, paired = TRUE)
APA_Ttest(m~time, varanax, var.equal=TRUE)
APA_Ttest(m~time, varanax, var.equal=FALSE)
#broom::tidy(t.test(m~time, varanax, var.equal=TRUE))
#broom::tidy(t.test(m~time, varanax, var.equal=FALSE))
#End()
# APA_Xtabs ################################
# Projekt("html")
dat<-GetData("
sex treatment neg pos
f KG 10 9
f UG 14 5
m KG 23 7
m UG 18 14",Tabel_Expand = TRUE, id.vars = 1:2, value="befund")
x1<-xtabs( ~ sex + treatment , dat)
x2<-xtabs( ~ sex + befund + treatment , dat)
APA2(x1)
APA2(x1, include.total=TRUE, percent=F)
APA2(x2, include.total=TRUE)
APA2(x1, include.total.columns=T)
APA2(x2, include.total.columns=T)
APA2(x1, include.total.rows=T)
APA2(x2,include.total.rows=T)
APA2(x2,include.total.sub=T)
APA2(x2,include.total.sub=T, include.total.rows=T)
APA2(x2,
include.total.columns = T,
include.total.rows = T)
hkarz$LAI<- factor(hkarz$lai, 0:1, c("pos", "neg"))
hkarz$Tzell<- cut(hkarz$tzell, 3, c("low", "med", "hig"))
xtab <- xtabs(~ gruppe+LAI, hkarz)
APA2(xtab,
caption="Harnblasenkarzinom", test=FALSE)
APA2(xtab, type="sens",
test=TRUE, caption = "type=sens")
APA2(xtab, type="sens",
caption = "geht nur mit teat=TRUE + type=sens")
APA2(xtabs(~ gruppe+Tzell, hkarz),
caption="APA_Xtabs: 2x3 Tabelle", test=FALSE)
APA2(xtabs(~ gruppe+LAI+Tzell, hkarz),
caption="APA_Xtabs: 2x2x3 Tabelle", test=FALSE)
APA2(xtab,
include.total.columns=TRUE, caption = "include.total.columns")
APA2(xtab,
include.total.sub=TRUE, caption = "include.total.sub")
xtab <- xtabs(~ gruppe+Tzell, hkarz)
APA2(xtab, test=FALSE, caption="APA2: 2x3 Tabelle")
#################################
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