我写这段代码是为了得到 Dunnet anova post hoc test
import rpy2.robjects as ro
import rpy2.robjects.numpy2ri as npr
from rpy2.robjects.numpy2ri import numpy2ri as np2r
from rpy2.robjects.packages import importr
base = importr("base")
stats = importr('stats')
multcomp = importr('multcomp')
val = np2r(vFC)
exp = base.gl(4,6,24)
tiempo = base.factor(base.rep(base.c(0,2,5,10,15,30),4))
fmla = ro.Formula('val ~ tiempo + exp')
env = fmla.environment
env['val'] = val
env['tiempo'] = tiempo
env['exp'] = exp
anova = stats.aov(fmla)
print base.summary(anova)
Phoc = multcomp.glht(anova, linfct = ro.r('mcp(tiempo="Dunnet")'))
sPhoc = base.summary(Phoc)
print sPhoc
工作正常，但在分析后形成输出我得到源代码，我怎样才能摆脱该代码并仅从 Dunnet 分析中获得最终表。
Df Sum Sq Mean Sq F value Pr(>F)
tiempo 5 91172 18234 8.788 0.000464 ***
exp 3 49402 16467 7.936 0.002108 **
Residuals 15 31125 2075
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts
Fit: function (formula, data = NULL, projections = FALSE, qr = TRUE,
contrasts = NULL, ...)
{
Terms <- if (missing(data))
terms(formula, "Error")
else terms(formula, "Error", data = data)
indError <- attr(Terms, "specials")$Error
if (length(indError) > 1L)
stop(sprintf(ngettext(length(indError), "there are %d Error terms: only 1 is allowed",
"there are %d Error terms: only 1 is allowed"), length(indError)),
domain = NA)
lmcall <- Call <- match.call()
lmcall[[1L]] <- as.name("lm")
lmcall$singular.ok <- TRUE
if (projections)
qr <- lmcall$qr <- TRUE
lmcall$projections <- NULL
if (is.null(indError)) {
fit <- eval(lmcall, parent.frame())
if (projections)
fit$projections <- proj(fit)
class(fit) <- if (inherits(fit, "mlm"))
c("maov", "aov", oldClass(fit))
else c("aov", oldClass(fit))
fit$call <- Call
return(fit)
}
else {
if (pmatch("weights", names(match.call()), 0L))
stop("weights are not supported in a multistratum aov() fit")
opcons <- options("contrasts")
options(contrasts = c("contr.helmert", "contr.poly"))
on.exit(options(opcons))
allTerms <- Terms
errorterm <- attr(Terms, "variables")[[1 + indError]]
eTerm <- deparse(errorterm[[2L]], width.cutoff = 500L,
backtick = TRUE)
intercept <- attr(Terms, "intercept")
ecall <- lmcall
ecall$formula <- as.formula(paste(deparse(formula[[2L]],
width.cutoff = 500L, backtick = TRUE), "~", eTerm,
if (!intercept)
"- 1"), env = environment(formula))
ecall$method <- "qr"
ecall$qr <- TRUE
ecall$contrasts <- NULL
er.fit <- eval(ecall, parent.frame())
options(opcons)
nmstrata <- attr(terms(er.fit), "term.labels")
nmstrata <- sub("^`(.*)`$", "\\1", nmstrata)
nmstrata <- c("(Intercept)", nmstrata)
qr.e <- er.fit$qr
rank.e <- er.fit$rank
if (rank.e < length(er.fit$coefficients))
warning("Error() model is singular")
qty <- er.fit$residuals
maov <- is.matrix(qty)
asgn.e <- er.fit$assign[qr.e$pivot[1L:rank.e]]
maxasgn <- length(nmstrata) - 1L
nobs <- NROW(qty)
if (nobs > rank.e) {
result <- vector("list", maxasgn + 2L)
asgn.e[(rank.e + 1):nobs] <- maxasgn + 1L
nmstrata <- c(nmstrata, "Within")
}
else result <- vector("list", maxasgn + 1L)
names(result) <- nmstrata
lmcall$formula <- form <- update(formula, paste(". ~ .-",
deparse(errorterm, width.cutoff = 500L, backtick = TRUE)))
Terms <- terms(form)
lmcall$method <- "model.frame"
mf <- eval(lmcall, parent.frame())
xvars <- as.character(attr(Terms, "variables"))[-1L]
if ((yvar <- attr(Terms, "response")) > 0L)
xvars <- xvars[-yvar]
if (length(xvars)) {
xlev <- lapply(mf[xvars], levels)
xlev <- xlev[!sapply(xlev, is.null)]
}
else xlev <- NULL
resp <- model.response(mf)
qtx <- model.matrix(Terms, mf, contrasts)
cons <- attr(qtx, "contrasts")
dnx <- colnames(qtx)
asgn.t <- attr(qtx, "assign")
if (length(wts <- model.weights(mf))) {
wts <- sqrt(wts)
resp <- resp * wts
qtx <- qtx * wts
}
qty <- as.matrix(qr.qty(qr.e, resp))
if ((nc <- ncol(qty)) > 1) {
dny <- colnames(resp)
if (is.null(dny))
dny <- paste0("Y", 1L:nc)
dimnames(qty) <- list(seq(nrow(qty)), dny)
}
else dimnames(qty) <- list(seq(nrow(qty)), NULL)
qtx <- qr.qty(qr.e, qtx)
dimnames(qtx) <- list(seq(nrow(qtx)), dnx)
for (i in seq_along(nmstrata)) {
select <- asgn.e == (i - 1)
ni <- sum(select)
if (!ni)
next
xi <- qtx[select, , drop = FALSE]
cols <- colSums(xi^2) > 1e-05
if (any(cols)) {
xi <- xi[, cols, drop = FALSE]
attr(xi, "assign") <- asgn.t[cols]
fiti <- lm.fit(xi, qty[select, , drop = FALSE])
fiti$terms <- Terms
}
else {
y <- qty[select, , drop = FALSE]
fiti <- list(coefficients = numeric(), residuals = y,
fitted.values = 0 * y, weights = wts, rank = 0L,
df.residual = NROW(y))
}
if (projections)
fiti$projections <- proj(fiti)
class(fiti) <- c(if (maov) "maov", "aov", oldClass(er.fit))
result[[i]] <- fiti
}
result <- result[!sapply(result, is.null)]
class(result) <- c("aovlist", "listof")
if (qr)
attr(result, "error.qr") <- qr.e
attr(result, "call") <- Call
if (length(wts))
attr(result, "weights") <- wts
attr(result, "terms") <- allTerms
attr(result, "contrasts") <- cons
attr(result, "xlevels") <- xlev
result
}
}(formula = val ~ tiempo + exp)
Linear Hypotheses:
Estimate Std. Error t value Pr(>|t|)
2 - 0 == 0 78.10 32.21 2.425 0.10346
5 - 0 == 0 152.39 32.21 4.731 0.00113 **
10 - 0 == 0 140.06 32.21 4.348 0.00246 **
15 - 0 == 0 158.90 32.21 4.933 < 0.001 ***
30 - 0 == 0 180.73 32.21 5.611 < 0.001 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)