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POHow to get rid code when printing summary in rpy2
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2012-06-28T05:20:26.420
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<p>I write this code to get Dunnet anova post hoc test</p> <pre><code>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 </code></pre> <p>Works fine but form the output I get sourcecode after the analysis, how can I get rid from that code and get only the final table from Dunnet analysis. </p> <pre><code> 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) </code></pre>
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How to get rid code when printing summary in rpy2
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