/dports/devel/R-cran-future/future/tests/ |
H A D | globals,formulas.R | 16 fit_i <- lm(weight ~ group - 1) 28 a = substitute({ lm(dist ~ . -1, data = cars) }), 30 b = substitute({ lm(dist ~ . +0, data = cars) }), 77 f <- future({ lm(weight ~ group - 1) }) 79 print(fit) 85 print(fit) 89 fit %<-% { lm(weight ~ group - 1) } 90 print(fit) 94 fit %<-% { lm(weight ~ group - 1) } %lazy% FALSE 95 print(fit) [all …]
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/dports/math/R/R-4.1.2/src/library/stats/man/ |
H A D | lmfit.Rd | 6 \name{lm.fit} 9 lm.fit (x, y, offset = NULL, method = "qr", tol = 1e-7, 15 .lm.fit(x, y, tol = 1e-7) 17 \alias{lm.fit} 19 \alias{.lm.fit} 23 directly unless by experienced users. \code{.lm.fit()} is bare bone 50 a \code{\link{list}} with components (for \code{lm.fit} and \code{lm.wfit}) 86 str(lm. <- lm.fit (x = X, y = y)) 90 lm.. <- .lm.fit(X,y) 91 lm.w <- .lm.fit(X*sqrt(w), y*sqrt(w)) [all …]
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H A D | predict.lm.Rd | 1 % File src/library/stats/man/predict.lm.Rd 6 \name{predict.lm} 8 \alias{predict.lm} 15 \method{predict}{lm}(object, newdata, se.fit = FALSE, scale = NULL, df = Inf, 22 \item{object}{Object of class inheriting from \code{"lm"}} 122 predict(lm(y ~ x)) 124 predict(lm(y ~ x), new, se.fit = TRUE) 125 pred.w.plim <- predict(lm(y ~ x), new, interval = "prediction") 126 pred.w.clim <- predict(lm(y ~ x), new, interval = "confidence") 133 fit <- lm(y ~ x) [all …]
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H A D | stats-defunct.Rd | 20 \alias{anovalist.lm} 21 \alias{lm.fit.null} 22 \alias{lm.wfit.null} 23 \alias{glm.fit.null} 39 print.anova.lm(.) 57 anovalist.lm(object, \dots, test = NULL) 58 lm.fit.null(x, y, method = "qr", tol = 1e-07, \dots) 60 glm.fit.null(x, y, weights, start = NULL, 98 \code{lm.fit.null} and \code{lm.wfit.null} are superseded by 99 \code{lm.fit} and \code{lm.wfit} which handle null models now. [all …]
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/dports/math/libRmath/R-4.1.1/src/library/stats/man/ |
H A D | lmfit.Rd | 6 \name{lm.fit} 9 lm.fit (x, y, offset = NULL, method = "qr", tol = 1e-7, 15 .lm.fit(x, y, tol = 1e-7) 17 \alias{lm.fit} 19 \alias{.lm.fit} 23 directly unless by experienced users. \code{.lm.fit()} is bare bone 50 a \code{\link{list}} with components (for \code{lm.fit} and \code{lm.wfit}) 86 str(lm. <- lm.fit (x = X, y = y)) 90 lm.. <- .lm.fit(X,y) 91 lm.w <- .lm.fit(X*sqrt(w), y*sqrt(w)) [all …]
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H A D | predict.lm.Rd | 1 % File src/library/stats/man/predict.lm.Rd 6 \name{predict.lm} 8 \alias{predict.lm} 15 \method{predict}{lm}(object, newdata, se.fit = FALSE, scale = NULL, df = Inf, 22 \item{object}{Object of class inheriting from \code{"lm"}} 122 predict(lm(y ~ x)) 124 predict(lm(y ~ x), new, se.fit = TRUE) 125 pred.w.plim <- predict(lm(y ~ x), new, interval = "prediction") 126 pred.w.clim <- predict(lm(y ~ x), new, interval = "confidence") 133 fit <- lm(y ~ x) [all …]
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H A D | stats-defunct.Rd | 20 \alias{anovalist.lm} 21 \alias{lm.fit.null} 22 \alias{lm.wfit.null} 23 \alias{glm.fit.null} 39 print.anova.lm(.) 57 anovalist.lm(object, \dots, test = NULL) 58 lm.fit.null(x, y, method = "qr", tol = 1e-07, \dots) 60 glm.fit.null(x, y, weights, start = NULL, 98 \code{lm.fit.null} and \code{lm.wfit.null} are superseded by 99 \code{lm.fit} and \code{lm.wfit} which handle null models now. [all …]
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/dports/math/R-cran-forecast/forecast/tests/testthat/ |
H A D | test-mforecast.R | 11 fit <- lm(mv_y ~ v_x) globalVar 14 fit <- lm(v_y ~ v_x) globalVar 20 fit <- lm(mv_y ~ v_x) globalVar 21 fit1 <- mlmsplit(fit, index = 1) 22 fit2 <- mlmsplit(fit, index = 2) 23 fit3 <- lm(mv_y[, 1] ~ v_x) 24 fit4 <- lm(mv_y[, 2] ~ v_x) 35 fit <- lm(mv_y ~ v_x) globalVar 37 fit2 <- lm(mv_y[, 1] ~ v_x) 52 fit <- lm(mv_y ~ v_x) globalVar [all …]
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/dports/math/R-cran-RcppArmadillo/RcppArmadillo/inst/tinytest/ |
H A D | test_fastLm.R | 26 fit <- lm(log(Volume) ~ log(Girth), data=trees) globalVar 28 expect_equal(as.numeric(flm$coefficients), as.numeric(coef(fit)))#,msg="fastLm.coef") 29 expect_equal(as.numeric(flm$stderr), as.numeric(coef(summary(fit))[,2]))#,msg="fastLm.stderr") 30 expect_equal(as.numeric(flm$df.residual), as.numeric(fit$df.residual))#,msg="fastLm.df.residual") 36 fit <- lm(log(Volume) ~ log(Girth), data=trees) globalVar 38 expect_equal(as.numeric(flm$coefficients), as.numeric(coef(fit)))#,msg="fastLm.default.coef") 47 sfit <- summary(lm(log(Volume) ~ log(Girth), data=trees)) 56 sfit <- summary(lm(log(Volume) ~ log(Girth) - 1, data=trees)) 64 sfit <- summary(lm(log(Volume) ~ log(Girth) - 1, data=trees)) 74 fit <- lm(log(Volume) ~ log(Girth), data=trees) globalVar [all …]
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/dports/math/R-cran-VGAM/VGAM/man/ |
H A D | df.residual.Rd | 12 df.residual_vlm(object, type = c("vlm", "lm"), \dots) 52 when \code{type = "lm"} this is a \eqn{M}-vector of 59 \code{\link[stats]{lm}}, 69 head(model.matrix(fit, type = "vlm")) 70 head(model.matrix(fit, type = "lm")) 73 nobs(fit, type = "vlm") # n * M 74 nvar(fit, type = "vlm") # p_VLM 76 df.residual(fit, type = "lm") # n - p_LM(j); Useful in some situations 77 nobs(fit, type = "lm") # n 78 nvar(fit, type = "lm") # p_LM [all …]
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H A D | model.matrixvlm.Rd | 89 \code{"orig.assign.lm"} (\code{"lm"}-type). 133 fit <- vglm(cbind(normal, mild, severe) ~ 136 class(fit) 138 fit@x # Not saved on the object 139 model.matrix(fit) 140 model.matrix(fit, linpred.index = 1, type = "lm") 141 model.matrix(fit, linpred.index = 2, type = "lm") 143 (Check1 <- head(model.matrix(fit, type = "lm"))) 144 (Check2 <- model.matrix(fit, data = head(pneumo), type = "lm")) 147 q0 <- head(predict(fit)) [all …]
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/dports/math/R-cran-VGAM/VGAM/R/ |
H A D | s.vam.q | 31 smooth.frame$n.lm <- dx[1] 32 smooth.frame$p.lm <- dx[2] 78 smooth.frame$n.lm * sum(ncolHlist[nwhich])) { 124 p.lm <- smooth.frame$p.lm 125 n.lm <- smooth.frame$n.lm 150 npetc = as.integer(c(n.lm, p.lm, length(which), se.fit, 0, 151 bf.maxit, qrank = 0, M, nbig = n.lm * M, pbig, 164 smomat = as.double(smomat), etamat = double(M * n.lm), 208 matrix(fit$etamat, n.lm, M, byrow = TRUE) else 265 df.residual = n.lm * M - qrank - sum(nl.df), # Decrement/increment ? [all …]
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/dports/math/R/R-4.1.2/src/library/stats/tests/ |
H A D | nafns.R | 32 fit <- lm(Ozone ~ ., data=airquality, na.action=na.omit) globalVar 33 summary(fit) 34 sm(fitted(fit)) 35 sm(resid(fit)) 36 sm(predict(fit)) 39 fit2 <- lm(Ozone ~ ., data=airquality, na.action=na.exclude) 52 try(fit3 <- lm(Ozone ~ ., data=airquality, na.action=na.fail)) 61 r1 <- resid(fit) 114 fit <- lm(y ~ x, na.action=na.exclude) globalVar 115 fit2 <- lm(y ~ x, subset=-10) [all …]
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/dports/math/libRmath/R-4.1.1/src/library/stats/tests/ |
H A D | nafns.R | 32 fit <- lm(Ozone ~ ., data=airquality, na.action=na.omit) globalVar 33 summary(fit) 34 sm(fitted(fit)) 35 sm(resid(fit)) 36 sm(predict(fit)) 39 fit2 <- lm(Ozone ~ ., data=airquality, na.action=na.exclude) 52 try(fit3 <- lm(Ozone ~ ., data=airquality, na.action=na.fail)) 61 r1 <- resid(fit) 114 fit <- lm(y ~ x, na.action=na.exclude) globalVar 115 fit2 <- lm(y ~ x, subset=-10) [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/ |
H A D | interactions_anova.py | 63 lm = ols(formula, salary_table).fit() variable 64 print(lm.summary()) 68 lm.model.exog[:5] 73 lm.model.data.orig_exog[:5] 77 lm.model.data.frame[:5] 81 infl = lm.get_influence() 92 resid = lm.resid 118 table1 = anova_lm(lm, interX_lm) 124 table2 = anova_lm(lm, interM_lm) 457 data=kt).fit() [all …]
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/dports/devel/R-cran-broom/broom/inst/doc/ |
H A D | broom_and_dplyr.R | 63 lm_fit <- lm(age ~ circumference, data = Orange) 73 fit = map(data, ~ lm(age ~ circumference, data = .x)), 74 tidied = map(fit, tidy) 86 fit = map(data, ~ lm(wt ~ mpg + qsec + gear, data = .x)), # S3 list-col 87 tidied = map(fit, tidy) 95 fit = map(data, ~ lm(wt ~ mpg + qsec + gear, data = .x)), 96 tidied = map(fit, tidy), 97 glanced = map(fit, glance), 98 augmented = map(fit, augment)
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/dports/textproc/py-nltk/nltk-3.4.1/nltk/test/ |
H A D | lm.doctest | 25 >>> lm.fit(train_data, vocab_data) 33 >>> lm.entropy(sent) 40 >>> lm.entropy(sent) 58 >>> lm.fit(train_data) 81 >>> from nltk.lm import Lidstone 83 >>> lm.fit(train_data, vocab_data) 87 >>> len(lm.vocab) 101 >>> lm.context_counts(context)[word] + lm.gamma 103 >>> lm.context_counts(context).N() + len(lm.vocab) * lm.gamma 129 >>> lm.fit(train_data, vocab_data) [all …]
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/dports/devel/R-cran-broom/broom/tests/testthat/ |
H A D | test-stats-lm.R | 12 fit <- lm(mpg ~ wt, mtcars) globalVar 13 fit2 <- lm(mpg ~ wt + log(disp), mtcars) 14 fit3 <- lm(mpg ~ 1, mtcars) 18 fit_0wts <- lm(mpg ~ 1, weights = wts, data = mtcars) 22 fit_na_row <- lm(mpg ~ cyl * qsec + gear, data = na_row_data) 26 fit_rd <- lm(y ~ x - 1, data = rd_data) 29 td <- tidy(fit) 56 gl <- glance(fit) 66 model = fit,
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/dports/math/R-cran-MatrixModels/MatrixModels/man/ |
H A D | lm.fit.sparse.Rd | 1 \name{lm.fit.sparse} 2 \alias{lm.fit.sparse} 11 lm.fit.sparse(x, y, w = NULL, offset = NULL, 61 \code{\link{lm.fit}()}. 90 system.time(fmDense <- lm.fit(Xd, y = dM[,"Y"])) 91 system.time( r1 <- MatrixModels:::lm.fit.sparse(X, y = dM[,"Y"]) ) # *is* faster 94 r2 <- MatrixModels:::lm.fit.sparse(X, y = dM[,"Y"], method = "chol") ) 99 system.time(fmD.w <- with(dM, lm.wfit(Xd, Y, w = wts))) 100 system.time(fm.w1 <- with(dM, MatrixModels:::lm.fit.sparse(X, Y, w = wts))) 101 system.time(fm.w2 <- with(dM, MatrixModels:::lm.fit.sparse(X, Y, w = wts,
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/dports/math/openturns/openturns-1.18/validation/src/ |
H A D | valid_LinearModelAnalysis.py | 34 fit = stats.lm(formula) variable 35 summary = stats.summary_lm(fit) 72 fit = stats.lm(formula) variable 73 summary = stats.summary_lm(fit) 92 fit = stats.lm(formula) variable 93 summary = stats.summary_lm(fit) 113 fit = stats.lm(formula) variable 114 summary = stats.summary_lm(fit) 135 fit = stats.lm(formula) variable 136 summary = stats.summary_lm(fit)
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/dports/finance/R-cran-AER/AER/man/ |
H A D | ivreg.fit.Rd | 1 \name{ivreg.fit} 2 \alias{ivreg.fit} 11 ivreg.fit(x, y, z, weights, offset, \dots) 21 \item{\dots}{further arguments passed to \code{\link[stats:lmfit]{lm.fit}} or 22 \code{\link[stats]{lm.wfit}}, respectively.} 26 \code{\link{ivreg}} is the high-level interface to the work-horse function \code{ivreg.fit}, 31 \code{ivreg.fit} is a convenience interface to \code{\link{lm.fit}} (or \code{\link{lm.wfit}}) 37 \code{ivreg.fit} returns an unclassed list with the following components: 52 \seealso{\code{\link{ivreg}}, \code{\link[stats:lmfit]{lm.fit}}} 69 ivreg.fit(x, y, z)$coefficients
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/dports/math/R-cran-robustbase/robustbase/man/ |
H A D | predict.lmrob.Rd | 15 %% the following is +- copy-pasted from predict.lm.Rd: 37 closely modeled after the method for \code{lm()}, 38 \code{\link{predict.lm}}, maybe see there for caveats with missing 40 %% Also lifted from predict.lm.Rd : 54 %% the following is +- copy-pasted from predict.lm.Rd: 59 \item{fit}{vector or matrix as above} 70 \code{\link{predict.lm}} method. 73 ## Predictions --- artificial example -- closely following example(predict.lm) 84 abline(lm (y ~ x), col = "gray40") 94 str(predict(fm, new, se.fit = TRUE)) [all …]
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/dports/devel/R-cran-gmodels/gmodels/man/ |
H A D | fit.contrast.Rd | 3 \name{fit.contrast} 4 \alias{fit.contrast} 5 \alias{fit.contrast.lm} 6 \alias{fit.contrast.lme} 7 %%\alias{fit.contrast.mer} 14 \method{fit.contrast}{lm}(model, varname, coeff, showall=FALSE, 22 \item{model}{regression (lm,glm,aov,lme) object for which the 57 \seealso{ \code{\link{lm}}, \code{\link{contrasts}}, 67 reg <- lm(y ~ x) 94 fit.contrast(reg,x,cmat) [all …]
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/dports/math/R-cran-outliers/outliers/R/ |
H A D | qtable.R | 14 fit <- lm(q0 ~ p0) functionVar 20 fit <- lm(q0 ~ p0) 32 fit <- lm(q0 ~ poly(p0,3)); 34 res <- c(res,predict(fit,newdata=list(p0=pp)))
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/dports/devel/R-cran-broom/broom/man/ |
H A D | augment.lm.Rd | 3 \name{augment.lm} 4 \alias{augment.lm} 17 \item{x}{An \code{lm} object created by \code{\link[stats:lm]{stats::lm()}}.} 32 \item{se_fit}{Logical indicating whether or not a \code{.se.fit} column should be 60 it \strong{must} be exactly the data that was used to fit the model 63 variable columns used to fit the model are present. If the original outcome 64 variable used to fit the model is not included in \code{newdata}, then no 73 so that \code{augment(fit)} will return the augmented training data. In these 87 We are in the process of defining behaviors for models fit with various 105 \code{.se.fit} columns. [all …]
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