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/dports/devel/R-cran-future/future/tests/
H A Dglobals,formulas.R16 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)
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/dports/math/R/R-4.1.2/src/library/stats/man/
H A Dlmfit.Rd6 \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))
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H A Dpredict.lm.Rd1 % 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)
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H A Dstats-defunct.Rd20 \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98 \code{lm.fit.null} and \code{lm.wfit.null} are superseded by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.
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/dports/math/libRmath/R-4.1.1/src/library/stats/man/
H A Dlmfit.Rd6 \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))
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H A Dpredict.lm.Rd1 % 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)
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H A Dstats-defunct.Rd20 \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.
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/dports/math/R-cran-forecast/forecast/tests/testthat/
H A Dtest-mforecast.R11 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
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/dports/math/R-cran-RcppArmadillo/RcppArmadillo/inst/tinytest/
H A Dtest_fastLm.R26 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$coefficients), as.numeric(coef(fit)))#,msg="fastLm.default.coef")
47 sfit <- summary(lm(log(Volume) ~ log(Girth), data=trees))
56 sfit <- summary(lm(log(Volume) ~ log(Girth) - 1, data=trees))
64 sfit <- summary(lm(log(Volume) ~ log(Girth) - 1, data=trees))
74 fit <- lm(log(Volume) ~ log(Girth), data=trees) globalVar
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/dports/math/R-cran-VGAM/VGAM/man/
H A Ddf.residual.Rd12 df.residual_vlm(object, type = c("vlm", "lm"), \dots)
52 when \code{type = "lm"} this is a \eqn{M}-vector of
59 \code{\link[stats]{lm}},
69 head(model.matrix(fit, type = "vlm"))
70 head(model.matrix(fit, type = "lm"))
73 nobs(fit, type = "vlm") # n * M
74 nvar(fit, type = "vlm") # p_VLM
76 df.residual(fit, type = "lm") # n - p_LM(j); Useful in some situations
77 nobs(fit, type = "lm") # n
78 nvar(fit, type = "lm") # p_LM
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H A Dmodel.matrixvlm.Rd89 \code{"orig.assign.lm"} (\code{"lm"}-type).
133 fit <- vglm(cbind(normal, mild, severe) ~
136 class(fit)
138 fit@x # Not saved on the object
139 model.matrix(fit)
140 model.matrix(fit, linpred.index = 1, type = "lm")
141 model.matrix(fit, linpred.index = 2, type = "lm")
143 (Check1 <- head(model.matrix(fit, type = "lm")))
144 (Check2 <- model.matrix(fit, data = head(pneumo), type = "lm"))
147 q0 <- head(predict(fit))
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/dports/math/R-cran-VGAM/VGAM/R/
H A Ds.vam.q31 smooth.frame$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 ?
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/dports/math/R/R-4.1.2/src/library/stats/tests/
H A Dnafns.R32 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)
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/dports/math/libRmath/R-4.1.1/src/library/stats/tests/
H A Dnafns.R32 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)
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/
H A Dinteractions_anova.py63 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()
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/dports/devel/R-cran-broom/broom/inst/doc/
H A Dbroom_and_dplyr.R63 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)
/dports/textproc/py-nltk/nltk-3.4.1/nltk/test/
H A Dlm.doctest25 >>> 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)
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/dports/devel/R-cran-broom/broom/tests/testthat/
H A Dtest-stats-lm.R12 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,
/dports/math/R-cran-MatrixModels/MatrixModels/man/
H A Dlm.fit.sparse.Rd1 \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,
/dports/math/openturns/openturns-1.18/validation/src/
H A Dvalid_LinearModelAnalysis.py34 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)
/dports/finance/R-cran-AER/AER/man/
H A Divreg.fit.Rd1 \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
/dports/math/R-cran-robustbase/robustbase/man/
H A Dpredict.lmrob.Rd15 %% 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))
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/dports/devel/R-cran-gmodels/gmodels/man/
H A Dfit.contrast.Rd3 \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)
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/dports/math/R-cran-outliers/outliers/R/
H A Dqtable.R14 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)))
/dports/devel/R-cran-broom/broom/man/
H A Daugment.lm.Rd3 \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.
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