| /dports/math/R-cran-VGAM/VGAM/man/ |
| H A D | model.matrixvlm.Rd | 5 model.matrixvlm(object, type = c("vlm", "lm", "lm2", "bothlmlm2"),
14 The value \code{"vlm"} is the VLM model matrix corresponding
16 The value \code{"lm"} is the LM model matrix corresponding
86 the model matrix has attributes:
118 \code{\link[stats]{model.matrix}},
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"))
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| H A D | model.matrixqrrvglm.Rd | 5 model.matrixqrrvglm(object, type = c("latvar", "lm", "vlm"), \dots)
12 \item{type}{Type of model (or design) matrix returned.
14 The value \code{"latvar"} is model matrix mainly comprising
17 The value \code{"lm"} is the LM matrix directly
19 The value \code{"vlm"} is the big VLM model matrix \emph{given C}.
30 Creates a model matrix. Two types can be
33 (such as returned if it were of class \code{"lm"}).
45 to construct the big model matrix \emph{given C}.
90 model.matrix(mycqo, type = "latvar")
91 model.matrix(mycqo, type = "lm")
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| H A D | df.residual.Rd | 12 df.residual_vlm(object, type = c("vlm", "lm"), \dots)
37 of rows of the LM-type model: \eqn{nM}.
41 columns of the `big' VLM matrix.
44 columns of the `ordinary' LM matrix corresponding
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"))
77 nobs(fit, type = "lm") # n
78 nvar(fit, type = "lm") # p_LM
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| /dports/math/R-cran-VGAM/VGAM/R/ |
| H A D | model.matrix.vglm.q | 62 vlm2lm.model.matrix <-
102 } # vlm2lm.model.matrix
109 lm2vlm.model.matrix <-
141 kronecker(matrix(1, nrow.X.lm, 1), allB)
250 } # lm2vlm.model.matrix
373 n.lm <- nobs(object, type = "lm") # Number of rows of the LM matrix
716 if (length(rownames(model.matrix(model, type = "lm"))))
857 n.lm <- nobs(model, type = "lm")
858 X.lm <- model.matrix(model, type = "lm")
863 offset <- matrix(model@offset, n.lm, M)
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| H A D | formula.vlm.q | 334 assign <- attr(model.matrix(model, type = "lm"), "assign")
391 x.lm <- model.matrix(object, type = "lm")
392 x.vlm <- model.matrix(object, type = "vlm")
399 "the model matrix; try vglm(..., x = TRUE) and rerun")
425 OOO <- matrix(0, n.lm, M)
426 Xm2 <- model.matrix(object, type = "lm2") # May be 0 x 0
435 Y <- as.matrix(Y)
571 OOO <- matrix(0, n.lm, M)
572 Xm2 <- model.matrix(object, type = "lm2") # May be 0 x 0
598 big.x.lm <- model.matrix(bigfit, type = "lm")
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| H A D | deviance.vlm.q | 109 lm = nobs(object, type = "lm") - nvar_vlm(object, type = "lm"),
128 nobs(object, type = "lm") * npred(object) -
146 allH <- matrix(unlist(constraints(object, type = "lm")), nrow = M)
155 X.lm.jay <- model.matrix(object, type = "lm", linpred.index = jay)
156 NumPars[jay] <- ncol(X.lm.jay)
198 coef(fit2, matrix = TRUE)
205 coef(fit3, matrix = TRUE)
209 constraints(fit2, type = "lm")
210 head(model.matrix(fit2, type = "term"))
211 head(model.matrix(fit2, type = "lm"))
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| /dports/math/R-cran-memisc/memisc/man/ |
| H A D | withVCov.Rd | 4 \alias{withVCov.lm}
7 \alias{summary.withVCov.lm}
20 \method{withVCov}{lm}(object, vcov, \dots)
23 \method{summary}{withVCov.lm}(object, \dots)
26 \item{object}{a fitted model object}
37 attributed to a fitted model object. Such a matrix may be produced by
41 \code{withVCov()} has no consequences on how a fitted model itself is
53 \code{withVCov} returns a slightly modified model object: It adds an
55 and modifies the class attribute. If e.g. the original model object has class
56 "lm" then the model object modified by \code{withVCov} has the class
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| /dports/math/R-cran-SparseM/SparseM/man/ |
| H A D | slm.Rd | 3 \title{Fit a linear regression model using sparse matrix algebra}
10 that it would be necessary to have a model.matrix function that
23 \code{lm()}, the response variable in the formula can be matrix valued.
50 default = \code{NULL} appearing in the model formula.
84 X <- model.matrix(lsq) #extract the design matrix
85 y <- model.response(lsq) # extract the rhs
86 X1 <- as.matrix(X)
88 lm.time <- system.time(lm(y~X1-1) -> lm.o) # very slow
94 cat("lm time =",lm.time,"\n")
96 sum.lm <- summary(lm.o)
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| /dports/math/R-cran-car/car/man/ |
| H A D | hccm.Rd | 5 % 2012-04-04: weighted lm now allowed. John
12 \alias{hccm.lm}
23 hccm(model, ...)
25 \method{hccm}{lm}(model, type=c("hc3", "hc0", "hc1", "hc2", "hc4"),
28 \method{hccm}{default}(model, ...)
32 \item{model}{a unweighted or weighted linear model, produced by \code{lm}.}
38 matrix that's returned.}
43 …The original White-corrected coefficient covariance matrix (\code{"hc0"}) for an unweighted model …
47 corrected covariance matrix.
54 The heteroscedasticity-corrected covariance matrix for the model.
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| /dports/devel/R-cran-gmodels/gmodels/man/ |
| H A D | make.contrasts.Rd | 7 Construct a user-specified contrast matrix.
13 \item{contr}{ vector or matrix specifying contrasts (one per row).}
14 \item{how.many}{ dimensions of the desired contrast matrix. This
26 \code{make.contrasts} returns a matrix with dimensions
30 This matrix can then be used as the argument to
32 functions (eg, \code{\link{lm}}).
48 reg <- lm(y ~ x)
66 summary(lm( y ~ x ) )
68 # or use contrasts.lm
69 reg <- lm(y ~ x)
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| /dports/finance/R-cran-AER/AER/man/ |
| H A D | ivreg.fit.Rd | 15 \item{x}{regressor matrix.}
17 \item{z}{instruments matrix.}
21 \item{\dots}{further arguments passed to \code{\link[stats:lmfit]{lm.fit}} or
22 \code{\link[stats]{lm.wfit}}, respectively.}
28 \code{hatvalues}, \code{predict}, \code{terms}, \code{model.matrix}, \code{bread},
31 \code{ivreg.fit} is a convenience interface to \code{\link{lm.fit}} (or \code{\link{lm.wfit}})
46 \item{rank}{the numeric rank of the fitted linear model.}
47 \item{df.residual}{residual degrees of freedom for fitted model.}
52 \seealso{\code{\link{ivreg}}, \code{\link[stats:lmfit]{lm.fit}}}
67 x <- model.matrix(fm, component = "regressors")
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| /dports/math/R/R-4.1.2/src/library/stats/man/ |
| H A D | vcov.Rd | 10 \alias{vcov.lm}
12 \alias{vcov.summary.lm}
18 Returns the variance-covariance matrix of the main parameters of
19 a fitted model object. The \dQuote{main} parameters of model
25 \S3method{vcov}{lm}(object, complete = TRUE, \dots)
38 full variance-covariance matrix should be returned also in case of
50 \item{vc}{a variance-covariance matrix, typically \dQuote{incomplete},
58 \code{summary.lm}, \code{summary.glm},
70 model fits encoded via NA coefficients: It augments a vcov--matrix
76 A matrix of the estimated covariances between the parameter estimates
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| H A D | lm.Rd | 26 model to be fitted. The details of model specification are given
56 components of the fit (the model frame, the model matrix, the
64 of \code{\link{model.matrix.default}}.}
68 This should be \code{NULL} or a numeric vector or matrix of extents
77 Models for \code{lm} are specified symbolically. A typical model has
92 If \code{response} is a matrix a linear model is fitted separately by
93 least-squares to each column of the matrix.
95 See \code{\link{model.matrix}} for some further details. The terms in
152 \item{x}{if requested, the model matrix used.}
248 stopifnot(identical(lm(weight ~ group, method = "model.frame"),
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| H A D | summary.lm.Rd | 6 \name{summary.lm}
23 \item{correlation}{logical; if \code{TRUE}, the correlation matrix of
49 statistics of the fitted linear model given in \code{object}, using
55 \item{coefficients}{a \eqn{p \times 4}{p x 4} matrix with columns for
71 the model},
77 \item{cov.unscaled}{a \eqn{p \times p}{p x p} matrix of (unscaled)
79 \item{correlation}{the correlation matrix corresponding to the above
86 The model fitting function \code{\link{lm}}, \code{\link{summary}}.
88 Function \code{\link{coef}} will extract the matrix of coefficients
99 ## model with *aliased* coefficient:
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| H A D | proj.Rd | 17 \method{proj}{lm}(object, onedf = FALSE, unweighted.scale = FALSE, \dots)
21 \alias{proj.lm}
25 \item{object}{An object of class \code{"lm"} or a class inheriting from
29 the columns of the model matrix. If \code{FALSE}, the single-column
30 projections are collapsed by terms of the model (as represented in
39 of the data onto the terms of a linear model. It is most frequently
47 A projection matrix or (for multi-stratum objects) a list of
59 The methods for \code{lm} and \code{aov} models add a column to the
61 onto the orthogonal complement of the model space).
65 model matrix corresponding to that term. In this case the matrix does
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| /dports/math/libRmath/R-4.1.1/src/library/stats/man/ |
| H A D | vcov.Rd | 10 \alias{vcov.lm}
12 \alias{vcov.summary.lm}
18 Returns the variance-covariance matrix of the main parameters of
19 a fitted model object. The \dQuote{main} parameters of model
25 \S3method{vcov}{lm}(object, complete = TRUE, \dots)
38 full variance-covariance matrix should be returned also in case of
50 \item{vc}{a variance-covariance matrix, typically \dQuote{incomplete},
58 \code{summary.lm}, \code{summary.glm},
70 model fits encoded via NA coefficients: It augments a vcov--matrix
76 A matrix of the estimated covariances between the parameter estimates
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| H A D | lm.Rd | 26 model to be fitted. The details of model specification are given
56 components of the fit (the model frame, the model matrix, the
64 of \code{\link{model.matrix.default}}.}
68 This should be \code{NULL} or a numeric vector or matrix of extents
77 Models for \code{lm} are specified symbolically. A typical model has
92 If \code{response} is a matrix a linear model is fitted separately by
93 least-squares to each column of the matrix.
95 See \code{\link{model.matrix}} for some further details. The terms in
152 \item{x}{if requested, the model matrix used.}
248 stopifnot(identical(lm(weight ~ group, method = "model.frame"),
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| H A D | summary.lm.Rd | 6 \name{summary.lm}
23 \item{correlation}{logical; if \code{TRUE}, the correlation matrix of
49 statistics of the fitted linear model given in \code{object}, using
55 \item{coefficients}{a \eqn{p \times 4}{p x 4} matrix with columns for
71 the model},
77 \item{cov.unscaled}{a \eqn{p \times p}{p x p} matrix of (unscaled)
79 \item{correlation}{the correlation matrix corresponding to the above
86 The model fitting function \code{\link{lm}}, \code{\link{summary}}.
88 Function \code{\link{coef}} will extract the matrix of coefficients
99 ## model with *aliased* coefficient:
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| H A D | proj.Rd | 17 \method{proj}{lm}(object, onedf = FALSE, unweighted.scale = FALSE, \dots)
21 \alias{proj.lm}
25 \item{object}{An object of class \code{"lm"} or a class inheriting from
29 the columns of the model matrix. If \code{FALSE}, the single-column
30 projections are collapsed by terms of the model (as represented in
39 of the data onto the terms of a linear model. It is most frequently
47 A projection matrix or (for multi-stratum objects) a list of
59 The methods for \code{lm} and \code{aov} models add a column to the
61 onto the orthogonal complement of the model space).
65 model matrix corresponding to that term. In this case the matrix does
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| /dports/math/R-cran-car/car/ |
| H A D | NAMESPACE | 8 S3method(Confint, lm)
17 S3method(S, lm)
43 S3method(brief, matrix)
70 "AIC", "BIC", "expand.model.frame")
84 glm, glm.fit, hatvalues, is.empty.model, lm, lm.fit, loess, loess.control,
85 logLik, lowess, lsfit, make.link, median, model.frame, model.matrix,
86 model.matrix.default, model.response, model.weights, na.action, na.omit, na.pass,
92 # importFrom(VGAM, vcovvlm, coefvlm, formulavlm, model.matrixvlm)
206 # scatterplot.matrix,
232 S3method(some, matrix)
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| /dports/finance/R-cran-plm/plm/vignettes/ |
| H A D | C_plmModelComponents.Rmd | 27 the model matrix `X` (with `model.matrix`),
52 Next, the `X` matrix is extracted using `model.matrix`. The usual way
54 `terms` object and a `data.frame` created with `model.frame`. `lm` uses
56 `data.frame` created with `model.frame`. Therefore, `model.matrix`
81 Xols <- model.matrix(mfols)
83 coef(lm.fit(Xols, y))
120 X1 <- model.matrix(mfSB1, rhs = 1)
121 W1 <- model.matrix(mfSB1, rhs = 2)
149 X2 <- model.matrix(mfSB2, rhs = 1)
150 W2 <- model.matrix(mfSB2, rhs = 2)
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| /dports/finance/R-cran-plm/plm/inst/doc/ |
| H A D | C_plmModelComponents.Rmd | 27 the model matrix `X` (with `model.matrix`),
52 Next, the `X` matrix is extracted using `model.matrix`. The usual way
54 `terms` object and a `data.frame` created with `model.frame`. `lm` uses
56 `data.frame` created with `model.frame`. Therefore, `model.matrix`
81 Xols <- model.matrix(mfols)
83 coef(lm.fit(Xols, y))
120 X1 <- model.matrix(mfSB1, rhs = 1)
121 W1 <- model.matrix(mfSB1, rhs = 2)
149 X2 <- model.matrix(mfSB2, rhs = 1)
150 W2 <- model.matrix(mfSB2, rhs = 2)
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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,
17 \item{x}{\emph{sparse} design matrix of dimension \code{n * p}, i.e.,
20 \code{\link{sparse.model.matrix}}.}
21 \item{y}{vector of observations of length \code{n}, or a matrix with
38 \code{FALSE}, a singular model is an error.}
59 \code{\link{sparse.model.matrix}} from the \pkg{Matrix} package;
61 \code{\link{lm.fit}()}.
83 X <- Matrix::sparse.model.matrix( ~ (a+b+c+d)^2 + c*x, data = dM)
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| /dports/math/apache-commons-math/commons-math3-3.6.1-src/src/test/R/ |
| H A D | multipleOLSRegressionTestCases | 38 errors <- as.vector(as.matrix(coefficients(summary(model)))[,2])
90 model <- lm(y ~ x1 + x2 + x3 + x4 + x5)
110 design <- matrix(c(60323,83.0,234289,2356,1590,107608,1947,
134 model <- lm(y ~ x1 + x2 + x3 + x4 + x5 + x6)
136 estimates <- matrix(c(-3482258.63459582,890420.383607373,
160 model <- lm(y ~ 0 + x1 + x2 + x3 + x4 + x5 + x6)
186 design <- matrix(c(80.2,17.0,15,12,9.96,
241 model <- lm(y ~ x1 + x2 + x3 + x4)
243 estimates <- matrix(c(91.05542390271397,6.94881329475087,
278 model <- lm(y ~ 0 + x1 + x2 + x3 + x4)
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| /dports/math/R-cran-robustbase/robustbase/man/ |
| H A D | splitFrame.Rd | 7 Splits the design matrix into categorical and continuous
12 splitFrame(mf, x = model.matrix(mt, mf),
16 \item{mf}{model frame (as returned by \code{\link{model.frame}}).}
17 \item{x}{(optional) design matrix, defaulting to the derived
18 \code{\link{model.matrix}}.}
45 categorical in the original design matrix}
65 fm1 <- lm(Y ~ Region + X1 + X2 + X3, education)
66 fmC <- lm(Y ~ Region + X1 + X2 + X3, educaCh )
68 splC <- splitFrame(fmC$model)
72 fm2 <- lm(Y ~ Region:X1:X2 + X1*X2, education)
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