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P � � @ t � r � c | � � � < � B H t 7 u � ; � � g > b x n 0 � V � J / � @ U > x � j � � � i � , & � � ; g � � � � ? � � Y d ^ m � v � � � � � b � D � � E � L ( � I � � � � ) � { � # � [ � E T X � V < � W 9 � 9 � � 3 � : � J � � s s 2 � = ~ � F � � M � � � * � : 0 ^ � K w � � � � e � " r ` u # � � � � } . 4 � Z / � � � ' � k ) � � y U ~ Q � S ` � m � � � h � � � Q O $ � � "fs" smooth cannot use a multiply penalized basis (wrong basis in xt) "fs" terms can not be fixed here %s link not available for negative binomial family; available links are "identity", "log" and "sqrt" 'a' has to be positive 'family' argument seems not to be a valid family object 'theta' must be specified , 0s in V(mu) 1<a<b<2 (strict) required A not square A term has fewer unique covariate combinations than specified maximum degrees of freedom AR not available with sparse marginals AR.start must be logical AR1 parameter rho unused with generalized model Algorithm did not converge Algorithm stopped at boundary value An object of length An object of length %d does not match the required parameter size At least three knots required in call to mono.con. Basis only handles 1D smooths Can not convert this smooth class to a random effect Can not have more eigenvalues than nrow(A) Can only deal with a sphere Can't find by variable Can't find valid starting values: please specify some Cannot extract the dimensions Cannot extract the inverse from an uninitialized object Cannot extract the matrix from an uninitialized object Cannot extract the matrix from an uninitialized pdMat object Cannot extract the matrix with uninitialized dimensions Deviance = %s Iterations - %d Discrete Theta search not available with performance iteration Don't know what to do with parametric terms that are not simple numeric or factor variables F is missing from cr smooth - refit model with current mgcv First argument is no sort of formula! Fitting terminated with step failure - check results carefully H has wrong dimension IRLS regularizing parameter must be a non-negative number. Invalid fitted means in empty model Invalid linear predictor values in empty model Iteration limit reached without full convergence - check carefully L has inconsistent dimensions. L has wrong dimension in `paraPen' L must be a matrix. L must have at least as many rows as columns. Later terms sharing an `id' can not have more smoothing parameters than the first such term Length of start should equal Length of start should equal %d and correspond to initial coefs for %s Length of start should equal %d and correspond to initial coefs. M$S and M$off have different lengths M$S[%d] is too large given M$off[%d] M$sp has different length to M$S and M$off MRF basis dimension set too high Model can not be updated Model does not seem to have enough terms to do anything useful Model has more coefficients than data Model matrix not full column rank Model seems to contain no terms Must give names when initializing pdIdnot from parameter. Must supply theta or R to ocat NA times supplied for cox.ph prediction NA values not allowed for the scaled t family NA's in min.sp. NA's in pdTens factor NA's in pdTens matrix NA's in supplied smoothing parameter vector - ignoring. NAs in V(mu) NAs in d(mu)/d(eta) Nested smooths must be fully random No data supplied to gam.setup No data supplied to gamm.setup No observations informative at iteration %d No suitable s (i.e. m[2]) try increasing m[1] No terms to plot - nothing for plot.gam() to do. No variance estimates available Non finite derivatives. Try decreasing fit tolerance! See `epsilon' in `gam.contol' Non-finite coefficients at iteration Non-finite coefficients at iteration %d Non-finite derivatives. Try decreasing fit tolerance! See `epsilon' in `gam.contol' Non-finite deviance Non-integer response variables are not allowed with ziP Non-integer response variables are not allowed with ziplss Not enough (non-NA) data to do anything meaningful Not enough informative observations. Number of prediction and fit constraints must match Only 1<p<=2 supported Partial residuals do not have a natural x-axis location for linear functional terms Pearson residuals not available for this family - returning deviance residuals Penalized model matrix must have no more columns than rows Please provide a single value for theta or use nb to estimate it Please put term with most levels last in 're' to avoid spoiling supplied penalties Possible divergence detected in fast.REML.fit Repeated variables as arguments of a smooth are not permitted Requires a gam or bam prefit object Reset optimizer to outer/newton Response should be integer class labels S.scale vector doesn't match S list - please report to maintainer S[[%d]] matrix is not +ve definite. Smoothness uncertainty corrected covariance not available Something wrong - matrix probably not +ve semi definite Something wrong with auto- penalty construction Something wrong with zlim Sorry, tensor products of smooths with multiple penalties are not supported. Step halved: new penalized deviance = %g Step size truncated due to divergence Step size truncated: out of bounds Step size truncated: out of bounds. Supplied df below min.df. min.df reset Supplied matrix not symmetric Supplied smoothing parameter vector is too short - ignored. Tensor product penalty rank appears to be too low: please email [email protected] with details. The following arguments to anova.glm(..) are invalid and dropped: This family should not have a matrix response Too many cluster nodes to use all efficiently Tweedie density may be unreliable - series not fully converged Tweedie p must be in interval (a,b) Unknown type, reset to terms. Using ziP for binary data makes no sense Using ziplss for binary data makes no sense View variables must contain more than one value. view = c(%s,%s). X lost dimensions in magic!! You've got no model.... `by' variable must be same dimension as smooth arguments `id' linked smooths must have same number of arguments `object' is not of class "gam" `rank' has wrong length in `paraPen' `sp' dimension wrong in `paraPen' `sp' too short a has wrong number of rows a parametric penalty has wrong dimension absorb.cons must be TRUE for multi-formula models algorithm did not converge all elements of random list must be named all.derivs only available in rho, theta parameterization all.terms argument not supported and correspond to initial coefs for anova.gam called with non gam object argument is not a gam object argument k must be positive. argument k of s() should be integer and has been rounded argument of mrf should be a factor variable argument pers is deprecated, please use scheme instead arguments of smooth not same dimension at least one element of lb must be non-zero attempt to select non existent basis function attempt to use unsuitable marginal smooth class bam can not discretize with this nesting structure bam fits only supported with discrete==TRUE basis dimension is larger than number of unique covariates basis dimension reset to minimum possible basis dimension too small for b-spline order basis dimension, k, increased to minimum possible basis not usable with reduced te bnd must be a list of boundary loops bnd must be a list. bs wrong length and ignored. burnin too large, reset by.resids argument not supported by=. not allowed can't handle [[ in formula can't soap smooth without a boundary cannot find valid starting values: please specify some chunk.size < number of coefficients. Reset to %d coefficient simulation data is missing coefficient vector wrong length color scheme not recognised components of knots relating to a single smooth must be of same length constraints for sub blocked tensor products un-tested cubic spline only deals with 1D data d can not be negative in call to null.space.dimension(). danger of round-off error data contain regions that are not contained in the knot specification data outside soap boundary data vectors are of different lengths deriv should be 1 or 2 df must be positive integers dimension of fx is wrong dimensions of supplied w wrong. discrete method does not use parallel cluster - use nthreads instead discretization only available with fREML dist not recognised does not match the required parameter size don't be silly dropping soap knots not inside boundary - use 'in.out' to investigate. dropping unidentifiable parametric terms from model edge.correct must be logical or a positive number elements of min.sp must be non negative. emtpy models not available er... no discrete methods for higher derivatives erroneous call to estimate.theta - no free parameters factor `by' variables can not be used with matrix arguments. failure of Davies method, falling back on Liu et al approximtion fam not a family object family are not designed for use with gamm! family not implemented yet family not recognised family not recognized fast REML optimizer reached iteration limit faulty bnd fitted probabilities numerically 0 or 1 occurred fitted rates numerically 0 occurred fs smooth not suitable for discretisation with more than one metric predictor fs smooths can only have one factor argument fx length wrong from t2 term: ignored gamm can not fix only some margins of tensor product. gamm can not handle linked smoothing parameters (probably from use of `id' or adaptive smooths) gamm is not designed to use extended families gamm models must have at least 1 smooth with unknown smoothing parameter or at least one other random effect gamm not converged, try increasing niterPQL gamm() can only handle random effects defined as named lists gammals requires 2 links specified as character strings gaulss requires 2 links specified as character strings general families not supported by bam gevlss requires 3 links specified as character strings grid vectors are different lengths gumbls requires 2 links specified as character strings handling `by' variables in smooth constructors may not work with the summation convention illegal `family' argument in.out incorrect: see documentation incorrect arguments to GP smoother incorrect number of linear predictors for family incorrect number of smoothing parameters supplied for a smooth term indefinite penalized likelihood in gam.fit5 initial parameters not feasible initial parameters very close to inequality constraints initial point very close to some inequality constraints inner groupings not nested in outer!! inner loop 1; can't correct step size inner loop 2; can't correct step size inner loop 3; can't correct step size integration not available with this family - insufficient derivatives internal error in vcorr, please report to [email protected] isa requires a dg/tCMatrix iteration %d iteration limit reached: max abs grad = %g iterative weights or data non-finite in gam.fit - regularization may help. See ?gam.control. iterms reset to terms jagam requires a file for the JAGS model specification k and bnd lengths are inconsistent knot range does not include data knot range is so wide that there is *no* information about some basis coefficients knots must be specified for soap knots supplied lambda and h should have the same length! length of min.sp is wrong. length of sp incorrect in t2: ignored length of sp must be number of free smoothing parameters in original model length(M$w) != length(M$y) length(drop.intercept) should be equal to number of model formulas lengths of k and bnd are not compatible. lengths of lb, df and nc must match linear predictor labels out of range link "%s" not available for Tweedie family. link not available for link not available for beta regression; available links are "logit", "probit", "cloglog" and "cauchit" link not available for coxph family; available link is "identity" link not available for mu parameter of gammals link not available for mu parameter of gaulss link not available for mu parameter of twlss link not available for negative binomial family; available links are "identity", "log" and "sqrt" link not available for ordered categorical family; available links are "identity" link not available for precision parameter of gaulss link not available for scale parameter of shash link not available for scaled t distribution; available links are "identity", "log", and "inverse" link not available for zero inflated; available link for `lambda' is only "loga" link not implemented for extended families link not recognised lower bound >= upper bound in call to mono.con() m can't be in re m wrong length and ignored. matrices must be all class dgCMatrix or all class matrix maximum number of iterations must be > 0 mean, mu, must be non negative method not recognised. min.sp not supported with fast REML computation, and ignored. mismatch between nb/polys supplied area names and data area names modal weight <=0 in integration step!! model has repeated 1-d smooths of same variable. more knots than data in a ds term: knots ignored. more knots than data in a tp term: knots ignored. more knots than data in an ms term: knots ignored. more knots than data in an sos term: knots ignored. more knots than unique data values is not allowed mu dimensions wrong multiple penalties of the same order is silly mvn dimension error mvn does not yet handle offsets mvn requires 2 or more dimensional data na.action not character or function names of z and pc must match ncol(M$C) != length(M$p) ncol(M$X) != length(M$p) need at least one interior knot negative values not allowed for the negative binomial family negative values not allowed for the zero inflated Poisson family newdata is a model.frame: it should contain all required variables nlm.fd only available for GCV/UBRE no NAs allowed in response data for this model no automatic plotting for smooths of more than four variables no automatic plotting for smooths of more than two variables no data to predict at no factor supplied to fs smooth no free coefs in sf smooth no good data in iteration no smooths, ignoring `discrete=TRUE' no spatial information provided! no valid set of coefficients has been found:please supply starting values non finite values in Hessian non-existent exclude terms requested - ignoring non-existent terms requested - ignoring non-finite coefficients at iteration %d non-integer binomial denominator: quantiles incorrect not a gam object not all required variables have been supplied in newdata! not an extended family not enough finite derivatives not enough unique values to find k nearest not positive definite not usable with bam fits nothing to do for this model nrow(M$Ain) != length(M$bin) nrow(M$Ain) != length(M$p) nrow(M$X) != length(M$y) nthreads must be a positive integer number of categories must be at least 2 number of linear predictors doesn't match number of supplied knots != k for a cc smooth number of supplied knots != k for a cr smooth object does not appear to be of class lme object is not a glm or gam object not fully initialized offset not still available for this family one or more supplied k too small - reset to default only deals with 2D case only first element of `id' used only one level of smooth nesting is supported by gamm only scalar `p' and `phi' allowed. only scalar `rho' and `theta' allowed. openMP not available: single threaded computation only ord contains out of range orders (which will be ignored) ord is wrong. reset to NULL. order too low p must be in (1,2) p must be in [1,2] p-values for any terms that can be penalized to zero will be unreliable: refit model to fix this. paraPen not supported for multi-formula models parameter of shashlss parameter of ziplss penalized deviance = %s penalty basis too large for smoothing basis penalty basis too small penalty column names don't match supplied area names! penalty matrix, boundary polygons and/or neighbours list must be supplied in xt penalty order too high for basis dimension predict.gam can only be used to predict from gam objects random argument must be a *named* list. random effects don't work with ids. rank deficient re-parameterization recov works with fitted gam objects only reparameterization unstable for margin: not done requested non-existent derivative in B-spline penalty requires a terms or formula object as first argument requires an object of class gam residuals argument not supported residuals argument to plot.gam is wrong length: ignored response not in 0 to number of predictors + 1 reverse indices missing from sparse discrete marginals rho missing from simulation data edf.type reset to 2 s value increased s value modified to give continuous function s value reduced s(.) not supported. s(.) not yet supported. samfrac too small - ignored saturated likelihood may be inaccurate scale parameter must be positive sd should have exactly one less entry than ld second derivs not available for non-linear penalties shared offsets not allowed shash requires 4 links specified as character strings side conditions not allowed for nested smooths silly tolerance supplied silly value supplied for rank.tol: reset to square root of machine precision. single linear predictor indices are ignored singular values not returned in order size must be in [1,120] smooth objects should not have a qrc attribute. smoothing parameter prior choise not recognised, reset to gamma soap basis ill-conditioned - changing 'xt$nmax' may help soap films are bivariate only something wrong with A index vector something wrong with argument d. something wrong with inputs to LAPACK routine something wrong with stratified prediction sorry link not yet handled sorry no option for contouring with errors: try plot.gam step failed: max abs grad = step failure in theta estimation supplied S matrices are wrong diminsion supplied dist negative supplied knots supplied penalty not square! supplied penalty wrong dimension! supply a value for each variable for a point constraint sweep and drop constraints unlikely to work well with self handling of by vars te smooths not useable with gamm4: use t2 instead test argument ignored the adaptive smooth class is limited to 1 or 2 covariates. there is *no* information about some basis coefficients there should be too few knots type iterms not available for multiple predictor cases type must be "link" or "response" un-supported smoothness selection method un-supported test unconditional argument not meaningful here unimplemented sparse constraint type requested uniquecombs has not worked properly unknown optimizer unknown outer optimization method. unknown smoothness selection criterion unknown tensor constraint type unrecognised na.action unrecognized (vector?) link unsupported order of differentiation requested of gam.fit3 update not positive definite value of epsilon must be > 0 values out of range variable names don't match boundary names variance function not recognized for quasi view variables must be one of %s w different length from y! weights ignored weights must be like glm weights for generalized case without a formula x and y must be same length x and y not same length x is null x out of range xt argument is faulty. y must be an integer multiple of phi for Tweedie(p=1) y must be strictly positive for a Gamma density y must be univariate unless binomial ziplss requires 2 links specified as character strings Project-Id-Version: mgcv 1.8-36
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"fs" smooth cannot use a multiply penalized basis (wrong basis in xt) "fs" terms can not be fixed here %s link not available for negative binomial family; available links are "identity", "log" and "sqrt" ‘a’ has to be positive ‘family’ argument seems not to be a valid family object ‘theta’ must be specified , 0s in V(mu) 1<a<b<2 (strict) required A not square A term has fewer unique covariate combinations than specified maximum degrees of freedom AR not available with sparse marginals AR.start must be logical AR1 parameter rho unused with generalized model Algorithm did not converge Algorithm stopped at boundary value An object of length An object of length %d does not match the required parameter size At least three knots required in call to mono.con. Basis only handles 1D smooths Can not convert this smooth class to a random effect Can not have more eigenvalues than nrow(A) Can only deal with a sphere Can't find by variable Can't find valid starting values: please specify some Cannot extract the dimensions Cannot extract the inverse from an uninitialized object Cannot extract the matrix from an uninitialized object Cannot extract the matrix from an uninitialized pdMat object Cannot extract the matrix with uninitialized dimensions Deviance = %s Iterations - %d Discrete Theta search not available with performance iteration Don't know what to do with parametric terms that are not simple numeric or factor variables F is missing from cr smooth - refit model with current mgcv First argument is no sort of formula! Fitting terminated with step failure - check results carefully H has wrong dimension IRLS regularizing parameter must be a non-negative number. Invalid fitted means in empty model Invalid linear predictor values in empty model Iteration limit reached without full convergence - check carefully L has inconsistent dimensions. L has wrong dimension in `paraPen' L must be a matrix. L must have at least as many rows as columns. Later terms sharing an `id' can not have more smoothing parameters than the first such term Length of start should equal Length of start should equal %d and correspond to initial coefs for %s Length of start should equal %d and correspond to initial coefs. M$S and M$off have different lengths M$S[%d] is too large given M$off[%d] M$sp has different length to M$S and M$off MRF basis dimension set too high Model can not be updated Model does not seem to have enough terms to do anything useful Model has more coefficients than data Model matrix not full column rank Model seems to contain no terms Must give names when initializing pdIdnot from parameter. Must supply theta or R to ocat NA times supplied for cox.ph prediction NA values not allowed for the scaled t family NA's in min.sp. NA's in pdTens factor NA's in pdTens matrix NA's in supplied smoothing parameter vector - ignoring. NAs in V(mu) NAs in d(mu)/d(eta) Nested smooths must be fully random No data supplied to gam.setup No data supplied to gamm.setup No observations informative at iteration %d No suitable s (i.e. m[2]) try increasing m[1] No terms to plot - nothing for plot.gam() to do. No variance estimates available Non finite derivatives. Try decreasing fit tolerance! See `epsilon' in `gam.contol' Non-finite coefficients at iteration Non-finite coefficients at iteration %d Non-finite derivatives. Try decreasing fit tolerance! See `epsilon' in `gam.contol' Non-finite deviance Non-integer response variables are not allowed with ziP Non-integer response variables are not allowed with ziplss Not enough (non-NA) data to do anything meaningful Not enough informative observations. Number of prediction and fit constraints must match Only 1<p<=2 supported Partial residuals do not have a natural x-axis location for linear functional terms Pearson residuals not available for this family - returning deviance residuals Penalized model matrix must have no more columns than rows Please provide a single value for theta or use nb to estimate it Please put term with most levels last in ‘re’ to avoid spoiling supplied penalties Possible divergence detected in fast.REML.fit Repeated variables as arguments of a smooth are not permitted Requires a gam or bam prefit object Reset optimizer to outer/newton Response should be integer class labels S.scale vector doesn't match S list - please report to maintainer S[[%d]] matrix is not +ve definite. Smoothness uncertainty corrected covariance not available Something wrong - matrix probably not +ve semi definite Something wrong with auto- penalty construction Something wrong with zlim Sorry, tensor products of smooths with multiple penalties are not supported. Step halved: new penalized deviance = %g Step size truncated due to divergence Step size truncated: out of bounds Step size truncated: out of bounds. Supplied df below min.df. min.df reset Supplied matrix not symmetric Supplied smoothing parameter vector is too short - ignored. Tensor product penalty rank appears to be too low: please email [email protected] with details. The following arguments to anova.glm(..) are invalid and dropped: This family should not have a matrix response Too many cluster nodes to use all efficiently Tweedie density may be unreliable - series not fully converged Tweedie p must be in interval (a,b) Unknown type, reset to terms. Using ziP for binary data makes no sense Using ziplss for binary data makes no sense View variables must contain more than one value. view = c(%s,%s). X lost dimensions in magic!! You've got no model.... `by' variable must be same dimension as smooth arguments `id' linked smooths must have same number of arguments `object' is not of class "gam" `rank' has wrong length in `paraPen' `sp' dimension wrong in `paraPen' `sp' too short a has wrong number of rows a parametric penalty has wrong dimension absorb.cons must be TRUE for multi-formula models algorithm did not converge all elements of random list must be named all.derivs only available in rho, theta parameterization all.terms argument not supported and correspond to initial coefs for anova.gam called with non gam object argument is not a gam object argument k must be positive. argument k of s() should be integer and has been rounded argument of mrf should be a factor variable argument pers is deprecated, please use scheme instead arguments of smooth not same dimension at least one element of lb must be non-zero attempt to select non existent basis function attempt to use unsuitable marginal smooth class bam can not discretize with this nesting structure bam fits only supported with discrete==TRUE basis dimension is larger than number of unique covariates basis dimension reset to minimum possible basis dimension too small for b-spline order basis dimension, k, increased to minimum possible basis not usable with reduced te bnd must be a list of boundary loops bnd must be a list. bs wrong length and ignored. burnin too large, reset by.resids argument not supported by=. not allowed can't handle [[ in formula can't soap smooth without a boundary cannot find valid starting values: please specify some chunk.size < number of coefficients. Reset to %d coefficient simulation data is missing coefficient vector wrong length color scheme not recognised components of knots relating to a single smooth must be of same length constraints for sub blocked tensor products un-tested cubic spline only deals with 1D data d can not be negative in call to null.space.dimension(). danger of round-off error data contain regions that are not contained in the knot specification data outside soap boundary data vectors are of different lengths deriv should be 1 or 2 df must be positive integers dimension of fx is wrong dimensions of supplied w wrong. discrete method does not use parallel cluster - use nthreads instead discretization only available with fREML dist not recognised does not match the required parameter size don't be silly dropping soap knots not inside boundary - use ‘in.out’ to investigate. dropping unidentifiable parametric terms from model edge.correct must be logical or a positive number elements of min.sp must be non negative. emtpy models not available er... no discrete methods for higher derivatives erroneous call to estimate.theta - no free parameters factor `by' variables can not be used with matrix arguments. failure of Davies method, falling back on Liu et al approximtion fam not a family object family are not designed for use with gamm! family not implemented yet family not recognised family not recognized fast REML optimizer reached iteration limit faulty bnd fitted probabilities numerically 0 or 1 occurred fitted rates numerically 0 occurred fs smooth not suitable for discretisation with more than one metric predictor fs smooths can only have one factor argument fx length wrong from t2 term: ignored gamm can not fix only some margins of tensor product. gamm can not handle linked smoothing parameters (probably from use of `id' or adaptive smooths) gamm is not designed to use extended families gamm models must have at least 1 smooth with unknown smoothing parameter or at least one other random effect gamm not converged, try increasing niterPQL gamm() can only handle random effects defined as named lists gammals requires 2 links specified as character strings gaulss requires 2 links specified as character strings general families not supported by bam gevlss requires 3 links specified as character strings grid vectors are different lengths gumbls requires 2 links specified as character strings handling `by' variables in smooth constructors may not work with the summation convention illegal `family' argument in.out incorrect: see documentation incorrect arguments to GP smoother incorrect number of linear predictors for family incorrect number of smoothing parameters supplied for a smooth term indefinite penalized likelihood in gam.fit5 initial parameters not feasible initial parameters very close to inequality constraints initial point very close to some inequality constraints inner groupings not nested in outer!! inner loop 1; can't correct step size inner loop 2; can't correct step size inner loop 3; can't correct step size integration not available with this family - insufficient derivatives internal error in vcorr, please report to [email protected] isa requires a dg/tCMatrix iteration %d iteration limit reached: max abs grad = %g iterative weights or data non-finite in gam.fit - regularization may help. See ?gam.control. iterms reset to terms jagam requires a file for the JAGS model specification k and bnd lengths are inconsistent knot range does not include data knot range is so wide that there is *no* information about some basis coefficients knots must be specified for soap knots supplied lambda and h should have the same length! length of min.sp is wrong. length of sp incorrect in t2: ignored length of sp must be number of free smoothing parameters in original model length(M$w) != length(M$y) length(drop.intercept) should be equal to number of model formulas lengths of k and bnd are not compatible. lengths of lb, df and nc must match linear predictor labels out of range link "%s" not available for Tweedie family. link not available for link not available for beta regression; available links are "logit", "probit", "cloglog" and "cauchit" link not available for coxph family; available link is "identity" link not available for mu parameter of gammals link not available for mu parameter of gaulss link not available for mu parameter of twlss link not available for negative binomial family; available links are "identity", "log" and "sqrt" link not available for ordered categorical family; available links are "identity" link not available for precision parameter of gaulss link not available for scale parameter of shash link not available for scaled t distribution; available links are "identity", "log", and "inverse" link not available for zero inflated; available link for `lambda' is only "loga" link not implemented for extended families link not recognised lower bound >= upper bound in call to mono.con() m can't be in re m wrong length and ignored. matrices must be all class dgCMatrix or all class matrix maximum number of iterations must be > 0 mean, mu, must be non negative method not recognised. min.sp not supported with fast REML computation, and ignored. mismatch between nb/polys supplied area names and data area names modal weight <=0 in integration step!! model has repeated 1-d smooths of same variable. more knots than data in a ds term: knots ignored. more knots than data in a tp term: knots ignored. more knots than data in an ms term: knots ignored. more knots than data in an sos term: knots ignored. more knots than unique data values is not allowed mu dimensions wrong multiple penalties of the same order is silly mvn dimension error mvn does not yet handle offsets mvn requires 2 or more dimensional data na.action not character or function names of z and pc must match ncol(M$C) != length(M$p) ncol(M$X) != length(M$p) need at least one interior knot negative values not allowed for the negative binomial family negative values not allowed for the zero inflated Poisson family newdata is a model.frame: it should contain all required variables nlm.fd only available for GCV/UBRE no NAs allowed in response data for this model no automatic plotting for smooths of more than four variables no automatic plotting for smooths of more than two variables no data to predict at no factor supplied to fs smooth no free coefs in sf smooth no good data in iteration no smooths, ignoring `discrete=TRUE' no spatial information provided! no valid set of coefficients has been found:please supply starting values non finite values in Hessian non-existent exclude terms requested - ignoring non-existent terms requested - ignoring non-finite coefficients at iteration %d non-integer binomial denominator: quantiles incorrect not a gam object not all required variables have been supplied in newdata! not an extended family not enough finite derivatives not enough unique values to find k nearest not positive definite not usable with bam fits nothing to do for this model nrow(M$Ain) != length(M$bin) nrow(M$Ain) != length(M$p) nrow(M$X) != length(M$y) nthreads must be a positive integer number of categories must be at least 2 number of linear predictors doesn't match number of supplied knots != k for a cc smooth number of supplied knots != k for a cr smooth object does not appear to be of class lme object is not a glm or gam object not fully initialized offset not still available for this family one or more supplied k too small - reset to default only deals with 2D case only first element of `id' used only one level of smooth nesting is supported by gamm only scalar `p' and `phi' allowed. only scalar `rho' and `theta' allowed. openMP not available: single threaded computation only ord contains out of range orders (which will be ignored) ord is wrong. reset to NULL. order too low p must be in (1,2) p must be in [1,2] p-values for any terms that can be penalized to zero will be unreliable: refit model to fix this. paraPen not supported for multi-formula models parameter of shashlss parameter of ziplss penalized deviance = %s penalty basis too large for smoothing basis penalty basis too small penalty column names don't match supplied area names! penalty matrix, boundary polygons and/or neighbours list must be supplied in xt penalty order too high for basis dimension predict.gam can only be used to predict from gam objects random argument must be a *named* list. random effects don't work with ids. rank deficient re-parameterization recov works with fitted gam objects only reparameterization unstable for margin: not done requested non-existent derivative in B-spline penalty requires a terms or formula object as first argument requires an object of class gam residuals argument not supported residuals argument to plot.gam is wrong length: ignored response not in 0 to number of predictors + 1 reverse indices missing from sparse discrete marginals rho missing from simulation data edf.type reset to 2 s value increased s value modified to give continuous function s value reduced s(.) not supported. s(.) not yet supported. samfrac too small - ignored saturated likelihood may be inaccurate scale parameter must be positive sd should have exactly one less entry than ld second derivs not available for non-linear penalties shared offsets not allowed shash requires 4 links specified as character strings side conditions not allowed for nested smooths silly tolerance supplied silly value supplied for rank.tol: reset to square root of machine precision. single linear predictor indices are ignored singular values not returned in order size must be in [1,120] smooth objects should not have a qrc attribute. smoothing parameter prior choise not recognised, reset to gamma soap basis ill-conditioned - changing ‘xt$nmax’ may help soap films are bivariate only something wrong with A index vector something wrong with argument d. something wrong with inputs to LAPACK routine something wrong with stratified prediction sorry link not yet handled sorry no option for contouring with errors: try plot.gam step failed: max abs grad = step failure in theta estimation supplied S matrices are wrong diminsion supplied dist negative supplied knots supplied penalty not square! supplied penalty wrong dimension! supply a value for each variable for a point constraint sweep and drop constraints unlikely to work well with self handling of by vars te smooths not useable with gamm4: use t2 instead test argument ignored the adaptive smooth class is limited to 1 or 2 covariates. there is *no* information about some basis coefficients there should be too few knots type iterms not available for multiple predictor cases type must be "link" or "response" un-supported smoothness selection method un-supported test unconditional argument not meaningful here unimplemented sparse constraint type requested uniquecombs has not worked properly unknown optimizer unknown outer optimization method. unknown smoothness selection criterion unknown tensor constraint type unrecognised na.action unrecognized (vector?) link unsupported order of differentiation requested of gam.fit3 update not positive definite value of epsilon must be > 0 values out of range variable names don't match boundary names variance function not recognized for quasi view variables must be one of %s w different length from y! weights ignored weights must be like glm weights for generalized case without a formula x and y must be same length x and y not same length x is null x out of range xt argument is faulty. y must be an integer multiple of phi for Tweedie(p=1) y must be strictly positive for a Gamma density y must be univariate unless binomial ziplss requires 2 links specified as character strings